AFADESI-MSI分析子宫内膜癌与正常组织的全局代谢重编程：脂质、多胺、谷氨酰胺、TCA循环及临床病理与分子亚型关联

Sensitive and spatial exploration of the metabolism of tumors at the metabolome level is highly challenging. In this study, we developed an in situ metabolomics method based on ambient mass spectrometry imaging using air flow-assisted desorption electrospray ionization (AFADESI), which can spatially explore the alteration of global metabolites in tissues with high sensitivity. Using this method, we discovered potential histopathological diagnosis biomarkers (including lipids, amino acids, choline, peptides and carnitine) from 52 postoperative lung cancer tissue samples and then subsequently used these biomarkers to generate images for rapid and label-free histopathological diagnosis. These biomarkers were validated with a sensitivity and a specificity of 93.5% and 100%, respectively. Moreover, a single imaging analysis of a cryosection that visualized all these biomarkers, taking tens of minutes, revealed the type and subtype of the cancer. This method could potentially be used as a molecular pathological tool for rapid clinical lung cancer diagnosis and immediate image-guided surgery.

No abstract available

Metabolic heterogeneity of cancer contributes significantly to its poor treatment outcomes and prognosis. As a result, studies continue to focus on identifying new biomarkers and metabolic vulnerabilities, both of which depend on the understanding of altered metabolism in cancer. In the recent decades, the rise of mass spectrometry imaging (MSI) enables the in situ detection of large numbers of small molecules in tissues. Therefore, researchers look to using MSI-mediated spatial metabolomics to further study the altered metabolites in cancer patients. In this review, we examined the two most commonly used spatial metabolomics techniques, MALDI-MSI and DESI-MSI, and some recent highlights of their applications in cancer studies. We also described AFADESI-MSI as a recent variation from the DESI-MSI and compare it with the two major techniques. Specifically, we discussed spatial metabolomics results in four types of heterogeneous malignancies, including breast cancer, esophageal cancer, glioblastoma and lung cancer. Multiple studies have effectively classified cancer tissue subtypes using altered metabolites information. In addition, distribution trends of key metabolites such as fatty acids, high-energy phosphate compounds, and antioxidants were identified. Therefore, while the visualization of finer distribution details requires further improvement of MSI techniques, past studies have suggested spatial metabolomics to be a promising direction to study the complexity of cancer pathophysiology.

No abstract available

Background: Nasopharyngeal carcinoma (NPC) is a malignancy highly prevalent in southern China. Metabolic reprogramming is a hallmark of cancer, yet the interplay between the systemic and local metabolic landscapes of NPC remains poorly characterized. This study aimed to identify serum metabolic biomarkers and characterize local tissue metabolic alterations through integrated metabolomics. Methods: We conducted a dual-compartment metabolomic investigation. Untargeted serum metabolomics was performed on 80 NPC patients and 35 healthy controls. Air flow-assisted desorption electrospray ionization mass spectrometry imaging (AFADESI-MSI) was applied to nasopharyngeal tissues (3 NPC vs. 3 chronically inflamed tissues). Cross-platform integration was performed to identify overlapping metabolic signatures, followed by least absolute shrinkage and selection operator (LASSO) regression for feature selection and logistic regression for biomarker panel construction. Results: Serum metabolomics revealed systemic perturbations involving amino acid and propanoate metabolism. Spatial metabolomics identified glycerophospholipid metabolism as the most significantly altered pathway in NPC tissues, with markedly reduced phosphatidylserine in tumor regions, and the ferroptosis pathway was significantly enriched. Cross-omics integration identified 8 overlapping metabolites, of which 5 were mapped to the biosynthesis of unsaturated fatty acids. Cysteine demonstrated excellent diagnostic performance (area under the receiver operating characteristic curve (AUC) = 0.903). A 5-metabolite combined model achieved an AUC of 0.904 (95% confidence interval (CI): 0.892–0.916). Conclusions: NPC exhibits compartmentalized metabolic reprogramming characterized by systemic amino acid and propanoate perturbations alongside local glycerophospholipid remodeling and ferroptosis susceptibility. The identified 5-metabolite serum panel shows considerable potential as a non-invasive diagnostic tool for NPC.

Innovations in spatial omics technologies applied to human tissues have led to breakthrough discoveries in various diseases, including cancer. Two of these approaches - spatial transcriptomics and spatial metabolomics - have blossomed independently, fueled by technologies such as spatial transcriptomics (ST) and mass spectrometry imaging (MSI). While powerful, these technologies only offer insights into the spatial distributions of restricted classes of molecules and have not yet been integrated to provide more holistic insights into biological questions. These techniques can be performed on adjacent serial sections from the same sample, but section-to-section variability can convolute data integration. We present a novel method combining desorption electrospray ionization mass spectrometry imaging (DESI-MSI) spatial metabolomics and Visium spatial transcriptomics on the same tissue sections. We show that RNA quality is maintained after performing DESI-MSI on a tissue and that ST data is unperturbed following DESI-MSI. We demonstrate this workflow on human breast and lung cancer tissues and identify novel correlations between metabolites and RNA transcripts in cancer specific regions.

Mass spectrometry (MS), particularly mass spectrometry imaging (MSI), is an important analytical technique that facilitates the detection and spatial visualization of biomolecules, and more specifically cancer biomarkers, in complex biological tissue samples. Over the past thirty years, innovations such as electrospray ionization (ESI) and matrix-assisted laser desorption/ionization (MALDI) have significantly expanded MS’s capabilities, enabling detailed molecular profiling of proteins, lipids, nucleic acids, and metabolites directly from clinical samples. It is here where MSI can uniquely contribute to cancer biomarker discovery by revealing the spatial distribution of these molecules in tissue sections, thereby providing crucial insights into tumor microenvironments. Despite its strengths, traditional matrix-based MSI faces limitations related to analyte specificity, reproducibility, and data interpretation. Matrix-free alternatives, such as desorption electrospray ionization (DESI) and rapid evaporative ionization mass spectrometry (REIMS), offer clinical promise but present challenges, including low ionization efficiency and complex data interpretation that require advanced processing, normalization, and machine learning to extract meaningful biological insights. While imaging techniques like the inclusion of heavy metal isotope (HMI) or photocleavable (PC) mass tags (MTs) can mitigate these factors by providing greater sensitivity and selectivity during MSI, powerful data processing and analysis is still needed to improve accuracy and reproducibility of datasets to allow for the use of MSI to permeate into routine clinical practice. In this tutorial, a variety of useful tools are provided to bolster each step of the data processing and analysis workflow. Furthermore, MSI has wide-reaching applications, not only in oncology but also in neurology, infectious disease, and drug development, offering molecular insights critical for diagnostics and personalized therapies. MS based surgical and diagnostic tools, such as iKnife, SpiderMass, and MasSpec Pen, may further enable intraoperative and point-of-care applications, positioning MSI at the forefront of next-generation clinical and translational research.

Mass Spectrometry Imaging (MSI) for small molecules has emerged as the optimal technique to ascertain meaningful biology from spatial distributions of metabolites, lipids, and other classes of molecules. The success or failure of this approach rests on the sample preparation. Each tissue can have its unique challenges. In addition, the established histological processes for embedding frozen tissue, such as optimal cutting temperature medium, produce large polyethylene glycol clusters that suppress and interfere with the signal of biological molecules. To address these challenges, we provide a reproducible protocol for sectioning lung cancer tissue using a commercially available histological embedding matrix using tissues from genetically engineered mouse models of lung adenocarcinoma with a fluorescent reporter cassette to highlight additional microscopy methods used in parallel with mass spectrometry imaging to select regions of interest to compare tumor and adjacent lung tissue. These improvements to existing techniques produce high-quality sections of frozen tissue for histology and mass spectrometry imaging.

Significance Various cellular populations in tumors continuously engage in highly active metabolic exchanges. However, the heterogeneous tumor microenvironment (TME) makes it challenging to characterize cell-specific metabolic interactions and their biological functions. By leveraging a spatial multiomics platform, our study uncovers a distinct tumor-immune-cancer-associated fibroblast (CAF) “interface” zone in hepatocellular carcinoma, where lactic acid and long-chain polyunsaturated fatty acids are elevated. Furthermore, our methodological advance—integrating single-cell mass spectrometry imaging with coculture systems—demonstrates that CAFs act as metabolic hubs. By enhancing glycolysis and secreting lactic acid, they drive macrophage polarization toward an immunosuppressive M2 phenotype, thereby facilitating tumor invasion. These insights into TME metabolism highlight metabolic targets for cancer therapy.

MALDI mass spectrometry imaging (MALDI imaging) uniquely advances cancer research, by measuring spatial distribution of endogenous and exogenous molecules directly from tissue sections. These molecular maps provide valuable insights into basic and translational cancer research, including tumor biology, tumor microenvironment, biomarker identification, drug treatment, and patient stratification. Despite its advantages, MALDI imaging is underutilized in studying rare cancers. Sarcomas, a group of malignant mesenchymal tumors, pose unique challenges in medical research due to their complex heterogeneity and low incidence, resulting in understudied subtypes with suboptimal management and outcomes.

Cholesterol is a vital component of the central nervous system and tissues, and understanding its spatial distribution is crucial for biology, pathophysiology, and diagnostics. However, direct imaging of cholesterol using matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) remains challenging and time-consuming due to the difficulty in ionizing the sterol molecule. To tackle this issue, a MALDI-MSI method is established for direct and rapid analysis of the spatial distribution of cholesterol in Alzheimer's disease (AD), different cancer tissues and organs via MALDI-MSI. This excellent imaging performance depends on the study and systemic optimization of various conditions that affect the imaging of MALDI-MSI. In this case, we report the distribution and levels of cholesterol across specific structures of the AD mouse brain and different tumor tissue and organs. According to the results, the content of cholesterol in the AD mouse cerebellum, especially in the arborvitae, was significantly higher than that in the wild type (WT) model. Furthermore, we successfully visualize the distribution of cholesterol in other organs, such as the heart, liver, spleen, kidney, pancreas, as well as tumor tissues parenchyma and interstitium using MALDI-MSI. Notably, the attribution of cholesterol MS/MS hydrocarbon fragments was systematically investigated. Our presented optimization strategy and established MALDI-MSI method can be easily generalized for different animal tissues or live samples, thereby facilitating the potential for applications of MALDI-MSI in clinical, medical and biological research.

No abstract available

BACKGROUND Mass spectrometry imaging (MSI) is an imaging method based on mass spectrometry technology that can simultaneously visualize the spatial distribution of various biological molecules. The use of MSI in cancer detection and drug discovery has been extensively investigated in recent years. OBJECTIVE This review aims to summarize the latest advances of MSI and its specific applications in cancer detection and drug discovery, providing a basic understanding of the development and application of MSI in the past five years and offering references for the further application of MSI in cancer detection and drug discovery. METHODS In the database, "mass spectrometry imaging", "cancer treatment", and "drug discovery" were used as keywords for literature retrieval, and the time range was limited to "2018- 2023". After organizing and analyzing the literature and patents, a review was conducted. RESULTS Based on the literature, it was found that the updated progress of MSI in the past five years mostly focused on concrete methods, operation procedures, facilities, and composite applications. The patents of MSI were mainly correlated with the mass spectrometry imaging system and its application in cancer treatment. MSI is conducive to investigating the therapeutic schedule of cancer and searching for new drugs. CONCLUSION MSI is a convenient, fast and powerful technology that has made great progress in sample preparation, instrumentation, quantitation, and multimodal imaging. MSI has emerged as a powerful technique in various biomedical applications, which has strong potential in cancer detection, treatment, formation mechanism research, discovery of biomarkers, and drug discovery process.

Cancer metabolic variability has a significant impact on both diagnosis and treatment outcomes. The discovery of novel biological indicators and metabolic dysregulation, can significantly rely on comprehension of the modified metabolism in cancer, is a research focus. Tissue histology is a critical feature in the diagnostic testing of many ailments, such as cancer. To assess the surgical margin of the tumour on patients, frozen section histology is a tedious, laborious, and typically arbitrary method. Concurrent monitoring of ion images in tissues facilitated by the latest advancements in mass spectrometry imaging (MSI) is far more efficient than optical tissue image analysis utilized in conventional histopathology examination. This article focuses on the “desorption electrospray ionization (DESI)-MSI” technique’s most recent advancements and uses in cancer research. DESI-MSI can provide wealthy information based on the variances in metabolites and lipids in normal and cancerous tissues by acquiring ion images of the lipid and metabolite variances on biopsy samples. As opposed to a systematic review, this article offers a synopsis of the most widely employed cutting-edge DESI-MSI techniques in cancer research.

