An updated meta-analysis of the association between fibroblast growth factor receptor 4 polymorphisms and susceptibility to cancer
Bibliographic record
Abstract
Fibroblast growth factor receptor 4 (FGFR4) is a cell surface receptor tyrosine kinases (RTKs) for FGFs. Several studies have focused on the association between FGFR4 polymorphisms and cancer development. This meta-analysis aimed to estimate the association between FGFR4 rs351855 (Gly388Arg), rs1966265 (Val10Ile), rs7708357, rs2011077, and rs376618 polymorphisms and cancer risk. Eligible studies were identified from electronic databases. All statistical analyses were achieved with the STATA 14.0 software. Pooled odds ratios (ORs) with 95% confidence intervals (CIs) were used to quantitatively estimate the association. Overall, no significant association was found among rs351855, rs2011077, and rs376618 polymorphisms with the risk of overall cancer. The rs1966265 polymorphism significantly decreased the risk of cancer in recessive (OR = 0.87, 95% CI = 0.78-0.97, P=0.009, TT vs CT+CC) genetic model. Whereas the rs7708357 polymorphism was positively associated with cancer risk in dominant (OR = 1.17, 95% CI = 1.02-1.36, P=0.028) genetic model. Stratified analysis revealed that rs351855 variant significantly increased the risk of prostate cancer in heterozygous (OR = 1.16, 95% CI = 1.02-1.32, P=0.025 AG vs GG), dominant (OR = 1.20, 95% CI = 1.06-1.35, P=0.004, AG+AA vs GG), and allele (OR = 1.22, 95% CI = 1.06-1.41, P=0.005, A vs G) genetic models. In summary, the findings of this meta-analysis indicate that rs1966265, rs7708357, and rs351855 polymorphisms are correlated to cancer development. Further well-designed studies are necessary to draw more precise conclusions.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.049 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".