Correlation of ERG Expression and DNA Methylation Biomarkers with Adverse Clinicopathologic Features of Prostate Cancer
Bibliographic record
Abstract
PURPOSE: Fusion of the TMPRSS2 gene with the ERG oncogene and aberrant DNA methylation patterns are commonly found in prostate cancer. The aim of this study was to analyze the relationship between ERG expression, DNA methylation of three biomarkers, and clinicopathologic features of prostate cancer. EXPERIMENTAL DESIGN: Immunohistochemistry for ERG protein was conducted as a surrogate for TMPRSS2-ERG fusions. We analyzed methylation of CYP26A1, TBX15, and HOXD3 in 219 prostatectomy specimens by the quantitative MethyLight assay. DNA methylation was compared between ERG-positive and -negative cases and correlations of ERG and DNA methylation with clinicopathologic features were analyzed using χ(2), Spearman correlation, logistic regression, and Cox regression. RESULTS: ERG expression varied according to Gleason pattern (almost absent in pattern II, highest in pattern III, and lower in pattern IV/V) and showed a strong positive correlation with methylation levels of CYP26A1, TBX15, and HOXD3 (Spearman P < 0.005). TBX15 and HOXD3 methylation were significantly associated with pathologic stage, Gleason score, and Gleason pattern (P ≤ 0.015). In multivariate regression analysis, PSA, TBX15 high methylation, and HOXD3 high methylation were significantly associated with stage (P < 0.05), whereas ERG expression was negatively correlated with Gleason score (P = 0.003). In univariate time-to-recurrence analysis, a combination of HOXD3/TBX15 high methylation predicted recurrence in ERG-positive and -negative cases (P < 0.05). CONCLUSIONS: CYP26A1, TBX15, and HOXD3 are methylation markers of prostate cancer associated with ERG expression and clinicopathologic variables, suggesting that incorporation of these markers may be useful in a pre- and posttreatment clinical setting.
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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 | 0.000 | 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".