Endometrial cancer (EC) is the most common gynaecological malignancy worldwide, yet the prognosis for advanced and recurrent disease remains poor, highlighting the need for improved diagnostic, prognostic, and therapeutic decision-making frameworks. Conventional approaches, including histopathology, imaging, and single-layer molecular profiling, provide essential clinical information but may not fully capture EC’s biological heterogeneity, especially within clinically challenging No Specific Molecular Profile (NSMP) and mismatch repair-deficient (MMRd) subgroups. Artificial intelligence (AI) and machine learning (ML) provide powerful approaches to analyse complex, high-dimensional datasets generated by multi-omics profiling, histopathology, imaging, and clinical records. In this review, we synthesize the latest evidence on AI-driven multi-omics research in EC, encompassing genomics, transcriptomics, proteomics, metabolomics, epigenomics, single-cell profiling, and spatial transcriptomics. Unlike other reviews that focus solely on AI, omics, or imaging, we integrate molecular, imaging, histopathological, and computational perspectives to underscore their collective impact on precision oncology in EC. We subsequently explore applications in molecular subtyping, predicting survival and recurrence, modelling treatment responses, discovering immunotherapy biomarkers, and identifying drug targets. Public resources such as The Cancer Genome Atlas (TCGA), Clinical Proteomic Tumour Analysis Consortium (CPTAC), Gene Expression Omnibus (GEO), cBioPortal, Human Protein Atlas, GTEx, and UCSC Xena have enabled large-scale reproducible analyses. However, challenges such as cohort heterogeneity, batch effects, ethnic underrepresentation, missing annotations, and the need for external validation remain significant hurdles. We then discuss the progression from conventional ML methods to deep learning architectures, including convolutional neural networks, transformers, graph neural networks, and multimodal fusion models applied to histopathological, radiological, and multi-omics data. Landmark models such as EndoNet, EndoRisk, and HECTOR illustrate the potential of AI-enabled approaches to support EC grading, molecular inference, lymph node metastasis prediction, and recurrence-risk stratification. Finally, we examine key translational barriers, including class imbalance, interpretability, data harmonization, regulatory requirements, and the implementation gap between high-performing retrospective models and routine clinical deployment. Ultimately, this review underscores how bridging these multi-modal computational approaches paves the way for precision oncology in EC.

Background Spatial metabolomics has revolutionized cancer research by offering unprecedented insights into the metabolic heterogeneity of the tumor microenvironment (TME). Unlike conventional metabolomics, which lacks spatial resolution, spatial metabolomics enables the visualization of metabolic interactions among cancer cells, stromal components, and immune cells within their native tissue context. Despite its growing significance, a systematic and visualized analysis of spatial metabolomics in cancer research remains lacking, particularly in the integration of multi-omics data and the standardization of methodologies for comprehensive tumor metabolic mapping. Objectives This study aims to conduct a bibliometric analysis to systematically evaluate the development trends, key contributors, research hotspots, and future directions of spatial metabolomics in cancer research. Methods A bibliometric approach was employed using data retrieved from the Web of Science Core Collection. Analytical tools such as VOSviewer and CiteSpace were utilized to visualize and assess co-citation networks, keyword co-occurrence, and institutional collaborations. Key metrics, including publication trends, authorship influence, country contributions, and journal impact, were analyzed to map the research landscape in this domain. Results A total of 182 publications on spatial metabolomics in cancer research were identified over the past two decades, with a notable surge in research output beginning in 2018. The field has experienced accelerated growth, with an annual average of 40 publications since 2021, reflecting its increasing relevance in cancer studies. Among 28 contributing countries, China (n=53), the United States (n=35), Germany (n=18), and the United Kingdom (n=13) have been the most active contributors. China leads in publication volume, while the United States exhibits the highest citation impact, indicating significant research influence. International collaboration networks are particularly strong among the United States, Germany, and China, underscoring the global interest in this emerging field. Analysis of key authors and institutions identifies He Jiuming as the most prolific author and Song Xiaowei as the researcher with the highest average citations. Other influential authors include Abliz Zeper and Sun Chenglong. Leading research institutions driving advancements in this field include the Chinese Academy of Medical Sciences, Peking Union Medical College, Harvard Medical School, and Stanford University. Regarding journal impact, Nature Communications (n=11), Journal of Pharmaceutical Analysis (n=9), and Nature Methods (n=8) are the most active publishing platforms in this domain. Citation analysis reveals that Cell, BioEssays, and Genome Medicine are among the most highly cited journals, reflecting the interdisciplinary nature of spatial metabolomics research.

Achyrocline satureioides have good therapeutic effects on nonsmall cell lung cancer (NSCLC). Nevertheless, it is still challenging to elucidate the active ingredients and mechanism of action due to their complex chemical composition. To address this, we innovatively combined network pharmacology with spatial metabolomics to comprehensively investigate the active components and the action mechanism in the present study. First, metabolomics of cells treated with the methanol extract of A. satureioides (ASM) utilizing high-resolution ultrahigh-performance liquid chromatography tandem mass spectrometry (HR-UHPLC-MS/MS) revealed 32 changed metabolites and 7 enriched metabolic pathways, confirming the anti-NSCLC effect of ASM and its impact on endogenous metabolites at the cellular level. Then, 69 chemical components in the ASM were identified using HR-UHPLC-MS/MS, followed by the screening of 6 core components and 10 core targets of anti-NSCLC with the help of network pharmacology and molecular docking. Lastly, quercetin, the most abundant compound among the six core active ingredients, was chosen for evaluating its anti-NSCLC effect and the potential mechanism using matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI). 51 altered endogenous metabolites were screened, and pathway enrichment analysis results were consistent with cell metabolomics, corroborating our network pharmacology predictions. In addition, we also observed the accumulation of three metabolites of quercetin in the tumor tissues. Network pharmacology combined with MSI elucidated the metabolic mechanisms by which A. satureioides treats NSCLC, offering new insights into herbal cancer therapies.

Papillary thyroid cancer (PTC) exhibits highly variable clinical behavior, ranging from indolent growth to aggressive lymph node (LN) metastasis. However, the specific molecular mechanisms involved in PTC evolution, driven by metabolic reprogramming and tumor-microenvironmental interactions, remain poorly understood. This study aims to elucidate the spatial metabolic and transcriptional mechanisms underlying PTC tumorigenesis and LN metastasis at multiple molecular levels. An integrated spatial multi-omics strategy combining spatial metabolomics and spatial transcriptomics was employed to map the distribution of metabolites and gene expression in heterogeneous PTC tissues. Metabolite profiles and transcriptomic data were analyzed to identify dysregulated pathways and to uncover the key molecular features contributing to PTC tumorigenesis and LN metastasis. Genes related to metastasis-driving metabolites were validated using the TCGA dataset, and further substantiated by zebrafish xenograft models. Spatial mapping revealed unique metabolic and transcriptional signatures across heterogeneous regions of PTC tissue. Key metabolic pathways, such as the arginine–polyamine axis, glycolysis and lipid metabolism, were prominently dysregulated in cancer regions. The surrounding stroma also exhibited metabolic adaptations, like polyamine recycling and glucose storage, which supported tumor growth via metabolic crosstalk. Most importantly, greater spatial heterogeneity, spatial evolutionary routes and 5 potential metastasis-driving metabolites, including FA (22:6), PC (36:4), PC (34:1), N-acetylaspartate and ascorbic acid, were discovered in the primary PTC tissue with LN metastasis. Knockdown of key genes NAT8L and SVCT-2 obviously decreased the metastatic ability of PTC cells. The TCGA database further confirmed that high expression of 10 pro-metastatic metabolite-related genes was significantly associated with poorer prognosis in PTC patients. This integrated spatial multi-omics approach provides novel insights into the molecular mechanisms underlying PTC evolution, potentially guiding the development of more effective diagnostic and therapeutic strategies for PTC patients.

The combination of spatial metabolomics and spatial transcriptomics can reveal powerful connections between gene expression and metabolism in heterogeneous tissues. However, section-to-section variability can convolute data integration. We present a novel approach, termed Desium, which integrates spatial metabolomics by Desorption Electrospray Ionization Mass Spectrometry Imaging (DESI-MSI) and Visium Spatial Transcriptomics (VST) on the same tissue section. This work demonstrates that DESI-MSI does not significantly impact RNA quality, enabling robust analysis of gene-metabolite relationships in human breast and lung cancer tissues. The Desium workflow involves sequentially performing DESI-MSI at 100 μm spatial resolution, hematoxylin and eosin (H&E) staining, and VST CytAssist analyses on the same tissue section. We applied this workflow to six human cancer tissues (3 breast, 3 lung) and analyzed a serial section of two samples (1 breast, 1 lung) with standard VST CytAssist as a control. H&E and VST data were automatically aligned, while DESI-MSI and H&E data were coregistered manually using nonrigid registration. The MSI data was then converted into VST spot coordinates using a Gaussian granularity matching algorithm. Pearson correlation scores were calculated for each tissue to link gene expression and metabolite abundances. Comparing sequencing results between Desium and the standard VST protocol using UMI counts/gene, we found a correlation coefficient of 1 for both lung and breast samples, indicating minimal impact of DESI-MSI on gene expression measurements. Across all samples, we identified 15, 111 transcript-metabolite correlations from 1, 136 transcripts and 439 metabolites, with correlation scores above 0.5 or below -0.5. Notably, we observed a 0.82 correlation between glycerophosphorylethanolamine and XBP1 in breast cancer and a 0.77 correlation between phosphatidylglycerol PG 22:6_22:5 and MSLN in lung cancer. Our study demonstrates that Desium enables unambiguous spatial correlations between metabolites and RNA transcripts, uncovering novel relationships and unique patterns of intratumor heterogeneity not evident through histologic analysis. These findings suggest that Desium could serve as a powerful tool to explore the gene expression-metabolic phenotype relationships within the complex tumor microenvironment. Trevor M. Godfrey, Yasmin Shanneik, Wanqiu Zhang, Thao Tran, Nico Verbeeck, Nathan H. Patterson, Faith E. Jackobs, Maheswhari Ramineni, Chandandeep Nagi, Livia S. Eberlin. Integrating spatial metabolomics and spatial transcriptomics on the same cancer tissue sections to detect gene-metabolite correlations [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 150.

Prostate cancer (PCa) exhibits significant intra-tumor metabolic heterogeneity. In this study, we developed a multi-platform workflow combining pathological staining, matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI), and laser capture microdissection-assistant gas chromatography-tandem mass spectrometry (LCM-GC-MS/MS) to investigate the metabolic spatial heterogeneity of a specific PCa tissue. Four suspicious glandular regions were initially identified by histology and immunofluorescence. MALDI-MSI revealed three distinct metabolic patterns for further analysis. It was found that only one glandular region showed PCa metabolic features, enabling precise differentiation from other regions of interest. The information from LCM-GC-MS/MS complemented that from MSI by confirming and quantifying fatty acid alterations. This workflow broadened the spatial metabolic coverage and enhanced intra-tumor metabolic heterogeneity analysis in PCa.

Significance Cribriform prostate cancer is a specific disease phenotype known for its high potential of transforming into incurable or even lethal disease. As conventional diagnostic methods tend to miss this disease subtype, identifying biologically informed methods for its early detection is important. Here, we show that one such method could be the imaging of tumor lipid metabolism using clinical imaging techniques: [1-11C]acetate positron emission tomography/computed tomography (PET/CT) and proton magnetic resonance spectroscopy. Both techniques, which are available to clinicians, showed promise for distinguishing between cribriform and noncribriform tumors in patients with biopsy-proven prostate cancer.

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BACKGROUND Cancer cachexia (CCx) presents a multifaceted challenge characterized by negative protein and energy balance and systemic inflammatory response activation. While previous CCx studies predominantly focused on mouse models or human body fluids, there's an unmet need to elucidate the molecular inter-organ cross-talk underlying the pathophysiology of human CCx. METHODS Spatial metabolomics were conducted on liver, skeletal muscle, subcutaneous and visceral adipose tissue, and serum from cachectic and control cancer patients. Organ-wise comparisons were performed using component, pathway enrichment and correlation network analyses. Inter-organ correlations in CCx altered pathways were assessed using Circos. Machine learning on tissues and serum established classifiers as potential diagnostic biomarkers for CCx. RESULTS Distinct metabolic pathway alteration was detected in CCx, with adipose tissues and liver displaying the most significant (P ≤ 0.05) metabolic disturbances. CCx patients exhibited increased metabolic activity in visceral and subcutaneous adipose tissues and liver, contrasting with decreased activity in muscle and serum compared to control patients. Carbohydrate, lipid, amino acid, and vitamin metabolism emerged as highly interacting pathways across different organ systems in CCx. Muscle tissue showed decreased (P ≤ 0.001) energy charge in CCx patients, while liver and adipose tissues displayed increased energy charge (P ≤ 0.001). We stratified CCx patients by severity and metabolic changes, finding that visceral adipose tissue is most affected, especially in cases of severe cachexia. Morphometric analysis showed smaller (P ≤ 0.05) adipocyte size in visceral adipose tissue, indicating catabolic processes. We developed tissue-based classifiers for cancer cachexia specific to individual organs, facilitating the transfer of patient serum as minimally invasive diagnostic markers of CCx in the constitution of the organs. CONCLUSIONS These findings support the concept of CCx as a multi-organ syndrome with diverse metabolic alterations, providing insights into the pathophysiology and organ cross-talk of human CCx. This study pioneers spatial metabolomics for CCx, demonstrating the feasibility of distinguishing cachexia status at the organ level using serum.

