Next-Generation Biomarkers and Artificial Intelligence in Colorectal Cancer: From Multi-Omic Data Integration to Clinical Application

Colorectal cancer (CRC) remains a leading cause of cancer-related mortality worldwide, although advances in molecular profiling and artificial intelligence (AI) are now reshaping precision oncology in ways that promise better patient outcomes. This review synthesises contemporary evidence on next-generation biomarkers and AI applications in CRC across multi-omic data integration, liquid biopsy, computational pathology, radiomics and clinical implementation. Several findings stand out. Integrated multi-omic approaches combining genomics, transcriptomics, proteomics, metabolomics and microbiomics outperform single-omic biomarkers for predicting prognosis and treatment response. Machine learning (ML) and deep learning (DL) models achieve clinical-grade performance for molecular biomarker prediction directly from routine histopathology, with a pooled area under the receiver operating characteristic curve (AUROC) of 0.94 for microsatellite instability (MSI) detection. Circulating tumour DNA (ctDNA) monitoring enables minimal residual disease (MRD) detection and real-time treatment guidance, postoperative ctDNA status separating a two-year recurrence-free survival of 91.1% from 50.4%; randomised evidence further shows that ctDNA-guided management can safely reduce adjuvant chemotherapy use in stage II colon cancer. Radiomics and pathomics extract prognostically significant quantitative features from imaging and histopathology, permitting non-invasive tumour characterisation, while multimodal models integrating clinical, genomic, imaging and pathological data support individualised treatment selection. MSI-high (MSI-H) status predicts exceptional immunotherapy benefit, carrying an overall survival hazard ratio of 0.35 against chemotherapy. Considerable difficulties nonetheless persist: harmonising data across institutions, generalising models to diverse populations, algorithmic bias and regulatory frameworks suited to clinical AI. Priorities for the coming years are prospective validation of AI-guided treatment algorithms, integration of spatial transcriptomics and single-cell approaches, federated learning to support multi-institutional collaboration without compromising privacy, and standardised protocols for biomarker testing and interpretation.