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Open Access Article

International Journal of Clinical Research. 2026; 10: (6) ; 35-41 ; DOI: 10.12208/j.ijcr.20260278.

Single-cell transcriptome-based analysis of t cell functional evolution and macrophage-T cell interactions in the immune microenvironment of lung adenocarcinoma
基于单细胞转录组的肺腺癌免疫微环境中T细胞功能演化及其与巨噬细胞互作机制研究

作者: 叶天1, 孙婕1, 晏菲2, 印明柱1 *, 李鑫1 *

1重庆大学附属三峡医院,临床研究中心,医学病理中心,肿瘤早期诊治中心,转化医学研究中心 重庆

2重庆大学附属三峡医院,重庆市老年疾病临床医学研究中心 重庆

*通讯作者: 印明柱,单位:重庆大学附属三峡医院,临床研究中心,医学病理中心,肿瘤早期诊治中心,转化医学研究中心 重庆 ;李鑫,单位:重庆大学附属三峡医院,临床研究中心,医学病理中心,肿瘤早期诊治中心,转化医学研究中心 重庆 ;

发布时间: 2026-06-07 总浏览量: 32

摘要

目的 探讨肺腺癌免疫微环境中T细胞亚群的组成特征、功能状态变化及其与巨噬细胞的潜在互作机制。方法 基于肺腺癌单细胞转录组测序数据,首先进行质量控制和细胞聚类分析,并依据经典标记基因完成主要细胞类型注释;在此基础上,引入深度学习模型对细胞亚群注释结果进行辅助验证。进一步对T细胞与髓系细胞进行亚群分析,并结合功能评分、轨迹推断及配体-受体分析,系统解析T细胞功能演化过程,探讨其与巨噬细胞之间的细胞互作关系。结果 肺腺癌样本中以T/NK细胞和髓系细胞为主要免疫组成。T细胞可分为效应性、耗竭性等多个亚群,其功能状态随疾病进展发生动态变化。轨迹分析显示,效应性CD8+ T细胞逐渐向耗竭状态演化。进一步分析发现,SPP1+巨噬细胞可能通过特定配体-受体通路与T细胞发生互作。结论 肺腺癌免疫微环境中T细胞功能具有显著异质性,其耗竭过程可能与特定巨噬细胞亚群的免疫调控作用相关。本研究为理解肺腺癌免疫调控机制提供了新的证据。

关键词: 肺腺癌;单细胞转录组;T细胞;免疫微环境

Abstract

Objective To investigate the composition of T cell subsets, their functional state dynamics, and potential interactions with macrophages in the immune microenvironment of lung adenocarcinoma.
Methods Based on single-cell RNA sequencing data from lung adenocarcinoma, quality control and unsupervised clustering were first performed, followed by annotation of major cell types using canonical marker genes. Subsequently, a deep learning-based approach was introduced for supportive comparison of cell subset annotation results from an independent computational perspective. Further subcluster analyses were conducted for T cells and myeloid cells. Functional scoring, trajectory inference, and ligand-receptor interaction analyses were integrated to systematically characterize T cell functional evolution and to explore potential interactions between T cells and macrophages.
Results T/NK cells and myeloid cells constituted the major immune cell populations in lung adenocarcinoma samples. T cells could be classified into multiple subsets, including effector and exhausted phenotypes, with functional states dynamically changing during disease progression. Trajectory analysis revealed a gradual transition of effector CD8+ T cells toward an exhausted state. In addition, macrophages characterized by SPP1+ expression were found to potentially interact with T cells through specific ligand–receptor signaling pathways.
Conclusion   T cells in the immune microenvironment of lung adenocarcinoma exhibit pronounced functional heterogeneity, and their exhaustion may be associated with immunomodulatory effects mediated by specific macrophage subsets. This study provides additional insights into immune regulatory mechanisms in lung adenocarcinoma.

Key words: Lung adenocarcinoma; Single-cell transcriptome; T cell; Immune microenvironment

参考文献 References

[1] Seguin, L., Durandy, M., and Feral, C.C. (2022). Lung Adenocarcinoma Tumor Origin: A Guide for Personalized Medicine. Cancers (Basel) 14. 10.3390/cancers14071759.

[2] 王佳庆, 宁霄, 余克富, 汪祺, and 史卫忠 (2024). 基于生物信息学的CD39对肺腺癌免疫微环境及其免疫治疗的影响分析. 药物评价研究 47, 490-495.

[3] Lim, J.U., Lee, E., Lee, S.Y., Cho, H.J., Ahn, D.H., Hwang, Y., Choi, J.Y., Yeo, C.D., Park, C.K., and Kim, S.J. (2023). Current literature review on the tumor immune micro-environment, its heterogeneity and future perspectives in treatment of advanced non-small cell lung cancer. Transl Lung Cancer Res 12, 857-876. 10.21037/tlcr-22-633.

[4] Seo, J.S., Kim, A., Shin, J.Y., and Kim, Y.T. (2018). Comprehensive analysis of the tumor immune micro-environment in non-small cell lung cancer for efficacy of checkpoint inhibitor. Sci Rep 8, 14576. 10.1038/s41598-018-32855-8.

[5] Bejarano, L., Jordao, M.J.C., and Joyce, J.A. (2021). Therapeutic Targeting of the Tumor Microenvironment. Cancer Discov 11, 933-959. 10.1158/2159-8290.CD-20-1808.

