Abstract
BACKGROUND: Gastrointestinal (GI) malignancies, including gastric and colorectal cancers, remain one of the primary contributors to cancer-related illness and death globally. Despite the availability of conventional diagnostic tools, early detection and personalized treatment remain significant clinical challenges. Integrated multi-omics methods encompassing genomic, transcriptomic, proteomic, metabolomic, and microbiome profiles have emerged as powerful tools for advancing precision oncology, improving diagnostic accuracy, and informing therapeutic strategies. AIM: To investigate the application of multi-omics approaches in the early detection, risk stratification, treatment optimization, and biomarker discovery of GI malignancies. METHODS: The systematic review process was conducted in accordance with the PRISMA 2020 guidelines. Five databases, PubMed, ScienceDirect, Scopus, ProQuest, and Web of Science, were searched for studies published in English from 2015 onwards. Eligible studies involved human subjects and focused on multi-omics integration in GI cancers, including biomarker identification, tumor microenvironment analysis, tumor heterogeneity, organoid modeling, and artificial intelligence (AI)-driven analytics. Data extraction included study characteristics, omics modalities, clinical applications, and evaluation of study quality conducted with the Cochrane risk of bias 2.0 instrument. RESULTS: A total of 17196 initially identified articles, 20 met the inclusion criteria. The findings highlight the superiority of multi-omics platforms over traditional biomarkers (e.g., carcinoembryonic antigen and carbohydrate antigen 19-9 in detecting early stage GI cancers. Key applications include the identification of circulating tumor DNA, extracellular vesicles, lipidomic and proteomic signatures, and the adoption of AI algorithms to enhance diagnostic precision. Multi-omics analysis has also revealed the mechanisms of immune modulation, tumor microenvironment regulation, metastatic behavior, and drug resistance. Organoid models and microbiota profiling have contributed to personalized therapeutic strategies and immunotherapy optimization. CONCLUSION: Multi-omics approaches offer significant advancements in the early diagnosis, prognostic evaluation, and personalized treatment of GI malignancies. Their integration with AI analytics, organoid biobanking, and microbiota modulation provides a pathway for precision oncology research.