Genetic variants in microRNAs and microRNA target sites predict biochemical recurrence after radical prostatectomy in localized prostate cancer
Bibliographic record
Abstract
Recent evidence indicates that microRNAs might participate in prostate cancer initiation, progression and treatment response. Germline variations in microRNAs might alter target gene expression and modify the efficacy of prostate cancer therapy. To determine whether genetic variants in microRNAs and microRNA target sites are associated with the risk of biochemical recurrence (BCR) after radical prostatectomy (RP). We retrospectively studied two independent cohorts composed of 320 Asian and 526 Caucasian men with pathologically organ-confined prostate cancer who had a median follow-up of 54.7 and 88.8 months after RP, respectively. Patients were systematically genotyped for 64 single-nucleotide polymorphisms (SNPs) in microRNAs and microRNA target sites, and their prognostic significance on BCR was assessed by Kaplan-Meier analysis and Cox regression model. After adjusting for known clinicopathologic risk factors, two SNPs (MIR605 rs2043556 and CDON rs3737336) remained associated with BCR. The numbers of risk alleles showed a cumulative effect on BCR [perallele hazard ratio (HR) 1.60, 95% confidence interval (CI) 1.16-2.21, p for trend = 0.005] in Asian cohort, and the risk was replicated in Caucasian cohort (HR 1.55, 95% CI 1.15-2.08, p for trend = 0.004) and in combined analysis (HR 1.57, 95% CI 1.26-1.96, p for trend <0.001). Results warrant replication in larger cohorts. This is the first study demonstrating that SNPs in microRNAs and microRNA target sites can be predictive biomarkers for BCR after RP.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".