Association between Polymorphisms in Cancer-Related Genes and Early Onset of Esophageal Adenocarcinoma
Bibliographic record
Abstract
There is an increasing incidence of esophageal adenocarcinoma (EA) among younger people in the western populations. However, the association between genetic polymorphisms and the age of EA onset is unclear. In this study, 1330 functional/tagging single-nucleotide polymorphisms (SNPs) from 354 cancer-related genes were genotyped in 335 white EA patients. Twenty important SNPs that have the highest importance scores and lowest classification error rate were identified by the random forest algorithm to be associated with early onset of EA (age ≤ 55 years). Subsequent logistic regression analysis indicated that 10 SNPs (rs2070744 of NOS3, rs720321 of BCL2, rs17757541 of BCL2, rs11775256 of TNFRSF10A, rs1035142 of CASP8, rs2236302 of MMP14, rs4740363 of ABL1, rs696217 of GHRL, rs2445762 of CYP19A1, and rs11941492 of VEGFR2/KDR) were significantly associated with early onset of EA (≤55 vs >55 years, all P < .05 after adjusting for co-variates and false discovery rate). Among them, five SNPs in the NOS3, BCL2, TNFRSF10A, and CASP8 genes were known to be involved in apoptosis processes. In Kaplan-Meier analyses, rs2070744 of NOS3, rs720321 of BCL2, and rs1035142 of CASP8 were also significantly associated with early onset of EA. Moreover, there was a higher risk of developing EA at a younger age when one had more risk genotypes. In conclusion, polymorphisms in cancer-related genes, especially those in the apoptotic pathway, play an important role in the development of younger-aged EA in a dose-response manner.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".