Microbiomics: Novel Biomarkers of Colorectal Cancer Diagnosis and Prognosis

Background: Colorectal cancer (CRC) caused over 1.9 million new cases and 930,000 deaths globally in 2020. There is an urgent need for novel biomarkers capable of predicting disease progression and therapeutic response. The gut microbiome has emerged as a promising source of diagnostic and prognostic indicators. Objective: This narrative review summarizes current evidences on gut microbiota and their metabolites as potential biomarkers for CRC diagnosis and prognosis. Main Content: Gut microbiomes influence CRC development through metabolism, immune modulation, inflammation, proliferation/apoptosis regulation, genotoxicity, and mucosal barrier disruption. Pathogenic species including Fusobacterium nucleatum and enterotoxigenic Bacteroides fragilis promote tumorigenesis via FadA-mediated signaling and Th17/IL-17 responses. Beneficial bacteria such as Faecalibacterium prausnitzii and Akkermansia muciniphila exert protective effects through short-chain fatty acid production. Macrophages phenotype physiological equilibrium is interrupted or maintained by different floras and inflammatory status fluctuates under the former. Metabolically, hydrogen sulfide damages mitochondrial DNA and secondary bile acids stimulate proliferation. Common detection methods include 16S rRNA sequencing and shotgun metagenomics, while organoids and gene arrays as innovate carriers are in exploratory stage so far. Clinical studies show F. nucleatum abundance correlates with advanced tumor stage, with combined F. nucleatum and colibactin-producing E. coli detection achieving 84.6% sensitivity for early CRC. A. muciniphila levels also predict response to PD-1 blockade immunotherapy. These microbiomes or metabolites support predictions in diagnosis, prognosis, therapeutic efficacy and even locations in earlier stages. Conclusion: Microbiome-based biomarkers represent a promising frontier in CRC management. Future research should focus on standardizing detection protocols, validating multi-marker panels, and exploring metabolite-based approaches to enhance clinical translation.