Epigenome-wide DNA methylation and transcriptome profiling of localized and locally advanced prostate cancer: Uncovering new molecular markers
Bibliographic record
Abstract
BACKGROUND: It has become increasingly important to identify molecular markers for accurately diagnosing prostate cancer (PCa) stages between localized PCa (LPC) and locally advanced PCa (LAPC). However, there is a lack of profiling both epigenome-wide DNA methylation and transcriptome for the same patients with PCa at different stages. This study aims to identify epitranscriptomic biomarkers screened in the peri-prostatic (PP) adipose tissue for predicting LPC and LAPC. METHODS: We profiled gene expression and DNA methylation of 10 PCa patients' PP adipose tissue (4 LPC and 6 LAPC). Differential analysis was used to identify differentially methylated CpG sites and expressed genes. An integrative analysis of the microarray gene expression profiles and DNA methylation profiles was conducted using LASSO (least absolute shrinkage and selection operator) between each studied gene and the CpG sites in their promoter region. This epitranscriptomic signature was constructed by combining the association and differential analyses. The signature was then refined using the genetic mutation data of >1500 primary PCa and metastasis PCa samples from 4 different studies. We determined genes that were the most significantly affected by mutations. Machine learning models were built to evaluate the classification ability of the identified signature using the gene expression profiles from three external cohorts. RESULTS: ). From the differential analysis, we detected 16,405 downregulated genes and 9485 genes containing at least one hypermethylated CpG site. We identified 30 genes that showed anti-correlation, down-regulation and hyper-methylation simultaneously. Using genetic mutation data, we determined that 6 of the 30 genes showed significant differences (adjusted p-value<0.05) in mutation frequencies between the primary PCa and metastasis PCa samples. The identified 30 genes performed well in distinguishing PCa patients with metastasis from PCa patient without metastasis (area under the receiver operating characteristic curve (AUC) = 0.81). The gene signature also performed well in distinguishing PCa patients with high risk of progression from PCa patients with low risk of progression (AUC = 0.88). CONCLUSIONS: We established an integrative framework to identify differentially expressed genes with an aberrant methylation pattern on PP adipose tissue that may represent novel candidate molecular markers for distinguishing between LPC and LAPC.
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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".