DNA methylation screening of primary prostate tumors identifies SRD5A2 and CYP11A1 as candidate markers for assessing risk of biochemical recurrence
Bibliographic record
Abstract
BACKGROUND: Altered DNA methylation in CpG islands of gene promoters has been implicated in prostate cancer (PCa) progression and can be used to predict disease outcome. In this study, we determine whether methylation changes of androgen biosynthesis pathway (ABP)-related genes in patients' plasma cell-free DNA (cfDNA) can serve as prognostic markers for biochemical recurrence (BCR). METHODS: Methyl-binding domain capture sequencing (MBDCap-seq) was used to identify differentially methylated regions (DMRs) in primary tumors of patients who subsequently developed BCR or not, respectively. Methylation pyrosequencing of candidate loci was validated in cfDNA samples of 86 PCa patients taken at and/or post-radical prostatectomy (RP) using univariate and multivariate prediction analyses. RESULTS: Putative DMRs in 13 of 30 ABP-related genes were found between tumors of BCR (n = 12) versus no evidence of disease (NED) (n = 15). In silico analysis of The Cancer Genome Atlas data confirmed increased DNA methylation of two loci-SRD5A2 and CYP11A1, which also correlated with their decreased expression, in tumors with subsequent BCR development. Their aberrant cfDNA methylation was also associated with detectable levels of PSA taken after patients' post-RP. Multivariate analysis of the change in cfDNA methylation at all of CpG sites measured along with patient's treatment history predicted if a patient will develop BCR with 77.5% overall accuracy. CONCLUSIONS: Overall, increased DNA methylation of SRD5A2 and CYP11A1 related to androgen biosynthesis functions may play a role in BCR after patients' RP. The correlation between aberrant cfDNA methylation and detectable PSA in post-RP further suggests their utility as predictive markers for PCa recurrence. .
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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 | 0.000 |
| 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) | 0.001 | 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".