Multi Omics Integration in Colorectal Cancer: From Molecular Insights to Precision Oncology

Area of Science:

• Oncology
• Genomics
• Proteomics
• Metabolomics
• Microbiome Research

Background:

• Colorectal cancer (CRC) is a complex disease where single-omics analyses fail to capture its full biological heterogeneity.
• Understanding tumor progression, immune evasion, and therapeutic resistance requires a multi-layered approach.

Purpose of the Study:

• To review how integrating genomics, transcriptomics, proteomics, metabolomics, and microbiome profiling is transforming CRC biology and precision oncology.
• To highlight landmark integrative studies and their impact on molecular subtyping and understanding CRC mechanisms.

Main Methods:

• Analysis of large-scale multi-omics datasets (e.g., TCGA, CPTAC, whole-genome sequencing).
• Integration of diverse data types including genomics, transcriptomics, proteomics, metabolomics, and microbiome profiling.
• Application of artificial intelligence (AI) methods like deep learning and explainable AI for analysis.

Main Results:

• Refined molecular subtyping and expanded understanding of driver mutations in CRC.
• Identified discordance between mRNA and protein activity, suggesting post-transcriptional/translational regulation.
• Uncovered tumor-microenvironment interactions through spatial profiling and microbiome-metabolite analyses.
• AI approaches show promise in improving CRC classification, biomarker discovery, and treatment prediction.

Conclusions:

• Multi-omics integration is essential for a comprehensive understanding of colorectal cancer biology and advancing precision oncology.
• AI-powered multi-omics analysis offers significant potential for improved CRC diagnosis and treatment strategies.
• Addressing barriers like data variability and cost is critical for clinical translation of multi-omics approaches in CRC management.