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Introduction writing example: single-cell sequencing of the thyroid cancer microenvironment

Run on 2026-10-26 · Acadwrite introduction writing · shown as generated

This is a full run of the first example on the introduction writing page. The input was only a topic and a few sentences of findings, with no references. The AI searched and read the literature, planned the argument, and saved a cited introduction as a note in the workspace. Below are the input, the process, the argument, the full text and the references.

Note: The input is the built-in example on the page. The introduction and references are the raw workflow output with no manual edits (output language: Chinese). Every reference was checked against Crossref: all DOIs resolve and the titles match.

What the author entered

Topic

单细胞测序揭示甲状腺癌微环境异质性 (Single-cell sequencing reveals heterogeneity of the thyroid cancer microenvironment)

Findings and conclusions

Single-cell RNA sequencing of 50 thyroid cancer samples built an atlas of about 400,000 cells. A "hub microenvironment" formed by APOE+ macrophages, EMT-like cancer-associated fibroblasts and RBP7+ endothelial cells plays a key role in tumor progression.

Search and output settings
Years: anySources: English journal papersOutput: Chinese

What the workflow did

The whole run happened in the workspace with no intervention. Each search left its results in the workspace "search" folder, so you can check which papers the AI relied on.

10
search rounds
4
papers read in full
17
references
6
paragraphs
  1. Read the task brief: topic, findings and output language
  2. Ran 8 searches on epidemiology, thyroid cancer single-cell atlases, APOE macrophages, CAF subtypes, tumor endothelium and immunotherapy resistance
  3. Read 4 key papers in full to check sample sizes, cell counts and wording
  4. Ran 2 more searches to close gaps in the argument
  5. Wrote the introduction as clinical problem → progress → cross-cancer evidence → gap → this study, with citations inline and only cited papers in the reference list

Argument plan

1Clinical problem

Incidence rose while mortality stayed flat, mostly due to overdiagnosis; yet SEER data show incidence-based mortality still rising after 2014, so part of the disease burden is aggressive.

2Turn to the microenvironment

ATC and PTC differ systematically in their microenvironments; progressive PTC loses immune cells; M2 macrophages drive anti-PD-1 resistance. The thyroid cancer microenvironment is described as "characterized poorly".

3Single-cell progress

Prior atlases: 11 patients / 158k cells across the clinical course, 8 PTC cases, bilateral PTC, and spatial transcriptomics across stages.

4Cross-cancer evidence

APOE+ macrophages, CAF phenotypes and EMT each have evidence in other cancers, but whether they co-occur in thyroid cancer is unanswered.

5Gap

Small cohorts (3–14 patients), mostly PTC, single-lineage analyses, and no robust test of subset combinations against stage.

6This study

A 50-patient, ~400k-cell atlas identifying the hub microenvironment, contributing a reference atlas and a shift from cell lists to cooperative modules.

The generated introduction (as is)

Underlined text marks inline citations that link to the DOI.

甲状腺癌是内分泌系统最常见的恶性肿瘤。自 20 世纪 80 年代以来，其全球发病率急剧上升，而死亡率在同期保持相对稳定，这一"发病升、死亡平"的分离现象在很大程度上归因于超声等影像学检查普及所导致的小尺寸、惰性乳头状甲状腺癌（papillary thyroid carcinoma，PTC）的过度诊断（Chen et al., 2025）。基于 SEER 数据的分析显示，美国甲状腺癌发病率在 2000—2014 年间持续攀升，2014 年后虽出现回落，但基于发病的死亡率在 2000—2018 年全程呈上升趋势，且直径大于 2 cm 的肿瘤发病率并未同步下降（Megwalu et al., 2022）。这意味着"过度诊断"并不能解释全部疾病负担：仍有一部分患者在初诊时即为局部晚期、碘难治性远处转移或未分化甲状腺癌（anaplastic thyroid cancer，ATC），预后极差且对常规治疗抵抗（Han et al., 2024；Pu et al., 2021）。因此，区分惰性与侵袭性疾病、阐明驱动甲状腺癌向恶性表型演进的生物学机制，是当前临床与基础研究共同面对的核心问题。

