Integrating Multi‐Omics Data to Uncover Prostate Tissue DNA Methylation Biomarkers and Target Genes for Prostate Cancer Risk
Why this work is in the frame
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Bibliographic record
Abstract
Previous studies have indicated that specific CpG sites may be linked to the risk of prostate cancer (PCa) by regulating the expression of PCa target genes. However, most existing studies aim to identify DNA methylation (DNAm) biomarkers through blood tissue genetic instruments, which impedes the identification of relevant biomarkers in prostate tissue. To identify PCa risk-associated CpG sites in prostate tissue, we established genetic prediction models of DNAm levels using data from normal prostate samples in the GTEx (N = 108) and assessed associations between genetically predicted DNAm in prostate and PCa risk by studying 122,188 cases and 604,640 controls. We observed significant associations for 3879 CpG sites, including 926 at novel genomic loci. Among them, DNAm levels of 80 CpG sites located at novel loci are significantly associated with expression levels of 45 neighboring genes in normal prostate tissue. Of these genes, 11 further exhibit significant associations with PCa risk for their predicted expression levels in prostate tissue. Intriguingly, a total of 31 CpG sites demonstrate consistent association patterns across the methylation-gene expression-PCa risk pathway. Our findings suggest that specific CpG sites may be related to PCa risk by modulating the expression of nearby target genes.
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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 it