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

Immunotherapy for colorectal cancer (CRC) currently faces significant dilemmas, but its specific mechanisms remain unclear. 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. Here, we integrated scRNA-seq and bulk RNA-seq databases and leveraged pseudotemporal trajectory model to identify core genes. Subsequently, with the help of 10 machine learning models, we constructed a TEX score prognostic model, whose clinical utility was externally validated in independent immunotherapy cohorts, demonstrating intra-tumoral CD8⁺ T cells occupy a continuum of exhaustion states. Besides, the TEX-score model, constructed from five exhaustion-related genes (KLF3, LMNA, SLC2A3, ARL4C, and TIMP1), stably predicted CRC prognosis and immunotherapy responsiveness, validating that patients with low TEX-score exhibited prolonged overall survival (OS), abundant immune infiltrates and better response to immunotherapy. Collectively, our findings elucidate T-cell exhaustion as a central mediator of immunotherapy failure in CRC and provide a clinically actionable guidance for patient stratification and treatment selection.