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Background <p>Immunotherapy has shown limited efficacy in a substantial subset of CRC patients, yet the mechanisms underlying therapeutic resistance remain incompletely understood. T-cell exhaustion (TEX) in the tumor microenvironment has been identified as a pivotal driver of immune evasion and tumor progression. Dissecting its contribution to CRC is essential for the development of rational therapeutic strategies.</p> Methods <p>We integrated scRNA-seq and bulk transcriptomic data to identify CD8⁺ T-cell exhaustion core genes via hdWGCNA and ten machine learning algorithms, constructed a multivariate Cox-based TEX score model validated across independent cohorts and immunotherapy datasets, and experimentally confirmed our findings by RT-qPCR, Western blot, and quantitative multiplex immunofluorescence in clinical CRC specimens.</p> Results <p>Our single-cell analysis revealed a continuum of intra-tumoral CD8⁺ T-cell exhaustion states, identified a five-gene TEX score (KLF3, LMNA, SLC2A3, ARL4C, TIMP1) that predicted poor prognosis and an immunosuppressive microenvironment. Further experimental validation confirmed the differential expression and spatial co-localization with CD8⁺ T cells in clinical specimens.</p> Conclusions <p>Our findings implicate TEX as a central mediator of immunotherapy resistance in CRC, offering a clinically actionable framework for patient stratification and therapeutic decision-making.</p> Graphical abstract <p></p>

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Dissecting T-cell exhaustion heterogeneity and immune ecosystem dynamics in colorectal cancer through multi-omics machine learning

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月19日

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  • Zhijing Zhang,
  • Peng Ouyang,
  • Kai Cui,
  • Jianqin Lai,
  • Yixiang Wen,
  • Xin Deng,
  • Wanyu Chen,
  • Zhenhong Xian,
  • Qi Qi,
  • Zhen Bao,
  • Jin Gong,
  • Xiao He

摘要

Background

Immunotherapy has shown limited efficacy in a substantial subset of CRC patients, yet the mechanisms underlying therapeutic resistance remain incompletely understood. T-cell exhaustion (TEX) in the tumor microenvironment has been identified as a pivotal driver of immune evasion and tumor progression. Dissecting its contribution to CRC is essential for the development of rational therapeutic strategies.

Methods

We integrated scRNA-seq and bulk transcriptomic data to identify CD8⁺ T-cell exhaustion core genes via hdWGCNA and ten machine learning algorithms, constructed a multivariate Cox-based TEX score model validated across independent cohorts and immunotherapy datasets, and experimentally confirmed our findings by RT-qPCR, Western blot, and quantitative multiplex immunofluorescence in clinical CRC specimens.

Results

Our single-cell analysis revealed a continuum of intra-tumoral CD8⁺ T-cell exhaustion states, identified a five-gene TEX score (KLF3, LMNA, SLC2A3, ARL4C, TIMP1) that predicted poor prognosis and an immunosuppressive microenvironment. Further experimental validation confirmed the differential expression and spatial co-localization with CD8⁺ T cells in clinical specimens.

Conclusions

Our findings implicate TEX as a central mediator of immunotherapy resistance in CRC, offering a clinically actionable framework for patient stratification and therapeutic decision-making.

Graphical abstract