Multiple highly methylated CpG sites as potential epigenetic markers for the diagnosis of prostate cancer
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Bibliographic record
Abstract
BACKGROUND: Prostate cancer (PCa) remains the leading cause of cancer deaths in men. The prostate-specific antigen (PSA) test is widely used for PCa screening, but it lacks specificity and can lead to over-diagnosis and over-treatment. New, effective and affordable markers are therefore needed. RESULTS: Using enzymatic methyl sequencing (EM-Seq), methylation-specific PCR (MS-PCR), and transcriptomics including a spatial approach, we analyzed tumor and non-tumor samples from radical prostatectomy specimens. Comprehensive methylome was performed in 15 paired samples of prostate cancer and their adjacent non-tumor tissue by EM-Seq. From over 4-million differentially methylated CpG sites, we identified 66 CpGs sites representing eight genes: CLDN5, GSTP1, NBEAL2, PRICKLE2, SALL3, TAMALIN/GRASP, TJP2, and TMEM106A which were hypermethylated in PCa tissues (p-value < 0.0001), and were confirmed by MS-PCR. A very good correlation between EM-Seq and MS-PCR results was observed (Pearson's correlation of 0.93). Differential expression of these candidate genes was analyzed first, using an Affymetrix RNA array dataset from a cohort of 68 non-tumor samples and 101 tumors with different aggressiveness patterns and, second, by in situ expression using Visium 10X spatial genomics transcriptomics on eight prostate tissue sections with different tumor grades and non-tumor glands. Lower expression level was found, using RNA arrays, in tumor compared to non-tumor tissues for six of the eight genes (p ≤ 0.0001) and in tumor glands with high aggressiveness compared to non-tumor glands (p < 0.0001) for the eight genes using in situ transcriptomics. CONCLUSIONS: Our study identifies promising DNA methylation markers for the diagnosis of prostate cancer.
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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 | 0.002 |
| 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