Single-cell RNA sequencing analysis reveals cellular heterogeneity and immune microenvironment dynamics in colorectal cancer

Background Despite treatment advances, prognoses of patients with advanced colorectal cancer remain poor. The limited effectiveness of programmed cell death protein 1 (PD-1) blockade immunotherapy in a subset of patients necessitates a deeper understanding of the colorectal cancer microenvironment. This study aimed to identify new therapeutic targets and potential biomarkers by investigating CD8 + CD101hiTim3 + (CCT) T cells and their progenitors in colorectal cancer, and their association with immunotherapy response at the transcriptomic level. Methods We used single-cell sequencing data and The Cancer Genome Atlas database for comprehensive bioinformatics analysis, including single-cell sequencing data analysis, Gene Set Variation Analysis, pseudotime analysis, cell communication analysis, and construction and validation of a prognostic model. Results Key findings include the identification and annotation of various T-cell subtypes in colorectal cancer, construction of a pseudotime trajectory of CCT T cells and their progenitors showing dynamic gene expression changes, and enhanced interactions between CCT T cell progenitors and other cells in the PD-1 immunotherapy group. We developed and validated a prognostic model comprising 15 gene features with strong prognostic stratification performance, revealing that high-risk patients exhibit transcriptomic associations with potentially reduced responses to immune checkpoint inhibitors (ICIs). Pathway enrichment analysis highlighted critical pathways, including leukocyte adhesion and T cell activation, indicating complex influences on disease progression and treatment responses within the colorectal cancer immune landscape. Conclusions This study emphasizes the potential importance of targeted therapies that modulate immune interactions and functional states to improve colorectal cancer prognosis, particularly in high-risk patients who show an association at the transcriptomic level with reduced ICI responsiveness.