Abstract B036: Non-steroidal anti-inflammatory drugs use and risk of colorectal cancer molecular subtypes: A systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background Current evidence suggests that the association between non-steroidal anti-inflammatory drug (NSAID) use and colorectal cancer (CRC) risk remains uncertain, and little is known about potential heterogeneity by molecular subtype. Clarifying these associations may provide insights into CRC carcinogenesis. The aim of this systematic review was to evaluate the association between NSAID use and CRC risk by four clinically relevant molecular subtypes. Methods PubMed, Web of Science and Embase were searched for studies assessing NSAID use in relation CRC risk by microsatellite instability (MSI), the CpG island methylator phenotype (CIMP), somatic mutations in the B-Raf proto-oncogene serine/threonine kinase (BRAF) gene or the Kirsten rat sarcoma viral oncogene homolog gene (KRAS) status. Meta-analyses were performed to calculate summary relative risks (sRR). Results Nine studies met the inclusion criteria (n = 4,726 overall study population). NSAID use was associated with a stronger inverse association for BRAF-wildtype CRC (sRR = 0.73; 95% CI: 0.66–0.80) than for BRAF-mutated CRC (sRR = 0.85; 95% CI: 0.71–1.02), although the difference was not statistically significant (P heterogeneity = 0.11). Associations were in a similar direction for CIMP-high (sRR = 0.71; 95% CI: 0.61–0.83) and CIMP-low CRC (sRR = 0.73; 95% CI: 0.66–0.80), with no difference by CIMP status (P heterogeneity = 0.80). Similarly, risk reductions were observed for KRAS-mutated (sRR = 0.78; 95% CI: 0.69–0.88) and KRAS-wildtype (sRR = 0.76; 95% CI: 0.69–0.84), and for both MSI-high (sRR = 0.80; 95% CI: 0.67–0.96) and MSI-low/MSS CRC (sRR = 0.75; 95% CI: 0.69–0.82), with no evidence of heterogeneity by KRAS or MSI status (P heterogeneity = 0.76 and P heterogeneity = 0.51 respectively). Conclusion NSAID use was associated with lower risk of CRC across molecular subtypes, with suggestive differences by BRAF mutation status. These findings highlight the need for further studies to clarify potential subtype-specific associations and their implications for targeted chemoprevention strategies. Citation Format: Mary Jose. Urruchúa-Rodríguez, Toktam Pour, Hermann Brenner, Michael Hoffmeister. Non-steroidal anti-inflammatory drugs use and risk of colorectal cancer molecular subtypes: A systematic review and meta-analysis [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: The Rise in Early-Onset Cancers—Knowledge Gaps and Research Opportunities; 2025 Dec 10-13; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(23_Suppl):Abstract nr B036.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.033 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".