Integrated Pharmacogenetic Signature for the Prediction of Prostatic Neoplasms in Men With Metabolic Disorders

Abstract

Background/aim: Oncogenic processes are delineated by metabolic dysregulation. Drug likeness is pharmacokinetically tested through the CYP450 enzymatic system, whose genetic aberrations under epigenetic stress could shift male organisms into prostate cancer pathways. Our objective was to predict the susceptibility to prostate neoplasia, focused on benign prostatic hyperplasia (BPH) and prostate cancer (PCa), based on the pharmacoepigenetic and the metabolic profile of Caucasians.

Materials and methods: Two independent cohorts of 47,389 individuals in total were assessed to find risk associations of CYP450 genes with prostatic neoplasia. The metabolic profile of the first cohort was statistically evaluated and frequencies of absorption-distribution-metabolism-excretion-toxicity (ADMET) properties were calculated. Prediction of miRNA pharmacoepigenetic targeting was performed.

Results: We found that prostate cancer and benign prostatic hyperplasia patients of the first cohort shared common cardiometabolic trends. Drug classes C08CA, C09AA, C09CA, C10AA, C10AX of the cardiovascular, and G04CA, G04CB of the genitourinary systems, were associated with increased prostate cancer risk, while C03CA and N06AB of the cardiovascular and nervous systems were associated with low-risk for PCa. CYP3A4*1B was the most related pharmacogenetic polymorphism associated with prostate cancer susceptibility. miRNA-200c-3p and miRNA-27b-3p seem to be associated with CYP3A4 targeting and prostate cancer predisposition. Metabolomic analysis indicated that 11β-OHT, 2β-OHT, 15β-OHT, 2α-OHT and 6β-OHT had a high risk, and 16α-OHT, and 16β-OHT had an intermediate disease-risk.

Conclusion: These findings constitute a novel integrated signature for prostate cancer susceptibility. Further studies are required to assess their predictive value more fully.

Keywords: BPH; CYP3A4; FoxO signaling pathway; PCa; Pharmacogenetics; diabetes; dislipidemia; epigenetics; estrogen signaling pathway; hypertension; metabolomics; miRNAs; p53 signaling pathway; prostatic neoplasia; steroid hormone pathway.

Figure 1

Summary of the steroid hormone pathway indicating the production of testosterone hydroxylated metabolites through the enzymatic activity of CYP450s involved in the synthesis and the metabolism of testosterone in humans. Red arrows: CYP3A4; Blue Arrow: CYP2B6; 2C9, 2C19, 3A4; Brown Arrow: CYP19A1 (aromatase).

Figure 2

Ring charts of metabolic profiles in: (a) Benign prostatic hyperplasia (BPH) subjects; (b) BPH in prostate cancer (PCa) subjects; (c) PCa*. The asterisk means that BPH in PCa subjects is excluded.

Figure 3

Comparative health records of patients with prostatic neoplasia. The bar chart illustrates the percentage of Cohort A patients with the registered comorbidities per subgroup of neoplastic disease in examination. Colors represent each subgroup; blue: only benign hyperplasia (BPH) individuals, red: BPH in prostate cancer (PCa) individuals, green: PCa individuals without previous BPH status. The prevalence of hypertension in patients stratified by subgroup of neoplastic disease is observed.

Figure 4

Most frequent ADMET categories observed in the 33 drugs of cohort A (Greek cohort). The relative frequency of each category is displayed on the vertical axis, while the ADMET category name is shown on the horizontal axis.

Figure 5

(a) Over-representation analysis (ORA) based on the hypergeometric distribution showing the significantly enriched biological entities that involve the CYP metabolic enzyme targeted by miRNAs as they were identified by the use of the miRabel prediction tool. The heatmap shows that the enriched “prostate cancer entity” term has a functional significance on the CYP3A4 gene, through its epigenetic regulation by hsa-miR-200c-3p; (b) Interactive visualization of maximum-coverage analysis based on miRPathDB 2.0 tool, indicating that numerous miRNAs can target a specific gene of interest. X and Y axes show the increasing number of miRNAs and the number of covered target genes, in this case CYP3A4, which is directly regulated by hsa-miR-27b-3p. This result was further confirmed by miRTarBase.

Figure 5

(a) Over-representation analysis (ORA) based on the hypergeometric distribution showing the significantly enriched biological entities that involve the CYP metabolic enzyme targeted by miRNAs as they were identified by the use of the miRabel prediction tool. The heatmap shows that the enriched “prostate cancer entity” term has a functional significance on the CYP3A4 gene, through its epigenetic regulation by hsa-miR-200c-3p; (b) Interactive visualization of maximum-coverage analysis based on miRPathDB 2.0 tool, indicating that numerous miRNAs can target a specific gene of interest. X and Y axes show the increasing number of miRNAs and the number of covered target genes, in this case CYP3A4, which is directly regulated by hsa-miR-27b-3p. This result was further confirmed by miRTarBase.