Colorectal cancer (CRC) is a biologically heterogeneous disease in which single-omics analyses incompletely capture the cross-layer mechanisms underlying tumor progression, immune evasion, and therapeutic resistance. This review critically examines how the integration of genomics, transcriptomics, proteomics, metabolomics, and microbiome profiling is redefining CRC biology and precision oncology. Landmark integrative efforts, including TCGA analyses of 276 colorectal cancer samples, CPTAC proteogenomic profiling of 95 tumors, and recent whole-genome sequencing studies of 2023 CRC cases, have refined molecular subtyping, expanded the driver landscape, and revealed clinically relevant discordance between mRNA abundance and protein activity. Integrative studies further show that oncogenic signaling may be driven by post-transcriptional and post-translational regulation, while spatially resolved profiling and microbiome–metabolite analyses are uncovering previously obscured tumor–microenvironment interactions. We also discuss how artificial intelligence-based approaches, including factor analysis, deep learning, graph-based models, and explainable AI, are improving subtype classification, biomarker discovery, and treatment-response prediction, with particular relevance to microsatellite instability-high and early-onset CRC. Finally, we critically evaluate the principal barriers to clinical translation, including batch effects, cross-platform variability, limited external validation, regulatory constraints, and cost, and outline priorities for building reproducible, clinically deployable multi-omics pipelines for CRC management.
Liu et al. (2026) studied this question.