Globally, endometrial cancer continues to impact a significant number of women. Immunotherapy provides those suffering from advanced or relapsed disease hope, but an important barrier is still the absence of trustworthy predictive biomarkers. To tackle this challenge, single-cell sequencing and spatial transcriptomics (ST) are increasingly applied. In cervical cancers of the no specific molecular profile (NSMP) subtype accompanied by p53 mutations. In many cases, the tumor microenvironment (TME) in endometrial cancer exhibits strong immunosuppression or poor immune cell infiltration, often leading to worse clinical outcomes. Single-cell sequencing reveals cellular heterogeneity and helps identify potential therapeutic targets and predict treatment responses. Conversely, ST assists in determining biomarkers that influence the effectiveness of immunotherapy by capturing the spatial organization of tumors. When combined, these technologies allow for integrated multi-omics analysis that aids in the development of immunotherapies, prognostication, and diagnosis. But there are still moral and legal issues. Clinicians may be able to improve outcomes for patients who don’t respond well to current immunotherapies by utilizing these combined approaches.

Endometrial cancer (EC) is the fourth most abundant gynecological cancer. There is an increase in the incidence of mortality from uterine cancers in the past few decades. A comprehensive systematic study to provide an overview on the relationship between autophagy, metabolomics and the risk of oestradiol valerate (OV) induced endometrial cancer was conducted correlated with the use of liposomal loaded-resvida as an innovate drug delivery system. This article explores how metabolomic technology can offer valuable insights on autophagy molecular aspects in EC by identifying new possible metabolite biomarkers that has the potential to improve the accuracy of diagnosis, prognosis and disease monitoring. Metabolomics approach, included orthogonal partial least squares discriminant analysis (OPLS-DA), thus revolutionizes the management of endometrial cancer. Autophagy described in endometrial cancer, includes the role of HSP-70/C-fos/PTEN/mTOR/ERDj-4/p53 signaling pathways that trigger/inhibit the process and consequently represent a potential molecular targets in therapeutic approaches. Endometrial cancer exhibits a molecular complexity and heterogeneity coherent with histopathologic and metabolomic variability. Multivariate statistical analyses pointed out a noteworthy deviation in serum chemical profiles among control, oestradiol valerate, and Resvida and liposomal-Resvida treated groups. Loading plot guided the selection of differential metabolites, elucidating significant variation in metabolite concentration. Improved characterization of molecular alterations of each histological type provides relevant information about the prognosis and potential response to new liposomal therapies. CA125 as EC biomarker was ameliorated post Resvida (108.7 IU/mL) and liposomal Resvida (82.2 IU/Ml) treatment at P ≤ 0.05 in addition to up regulating autophagy biomarkers including mTOR/Cfos/ERDj-4/ PTEN by 20, 25, 14, and 17 fold change respectively and down regulating p53 protein expression by 0.4 ng/ml at P ≤ 0.05 post OV intoxication with liposomal regimen reflecting the most significant impact in modulating these altered genes. The current metabolomics study is the integration of histopathologic and autophagy molecular factors to improve the diagnosis, prognosis, and treatment of endometrial cancer in coherent with liposomal drug delivery system as a targeted therapy.

Objective Endometrial cancer (EC) is the most frequent gynecological malignancy in women worldwide. This study aims to develop a predictive model integrating machine learning (ML) approaches with explainable artificial intelligence (XAI) using metabolomics panel data for significant biomarker discovery in EC. Materials and Methods This study applied metabolomics and XAI to uncover diagnostic biomarkers for EC, the most common gynecologic malignancy. A total of 191 EC cases and 204 controls were analyzed using mass spectrometry. ML and XAI techniques were incorporated, including SHapley Additive exPlanation, Random Forest, BaggedCART, LightGBM, Adaptive Boosting, and Extreme Gradient Boosting. Results Statistically significant differences (adjusted p<0.05) were found in 25 metabolites. Effect sizes (ES) of m/z=219.125 (ES=1.516), m/z=672.6961 (ES=0.913), and m/z=203.1564 (ES=0.839) were notably large, suggesting strong discriminatory ability. These metabolites are involved in lipid dysregulation, steroid hormone pathways, and oxidative stress, reflecting cancer-specific metabolic reprogramming. The ML models, particularly LightGBM, demonstrated high accuracy and good calibration. After training with the final feature dataset, SHapley Additive exPlanations (SHAP) analysis identified m/z=219.125, m/z=672.6961, and m/z=127.0769 as the top contributing features, aligning with their biological impact on EC pathogenesis. Conclusion This study suggests non-invasive biomarkers for early detection of EC screening, highlighting the heterogeneity of metabolic adaptation in EC and the need for multi-omics approaches to understand disease mechanisms. Limitations include diverse cohorts and reliance on tandem mass spectrometry. Nonetheless, these findings represent a step forward in precision oncology.

We focused on how the immunophenotypes based on the distribution of CD8-positive tumor-infiltrating lymphocytes (TILs) relate to the endometrial cancer (EC) molecular subtypes and patients’ prognosis. Two cohorts of EC patients (total n = 145) were analyzed and categorized using the Molecular Risk Classifier for Endometrial cancer (ProMisE): POLEmut (POLE mutation), MMRd (mismatch repair deficiency), NSMP (no specific molecular profile), and p53abn (p53 abnormality). CD8-positive TILs, within the central tumor and the invasive margin, were examined by using immunohistochemical staining and advanced image-analysis software. It was investigated whether these immunophenotypes correlate with the molecular subtypes and patients' survival. RNA-sequencing (RNA-seq) was used to explore tumor-derived factors influencing these immunophenotypes. Three distinct immunophenotypes (inflamed, excluded, and desert) based on the CD8-positive TIL patterns were identified in EC patients. Notably, the inflamed phenotype was most frequently observed in the POLEmut and MMRd subtypes, while the desert phenotype was predominant in the NSMP subtype; however, other immunophenotypes were also observed. All p53abn subtype showed the non-inflamed (excluded or desert) phenotype. The prognosis was markedly poorer in the patients with the non-inflamed phenotype than in those with the inflamed phenotype. The RNA-seq analysis showed that the expression of MYC target genes and type-1 interferon response genes was enriched in the non-inflamed phenotype in MMRd and NSMP subtypes, respectively. Evaluating not only the molecular classification but also the immunophenotype may lead to more personalized immunotherapy in EC and elucidating the mechanisms that underlie the formation of the three immunophenotypes could lead to the discovery of new immunotherapy targets.

Mass spectrometry imaging (MSI) is an emerging technology in cancer metabolomics. Desorption electrospray ionization (DESI) and matrix-assisted laser desorption ionization (MALDI) MSI are complementary techniques to identify hundreds of metabolites in space with close to single-cell resolution. This technology leap enables research focusing on tumor heterogeneity, cancer cell plasticity, and the communication signals between cancer and stromal cells in the tumor microenvironment (TME). Currently, unprecedented knowledge is generated using spatial metabolomics in fundamental cancer research. Yet, also translational applications are emerging, including the assessment of spatial drug distribution in organs and tumors. Moreover, clinical research investigates the use of spatial metabolomics as a rapid pathology tool during cancer surgeries. Here, we summarize MSI applications, the knowledge gained by this technology in space, future directions, and developments needed.

Current therapeutic strategies for endometrial cancer are mainly based on aggressive histological types and molecular subtypes. However, ignoring the spatial distribution of immune/stromal cells fails to account for the heterogeneity of the local tumour microenvironment, leading to biased prediction of treatment response. The goal of precision medicine is to delineate the biological characteristics of local functional units based on molecular labelling, which adequately reflects spatially adaptive changes during treatment or metastasis. Single-cell resolution analysis of 40 endometrial cancer cases across four molecular subtypes was performed using imaging mass cytometry (IMC) to quantify the frequency, spatial distribution, and intercellular crosstalk of distinct immune and stromal cell populations. These ecosystem-level features were systematically correlated with clinical features and outcomes, including treatment response and survival. We further identified CD90 + clusters as key regulators of macrophage polarization and T-cell infiltration dynamics, with flow cytometry used to validate their functional role in tumour subtype specification and microenvironmental remodelling. Finally, machine learning-based spatial phenotyping was employed to construct molecular subtype-specific signatures and a highly accurate recurrence prediction model for high-risk endometrial cancer. Single-cell profiling revealed that CD90 + clusters constitute a critical immunomodulatory component within the tumour microenvironment, demonstrating significant enrichment in macrophage differentiation pathways and serving as key mediators of intercellular signalling networks. Furthermore, computational models integrating functional molecular signatures with cell–cell interaction profiles demonstrated high predictive accuracy for both molecular subtyping and recurrence risk stratification in patients with endometrial carcinoma. Our study establishes a spatial eco-context framework for molecular subtypes of endometrial cancer by integrating single-cell spatial multiomics data. This approach enables high-resolution mapping of tumour-immune-stromal interaction networks and reveals novel targets for personalized therapeutic strategies.

No abstract available

Triclosan (TCS), an antimicrobial compound, is known to have potential endocrine-disruptive properties, but the underlying toxic mechanisms at the metabolic level are not well understood. Here, we applied metabolomics and lipidomics combined with mass spectrometry imaging (MSI) to unveil the mechanisms of the enhanced growth of MCF-7 breast cancer cell spheroids (CCS) exposed to TCS. To obtain a wide coverage of metabolites and lipids by using MSI, we used techniques of matrix-assisted laser desorption/ionization (MALDI) and MALDI coupled with laser-postionization. The results showed that TCS and TCS sulfate penetrated into the entire area at 0–3 h and both localized in the inner area at 6 h. After 24 h, a portion of two compounds was released from CCS. Omic data indicated that TCS exposure induced alterations via several pathways, including energy metabolism and biosynthesis of glycerophospholipids and glycerolipids. Further MSI data revealed that the enhancement of energy supply in the peripheral area and the increase of energy storage in the inner area might contribute to the enhanced growth of MCF-7 breast CCS exposed to TCS. This study highlights the importance of integrating metabolite distributions and metabolic profiles to reveal the novel mechanisms of TCS-triggered endocrine disrupting effects.

Background/Objectives: Lipid metabolism plays a crucial role in uterine corpus endometrial carcinoma (UCEC); however, its specific mechanisms remain to be fully elucidated. This study aimed to construct a lipid-metabolism-related prognostic model and explore its association with the tumor immune microenvironment. Methods: A total of 552 UCEC and 35 normal tissue samples from The Cancer Genome Atlas (TCGA) database were analyzed to identify differentially expressed lipid-metabolism-related genes (DE-LMRGs). A prognostic risk model was established using univariate Cox analysis, least absolute shrinkage and selection operator (LASSO) regression, and multivariate Cox regression, and its clinical utility was assessed through nomogram construction. Functional enrichment analysis was performed to explore the biological pathways involved. Tumor immune infiltration patterns were evaluated using single-sample Gene Set Enrichment Analysis (ssGSEA), Estimation of Stromal and Immune Cells in Malignant Tumors using Expression Data (ESTIMATE), and Tumor Immune Dysfunction and Exclusion (TIDE) algorithms. Results: Multivariate analysis indicated that the prognostic model had robust predictive value, with AUCs of 0.701, 0.746, and 0.790 for 1-, 3-, and 5-year overall survival predictions. High-risk patients exhibited a suppressed immune microenvironment characterized by reduced immune cell infiltration, lower tumor mutation burden (TMB), and elevated TIDE scores, suggesting potential resistance to immunotherapy. Furthermore, LIPG was identified as a key hub gene through the intersection of nine machine learning algorithms, demonstrating strong associations with both cancer progression and immune infiltration. Functional validation using Cell Counting Kit-8 (CCK-8), wound healing, and transwell migration assays following small interfering RNA (siRNA) transfection demonstrated that LIPG promotes UCEC cell proliferation and migration in vitro. Conclusions: These findings highlight the critical role of lipid metabolism in UCEC progression and immune modulation, with LIPG emerging as a potential prognostic biomarker. The identified lipid-metabolism-related gene signature may provide new insights into tumor microenvironment interactions.

Background: Endometrial cancer (EC) is a malignancy of the inner epithelial lining of the uterus, with an increasing incidence and disease-associated mortality worldwide. Inflammation and lipid metabolism contribute to EC risk. Materials and Methods: Differential expression genes (DEGs) in EC and normal samples were analyzed based on the TCGA-UCEC database, and DEGs associated with inflammation and lipid metabolism were screened out to be candidate genes. Prognosis-related genes were analyzed using Cox regression and LASSO regression, and a prognostic model was established. Receiver operating characteristic curves and Kaplan–Meier survival analysis were performed to assess the predictive performance of the prognostic model. Gene Set enrichment analysis, immune infiltration analysis, and gene set variation analysis were performed. Expression of prognostic genes in local tissues was examined by Reverse Transcription Quantitative PCR (RT-qPCR) and immunohistochemistry. Methylthiazolyldiphenyl-tetrazolium bromide assay, migration assay, and wound-healing assay were applied to examine the role of CKMT1B on cell proliferation and migration in EC cell lines. Results: A prognostic model based on six prognosis-related genes (CKMT1B, NTS, NSG2, H3C1, MAL, ELOA2) was established in EC, and this model had a favorable predictive performance. Respective different pathways and immune cell infiltration were associated with prognostic genes. 5/6 prognostic genes were highly expressed in local EC tissues compared with normal tissues. Knockdown of CKMT1B significantly suppressed cell proliferation and metastasis in EC cell lines. Conclusion: CKMT1B, NTS, NSG2, H3C1, MAL, and ELOA2 (especially CKMT1B) were important factors in human EC and could be potentially used for risk stratification and prognosis prediction in EC.

