Epigenetic analysis identifies factors driving racial disparity in prostate cancer
Bibliographic record
Abstract
Abstract Background Prostate cancer (PCa) is the second most leading cause of death in men worldwide. African‐American men (AA) represent more aggressive form of the disease compared to Caucasian (CA) counterparts. Several lines of evidences suggest that biological factors are responsible for the observed racial disparity. Aim This study was aimed at identifying the epigenetic variation among AA and CA PCa patients and whether DNA methylation differences have an association with clinical outcomes in the two races. Methods and results The cancer genome atlas (TCGA) dataset (2015) was used to identify existing epigenetic variation in AA and CA PCa patients. Reduced Representation Bisulfite Sequencing (RRBS) was performed to identify global DNA methylation changes in a small cohort of AA and CA PCa patients. The RRBS data were then used to identify survival and recurrence outcomes in AA and CA PCa patients using publicly available datasets. The TCGA data analysis revealed epigenetic heterogeneity, which could be categorized into four classes. AA associated primarily to methylation cluster 1 ( p = 0.048), and CA associated to methylation cluster 3 ( p = 0.000146). Enrichment of the Wnt signaling pathway was identified in both the races; however, they were differentially activated in terms of canonical and non‐canonical Wnt signaling. This was further validated using the Decipher Genomics Resource Information Database (GRID). The RRBS data also identified discrete methylation patterns in AA compared with CA and, in part, validated our TCGA findings. Survival analysis using the RRBS data suggested hypomethylated genes to be significantly associated with recurrence of PCa in CA ( p = 6.07 × 10 −6 ) as well as in AA ( p = 0.0077). Conclusion Overall, we observed epigenetic‐based racial disparity in PCa which could affect survival and should be considered during prognosis and treatment.
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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 | 0.000 | 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".