仅从肿瘤细胞自身的驱动基因改变出发，难以完整解释同一组织学类型内部悬殊的临床结局。近年来的共识是，肿瘤细胞所处的微环境——包括肿瘤相关巨噬细胞、癌相关成纤维细胞（cancer-associated fibroblast，CAF）、血管内皮细胞与各类淋巴细胞——在肿瘤进展、免疫逃逸和治疗反应中发挥决定性作用。在 ATC 与 PTC 的对比中，两者的肿瘤微环境（tumor microenvironment，TME）组分存在系统性差异：ATC 中富集 CXCL13⁺ T 淋巴细胞并伴有早期三级淋巴结构形成，这被认为是其虽更致命、却对免疫治疗相对敏感的原因之一（Han et al., 2024）。在进展型 PTC 中，总体免疫细胞数量显著减少、肿瘤细胞抗原提呈功能受损，LAMP3⁺ 树突状细胞通过 NECTIN2–TIGIT 与 CCL17–CCR4 轴促进 CD8⁺ T 细胞耗竭与调节性 T 细胞浸润（Wang et al., 2024）；M2 样巨噬细胞还可经外泌体途径介导抗 PD-1 治疗抵抗（Ning et al., 2023）。然而，甲状腺癌的 TME 在很长一段时间内被明确描述为"characterized poorly"——其细胞组成、亚群身份及其与临床分期的对应关系都缺乏系统刻画（Pu et al., 2021；Pan et al., 2021）。

单细胞 RNA 测序（single-cell RNA sequencing，scRNA-seq）技术为解决上述问题提供了关键手段：它不再把组织匀浆成一个平均表达谱，而是逐细胞地读取转录组，从而在同一份标本内区分恶性细胞、免疫细胞、成纤维细胞与内皮细胞的亚群状态。将该技术应用于甲状腺癌后，研究者已取得一系列进展。对 11 例患者、覆盖瘤旁组织、局限与晚期肿瘤、初治与复发淋巴结以及碘难治性远处转移等完整临床病程的 158 577 个细胞进行测序，鉴定出形态正常但转录组已改变的"癌前启动"甲状腺细胞群体，以及滤泡样、部分上皮–间质转化样和去分化样三种恶性甲状腺细胞表型，并提示 PTC 中存在血管–免疫交互，为抗血管生成与免疫治疗联用提供了依据（Pu et al., 2021）。对 8 例 PTC（含 3 例合并桥本甲状腺炎）的图谱分析揭示了瘤内免疫细胞的多种转录状态、肿瘤浸润 B 细胞与桥本甲状腺炎的来源关联，以及非免疫细胞、髓系细胞与淋巴细胞之间多样的配体–受体互作（Pan et al., 2021）。在双侧 PTC 中，29 561 个细胞的比较显示抑炎性 M2 样与促炎性 M1 样巨噬细胞的比例随疾病分期升高而增大，即使携带相同 BRAF V600E 突变，不同病灶的富集通路也显著不同（Wang et al., 2022）。空间转录组与单细胞的联合分析进一步把这种异质性放回组织坐标中：跨瘤旁甲状腺、PTC、局部晚期 PTC 与 ATC 的图谱显示，进展过程中出现阶段特异的"肿瘤前沿"重塑，以及 COL8A1–ITGB1、LAMB2–ITGB4、SERPINE1–PLAUR 等随分期切换的细胞互作（Liao et al., 2025）。