Background Endometrial cancer (EC) is one of the common malignant tumors among women, and in recent years, the role of gut microbiota in tumorigenesis has been increasingly gaining attention.Existing research has shown that the gut microbiome, establishes axis connections with multiple extra-intestinal organs. However, whether gut microbes affect the process of endometrial carcinogenesis through metabolic pathways and the specific mechanisms by which they promote the development of EC remain unclear. This study aims to explore the impact of overweight-mediated gut microbiota on the initiation or progression of EC and to assess its relationship with metabolites, thereby providing new insights for early diagnosis and treatment. Methods In this study, we analyzed gut microbiota differences among normal-weight, overweight EC patients, and healthy controls using 16S rRNA sequencing. Liquid chromatography-mass spectrometry (LC-MS) and KEGG analysis identified group-specific metabolites and pathways, while Spearman correlation analysis revealed associations between microbiota and metabolites. Results This study revealed that in the ECMO group, the genus Megamonas exhibited the highest abundance and significant intergroup differences (H=13.46, P<0.05). Additionally, the Bacillota/Bacteroidota ratio (B/B ratio) gradually increased in the CN, ECMN, ECMO group. LEfSe analysis identified Megamonas and Amedibacillus as potential biomarkers for the ECMO group. Serum metabolomics of overweight EC patients highlighted lipid metabolism-related metabolites with the most specific expression. KEGG enrichment analysis of differential metabolites highlighted that the Glycerophospholipid metabolism and Purine metabolism pathways were notably significant in both the ECMN and ECMO groups. Conclusion The study found significantly elevated abundance of Megamonas in the gut microbiota of overweight EC patients, which may promote EC progression by degrading inositol to enhance lipid absorption. This reveals the role of gut microbiota in EC pathogenesis through lipid metabolism regulation, providing a theoretical basis for microbiota-based diagnostic and therapeutic strategies.

Endometrial cancer is one of the major gynecological cancers, with increasing incidence and mortality in the past decades. Emerging preclinical and clinical data have indicated its close association with obesity and dyslipidemia. Metabolism reprogramming has been considered as the hallmark of cancer, to satisfy the extensive need of nutrients and energy for survival and growth. Particularly, lipid metabolism reprogramming has aroused the researchers’ interest in the field of cancer, including tumorigenesis, invasiveness, metastasis, therapeutic resistance and immunity modulation, etc. But the roles of lipid metabolism reprogramming in endometrial cancer have not been fully understood. This review has summarized how lipid metabolism reprogramming induces oncogenesis and progression of endometrial cancer, including the biological functions of aberrant lipid metabolism pathway and altered transcription regulation of lipid metabolism pathway. Besides, we proposed novel therapeutic strategies of targeting lipid metabolism pathway and concentrated on its potential of sensitizing immunotherapy and hormonal therapy, to further optimize the existing treatment modalities of patients with advanced/metastatic endometrial cancer. Moreover, we expect that targeting lipid metabolism plus hormone therapy may block the endometrial malignant transformation and enrich the preventative approaches of endometrial cancer. Lipid metabolism reprogramming plays an important role in tumor initiation and cancer progression of endometrial cancer. Targeting the core enzymes and transcriptional factors of lipid metabolism pathway alone or in combination with immunotherapy/hormone treatment is expected to decrease the tumor burden and provide promising treatment opportunity for patients with advanced/metastatic endometrial cancer.

BACKGROUND Lactamase β (LACTB) inhibits the metastasis and progression of multiple malignant tumors. However, little is known about its role in endometrial cancer (EC). Our study aimed to investigate the function and potential molecular mechanism of LACTB in modulating EC progression. METHODS LACTB expression was measured via immunohistochemistry staining, western blot and qRT-PCR. The role of LACTB in EC was investigated both in vivo and in vitro by employing xenograft mice models and using colony formation, EdU, and Transwell assays, along with flow cytometric analysis. In addition, to assess LACTB function on lipid metabolism, lipid droplets in EC cells were labeled with Nile red. Western blot, immunofluorescence staining, co-immunoprecipitation, ubiquitination assay, and cycloheximide chase assay and rescue experiments were performed to confirm the interaction between LACTB, p53, and MDM2 in EC. RESULTS LACTB expression was downregulated in EC. LACTB inhibited the malignant phenotypes and reprogramed lipid metabolism in EC cells. Moreover, LACTB significantly upregulated p53 by attenuating the MDM2-mediated ubiquitination and degradation of p53. Besides, LACTB silencing facilitated the malignant phenotypes and reprogramed lipid metabolism in EC cells; this was reversed with p53 overexpression. LACTB knockdown facilitated EC progression via downregulating p53 in vivo. CONCLUSION LACTB repressed EC cell proliferation and metastasis, and reprogramed lipid metabolism via attenuating the MDM2-mediated ubiquitination and degradation of p53.

Abstract Endometrial cancer is strongly associated with obesity and other metabolic disorders, but host-level associations do not necessarily imply that tumor cells depend on a specific lipid pathway. This review therefore considers two related questions: how obesity, insulin resistance, and dyslipidemia influence endometrial cancer risk and outcome, and what evidence supports tumor-intrinsic changes in lipid uptake, fatty acid synthesis and desaturation, fatty acid oxidation, and cholesterol or oxysterol signaling. Endometrial cancer-specific studies provide preclinical support for ATP-citrate lyase (ACLY), fatty acid synthase (FASN), stearoyl-CoA desaturase 1 (SCD1), acyl-CoA synthetase long-chain family member 1 (ACSL1), squalene epoxidase (SQLE), and liver X receptor alpha (LXRα), which is encoded by nuclear receptor subfamily 1 group H member 3 (NR1H3). Evidence for circulating lipids and tissue markers is less mature: most studies are observational, retrospective, or based on cell lines and xenografts, and standardized assays and clinically validated cutoffs are lacking. Statins, fatostatin, SQLE inhibition, and fatty acid synthase inhibition illustrate possible therapeutic approaches, but no lipid-directed treatment has established efficacy in an endometrial cancer-specific clinical trial. The available literature supports continued mechanistic and biomarker work, provided that host metabolic measures, tumor pathway dependence, and treatment-predictive value are evaluated as separate evidence categories.

No abstract available

Objective This study aimed to predict tumor remission by monitoring obesity-parameter changes during weight loss in EC patients and to identify the most reliable indicators for forecasting tumor complete remission (CR). Methods A prospective longitudinal study was conducted to track changes in obesity parameters during a 6-month weight-loss period and to predict 9-month CR. Body morphology indicators, including weight, body mass index (BMI), waist circumference (WC), hip circumference (HC), waist-to-height ratio (WHtR), waist-to-hip ratio (WHR), lipid accumulation product (LAP), and body roundness index (BRI), and metabolic measures such as triglycerides (TG), hemoglobin A1c (HbA1c), fasting insulin (FINS), and insulin resistance (IR) were assessed. Multivariable logistic regression was used to evaluate associations between these parameters and CR, and receiver operating characteristic (ROC) analyses were performed to determine optimal cut-off values for predicting remission. Results A total of 84 patients were included. Multivariable logistic regression identified WHR as a significant independent predictor for CR (P = 0.015, OR=1.111 95% CI: 1.021–1.209). ROC analysis revealed optimal cut-off values for WHR (0.90), TG (1.38), and FINS (18.93). The AUC values were 0.71, 0.63, and 0.67 respectively, indicating moderate predictive value. Calibration and decision curve analyses confirmed the models' stability and clinical benefit. Conclusions WHR may serve as an additional predictor of obesity in CR. of obesity for CR. It is a convenient and quick method for monitoring and developing individualized treatments for fertility preservation and weight reduction strategies.

Metabolic syndrome increases the risk of endometrial cancer development and progression, but the mechanism remains unclear. We find that polyamine metabolites are notably elevated in the sera and tumor tissues of endometrial cancer patients with metabolic syndrome. Oleic acid, one of the many components in hyperlipidemia, is the key factor for upregulating Ornithine Decarboxylase 1 (ODC1) (the rate-limiting enzyme in polyamine metabolism) and downstream polyamines. Mechanistically, Oleic acid binds to and stabilizes Homeobox B9 (HOXB9) by inhibiting the binding of HOXB9 to E3 ligase Praja2. Stable HOXB9 then competes with OAZ1 and combines with ODC1 to block ODC1 degradation. Targeting HOXB9 or ODC1 reduces polyamine levels and suppresses tumor growth/spread. Oleic acid-HOXB9-ODC1 stable cascading axis then is confirmed in patient tissues, and ODC1 inhibitors boost patient-derived tumor cells’ chemosensitivity. This study links fatty acids to polyamine buildup, reveals a mechanism for metabolic syndrome-driven endometrial cancer, and points to HOXB9 and ODC1 as potential therapeutic targets. Metabolic syndrome (MS) is associated with enhanced risk to develop endometrial cancer (EC). Here, the authors show that oleic acid, increased in MS, promotes endometrial cancer by supporting the stability of the rate-limiting enzyme in polyamine metabolism ODC1 and polyamine accumulation.

No abstract available

Endometrial cancer is the 6th most common carcinoma as well as the 2nd most common malignancy worldwide in women. It is closely related to fat content, and dyslipidemia is among the most significant metabolic changes in this cancer. Therefore, further understanding of the regulation mechanism in lipid metabolism of endometrial cancer is conducive to the development of better therapeutic strategies and methods. Here, we systematically review the signaling pathways that regulate lipid metabolism in endometrial cancer and the research progress of drugs and targeted therapies that act on lipid metabolism by retrieving relevant articles. The underlying mechanism of occurrence and development of endometrial cancer is relatively clear and comprehensively reviewed here. But following more research studies will help to illuminate more specific regulatory roles of lipid metabolism in endometrial cancer and explore new possible mechanisms, prognostic and therapeutic targets, and subsequent drugs. Our review will provide a full view for the following investigation of lipid metabolism in endometrial cancer.

Metabolic reprogramming is crucial in developing endometrial cancer (EC); however, the mechanisms through which tumor suppressors control metabolites that drive cell proliferation and tumor growth remain unclear. ARID1A, an SWI/SNF chromatin remodeling complex subunit, is frequently mutated in endometrium-related malignancies. Here, EC tumors with ARID1A deleted exhibit increased polyamine production, which enhances malignant proliferative capacity while inhibiting the efficacy of functional CD8+ T cells. Mechanistically, ARID1A depletion in tumor cells interrupts the competitive binding of ARID1A to YAP, causing excessive YAP activation and transcriptionally increasing the expression of polyamine metabolic enzymes, thereby enhancing polyamine synthesis. Increased spermidine production from polyamines can directly hypusinate eukaryotic translation initiation factor 5A (eIF5A) at lysine residues, resulting in efficient histone demethylase LSD1 protein translation. Moreover, polyamine accumulation suppresses the recruitment of CD8+ T cells and hampers antitumor immune responses in vivo. Notably, polyamine depletion induced by eflornithine (DFMO) significantly reduces EC cell proliferative capacity and enhances CD8+ T-cell efficacy. Together, these findings highlight the role of ARID1A in regulating polyamine metabolism and suggest that elevated polyamine levels in tumors enhance malignant cellular behaviors and contribute to immune evasion by inhibiting CD8+ T cell-mediated cytotoxic responses. Therefore, targeting polyamine biosynthesis could be an important therapeutic strategy for ARID1A-inactivated EC.

Arginine metabolism, polyamine homeostasis and ferroptosis are interconnected molecular processes that influence tumor growth, immune remodeling and therapeutic responses. The availability of arginine is affected by tumor-cell biosynthesis and uptake, myeloid arginase (ARG)-mediated depletion, nitric oxide synthase (NOS)-driven diversion and stromal-vascular nutrient transport and microenvironmental remodeling. These input signals regulate the synthesis, uptake-efflux cycling and catabolic turnover of polyamines, thereby creating an inhibitory buffering state or an oxidative state that can lower the ferroptosis threshold. Ferroptosis is controlled by the balance of lipid peroxidation triggers, including polyunsaturated fatty acid-phospholipid load, iron-dependent amplification and lipoxygenase activity, as well as buffering systems such as the cystine/glutamate antiporter system Xc−-glutathione-glutathione peroxidase 4 axis, ferroptosis suppressor protein 1-coenzyme Q10, dihydroorotate dehydrogenase and GTP cyclohydrolase 1-tetrahydrobiopterin (BH4). The immunological consequences of ferroptotic tumor-cell death are context-dependent, as dying cells may promote interferon-gamma-driven antitumor immunity or, conversely, reinforce myeloid-mediated immunosuppression. The present review summarized the molecular links among arginine metabolism, polyamine homeostasis and ferroptosis in cancer and discusses therapeutic opportunities involving biomarker-guided combinations targeting arginine, polyamine blockade, ferroptosis sensitization and immune checkpoint blockade.

