Association between the flap endonuclease 1 gene polymorphisms and cancer susceptibility: An updated meta‐analysis
Bibliographic record
Abstract
Flap endonuclease 1 (FEN1) has emerged as an important enzyme in the maintenance of genomic instability and preventing carcinogenesis. The relationship between FEN1 -69G>A (rs174538)+4150G>T (rs4246215) polymorphisms and cancer susceptibility has been reported; however, results were inconclusive. In the present study, a meta-analysis of data from eligible reports was carried out to summarize the possible relationship between FEN1 polymorphisms and cancer risk. A total of 11 articles, including 20 studies with 7366 cases and 9028 controls and 18 studies with 6649 cases and 8325 controls for FEN1 rs174538 and FEN1 rs4246215 polymorphisms, respectively, were recruited for meta-analysis. Overall, meta-analyses showed that FEN1 rs174538 and rs4246215 polymorphisms are significantly associated with the decreased risk of cancer. The stratified analysis proposed that both variants were associated with protection against gastrointestinal cancer, breast cancer, hepatocellular cancer, esophageal cancer, gastric cancer, colorectal cancer, and lung cancer. In conclusion, this meta-analysis revealed an association between FEN1 polymorphisms and cancer risk. Additional studies in a larger study population that include subjects from a variety of ethnicities are warranted to further verify our findings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.015 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".