这些工作共同表明，TME 中的基质与免疫细胞并非同质背景，而是由功能迥异的亚群构成；亚群的组合方式而非单一亚群的存在，才真正决定微环境的功能倾向。这一判断在多种实体瘤中已获充分证据。巨噬细胞方面，表达载脂蛋白 E（apolipoprotein E，APOE）的巨噬细胞群体具有明确的促瘤功能：M2 巨噬细胞来源外泌体把 ApoE 转移至胃癌细胞，经 PI3K–Akt 通路重塑细胞骨架并促进迁移（Zheng et al., 2018）；同一 ApoE 转移还可下调肿瘤细胞 MHC-I、削弱其内在免疫原性，从而造成免疫检查点阻断抵抗（Zheng et al., 2023）。在 PTC 中，APOE 表达与肿瘤大小、分期及淋巴结转移显著相关，并能通过 IL-10 驱动巨噬细胞向 M2 表型极化（Huo et al., 2024）。成纤维细胞方面，CAF 的表型与空间分布本身即为独立预后因素，肿瘤样 CAF 与不良预后强相关，而炎症型与干扰素应答型 CAF 则对应炎症活跃、生存更好的微环境（Cords et al., 2024）；跨癌种的空间–单细胞整合分析显示，基质型 CAF 主导肿瘤血管生成，炎症型 CAF 塑造免疫抑制环境并促进上皮–间质转化（Ma et al., 2023；Jia et al., 2025），而 CAF 对瘤巢结构的塑造可直接把肿瘤细胞推向侵袭性（EMT）状态（Ligorio et al., 2019）。可见，APOE⁺ 巨噬细胞、EMT 样 CAF 与特定内皮细胞状态分别在不同瘤种中被证实具有促进展作用，但在甲状腺癌中，它们是否同时出现、是否在空间与信号上彼此耦合，仍缺乏系统回答。

就甲状腺癌而言，现有单细胞研究虽已勾画出多种细胞亚群，却仍存在三方面局限。其一，样本规模与疾病谱覆盖不足：代表性研究的患者数多在 3—14 例之间，细胞数从数万到十余万不等，且多数聚焦于 PTC 单一组织学类型（Wang et al., 2022；Pan et al., 2021；Wang et al., 2024），跨组织学类型、跨临床阶段的统一参考图谱尚未建立，亚群定义与命名在不同研究间也难以对齐。其二，分析视角多以单一细胞谱系或单条配体–受体轴为中心，例如仅关注巨噬细胞亚群（Pan et al., 2022）或仅关注树突状细胞与 T 细胞的互作（Wang et al., 2024），很少把来源不同、功能互补的免疫、基质与血管亚群作为协同模块加以考察，因而无法判断哪些亚群组合共同构成支撑肿瘤进展的微环境"核心"。其三，多数研究以单一时间截面的细胞比例差异描述进展，缺少在足够大的样本中检验特定亚群组合与分期、组织学类型之间稳健对应关系的能力。由此留下一个具体而尚未解决的问题：在甲状腺癌从惰性向侵袭性演进的过程中，是否存在由特定巨噬细胞、成纤维细胞与内皮细胞亚群共同组成的、可重复识别且功能上相互依赖的微环境枢纽？

本研究即针对这一问题展开。我们对 50 例甲状腺癌样本进行单细胞 RNA 测序，构建了包含约 40 万个细胞的综合图谱，在统一的分析框架下注释免疫、基质与血管各谱系的亚群，并系统解析其在不同组织学类型与临床阶段中的组成变化与细胞间通讯网络。通过这一图谱，我们识别出由 APOE⁺ 巨噬细胞、EMT 样癌相关成纤维细胞与 RBP7⁺ 内皮细胞共同构成的"枢纽微环境"，并对其在肿瘤进展中的潜在作用进行了刻画。本研究的贡献在于：一方面提供了目前规模较大、覆盖疾病谱较广的甲状腺癌单细胞参考图谱，为后续研究的细胞注释与亚群命名提供可比对的基准；另一方面把微环境分析从"细胞清单"推进到"协同模块"，为理解甲状腺癌发生发展机制、寻找可用于风险分层与联合治疗的微环境靶点提供了新的切入点。