No abstract available

Progesterone resistance (ProR) remains a major obstacle in the conservative management of endometrial cancer (EC). Here, a metabolic‐stromal signaling loop centered on the OLR1/FOXM1/FGF19 axis is identified that drives progesterone resistance in EC. Single‐cell transcriptomic profiling first revealed a striking correlation between epithelial cells and fibroblasts in EC tissues with ProR. Tumor epithelial cells display profound alterations in lipid metabolism, whereas fibroblasts exhibited enhanced oxidative stress signatures. Clinical samples analyses indicated that oxidized low density lipoprotein (oxLDL), a product of LDL oxidation, is associated with adverse outcomes. The binding of oxLDL to its receptor OLR1 promoted the expression of FOXM1, a transcription factor that directly upregulates fibroblast growth factor 19 (FGF19). Immunofluorescence confirmed not only the spatial co‐localization of epithelial cells and fibroblasts but also the enrichment of OLR1 within epithelial compartments. Furthermore, treatment with the antioxidant resveratrol (RSV) and its nanoformulation (RSV‐NPs) markedly inhibited tumor growth in mice with lipid metabolic disorders, highlighting their potential to counteract progesterone resistance by disrupting this OLR1/FOXM1/FGF19 axis. This work highlights the therapeutic potential of targeting the tumor–stroma metabolic axis to increase progesterone sensitivity and improve outcomes in EC patients with fertility‐preserving demands.

BACKGROUND While lipids and inflammation are recognized as key modulators of tumor progression, their causal interplay in endometrial carcinoma in situ (ECIS)-the precursor of endometrial cancer-remains mechanistically undefined. Despite broad associations between lipid metabolism and cancer, the specific pathways driving ECIS initiation through inflammatory mediators are unknown. METHOD We pioneer an integrative Mendelian randomization (MR) framework combining multivariable MR, Bayesian weighted MR (BWMR), and sensitivity analyses to address pleiotropy. This approach was systematically applied to 179 lipids and 91 inflammatory factors. Methodological novelty further includes mediation analysis quantifying inflammatory factors' role in lipid-ECIS pathways. RESULTS First evidence reveals a phosphatidylcholine (O-18:0_16:1)-TNFSF12-ECIS axis: TNFSF12 mediates 4.894 % of phosphatidylcholine's effect (OR: 2.925; beta: 1.073; 95 % CI: 1.752-4.884; p = 4.032E-05), attenuating the direct lipid-ECIS association. This represents the inaugural demonstration of an inflammation-mediated lipid pathway in ECIS pathogenesis. CONCLUSION As the first MR study decoding lipid-ECIS causality, we establish the following.

Background The dysregulation of genes related to lipid metabolism and lysosomal function has been reported to significantly contribute to tumor progression. In this study, we systematically explored the roles played by lipid metabolism and lysosomes in uterine corpus endometrial carcinoma (UCEC), aiming to identify potential biomarkers for predicting prognosis and immune checkpoint therapy efficacy. Methods Genes associated with lipid metabolism and lysosomal function were retrieved from the MSigDB and GO databases. Transcriptomic data and clinical information of patients were acquired from The Cancer Genome Atlas database. A prognostic model was constructed using consensus clustering, univariate Cox regression, and LASSO regression. ROC curves, Kaplan-Meier plots, and calibration curves were employed to assess the predictive capacity of the model, while ssGSEA, TIDE, and IPS were used to evaluate the response of high- and low-risk groups to immunotherapy. Drug sensitivity was assessed with the “oncoPredict” R package. Given that we identified a strong association between PLAAT1 and CD8+ T-cell infiltration, this gene was selected for loss-of-function assays in UCEC cells, including the evaluation of their proliferative, invasive, and migratory potential. Results An eight-gene (LAMP3, RNF183, EEF1A2, PLAAT1, ELAPOR1, B4GALT1, ATP10B, and PLA2G10) risk signature based on lipid metabolism and lysosomal function was constructed to distinguish high-risk and low-risk UCEC patients. Subsequent analyses showed that patients classified as high risk had higher TIDE scores, whereas those categorized as low risk exhibited higher MSI scores and greater levels of CD8+ T-cell infiltration. All evidence suggested that patients in the low-risk group displayed greater immunogenicity and sensitivity to both immunotherapy and chemotherapy. Analysis using the TIMER database indicated that among the eight risk genes, PLAAT1 showed the strongest association with CD8+ T-cell immune infiltration in UCEC. Cytological experiments confirmed that the knockdown of PLAAT1 effectively suppressed the proliferation and motility of endometrial cancer cells. Conclusion We constructed a risk prognostic model for UCEC based on a combination of lysosomal- and lipid metabolism-related genes. Our findings highlight the oncogenic potential of PLAAT1 in endometrial cancer and provide novel insights into the diagnosis and therapy of this cancer type.

e16359 Background: Pancreatic cancer is one of the most malignant cancers with an occult presentation and difficult diagnosis. Liver metastasis, which accounts for 90% of metastatic pancreatic cancer, is particularly associated with poor prognosis and high mortality. Glutamine metabolic reprogramming is a major source of energy for metastatic cancer cells. However, whether it is involved in the crosstalk between pancreatic stellate cells (PSCs) and cancer cells and cancer metastasis remains unclear. Methods: In this study, Pan02 cell lines and C57 / BL6 mouse PSCs were used to investigate the function of upstream transcription factors, glutamate ammonia ligase (GLUL) and early growth response gene 1 (EGR1), on PSC activation and tumour metastasis. Results: ELISA and Western blot showed that GLUL promoted glutamine synthesis and secretion in PSCs and inhibited mTOR-mediated activation of PSCs through EGR1. When co-cultured with EGR1/GLUL-overexpressed PSCs, Pan02 cells exhibited metabolic reprogramming manifested by suppressed autophagy levels and vasculogenesis, resulting in insufficient energy intake. The crosstalk between PSCs and pancreatic cancer cells reduced the proliferation, migration and invasion of pancreatic cancer cells and ultimately inhibited pancreatic cancer liver metastasis. Conclusions: Our results indicate that EGR1-mediated glutamine metabolic reprogramming is involved in the interaction of PSCs and pancreatic cancer cells and inhibits pancreatic cancer liver metastasis. In the future, EGR1 may be the potential drug target and better treatment for liver metastatic pancreatic cancer.

Background This study aims to identify precise biomarkers for breast cancer to improve patient outcomes, addressing the limitations of traditional staging in predicting treatment responses. Methods Our analysis encompassed data from over 7,000 breast cancer patients across 14 datasets, which included in-house clinical data and single-cell data from 8 patients (totaling 43,766 cells). We utilized an integrative approach, applying 10 machine learning algorithms in 54 unique combinations to analyze 100 existing breast cancer signatures. Immunohistochemistry assays were performed for empirical validation. The study also investigated potential immunotherapies and chemotherapies. Results Our research identified five consistent glutamine metabolic reprogramming (GMR)-related genes from multi-center cohorts, forming the foundation of a novel GMR-model. This model demonstrated superior accuracy in predicting recurrence and mortality risks compared to existing clinical and molecular features. Patients classified as high-risk by the model exhibited poorer outcomes. IHC validation in 30 patients reinforced these findings, suggesting the model’s broad applicability. Intriguingly, the model indicates a differential therapeutic response: low-risk patients may benefit more from immunotherapy, whereas high-risk patients showed sensitivity to specific chemotherapies like BI-2536 and ispinesib. Conclusions The GMR-model marks a significant leap forward in breast cancer prognosis and the personalization of treatment strategies, offering vital insights for the effective management of diverse breast cancer patient populations.

Background Triple-negative breast cancer (TNBC) is a heterogeneous disease that is characterized by metabolic disruption. Metabolic reprogramming and tumor cell immune escape play indispensable roles in the tumorigenesis that leads to TNBC. Methods In this study, we constructed and validated two prognostic glutamine metabolic gene models, Clusters A and B, to better discriminate between groups of TNBC patients based on risk. Compared with the risk Cluster A patients, the Cluster B patients tended to exhibit better survival outcomes and higher immune cell infiltration. In addition, we established a scoring system, the glutamine metabolism score (GMS), to assess the pattern of glutamine metabolic modification. Results We found that solute carrier family 7 member 5 (SLC7A5), an amino acid transporter, was the most important gene and plays a vital role in glutamine metabolism reprogramming in TNBC cells. Knocking down SLC7A5 significantly inhibited human and mouse TNBC cell proliferation, migration, and invasion. In addition, downregulation of SLC7A5 increased CD8+ T-cell infiltration. The combination of a SLC7A5 blockade mediated via JPH203 treatment and an anti-programmed cell death 1 (PD-1) antibody synergistically increased the immune cell infiltration rate and inhibited tumor progression. Conclusions Hence, our results highlight the molecular mechanisms underlying SLC7A5 effects and lead to a better understanding of the potential benefit of targeting glutamine metabolism in combination with immunotherapy as a new therapy for TNBC.

Hepatocellular carcinoma (HCC) is a lethal disease with limited management strategies and poor prognosis. Metabolism alternations have been frequently unveiled in HCC, including glutamine metabolic reprogramming. The components of glutamine metabolism, such as glutamine synthetase, glutamate dehydrogenase, glutaminase, metabolites, and metabolite transporters, are validated to be potential biomarkers of HCC. Increased glutamine consumption is confirmed in HCC, which fuels proliferation by elevated glutamate dehydrogenase or upstream signals. Glutamine metabolism also serves as a nitrogen source for amino acid or nucleotide anabolism. In addition, more glutamine converts to glutathione as an antioxidant in HCC to protect HCC cells from oxidative stress. Moreover, glutamine metabolic reprogramming activates the mTORC signaling pathway to support tumor cell proliferation. Glutamine metabolism targeting therapy includes glutamine deprivation, related enzyme inhibitors, and transporters inhibitors. Together, glutamine metabolic reprogramming plays a pivotal role in HCC identification, proliferation, and progression.

Cadmium (Cd) exposure is known to enhance breast cancer (BC) progression. Cd promotes epithelial-mesenchymal transition (EMT) in BC cells, facilitating BC cell aggressiveness and invasion, but the underlying molecular mechanisms are unclear. Hence, transgenic MMTV-Erbb2 mice (6 weeks) were orally administered Cd (3.6 mg/L, approximately equal to 19.64 μΜ) for 23 weeks, and BC cells (BT474 cells) were exposed to Cd (0, 0.1, 1 or 10 μΜ) for 72 h to investigate the effect of Cd exposure on EMT in BC cells. Chronic Cd exposure dramatically expedited tumor metastasis to multiple organs; decreased E-cadherin density; and increased Vimentin, N-cadherin, ZEB1, and Twist density in the tumor tissues of MMTV-Erbb2 mice. Notably, transcriptomic analysis of BC tumors revealed cytochrome P450 1B1 (CYP1B1) as a key factor that regulates EMT progression in Cd-treated MMTV-Erbb2 mice. Moreover, Cd increased CYP1B1 expression in MMTV-Erbb2 mouse BC tumors and in BT474 cells, and CYP1B1 inhibition decreased Cd-induced BC cell malignancy and EMT in BT474 cells. Importantly, the promotion of EMT by CYP1B1 in Cd-treated BC cells was presumably controlled by glutamine metabolism. This study offers novel perspectives into the effect of environmental Cd exposure on driving BC progression and metastasis, and this study provides important guidance for comprehensively assessing the ecological and health risks of Cd.

Introduction The reprogramming of glutamine metabolism holds a pivotal position in the energy provision and biosynthesis of tumors. However, the regulatory mechanism of this phenomenon in non-small cell lung cancer (NSCLC) is still not well-understood. NSCLC is a type of malignancy that has a high incidence and mortality rate globally. There is an urgent need to elucidate the role of glutamine metabolism in its pathological mechanism. This clarification may provide theoretical guidance for developing new therapeutic approaches. Methods Core targets of glutamine metabolism were screened by integrating single-cell transcriptomic and RNA sequencing data from public databases. Target expression was validated in clinical samples by immunohistochemistry (IHC) and Western blot (WB), and its association with clinical features was analyzed. Lentiviral gene silencing was employed to establish glutamine-deprived cell models and xenograft mouse models. To evaluate the effects of the target on cell proliferation, redox balance, and migratory/invasive behavior in cell culture and animal models, we utilized Transwell assays, colony formation assays, redox detection kits, and Seahorse metabolic flux analysis. Subsequently, WB and IHC served to elucidate the downstream pathways and potential synergistic effects of the drugs. Results Analysis of the single-cell atlas revealed a marked increase in epithelial (Epi) cell populations in the tumor milieu of NSCLC. By integrating weighted gene co-expression network analysis (WGCNA) with RNA sequencing, fibroblast growth factor 17 (FGF17) was pinpointed as a crucial regulatory factor. High FGF17 expression showed a strong association with poor prognosis in patient (p = 0.0078). Consistent clinical data further demonstrated that FGF17 upregulation was associated with higher TNM stages and the presence of lymph node metastasis. Functional and mechanistic analyses revealed that silencing FGF17 suppressed the FGFR4/MEK5/ERK5 signaling cascade, disturbed NRF2-dependent redox homeostasis, and consequently impaired epithelial–mesenchymal transition (EMT), leading to a marked reduction in cancer cell motility and invasiveness. In vivo, targeting FGF17 was shown to synergistically enhance cisplatin antitumor activity and reverse the EMT phenotype. Conclusion As a critical driver of glutamine metabolic reprogramming, FGF17—activated under conditions of GLUL overexpression—stimulates the FGFR4/MEK5/ERK5/NRF2 signaling cascade to maintain redox homeostasis and promote invasion, thereby accelerating NSCLC progression. Targeted intervention of the pathway reverses malignant phenotypes and enhances chemosensitivity. These findings highlight FGF17 as a potential therapeutic target for NSCLC and provide new insights into tumor metabolism and EMT, thereby may paving the way for novel combination therapies.