[6] de Visser, K.E., and Joyce, J.A. (2023). The evolving tumor microenvironment: From cancer initiation to metastatic outgrowth. Cancer Cell 41, 374-403. 10.1016/j.ccell. 2023.02.016.

[7] Mun, J.Y., Leem, S.H., Lee, J.H., and Kim, H.S. (2022). Dual Relationship Between Stromal Cells and Immune Cells in the Tumor Microenvironment. Front Immunol 13, 864739. 10.3389/fimmu.2022.864739.

[8] Seager, R.J., Hajal, C., Spill, F., Kamm, R.D., and Zaman, M.H. (2017). Dynamic interplay between tumour, stroma and immune system can drive or prevent tumour progression. Converg Sci Phys Oncol 3. 10.1088/2057-1739/aa7e86.

[9] Hanahan, D., Michielin, O., and Pittet, M.J. (2025). Convergent inducers and effectors of T cell paralysis in the tumour microenvironment. Nat Rev Cancer 25, 41-58. 10.1038/s41568-024-00761-z.

[10] 林贯川, and 潘星华 (2025). 单细胞与空间组学的技术前沿、计算范式及新兴挑战. 中国生物化学与分子生物学报 41, 1559-1565. 10.13865/j.cnki.cjbmb.2025.10.0001.

[11] Puram, S.V., Tirosh, I., Parikh, A.S., Patel, A.P., Yizhak, K., Gillespie, S., Rodman, C., Luo, C.L., Mroz, E.A., Emerick, K.S., et al. (2017). Single-Cell Transcriptomic Analysis of Primary and Metastatic Tumor Ecosystems in Head and Neck Cancer. Cell 171, 1611-1624 e1624. 10.1016/j.cell. 2017.10.044.

[12] Lavin, Y., Kobayashi, S., Leader, A., Amir, E.D., Elefant, N., Bigenwald, C., Remark, R., Sweeney, R., Becker, C.D., Levine, J.H., et al. (2017). Innate Immune Landscape in Early Lung Adenocarcinoma by Paired Single-Cell Analyses. Cell 169, 750-765 e717. 10.1016/j.cell.2017. 04.014.

[13] Kim, N., Kim, H.K., Lee, K., Hong, Y., Cho, J.H., Choi, J.W., Lee, J.I., Suh, Y.L., Ku, B.M., Eum, H.H., et al. (2020). Single-cell RNA sequencing demonstrates the molecular and cellular reprogramming of metastatic lung adenocarcinoma. Nat Commun 11, 2285. 10.1038/s41467-020-16164-1.

[14] Cui, H., Wang, C., Maan, H., Pang, K., Luo, F., Duan, N., and Wang, B. (2024). scGPT: toward building a foundation model for single-cell multi-omics using generative AI. Nat Methods 21, 1470-1480. 10.1038/s41592-024-02201-0.

[15] Qiu, X., Mao, Q., Tang, Y., Wang, L., Chawla, R., Pliner, H.A., and Trapnell, C. (2017). Reversed graph embedding resolves complex single-cell trajectories. Nat Methods 14, 979-982. 10.1038/nmeth.4402.

[16] Jin, S., Guerrero-Juarez, C.F., Zhang, L., Chang, I., Ramos, R., Kuan, C.H., Myung, P., Plikus, M.V., and Nie, Q. (2021). Inference and analysis of cell-cell communication using CellChat. Nat Commun 12, 1088. 10.1038/s41467-021-21246-9.

[17] Fagerberg, E., Attanasio, J., Dien, C., Singh, J., Kessler, E.A., Abdullah, L., Shen, J., Hunt, B.G., Connolly, K.A., De Brouwer, E., et al. (2025). KLF2 maintains lineage fidelity and suppresses CD8 T cell exhaustion during acute LCMV infection. Science 387, eadn2337. 10.1126/science. adn2337.

[18] Bai, Y., Hu, M., Chen, Z., Wei, J., and Du, H. (2021). Single-Cell Transcriptome Analysis Reveals RGS1 as a New Marker and Promoting Factor for T-Cell Exhaustion in Multiple Cancers. Front Immunol 12, 767070. 10.3389/fimmu.2021.767070.

[19] Worboys, J.D., Vowell, K.N., Hare, R.K., Ambrose, A.R., Bertuzzi, M., Conner, M.A., Patel, F.P., Zammit, W.H., Gali-Moya, J., Hazime, K.S., et al. (2023). TIGIT can inhibit T cell activation via ligation-induced nanoclusters, independent of CD226 co-stimulation. Nat Commun 14, 5016. 10.1038/s41467-023-40755-3.

[20] Jo, Y., Jin, H.S., and Park, Y. (2024). No more LAGging behind PD-1: uncovering the unique role of LAG-3 in T-cell exhaustion. Cell Mol Immunol 21, 1351-1353. 10.1038/s41423-024-01227-w.

[21] Hou, L., Jiang, M., Li, Y., Cheng, J., Liu, F., Han, X., Guo, J., Feng, L., Li, Z., Yi, J., et al. (2025). Targeting SPP1(+) macrophages via the SPP1-CD44 axis reveals a key mechanism of immune suppression and tumor progression in ovarian cancer. Int Immunopharmacol 160, 114906. 10.1016/j.intimp.2025.114906.


引用本文

叶天, 孙婕, 晏菲, 印明柱, 李鑫, 基于单细胞转录组的肺腺癌免疫微环境中T细胞功能演化及其与巨噬细胞互作机制研究[J]. 国际临床研究杂志, 2026; 10: (6) : 35-41.