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

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.