Microbiome-Based Biomarkers for Early Detection of Colorectal Cancer
DOI:
https://doi.org/10.5281/zenodo.22688272Keywords:
colorectal cancer, gut microbiome, Fusobacterium nucleatum, non-invasive biomarker, metagenomics, screening, systematic reviewAbstract
olorectal cancer screening relies on colonoscopy and stool-based occult blood or DNA testing, methods that are
either invasive and resource-intensive or limited in sensitivity for early-stage and pre-malignant lesions, motivating
sustained interest in gut microbiome composition as a source of non-invasive diagnostic biomarkers. We
conducted a systematic review of studies reporting fecal or mucosal microbiome-based biomarkers for colorectal
cancer or adenoma detection, following PRISMA 2020 reporting guidance, searching the literature through early
2026 and assessing methodological quality using QUADAS-2. Twenty-six studies meeting inclusion criteria
reported diagnostic accuracy data for a microbiome-based classifier distinguishing colorectal cancer cases from
controls, most commonly using shotgun metagenomic or 16S rRNA amplicon sequencing of stool samples
combined with a machine-learning classifier. Pooled sensitivity and specificity for colorectal cancer detection,
synthesised across the fourteen studies providing sufficient data for bivariate meta-analysis, were 0.83 (95% CI
0.78-0.87) and 0.85 (95% CI 0.80-0.89) respectively, with an area under the summary receiver operating
characteristic curve of 0.90. Diagnostic performance for adenoma detection, reported in eleven studies, was
consistently lower, with pooled sensitivity of 0.61 (95% CI 0.53-0.68), reflecting the more subtle microbial
community shifts associated with pre-malignant lesions. Fusobacterium nucleatum enrichment was the single
most consistently replicated taxonomic signal across studies, reported in nineteen of the twenty-six included
studies, though its individual diagnostic accuracy as a stand-alone marker was substantially lower than that of
multi-taxon or metagenomic classifiers combining dozens of microbial features. Cross-cohort external validation,
tested in only six of the twenty-six studies, showed a consistent, moderate drop in both sensitivity and specificity
relative to within-cohort performance, highlighting population and technical batch-effect generalisability as the
principal unresolved barrier to clinical translation. We conclude that microbiome-based classifiers show diagnostic
performance approaching, but not yet matching, that of the most sensitive current non-invasive tests for
established colorectal cancer, with adenoma detection and cross-population generalisability remaining the most
significant unresolved gaps before clinical adoption can be reasonably considered.
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