Triple-negative breast cancer (TNBC) represents the most aggressive breast cancer subtype with limited therapeutic options and poor prognosis, highlighting an urgent need to identify novel metabolic vulnerabilities and prognostic biomarkers to improve patient outcomes. While glutamine metabolism has been implicated in cancer, the specific roles of glutamine metabolism-related genes (GMRGs) in TNBC remain poorly understood. Herein, through an integrating analysis encompassing multi-omics bioinformatics, consensus clustering and tumor microenvironment (TME) analysis, we established a glutamine metabolism-based prognostic classification for TNBC patients, which correlates with distinct survival outcomes and TME features. Furthermore, we identified ALDH18A1, one of the GMRGs that encodes P5CS for proline synthesis, as a novel prognostic biomarker and oncogenic driver. ALDH18A1 is overexpressed in TNBC and associated with larger tumor size, lymph node metastasis and poor survival, as well as an immunosuppressive TME. In vitro experiments confirmed ALDH18A1 activated the AKT/mTOR signaling pathway, promoted the proliferation, migration and invasion of TNBC cells, and increased proline synthesis. Computational drug screening predicted six compounds with potential efficacy against ALDH18A1-high tumors. Collectively, our findings demonstrate that reprogramming of glutamine metabolism plays a crucial role in the malignant progression of TNBC and provide translational insights for precision metabolomic-immunotherapeutic strategies in ALDH18A1-high TNBC.

Metabolic alterations that support the supply of biosynthetic molecules necessary for rapid and sustained proliferation are characteristic of cancer. Some cancer cells rely on glutamine to maintain their energy requirements for growth. Glutamine is an important metabolite in cells because it not only links to the tricarboxylic acid cycle by producing α-ketoglutarate by glutaminase and glutamate dehydrogenase but also supplies other non-essential amino acids, fatty acids, and components of nucleotide synthesis. Altered glutamine metabolism is associated with cancer cell survival, proliferation, metastasis, and aggression. Furthermore, altered glutamine metabolism is known to be involved in therapeutic resistance. In recent studies, lncRNAs were shown to act on amino acid transporters and glutamine-metabolic enzymes, resulting in the regulation of glutamine metabolism. The lncRNAs involved in the expression of the transporters include the abhydrolase domain containing 11 antisense RNA 1, LINC00857, plasmacytoma variant translocation 1, Myc-induced long non-coding RNA, and opa interacting protein 5 antisense RNA 1, all of which play oncogenic roles. When it comes to the regulation of glutamine-metabolic enzymes, several lncRNAs, including nuclear paraspeckle assembly transcript 1, XLOC_006390, urothelial cancer associated 1, and thymopoietin antisense RNA 1, show oncogenic activities, and others such as antisense lncRNA of glutaminase, lincRNA-p21, and ataxin 8 opposite strand serve as tumor suppressors. In addition, glutamine-dependent cancer cells with lncRNA dysregulation promote cell survival, proliferation, and metastasis by increasing chemo- and radio-resistance. Therefore, understanding the roles of lncRNAs in glutamine metabolism will be helpful for the establishment of therapeutic strategies for glutamine-dependent cancer patients.

No abstract available

Metabolic reprogramming induced by the glutamine/glutamate (Gln/Glu) metabolic pathway is a key mechanism in ATP production, precursor biosynthesis, and redox homeostasis, promoting prostate cancer (PCa) growth and proliferation. This evolutionarily acquired hallmark of cancers enables malignant cells to adapt their bioenergetic and biosynthetic pathways in response to microenvironmental stresses. Therefore, Gln/Glu metabolism orchestrates epigenetic regulation, metastatic capacity, and oxidative homeostasis in PCa, supporting the survival of PCa tumors. Fluctuations in Glu metabolite levels and oxygen tension shape the PCa epigenome by facilitating Glu-derived α-ketoglutarate (α-KG) activation of TET and KDM enzymes, which drive histone and DNA demethylation. Furthermore, tumor progression toward metastatic castration-resistant PCa is characterized by heightened Gln/Glu dependency and increased Gln uptake. Within the tumor microenvironment (TME), a dynamic tug-of-war occurs between tumor and immune cells, competing for Gln metabolites. Gln/Glu converges on critical oncogenic signaling axes, including NF-κB/Nrf2, c-Myc/androgen receptor, MAPK/ERK, and PI3K/AKT/mTOR. Additionally, extracellular Glu release via SLC7A11 and PSMA triggers metabotropic glutamate receptor (mGluR) signaling, further potentiating oncogenic programs. Targeting this Gln/Glu metabolic network thus presents a promising therapeutic approach against PCa. In this review, we summarize the role of Gln/Glu in PCa progression based on the compartmentalization of the Gln/Glu metabolic pathway to elucidate why PCa cells manifest dependence on Gln/Glu. Eventually, we highlight potential therapeutic targets that can be exploited for PCa treatment.

Background: Despite advances in gene-targeted and immunotherapies, many aggressive cancers—including glioblastoma and triple-negative breast cancer—remain refractory to treatment. Mounting evidence implicates metabolic reprogramming, especially dysregulation of glucose and glutamine metabolism, as a core hallmark of tumor progression. Natural compounds with metabolic-modulatory effects have emerged as promising adjuncts in oncology. Research Question and Objectives: This review investigates the following question: How can metabolic-targeted therapies—particularly those modulating the Warburg effect and glutamine metabolism—improve cancer treatment outcomes, and what role do natural compounds play in this strategy? The objectives were to (1) evaluate the therapeutic potential of metabolic interventions targeting glucose and glutamine metabolism, (2) assess natural compounds with metabolic regulatory activity, (3) examine integration of metabolic-targeted therapies with conventional treatments, and (4) identify metabolic vulnerabilities in resistant malignancies. Methods: A qualitative systematic review (QualSR) was conducted following PRISMA guidelines. A total of 87 peer-reviewed studies published between 2000 and 2024 were included. Inclusion criteria required clearly defined mechanistic or clinical endpoints and, for clinical trials, sample sizes ≥ 30. Data extraction focused on tumor response, survival, metabolic modulation, and safety profiles. Results: Curcumin significantly reduced serum TNF-α and IL-6 (both p = 0.001) and improved antioxidant capacity (p = 0.001). EGCG downregulated ERα (p = 0.002) and upregulated tumor suppressors p53 and p21 (p = 0.001, p = 0.02). High-dose intravenous vitamin C combined with chemoradiotherapy yielded a 44.4% pathologic complete response rate in rectal cancer. Berberine suppressed Akt/mTOR signaling and glutamine transporter SLC1A5 across tumor types (q < 10−10). However, poor bioavailability (e.g., EGCG t½ = 3.4 ± 0.3 h) and systemic toxicity limit their standalone clinical application. Conclusions: Metabolic-targeted therapies—particularly natural compounds acting on glucose and glutamine pathways—offer a viable adjunct to standard cancer therapies. Clinical translation will require biomarker-driven patient stratification, improved delivery systems, and combination trials to optimize the therapeutic impact in treatment-resistant cancers.

Glutamine metabolism in tumor microenvironments critically regulates antitumor immunity. Using the glutamine-antagonist prodrug JHU083, we report potent tumor growth inhibition in urologic tumors by JHU083-reprogrammed tumor-associated macrophages (TAMs) and tumor-infiltrating monocytes (TIMs). We show JHU083-mediated glutamine antagonism in tumor microenvironments induced TNF, pro-inflammatory, and mTORC1 signaling in intratumoral TAM clusters. JHU083-reprogrammed TAMs also exhibited increased tumor cell phagocytosis and diminished pro-angiogenic capacities. In vivo inhibition of TAM glutamine consumption resulted in increased glycolysis, a broken TCA cycle, and purine metabolism disruption. Although the antitumor effect of glutamine antagonism on tumor-infiltrating T cells was moderate, JHU083 promoted a stem cell–like phenotype in CD8+ T cells and decreased the abundance of regulatory T cells (Treg). Finally, JHU083 caused a global shutdown in glutamine-utilizing metabolic pathways in tumor cells, leading to reduced HIF-1α, c-MYC phosphorylation, and induction of tumor cell apoptosis, all key antitumor features. Altogether, our findings demonstrate that targeting glutamine with JHU083 led to suppressed tumor growth as well as reprogramming of immune-suppressive TAMs within prostate and bladder tumors that promoted antitumor immune responses. JHU083 can offer an effective therapeutic benefit for tumor types that are enriched in immune-suppressive TAMs. Synopsis: The authors show in myeloid-rich urologic tumors that glutamine antagonism in TAMs results in functional alterations including increased tumor-cell phagocytosis, inflammatory signaling, immune-proliferative pathways, and glycolysis. This promotes antitumor immunity, suggesting new therapeutic approaches for these immunotherapy-unresponsive tumors.

Colorectal cancer (CRC) cells exhibit a pronounced dependence on L-glutamine to support anabolic growth, redox balance, and mitochondrial metabolism, a phenomenon known as "glutamine addiction." The canonical glutaminase I pathway is mediated by a liver type glutaminase isozyme (LGA; GLS1), a kidney type glutaminase isozyme (KGA; GLS2), and a shortened form (glutaminase C, GAC). GLS1 and GLS2 convert glutamine to glutamate and subsequently to α-ketoglutarate (α-KG) by glutamate dehydrogenase, fueling the TCA cycle. GLS1 inhibitors, such as CB-839 (Telaglenastat), are under clinical evaluation and shows promise in treatment of CRC, particularly in combination therapies. In addition to the canonical pathway, the glutamine transaminase-ω-amidase (GTωA) pathway, a noncanonical route involving transamination of glutamine to α-ketoglutaramate (KGM) by KYAT1/2 and subsequent hydrolysis by ω-amidase (NIT2), offers metabolic flexibility under hypoxic or nutrient-limited conditions. Preclinical studies suggest that GTωA may compensate for GLS1 inhibition, contributing to therapeutic resistance. This review explores the dual roles of glutamine metabolism in CRC, emphasizing the GTωA pathway as a potentially targetable metabolic route that may contribute to therapeutic resistance. While GLS1 inhibitors are under clinical evaluation, emerging evidence suggests that dual targeting of both pathways may enhance treatment efficacy by overcoming metabolic compensation. Understanding the regulatory mechanisms driving the "glutamine shift" between these pathways is critical for developing effective metabolic interventions in CRC.

At present, mass spectrometry (MS) and mass spectrometry imaging (MSI) have developed into versatile analytical techniques, applicable for the analysis of both small molecules and macromolecular compounds and complexes. Desorption electrospray ionization (DESI), as an ambient MS (AMS) technique, enables rapid analysis and imaging under open conditions with minimal or no sample preparation required. In this review, the principles and mechanisms of DESI were described, especially for the factors affecting the analysis including suppression effects, device condition parameter settings, and ionization and transmission efficiency. Moreover, the relevant improvements of DESI technique were introduced in detail. Emphasis has been made to discuss the recent research on the application of DESI MS and MSI in the fields of metabolomics, lipidomics, and proteomics. In these applications, DESI MS and MSI exhibit exceptional capabilities for qualitative analysis.

Mass spectrometry imaging (MSI) is emerging as a powerful tool for uncovering the distribution of metabolites in the tumor microenvironment and studying tumor metabolism in vivo. However, to date, MSI of primary patient biobanked tissues contextualized by patient data has been limited to peptides, proteins, and glycans – with few examples for metabolites. This is because most biobanked fresh-frozen tissue required for spatial metabolomics is embedded in optimal cutting temperature compound (OCT), which introduces high-abundance polymeric interferents. Herein, we use nanospray desorption electrospray ionization (nano-DESI) to demonstrate the MSI of metabolites in OCT-embedded tissue. Metabolite coverage and sensitivity for prepared tissue mimetic homogenates embedded in OCT and an MSI-compatible material, carboxymethylcellulose (CMC), showed excellent agreement. We apply our ambient MSI workflow to detect changes in intratumoral methionine using a preclinical cancer mouse model undergoing adoptive T-cell therapy. Eight days after tumor incubation, lymphoma-bearing mice were maintained on a complete or methionine-restricted diet for 2 days. Nano-DESI MSI revealed a heterogeneous tumor microenvironment, with multiple methionine-cycle intermediates (S-adenosylmethionine, S-adenosylhomocysteine) and related metabolites, including known T-cell modulators (1-methylnicotinamide, polyamines) localizing to tumor subregions. Methionine-restricted tumors exhibited reduced methionine levels and elevated S-adenosylmethionine, relative to the control group. Overall, this work demonstrates spatial metabolomics on fresh-frozen OCT-embedded tissue, unlocking the wealth of information stored in primary tissue biobanks and consequently accelerating our understanding of cancer metabolism and treatment.

