Impact of Gut Microbiota on the Clinical Course and Treatment Outcomes of Colorectal Cancer-A Systematic Review.

Background and Perspectives: As colorectal cancer research focuses on improving screening policies and treatment strategies, the gut microbiome is emerging as a novel diagnostic and prognostic biomarker. This systematic review aims to present the available data on the role of gut microbiota in colorectal cancer diagnosis, prognosis, and treatment response. Materials and

Methods

A systematic search under the PRISMA recommendation was conducted in PubMed database, until February 2026. Original human studies evaluating associations between gut microbiome composition and CRC diagnosis, survival outcomes, or therapeutic response were included. Both stool- and tissue-based analyses were considered. A qualitative synthesis of the data was performed.

Results

Thirty-six studies met the inclusion criteria, encompassing case-control cohorts, prospective survival analyses, and early-phase translational trials. Across populations and sequencing methodologies, gut microbiome alterations were consistently identified, with enrichment of oral-derived anaerobes, particularly Fusobacterium nucleatum, and depletion of beneficial commensal taxa in CRC patients compared with controls. Beta-diversity analyses frequently showed distinct clustering of microbial communities between the CRC and control groups, whereas alpha-diversity findings were heterogeneous. Several stool-based multi-species classifiers demonstrated good to excellent diagnostic performance, particularly when combined with established screening modalities. Tumor-associated microbial signatures were further associated with adverse survival outcomes and, in exploratory cohorts, with differential treatment response. Emerging evidence suggests that the microbiome may represent a modifiable environmental factor, particularly relevant in early-onset CRC.

Conclusions

The gut microbiome represents a promising adjunctive biomarker for CRC diagnosis and prognostic stratification, with potential implications for precision oncology. However, methodological heterogeneity and the need for prospective validation currently limit its routine clinical implementation.