Epigenetic analysis identifies factors driving racial disparity in prostate cancer
Bibliographic record
Abstract
Abstract Prostate cancer (PCa) is the second most leading cause of death in men worldwide. African American men (AA) represent more aggressive form of PCa as compared to Caucasian (CA) counterparts. Evidence suggests that genetic and other biological factors could account for the observed racial disparity. We analyzed the cancer genome atlas (TCGA) dataset (2015) for existing epigenetic variation in AA and CA prostate cancer patients, and carried out Reduced Representation Bisulphite Sequencing (RRBS) analysis to identify global methylation changes in AA and CA prostate cancer patients. The TCGA dataset analysis revealed that the epigenetic heterogeneity could be categorized into 4 classes, where AA associated primarily to methylation cluster 1 (p value 0.048), and CA associated to methylation cluster 3 (p value 0.000146). We identified enrichment of Wnt signaling genes in both AA and CA, however they were differentially activated in terms of canonical and non-canonical Wnt signaling pathway activation. This was further validated using the GenomeDx expression data. Our RRBS data also suggested distinct methylation patterns in AA compared to CA, and in part validated our TCGA findings. Survival analysis using the RRBS data suggested hypomethylated genes to be significantly associated with recurrence of prostate cancer in CA (p=6.07×10 −6 ) as well as in AA (p=0.0077). Overall, the observed racial disparity in the molecular mechanism involved in the pathogenesis of prostate cancer suggests diverse heterogeneity that potentially could affect survival and should be considered during prognosis and treatment.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".