Comprehensive Immune Subtyping and Multi-Omics Profiling of the Tumor Microenvironment in Colorectal Cancer: Implications for Prognosis and Personalized Immunotherapy.

Introduction: The Tumor microenvironment (TME) plays a crucial role in colorectal cancer (CRC) prognosis and treatment response. However, comprehensive understandings of TME-related immune subtypes and their mechanisms for precision medicine remain insufficient. This study aims to identify immune subtypes in CRC, develop a prognostic model, and explore the role of microbial diversity in tumor progression.

Methods: Multi-omics data and non-negative matrix factorization (NMF) were used to classify CRC into immune subtypes. Differentially expressed TME-related genes were identified, and a prognostic risk model was developed using Cox and LASSO regression. Single-cell RNA sequencing (scRNA-seq) assessed cellular interactions and gene set variations. Microbiome profiling was integrated to evaluate the impact of microbial diversity on CRC progression and immune modulation. Key findings were validated using immunohistochemistry, external datasets, and qPCR in patient-derived organoids.

Results: Four TME-related immune subtypes were identified: immune-exhausted C1 (poor prognosis, high immune infiltration), immune-activated C2/C3 (better prognosis), and immune-desert C4 (worst prognosis). A risk model based on genes (SOX9, CLEC10A, RAB15, RAB6B, PCOLCE2, FUT1) stratified patients into high- and low-risk groups. High-risk groups exhibited increased Enterobacteriaceae and Clostridium, while low-risk groups showed higher Porphyromonadaceae and Peptostreptococcaceae, correlating with better immunotherapy responses. scRNA-seq revealed distinct cell-cell communication patterns across subtypes.

Discussion: The study highlights the complexity of CRC's TME and its role in prognosis and treatment. Findings support personalized treatment strategies, considering immune and microbial factors.

Conclusion: This research integrates TME subtyping, risk modeling, single-cell analysis, and microbiome profiling to advance CRC prognosis and precision therapy, emphasizing personalized strategies for better outcomes.