Human papillomavirus (HPV) infection and prevalence of colorectal cancer: an updated systematic review and meta-analysis of global data
Bibliographic record
Abstract
BACKGROUND: Human papillomavirus (HPV), a known oncogenic virus in cervical and anal cancers, has also been detected in colorectal tissues. However, evidence regarding its association with colorectal cancer (CRC) remains inconsistent. We conducted an updated systematic review and meta-analysis to clarify this relationship. METHODS: Following PRISMA 2020 guidelines, we systematically searched PubMed, Embase, Web of Science, and Cochrane Library through May 2025 for observational studies (case-control and cross-sectional) assessing HPV prevalence in CRC patients versus controls. Data extraction was performed in duplicate. Pooled odds ratios (ORs) were estimated using random-effects models with logit transformation. Subgroup analyses and meta-regression examined the effects of geographic region, HPV genotype, detection method, and sample type. Risk of bias was assessed using the Newcastle-Ottawa Scale. FINDINGS: Twenty case-control studies encompassing 1424 CRC cases and 1363 controls were included. The pooled OR for the association between HPV infection and CRC was 2.39 (95% CI: 1.69-3.09), with no significant heterogeneity ( I2 = 0%). Associations were strongest in Asian studies (OR = 3.73) and in those using formalin-fixed paraffin-embedded (FFPE) tissues (OR = 3.56). Studies targeting HPV16 alone yielded higher effect sizes than those evaluating mixed or unspecified genotypes. Meta-regression confirmed region and genotype group as significant effect modifiers. Leave-one-out sensitivity analysis confirmed robustness. Egger's test indicated marginal small-study bias ( P = 0.074), but funnel plot symmetry suggested no serious publication bias. CONCLUSION: This meta-analysis confirms a significant link between HPV infection and colorectal cancer, suggesting HPV may play a broader oncogenic role beyond the anogenital tract. The findings highlight the need for genotype-aware, region-specific screening strategies, and support incorporating viral profiling into CRC prevention efforts, especially in underserved populations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.032 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".