Examining the Polymorphisms in the Hypoxia Pathway Genes in Relation to Outcome in Colorectal Cancer
Bibliographic record
Abstract
INTRODUCTION: Colorectal cancer is a common malignancy. Identification of genetic prognostic markers may help prognostic estimations in colorectal cancer. Genes that regulate response to hypoxia and other genes that are regulated under the hypoxic conditions have been shown to play roles in cancer progression. In this study, we hypothesized that genetic variations in the hypoxia pathway genes were associated with the risk of outcome in colorectal cancer patients. METHODS: This study was performed in two phases. In the first phase, 49 SNPs from six hypoxia pathway genes (HIF1A, HIF1B, HIF2A, LOX, MIF and CXCL12) in 272 colorectal cancer patients were analyzed. In the second phase, 77 SNPs from seven hypoxia pathway genes (HIF1A, HIF1B, HIF2A, HIF2B, HIF3A, LOX and CXCL12) were analyzed in an additional cohort of 535 patients. Kaplan Meier, Cox univariate and multivariable regression analyses were performed to analyze the relationship between the SNPs and overall survival (OS), disease free survival (DFS) or disease specific survival (DSS). Since this was a hypothesis-generating study, no correction for multiple testing was applied. RESULTS: In phase I, one SNP (HIF2A rs11125070) was found to be associated with DFS in multivariable analysis; yet association of a proxy polymorphism (HIF2A rs4953342) was not detected in the phase II patient cohort. In phase II, associations of two SNPs (HIF2A rs4953352 and HIF2B rs12593988) were significant in both OS and DFS multivariable analyses. However, association of HIF2A rs4953352 was not replicated in the phase I cohort using a proxy SNP (HIF2A rs6706003). CONCLUSION: Overall, our study did not find a convincing evidence of association of the investigated polymorphisms with the disease outcomes in colorectal cancer.
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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 | 0.001 | 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".