ArticleBMC cancer2026
Dissecting T-cell exhaustion heterogeneity and immune ecosystem dynamics in colorectal cancer through multi-omics machine learning.
Article in BMC cancer, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundImmunotherapy 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.
methodsWe 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.
resultsOur 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.
conclusionsOur findings implicate TEX as a central mediator of immunotherapy resistance in CRC, offering a clinically actionable framework for patient stratification and therapeutic decision-making.
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