Abstract
AIM: Colorectal polyps serve as precursors to colorectal cancer and pose a growing public health challenge with their increasing incidence. The potential role of gut microbiota (GM) dysbiosis in colorectal polyp pathogenesis has garnered attention, yet existing evidence remains inconsistent. This study aimed to compare gut microbiota differences between colorectal polyp patients and healthy controls using systematic review and meta-analysis using 16S rRNA sequencing data. MATERIALS AND METHODS: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, a systematic search was performed across multiple databases (PubMed, Web of Science, Embase, Cochrane Library) up to April 2025. Only studies comparing gut microbiota profiles between colorectal polyp patients and healthy controls were included. Data was independently screened and extracted by two reviewers, and study quality was assessed using the Newcastle-Ottawa Scale. Meta-analyses were conducted with R (version 4.4.1) and Stata (version 18.0), with heterogeneity assessed via the I(2) statistic and publication bias through funnel plots, Egger's test, Begg's test, and sensitivity analyses.Logit transformation was applied to enhance the accuracy and reproducibility of the analysis. Additionally, KEGG pathway data was utilized to explore the distinct metabolic pathway patterns between polyp patients and healthy controls. KEY FINDINGS: Systematic review and meta-analysis were performed by synthesizing 11 independent 16S rRNA-sequenced studies. Our analysis revealed that patients with colorectal polyps exhibited significantly reduced GM diversity, decreased Firmicutes abundance, and increased Fusobacteria abundance. KEGG pathway analysis indicated enrichment of the TCA cycle in polyp patients and more active amino acid metabolism in healthy controls. SIGNIFICANCE: Patients with colorectal polyps have distinct gut microbiota characteristics and specific metabolic shifts. These findings may facilitate the discovery of non-invasive biomarkers, guide personalized prevention strategies, and improve risk stratification for early intervention.