Gene methylation status in focus of advanced prostate cancer diagnostics and improved individual outcomes

Background: Prostate cancer (PCa) is the most prevalent type of male genitourinary tumor, remains the second leading cause of deaths due to cancer in the United States in men. The aim of this study was to perform an integrative epigenetic analysis to explore the epigenetic abnormalities involved in the development and progression of PCa, and present advanced diagnostics and improved individual outcomes. Methods: Genome-wide DNA methylation profiles obtained from The Cancer Genome Atlas (TCGA) were analyzed and a diagnostic model was constructed. For validation, we employed profiles from the Gene Expression Omnibus (GEO) and methylation data derived from clinical samples. Gene set enrichment analysis (GSEA) and the Tumor Immune Estimation Resource (TIMER) were employed for GSEA and to assess immune cell infiltration, respectively. Results: An accurate diagnostic method for PCa was established based on the methylation level of Cyclin-D2 (CCND2) and glutathione S-transferase pi-1 (GSTP1), with an impressive area under the curve (AUC) value of 0.937. The model's reliability was further confirmed through validation using four GEO datasets GSE76938 (AUC =0.930), GSE26126 (AUC =0.906), GSE112047 (AUC =1.000), GSE84749 (AUC =0.938) and clinical samples (AUC =0.980). Notably, the TIMER analysis indicated that hypermethylation of CCND2 and GSTP1 was associated with reduced immune cell infiltration, higher tumor purity, and an increased risk of tumor progression. Conclusions: In conclusion, our study provides a robust and reliable methylation-based diagnostic model for PCa. This model holds promise as an improved approach for screening and diagnosing PCa, potentially enhancing early detection and patient outcomes, as well as for an advanced clinical management for PCa in the framework of predictive, preventive and personalised medicine.

Automated summary

In this study, an integrative epigenetic analysis was conducted to explore the epigenetic abnormalities in prostate cancer (PCa). A diagnostic model based on DNA methylation profiles was constructed, which showed high accuracy and reliability. This model has the potential to improve early detection and patient outcomes.