No abstract available

Zebrafish (Danio rerio) represent an effective model biological material for human disease research, even for personalized precision medicine. Thus, it is necessary to fully characterize their molecular information in order to obtain a global metabolic profile. Here, a spatially resolved metabolomics method for whole-body zebrafish analysis was established based on an air-flow-assisted desorption electrospray ionization-mass spectrometry imaging (AFADESI-MSI) system. Using the optimized experimental conditions, the method provided high-quality visual distribution information for >1000 functional metabolites, thereby organ-specific metabolites characterizing nine regions were obtained comprehensively, including the eyes, brain, gill, heart, liver, kidney, intestine, muscle, and spinal cord. Then, combined with metabolic pathway analysis, a global metabolic network with in situ information on zebrafish was mapped for the first time. We also tried to use the recently published MSI database to annotate the metabolites in this study; however, the annotation rate was only 33.7 and 10.4% in positive and negative modes, respectively. This further demonstrated the necessity of establishing a suitable AFADESI-MSI method for zebrafish samples. These results offer comprehensive and in-depth molecular information about zebrafish at the metabolic level, which facilitates the use of zebrafish models to understand metabolic reprogramming in human diseases and the development of zebrafish disease models.

Ketamine is a new, fast, and effective antidepression treatment method; however, the possible dissociation effects, sensory changes, abuse risk, and the inability to accurately identify whether patients have a significant response to ketamine limit its clinical use. Further exploration of the antidepressant mechanisms of ketamine will contribute to its safe and practical application. Metabolites, the products of upstream gene expression and protein regulatory networks, play an essential role in various physiological and pathophysiological processes. In traditional metabonomics it is difficult to achieve the spatial localization of metabolites, which limits the further analysis of brain metabonomics by researchers. Here, we used a metabolic network mapping method called ambient air flow-assisted desorption electrospray ionization (AFADESI)-mass spectrometry imaging (MSI). We found the main changes in glycerophospholipid metabolism around the brain and sphingolipid metabolism changed mainly in the globus pallidus, which showed the most significant metabolite change after esketamine injection. The spatial distribution of metabolic changes was evaluated in the whole brain, and the potential mechanism of esketamine’s antidepressant effect was explored in this research.

No abstract available

Metabolic networks and their dysfunction in the brain are closely associated with central nervous function and many psychogenic diseases. Thus, it is of utmost importance to develop a high-throughput imaging method for metabolic network mapping. Here, we developed a metabolic network mapping method to discover the metabolic contexts and alterations with spatially resolved information from the microregion of the brain by ambient-air flow-assisted desorption electrospray ionization mass spectrometry imaging and metabolomics analysis, which can be performed without any chemical derivatization, labels, or complex sample pretreatment. This method can map hundreds of different polar functional metabolites involved in multiple metabolic pathways, including not only neurotransmitters but also purines, organic acids, polyamines, cholines, and carbohydrates, in the rat brain. These high-coverage metabolite profile and microregional distribution information constitute complex networks that regulate advanced functions in the central nervous system. Moreover, this methodology was further used to discover not only the dysregulated metabolites but also the brain microregions involved in the pathology of a scopolamine-treated Alzheimer's model. Furthermore, this methodology was demonstrated to be a powerful visualizing tool that could offer novel insight into the metabolic events and provide spatial information about these events in central nervous system diseases.

Casper, a type of transparent mutant-line zebrafish, was generated to overcome the opaque trunk of an adult zebrafish for tumor modeling to realize real-time visualization of transplanted cells in vivo. However, the molecular information at the metabolic level has not received much attention. Herein, a spatially resolved metabolomics method based on an airflow-assisted desorption electrospray ionization–mass spectrometry imaging (AFADESI-MSI) system for whole-body zebrafish was used to investigate small molecules and the distribution of adult casper (Mitfaw2/w2, roya9/a9) and the differences from wild-type zebrafish. Finally, the spatial distribution information of more than 1500 endogenous ions was obtained in positive and negative detection modes, and 186 metabolites belonging to a variety of structural categories were identified or annotated. Compared with wild-type samples, 85 variables, including 37 known metabolites, were screened out. In addition, the disordered metabolic pathways caused by the genetic mutation were excavated, involving downregulation of purine metabolism and arachidonic acid metabolism, upregulation of glycerophospholipid metabolism, and biosynthesis of unsaturated fatty acids. All these results were observed in the most intuitive way through MSI. This study revealed important metabolic characteristics of and perturbation in adult casper zebrafish, and provides indispensable fundamental knowledge for tumor research based on it.

Rapid and accurate metabolite annotation in mass spectrometry imaging (MSI) can improve the efficiency of spatially resolved metabolomics studies and accelerate the discovery of reliable in situ disease biomarkers. To date, metabolite annotation tools in MSI generally utilize isotopic patterns, but high-throughput fragmentation-based identification and biological and technical factors that influence structure elucidation are active challenges. Here, we proposed an organ-specific, metabolite-database-driven approach to facilitate efficient and accurate MSI metabolite annotation. Using data-dependent acquisition (DDA) in liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) to generate high-coverage product ions, we identified 1620 unique metabolites from eight mouse organs (brain, liver, kidney, heart, spleen, lung, muscle, and pancreas) and serum. Following the evaluation of the adduct form difference of metabolite ions between LC-MS and airflow-assisted desorption electrospray ionization (AFADESI)-MSI and deciphering organ-specific metabolites, we constructed a metabolite database for MSI consisting of 27,407 adduct ions. An automated annotation tool, MSIannotator, was then created to conduct metabolite annotation in the MSI dataset with high efficiency and confidence. We applied this approach to profile the spatially resolved landscape of the whole mouse body and discovered that metabolites were distributed across the body in an organ-specific manner, which even spanned different mouse strains. Furthermore, the spatial metabolic alteration in diabetic mice was delineated across different organs, exhibiting that differentially expressed metabolites were mainly located in the liver, brain, and kidney, and the alanine, aspartate, and glutamate metabolism pathway was simultaneously altered in these three organs. This approach not only enables robust metabolite annotation and visualization on a body-wide level but also provides a valuable database resource for underlying organ-specific metabolic mechanisms.

Ambient mass spectrometry imaging (AMSI) is a molecular imaging technique developed in recent years for in situ and real time visualization of the distribution of chemical compounds in biological tissues without the need of labeling or staining. With the development for more than one decade, AMSI becomes a powerful molecular imaging technique in variousfields such as forensics, metabolomics, cancer diagnosis, and drug monitoring. In this review, we describe the recent advances of AMSI for imaging biological tissues in details. Three types of AMSI techniques based on different ionization mechanisms and analytical strategies are summarized, i.e., direct desorption/ionization of analytes for AMSI, desorption and then ionization of analytes for AMSI, and extraction of analytes for AMSI, and the features of them are presented from the aspects of tissue origin, target image molecule, and spatial resolution among others. In addition, future development directions for AMSI are discussed.

The Single-probe single-cell mass spectrometry (SCMS) is an innovative analytical technique designed for metabolomic profiling, offering a miniaturized, multifunctional device capable of direct coupling to mass spectrometers. It is an ambient technique leveraging microscale sampling and nanoelectrospray ionization (nanoESI), enabling the analysis of cells in their native environments without the need for extensive sample preparation. Due to its miniaturized design and versatility, this device allows for applications in diverse research areas, including single-cell metabolomics, quantification of target molecules in single cell, MS imaging (MSI) of tissue sections, and investigation of extracellular molecules in live single spheroids. This review explores recent advancements in Single-probe-based techniques and their applications, emphasizing their potential utility in advancing MS methodologies in microscale bioanalysis.

Spatially resolved metabolomics is an excellent tool for elucidating in situ molecular events, but its use remains challenging due to the complexity of the endogenous metabolites in bio-tissue and tissue heterogeneity. In this study, a data processing pipeline for spatially resolved metabolomics analysis of tumor microregion heterogeneity was developed and built into a graphical interface with MSI software. Biological tissue sections were analysed by ambient air-flow assisted desorption electrospray ionization mass spectrometry imaging. Histology-driven and characterized ion images overlay combined with metabolic feature-based spatial segmentation were developed to accurately extract the metabolic profile from the tissue microregion of interest. In addition, appropriate data pretreatment methods were investigated to evaluate their ability to identify biological variations from the complicated spatially resolved metabolomics data. Diverse graphical metabolic feature extraction and various data pretreatment methods enable not only the achievement of the best multivariate statistical results in an intuitive and simple way but also the discovery of low-abundance but reliable biomarkers. The results from a papillary thyroid cancer tissue study demonstrated that this data processing pipeline is a powerful and easy-to-use tool for investigating the spatial molecular events in tumor microenvironments and to therefore thoroughly understand their metabolic heterogeneity.

No abstract available

No abstract available

Colorectal cancer (CRC) still remains the leading cause of cancer death worldwide. This study aimed to profile the metabolic differences of colorectal cancer tissues (CCT) at different stages and sites, as compared with their distant noncancerous tissues (DNT), to investigate the temporal and spatial heterogeneities of metabolic characterization. Our NMR‐based metabolomics fingerprinting revealed that many of the metabolite levels were significantly altered in CCT compared to DNT and esophageal cancer tissues, indicating deregulations of glucose metabolism, one‐carbon metabolism, glutamine metabolism, amino acid metabolism, fatty acid metabolism, TCA cycle, choline metabolism, and so forth. A total of five biomarker metabolites, including glucose, glutamate, alanine, valine and histidine, were identified to distinguish between early and advanced stages of CCT. Metabolites that distinguish the different anatomical sites of CCT include glucose, glycerol, glutamine, inositol, succinate, and citrate. Those significant metabolic differences in CRC tissues at different pathological stages and sites suggested temporal and spatial heterogeneities of metabolic characterization in CCT, providing a metabolic foundation for further study on biofluid metabolism in CRC early detection.

Altered hepatic mitochondrial fatty acid β-oxidation and associated tricarboxylic acid (TCA) cycle activity contributes to lifestyle-related diseases, and circulating biomarkers reflecting these changes could have disease prognostic value. This study aimed to determine hepatic and systemic changes in TCA-cycle-related metabolites upon the selective pharmacologic enhancement of mitochondrial fatty acid β-oxidation in the liver, and to elucidate the mechanisms and potential markers of hepatic mitochondrial activity. Male Wistar rats were treated with 3-thia fatty acids (e.g., tetradecylthioacetic acid (TTA)), which target mitochondrial biogenesis, mitochondrial fatty acid β-oxidation, and ketogenesis predominantly in the liver. Hepatic and plasma concentrations of TCA cycle intermediates and anaplerotic substrates (LC-MS/MS), plasma ketones (colorimetric assay), and acylcarnitines (HPLC-MS/MS), along with associated TCA-cycle-related gene expression (qPCR) and enzyme activities, were determined. TTA-induced hepatic fatty acid β-oxidation resulted in an increased ratio of plasma ketone bodies/nonesterified fatty acid (NEFA), lower plasma malonyl-CoA levels, and a higher ratio of plasma acetylcarnitine/palmitoylcarnitine (C2/C16). These changes were associated with decreased hepatic and increased plasma pyruvate concentrations, and increased plasma concentrations of succinate, malate, and 2-hydroxyglutarate. Expression of several genes encoding TCA cycle enzymes and the malate–oxoglutarate carrier (Slc25a11), glutamate dehydrogenase (Gdh), and malic enzyme (Mdh1 and Mdh2) were significantly increased. In conclusion, the induction of hepatic mitochondrial fatty acid β-oxidation by 3-thia fatty acids lowered hepatic pyruvate while increasing plasma pyruvate, as well as succinate, malate, and 2-hydroxyglutarate.

SUMMARY The tricarboxylic acid (TCA) cycle couples nutrient oxidation with the generation of reducing equivalents that power oxidative phosphorylation. Nevertheless, the requirement for components of the TCA cycle is context-specific, raising the question of which TCA cycle outputs support cell fitness. Here, we demonstrate that citrate clearance is an essential function of the TCA cycle. As citrate production increases, so do TCA cycle activity and dependence upon aconitase 2 (ACO2), the enzyme that initiates citrate catabolism in the TCA cycle. Disrupting citrate catabolism activates the integrated stress response and impairs cell fitness, and these effects are reversed by preventing citrate production or promoting mitochondrial citrate efflux. In vivo, ACO2 deficiency induces citrate accumulation and triggers tubular degeneration in the kidney, a tissue that physiologically takes up circulating citrate. Thus, intracellular citrate accumulation can be a metabolic liability, and citrate clearance is a major function of ACO2 in the TCA cycle.

The TCA cycle serves as a central hub to balance catabolic and anabolic needs of the cell, where carbon moieties can either contribute to oxidative metabolism or support biosynthetic reactions. This differential TCA cycle engagement for glucose-derived carbon has been extensively studied in cultured cells, but the fate of fatty acid (FA)-derived carbons is poorly understood. To fill the knowledge gap, we have developed a strategy to culture cells with long-chain FAs without altering cell viability. By tracing 13C-FA we show that FA oxidation (FAO) is robust in both proliferating and oxidative cells while the metabolic pathway after citrate formation is distinct. In proliferating cells, a significant portion of carbon derived from FAO exits canonical TCA cycle as citrate and converts to unlabeled malate in cytosol. Increasing FA supply or β-oxidation does not change the partition of FA-derived carbon between cytosol and mitochondria. Oxidation of glucose competes with FA derived carbon for the canonical TCA pathway thus promoting FA carbon flowing into the alternative TCA pathway. Moreover, the coupling between FAO and the canonical TCA pathway changes with the state of oxidative energy metabolism.

