MicroRNA polymorphisms and esophageal cancer outcome.
Bibliographic record
Abstract
32 Background: Better understanding of the biology of esophageal cancer may help improve its treatment. MicroRNAs (miRs) regulate mRNA and can exert some influence on carcinogenesis. Identification of the microRNAs which regulate esophageal cancer development could potentially yield alternative therapeutic options. Objectives: We evaluated polymorphisms in miRs, miR biogenesis, binding sites of miR and their role in the survival of esophageal cancer patients. Methods: 324 esophageal cancer patients of all stages and histological subtypes were evaluated. Using Illumina Custom GoldenGate, 43 polymorphisms in miR pathways were evaluated. Cox proportional hazards models adjusted for clinical prognostic variables and determined the association of polymorphisms with overall survival (OS) and progression free survival (PFS). Adjusted hazard ratio (aHR) and 95% confidence intervals (CI) were calculated. Results: Among our patients, 83% were male while the mean age was 65 years. 73% had adenocarcinomas while 33.6% had advanced tumors (Stage IV). The median PFS was 1.20 years, while median OS was 2.17 years. After adjustment for clinical variables, a 5’UTR polymorphism in pri-mir26a-1 (rs7372209) was significantly associated with reduced PFS [aHR=0.78, CI:0.62-0.98, p=0.04] and OS [aHR 0.71 (0.56-0.89), p=0.003]. Three other polymorphisms were significantly associated with OS but not PFS: these included two polymorphisms of miR processing genes, DDX20 (rs197412) [aHR 1.31 (1.04-1.64), p=0.02] and EIF2C1 (rs595961) [aHR 0.76 (0.60-0.97), p=0.03] as well as the CD86 3’UTR C>G (rs17281995) polymorphism, which has been predicted to affect the binding of miR337, miR582, miR200a, miR184, and miR212 [aHR 1.38 (1.03-1.85), p=0.03]. Conclusions: We report the initial association of miR related polymorphisms with survival in esophageal cancer. We plan to explore additional relationships and validate these findings in other datasets.
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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 | 0.001 | 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".