The relationship between <em>IGF2BP2</em> and <em>PPARG</em> polymorphisms and susceptibility to esophageal squamous-cell carcinomas in the eastern Chinese Han population
Bibliographic record
Abstract
Abstract: The aim of this case–control study was to assess whether PPARG and IGF2BP2 polymorphisms confer susceptibility to esophageal squamous-cell carcinoma (ESCC). A total of 507 patients pathologically confirmed for ESCC and 1,496 age-, sex-, and residence-matched healthy individuals were enrolled. The PPARG rs1801282 C>G and rs3856806 C>T and IGF2BP2 rs1470579 A>C and rs4402960 G>T polymorphisms were selected and genotyped by SNPscan genotyping assays. Multivariable logistic analysis suggested that the PPARG rs3856806 C>T polymorphism might increase the risk of ESCC. In different stratified analyses, there were significant associations between PPARG rs3856806 C>T and risk of ESCC in female, never-smoking, drinking, and never-drinking subgroups. In addition, we also found that PPARG rs1801282 C>G increased ESCC risk in the never-smoking subgroup. There was significant difference in C rs1470579 G rs4402960 C rs1801282 C rs3856806 -haplotype distribution among ESCC cases and control subjects. In conclusion, our findings highlight that PPARG rs1801282 C>G and rs3856806 C>T polymorphisms are candidates for susceptibility to ESCC in the eastern Chinese Han population. The C rs1470579 G rs4402960 C rs1801282 C rs3856806 haplotype is associated with susceptibility to ESCC. Keywords: PPARG , IGF2BP2 , polymorphism, risk, ESCC
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".