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

Abstract

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.

Keywords: DNA methylation; Prostate cancer (PCa); advanced diagnostics; improved individual outcomes; patient stratification.

Figure 1

Identification and verification of the differentially expressed genes in prostate cancer. (A) Heat map of RARB, APC, GSTP1, PRKY, CCND2, and RASSF1 genes DNA methylation in prostate cancer tissues (tumor) and paracancerous tissues (normal) in TCGA database. (B) Violin plot of methylation levels of APC, CCND2, GSTP1, PRKY, RARB, and RASSF1 genes in prostate cancer tissues (T) and paracancerous tissues (N). (C) ROC of single-gene diagnostic performance. CCND2, Cyclin-D2; GSTP1, glutathione S-transferase pi-1; AUC, area under the curve; TCGA, The Cancer Genome Atlas; ROC, receiver operating characteristic.

Figure 2

Dual-gene diagnostic model for prostate cancer. (A,B) BIC and Mallows’ Cp value for the best diagnostic model. (C) Correlation between the expression of CCND2 and GSTP1 gene, VIF =1.314. (D) ROC of GSTP1-CCND2 dual-gene diagnostic model in TCGA database. (E) ROC of GSTP1 single-gene diagnostic model in TCGA database. (F) ROC of CCND2 single-gene diagnostic model in TCGA database. BIC, Bayesian information criterion; TPM, transcripts per million; CCND2, Cyclin-D2; GSTP1, glutathione S-transferase pi-1; AUC, area under the curve; VIF, Variance Inflation Factor; TCGA, The Cancer Genome Atlas; ROC, receiver operating characteristic.

Figure 3

Evaluation of diagnostic efficacy. Methylation level and single-gene diagnostic performance of CCND2 and GSTP1 gene in GSE76938 (A), GSE26126 (B), GSE112047 (C), and GSE84749 (D) datasets. ROC shows the dual-gene diagnostic model in each dataset. T = prostate cancer tissues; N = paracancerous tissues. CCND2, Cyclin-D2; GSTP1, glutathione S-transferase pi-1; AUC, area under the curve; ROC, receiver operating characteristic.

Figure 4

CCND2 and GSTP1 may be methylation driver genes. (A) Correlation of DNA methylation and CCND2 and GSTP1 gene expression in TCGA prostate cancer dataset. (B) Correlation of DNA methylation and CCND2 and GSTP1 gene expression in the GSE84749 dataset. (C) Correlation of DNA methylation and CCND2 and GSTP1 gene expression in the CCLE pan-cancer dataset. (D) Correlation of DNA methylation and CCND2 and GSTP1 gene expression in the CCLE prostate cancer dataset. CCND2, Cyclin-D2; GSTP1, glutathione S-transferase pi-1; EXP, expression; MET, methylation; TCGA, The Cancer Genome Atlas; CCLE, Cancer Cell Line Encyclopedia.

Figure 5

Pathway enrichment and immunoassay. (A) Differentially expressed pathways in Gene Set Enrichment Analysis of multiple gene sets. (B) Correlation of gene expression and immune infiltration in CCND2 and GSTP1 gene. CCND2, Cyclin-D2; GSTP1, glutathione S-transferase pi-1; TPM, transcripts per million; PRAD, prostate adenocarcinoma; cor, correlation.

Figure 6

Validation in clinical samples. (A) Heat map of PRKY, RASSF1, CCND2, GSTP1, APC, and RARB genes DNA methylation in prostate cancer tissues (tumor) and paracancerous tissues (normal) of clinical samples. (B) Violin plot of methylation levels and ROC of single-gene diagnostic performance in CCND2. (C) Violin plot of methylation levels and ROC of single-gene diagnostic performance in GSTP1. (D) ROC of the dual-gene diagnostic model in of clinical samples. T = prostate cancer tissues; N = paracancerous tissues. CCND2, Cyclin-D2; GSTP1, glutathione S-transferase pi-1; AUC, area under the curve; ROC, receiver operating characteristic.