AIMS Short-chain fatty acids (SCFAs) are produced by gut microbiota from dietary fiber. Since absorbed SCFAs could be introduced into the tricarboxylic acid (TCA) cycle in host cells, the relationships between SCFAs and TCA cycle intermediates might influence to energy metabolism in the human body. For this reason, information on profile changes between SCFAs and TCA cycle intermediates could help unveil pathological mechanisms of gastric cancer. MAIN METHODS A gas chromatography-tandem mass spectrometry (GC-MS/MS) method was developed to simultaneously determine SCFAs and TCA cycle intermediates in human plasma from patients with chronic superficial gastritis (CSG), intestinal metaplasia (IM), and gastric cancer. We applied a tetra-alkyl ammonium pairing method to prevent loss of volatile SCFAs and base decarboxylation of TCA cycle intermediates during sample preparation. To assess gastric diseases, metabolic alterations of SCFAs and TCA cycle intermediates in human plasma with gastric disorders were analyzed by their plasma levels. KEY FINDINGS Significantly different metabolic alterations based on the plasma levels of SCFAs and TCA cycle intermediates were investigated in cancer metabolic pathways. Not only propionate and butyrate, mainly produced by gut microbiota, were significantly decreased, but also cis-aconitate, α-ketoglutarate, and fumarate were significantly increased in plasma with IM or gastric cancer, compared to CSG. Further, based on ratios of product to precursor, three metabolic pathways (succinate/propionate, succinate/α-ketoglutarate, and cis-aconitate/citrate) were supposed to be distorted between gastric diseases. SIGNIFICANCE In conclusion, propionate, cis-aconitate, α-ketoglutarate, and fumarate could be used to assess the progression of gastric cancer.

ABSTRACT The tricarboxylic acid (TCA) cycle is a central route for generating cellular energy and precursors for biosynthetic pathways. Emerging evidences have shown that the aberrations of metabolic enzymes which affect the integrity of TCA cycle are implicated in various tumour pathological processes. Interestingly, several TCA enzymes exhibit the characteristics of RNA binding properties, and their long non-coding RNA (lncRNA) partners play critical regulatory roles in regulating the function of TCA cycle and tumour progression. In this review, we will discuss the functional roles of RNA binding proteins and their lncRNA partners in TCA cycle, with emphasis placed on the cancer progression. A further understanding of RNA binding proteins and their lncRNA partners in TCA cycle, as well as their molecular mechanisms in oncogenesis, will aid in developing novel layers of metabolic targets for cancer therapy in the near future. Abbreviations: CS: citrate synthase. AH: aconitase, including ACO1, and ACO2. IDH: isocitrate dehydrogenase, including IDH1, IDH2, and IDH3. KGDHC: α-ketoglutarate dehydrogenase complex, including OGDH, DLD, and DLST. SCS: succinyl-CoA synthase, including SUCLG1, SUCLG2, and SUCLA2. SDH: succinate dehydrogenase, including SDHA, SDHB, SDHC, and SDHD. FH: fumarate hydratase. MDH: malate dehydrogenase, including MDH1 and MDH2. PC: pyruvate carboxylase. ACLY: ATP Citrate Lyase. NIT: nitrilase. GAD: glutamate decarboxylase. ABAT: 4-aminobutyrate aminotransferase. ALDH5A1: aldehyde dehydrogenase 5 family member A1. ASS: argininosuccinate synthase. ASL: adenylosuccinate synthase. DDO: D-aspartate oxidase. GOT: glutamic-oxaloacetic transaminase. GLUD: glutamate dehydrogenase. HK: hexokinase. PK: pyruvate kinase. LDH: lactate dehydrogenase. PDK: pyruvate dehydrogenase kinase. PDH: pyruvate dehydrogenase complex. PHD: prolyl hydroxylase domain protein.

Tryptophan is an essential amino acid that is extensively characterized as a regulator of cellular function through its metabolism by indoleamine 2,3-deoxygenase (IDO) into the kynurenine pathway. However, despite decades of research on tryptophan metabolism, the metabolic regulatory roles of it and its metabolites are not well understood. To address this, we performed an activity metabolomics screen of tryptophan and most of its known metabolites in cell culture. We discovered that treatment of human colon cancer cells (HCT116) with 3-hydroxykynurenine (3-HK), a metabolite of kynurenine, potently disrupted TCA cycle function. Citrate and aconitate levels were increased, while isocitrate and all downstream TCA metabolites were decreased, suggesting decreased aconitase function. We hypothesized that 3HK or one of its metabolites increased reactive oxygen species (ROS) and inhibited aconitase activity. Accordingly, we observed almost complete depletion of reduced glutathione and a decrease in total glutathione levels. We observed a dose-dependent decrease in cell viability after 48 hours of 3HK treatment. These data suggest that raising the intracellular levels of 3HK could be sufficient to induce ROS-mediated apoptosis. We modulated the intracellular levels of 3HK by combined induction of IDO and knockdown of kynureninase (KYNU) in HCT116 cells. Cell viability decreased significantly after 48 hours of KYNU knockdown compared to controls, which was accompanied by increased ROS production and Annexin V staining revealing apoptosis. Finally, we identify xanthommatin production from 3-HK as a candidate radical-producing, cytotoxic mechanism. Our work indicates that KYNU may be a target for disrupting tryptophan metabolism. Interestingly, many cancers exhibit overexpression of IDO, providing a cancer-specific metabolic vulnerability that could be exploited by KYNU inhibition.

Simple Summary Oestrogen and progesterone are two sex hormones that are important in the development of cancer of the inner lining of the uterus and endometrial cancer (EC). Oestrogen binds to the oestrogen receptor (ER) and progesterone to the progesterone receptor (PR). The presence of these receptors is important because the response to hormonal therapy is higher when the receptors are present. However, as EC grows and spreads throughout the body, ER and PR may be lost. In this study, we found that tumours that have spread to other organs throughout the blood and tumours that have spread in the abdomen have a relatively high presence of ER/PR, while tumours located in the lymph nodes have a lower presence of PR. ER/PR-IHC were not lower in tumours that had previously been treated with radiotherapy. This might influence the application of hormonal therapy in the future. Abstract Background: Response to hormonal therapy in advanced and recurrent endometrial cancer (EC) can be predicted by oestrogen and progesterone receptor immunohistochemical (ER/PR-IHC) expression, with response rates of 60% in PR-IHC > 50% cases. ER/PR-IHC can vary by tumour location and is frequently lost with tumour progression. Therefore, we explored the relationship between ER/PR-IHC expression and tumour location in EC. Methods: Pre-treatment tumour biopsies from 6 different sites of 80 cases treated with hormonal therapy were analysed for ER/PR-IHC expression and classified into categories 0–10%, 10–50%, and >50%. The ER pathway activity score (ERPAS) was determined based on mRNA levels of ER-related target genes, reflecting the actual activity of the ER receptor. Results: There was a trend towards lower PR-IHC (33% had PR > 50%) and ERPAS (27% had ERPAS > 15) in lymphogenic metastases compared to other locations (p = 0.074). Hematogenous and intra-abdominal metastases appeared to have high ER/PR-IHC and ERPAS (85% and 89% ER-IHC > 50%; 64% and 78% PR-IHC > 50%; 60% and 71% ERPAS > 15, not significant). Tumour grade and previous radiotherapy did not affect ER/PR-IHC or ERPAS. Conclusions: A trend towards lower PR-IHC and ERPAS was observed in lymphogenic sites. Verification in larger cohorts is needed to confirm these findings, which may have implications for the use of hormonal therapy in the future.

OBJECTIVE To investigate the effect of estrogen and progesterone receptor status score on the prognosis of endometrial cancer (EC) patients. METHODS We chose 187 endometrial cancer patients hospitalized in The Affiliated Hospital of Southwest Medical University between March 2021 and March 2023. The histologic score (H score) and the Allred score were used to evaluate and analyze their correlation with clinical pathological parameters and prognosis. RESULTS The hormone receptor status score was negatively correlated with lymph node metastasis (LNMs), differentiation degree, depth of invasion (DOI), and the clinical stage (P<0.05). The hormone receptor status score was significantly higher in the survival group compared to that in the dead group (P<0.05). According to multivariate logistic regression analysis, the hormone receptor status score was identified as an independent protective factor for endometrial cancer patient prognosis (P<0.05). CONCLUSION The hormone receptor status score has a definite correlation with LNMs, DOI, differentiation degree, and clinical stage in EC patients, and is related to prognosis.

Background: Endometrial carcinoma is the most common malignant tumor of the female genital tract with increasing incidence in developed countries. In the era of targeted therapy, immunohistochemical markers play an important role in the treatment and prognosis of endometrial carcinoma. The aim of the study was to study the immunohistochemical expression of B-cell lymphoma 2 (BCL-2) in endometrial carcinoma and to study the correlation of BCL-2 expression with hormone receptor status and transforming growth factor receptors in endometrial carcinoma. Methods: Endometrial carcinoma reported between the period from January 2010 to December 2014 in the department of pathology of this institute was considered in the study. The study included cases of endometrial carcinoma reported on both curetting and hysterectomy specimens. In the samples where both curetting and hysterectomy were received only hysterectomy blocks were included and the curetting was excluded. Results: The total number of malignancies reported during this period was 3478. Of these 3478 malignancies, 59 were endometrial carcinomas with an incidence of 1.6%. Out of 59 endometrial carcinomas, 46 cases were diagnosed on hysterectomy specimens and 13 cases were diagnosed on endometrial curetting biopsies. Conclusion: The positive expressions of estrogen receptor (ER) and progesterone receptor (PR) in endometrial carcinoma suggest that steroid receptor studies may be of potential benefit in the management of some patients with endometrial carcinoma.

Background: To compare the expression of ER, PR, and Her-2/neu in cases of endometrial hyperplasia and endometrial carcinoma within glands and stroma with normal endometrium. Methods: Samples of formalin-fixed, paraffin-embedded tissue of 62 patients coming to a tertiary care center were immunohistochemically studied for expression of ER and PR. Results: Steroid Hormone receptor levels of both ER and PR were overexpressed in endometrial hyperplasia and carcinoma compared to normal endometrium. However, same was significantly reduced in adenocarcinoma compared to simple hyperplasia and revealed a statistical significance with a p-value <0.05. Conclusions: Immunohistochemical expression of ER and PR should be evaluated in all cases of simple and atypical hyperplasia, and adenocarcinoma as their expression can facilitate a prognostic marker and can entail hormonal therapy in future for such cases.

Endometrial cancer (EC) incidence and mortality is rising globally, largely driven by the obesity epidemic. Treatment options remain limited for obesity-associated, hormone-resistant, or fertility-preserving EC, highlighting the need for novel therapies that address both tumor biology and metabolic dysfunction. Here, we synthesize the molecular rationale, preclinical, population-based, and emerging clinical trial evidence on glucagon-like peptide-1 receptor (GLP-1R) agonists (GLP-1 RAs) in EC. GLP-1R is expressed in benign and malignant endometrial tissues, where activation of cAMP-PKA and AMPK-mTOR signaling has been shown to mediate antiproliferative, proapoptotic, and autophagy-inducing effects. Preclinical models demonstrate that class-wide GLP-1 RAs may restore progesterone receptor expression, overcome hormone resistance, and synergize with progestin therapy; however, the effects may vary by agent. Retrospective studies suggest that combining GLP-1 RAs with local progestin therapy, commonly a levonorgestrel-releasing intrauterine device (preferred in obesity because oral progestin bioavailability is reduced), is associated with reduced EC in high-risk women. Ongoing clinical trials are assessing their role in fertility-sparing settings-evaluating complete response to progestin-based therapy, relapse rates, and time to conception-as well as in adjuvant settings, where disease-free survival is a key endpoint; however, gastrointestinal tolerability and the absence of long-term safety data in EC populations remain important considerations. As a class, GLP-1 RAs represent a promising therapeutic approach to targeting both the obesogenic milieu and tumor-intrinsic pathways in EC; however, agent-specific differences warrant attention. Prospective, subtype-stratified trials are essential to establish their role in comprehensive EC care.

Endometrial carcinoma (EMC) is associated with obesity; however, the underlying mechanisms have not yet been elucidated. Peroxisome proliferator-activated receptor alpha (PPARα) is a nuclear receptor that is involved in lipid, glucose, and energy metabolism. PPARα reportedly functions as a tumor suppressor through its effects on lipid metabolism; however, the involvement of PPARα in the development of EMC remains unclear. The present study demonstrated that the immunohistochemical expression of nuclear PPARα was lower in EMC than in normal endometrial tissues, suggesting the tumor suppressive nature of PPARα. A treatment with the PPARα activator, irbesartan, inhibited the EMC cell lines, Ishikawa and HEC1A, by down-regulating sterol regulatory element-binding protein 1 (SREBP1) and fatty acid synthase (FAS) and up-regulating the tumor suppressor genes p21 and p27, antioxidant enzymes, and AT-rich interaction domain 1A (ARID1A). These results indicate the potential of the activation of PPARα as a novel therapeutic approach against EMC.

Estrogen receptor (ER) immunohistochemical evaluation has emerged as a key variable in the risk assessment of endometrial carcinoma. In the most common molecular subtype of endometrial carcinomas, the no specific molecular profile, ER is used in conjunction with stage, grade, and lymphovascular space invasion (LVSI) for risk assessment and guiding adjuvant treatments. Additionally, ER is a useful tool for aiding primary site and histotype assessment and has a role in predicting response to hormonal therapies for endometrial carcinoma. These applications of ER immunostaining will be reviewed.