References

  1. Chen DW, Haymart MR. Unravelling the rise in thyroid cancer incidence and addressing overdiagnosis. Nature Reviews Endocrinology, 2025. https://doi.org/10.1038/s41574-025-01168-y
  2. Megwalu UC, Moon PK. Thyroid Cancer Incidence and Mortality Trends in the United States: 2000–2018. Thyroid, 2022. https://doi.org/10.1089/thy.2021.0662
  3. Han P, Ye WD, Yu PC, et al. A distinct tumor microenvironment makes anaplastic thyroid cancer more lethal but immunotherapy sensitive than papillary thyroid cancer. JCI Insight, 2024. https://doi.org/10.1172/jci.insight.173712
  4. Pu W, Shi X, Yu P, et al. Single-cell transcriptomic analysis of the tumor ecosystems underlying initiation and progression of papillary thyroid carcinoma. Nature Communications, 2021. https://doi.org/10.1038/s41467-021-26343-3
  5. Pan J, Ye F, Yu C, et al. Papillary Thyroid Carcinoma Landscape and Its Immunological Link With Hashimoto Thyroiditis at Single-Cell Resolution. Frontiers in Cell and Developmental Biology, 2021. https://doi.org/10.3389/fcell.2021.758339
  6. Wang TT, Shi JF, Li L, et al. Single-Cell Transcriptome Analysis Reveals Inter-Tumor Heterogeneity in Bilateral Papillary Thyroid Carcinoma. Frontiers in Immunology, 2022. https://doi.org/10.3389/fimmu.2022.840811
  7. Wang ZY, Ji X, Zhang Y, et al. Interactions between LAMP3+ dendritic cells and T-cell subpopulations promote immune evasion in papillary thyroid carcinoma. Journal for ImmunoTherapy of Cancer, 2024. https://doi.org/10.1136/jitc-2024-008983
  8. Ning J, Hou XK, Hao J, et al. METTL3 inhibition induced by M2 macrophage-derived extracellular vesicles drives anti-PD-1 therapy resistance via M6A-CD70-mediated immune suppression in thyroid cancer. Cell Death and Differentiation, 2023. https://doi.org/10.1038/s41418-023-01217-x
  9. Liao T, Zeng Y, Xu WB, et al. A spatially resolved transcriptome landscape during thyroid cancer progression. Cell Reports Medicine, 2025. https://doi.org/10.1016/j.xcrm.2025.102043
  10. Pan Z, Bao LS, Lu XX, et al. IL2RA+VSIG4+ tumor-associated macrophage is a key subpopulation of the immunosuppressive microenvironment in anaplastic thyroid cancer. Biochimica et Biophysica Acta - Molecular Basis of Disease, 2022. https://doi.org/10.1016/j.bbadis.2022.166591
  11. Zheng P, Luo Q, Wang W, et al. Tumor-associated macrophages-derived exosomes promote the migration of gastric cancer cells by transfer of functional Apolipoprotein E. Cell Death and Disease, 2018. https://doi.org/10.1038/s41419-018-0465-5
  12. Zheng N, Wang T, Luo Q, et al. M2 macrophage-derived exosomes suppress tumor intrinsic immunogenicity to confer immunotherapy resistance. Oncoimmunology, 2023. https://doi.org/10.1080/2162402X.2023.2210959
  13. Huo R, Zhao RH, Li Z, et al. APOE expression in papillary thyroid carcinoma: Influencing tumor progression and macrophage polarization. Immunobiology, 2024. https://doi.org/10.1016/j.imbio.2024.152821
  14. Cords L, Engler S, Haberecker M, et al. Cancer-associated fibroblast phenotypes are associated with patient outcome in non-small cell lung cancer. Cancer Cell, 2024. https://doi.org/10.1016/j.ccell.2023.12.021
  15. Ma C, Yang CZ, Peng A, et al. Pan-cancer spatially resolved single-cell analysis reveals the crosstalk between cancer-associated fibroblasts and tumor microenvironment. Molecular Cancer, 2023. https://doi.org/10.1186/s12943-023-01876-x
  16. Jia HY, Chen X, Zhang L, et al. Cancer associated fibroblasts in cancer development and therapy. Journal of Hematology & Oncology, 2025. https://doi.org/10.1186/s13045-025-01688-0
  17. Ligorio M, Sil S, Malagon-Lopez J, et al. Stromal Microenvironment Shapes the Intratumoral Architecture of Pancreatic Cancer. Cell, 2019. https://doi.org/10.1016/j.cell.2019.05.012

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