The genetic landscape of CYP24A1 polymorphisms in cancer risk: evidence from a systematic review
Bibliographic record
Abstract
Cancer remains a significant global health challenge, with a multifaceted etiology that includes genetic factors. Among these, CYP24A1 stands out for its pivotal role in vitamin D metabolism and regulates biological processes influencing cancer risk. Single nucleotide polymorphisms in CYP24A1 are associated with variations in vitamin D bioavailability, potentially impacting the initiation and progression of cancer. To date, no comprehensive review has been conducted on this topic. Therefore, this systematic review aims to investigate the association between common CYP24A1 polymorphisms and cancer susceptibility, by analyzing studies retrieved from PubMed, Scopus, Cochrane Library, and Web of Science databases up to January 2024. Using PRISMA guidelines and quality assessments with the Newcastle Ottawa Scale (NOS), 22 studies, with 28,132 participants (12,751 cases and 15,381 controls) were included. The reported odds ratio and p-value were used to assess the association between CYP24A1 SNPs (rs2296241, rs6068816, rs927650, rs2181874 and rs2585428) with various cancer risks. Key findings revealed that SNP rs2296241 was linked to increased risk of follicular thyroid cancer but lower risks in papillary thyroid cancer, esophageal squamous cell carcinoma, oral cancer, and prostate cancer. SNP rs6068816 correlated with reduced breast and lung cancer risks, while rs927650 and rs2181874 were associated with lower risks in papillary thyroid cancer and prostate cancer, respectively. SNP rs2585428 showed a lower risk in breast and prostate cancers suggesting a protective effect against these malignancies. These results highlight CYP24A1 polymorphisms as potential molecular markers for cancer susceptibility, underscoring their clinical relevance in risk assessment and personalized interventions.
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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 | 0.008 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".