Association between bivariate expression of key oncogenes and metabolic phenotypes of patients with prostate cancer

AKT and MYC are two of the most prevalent oncogenes associated with prostate cancer. The precise effects of overexpression of these two key oncogenes on the regulation of metabolic pathways in prostate cancer are under active investigation; however, few studies have investigated their bivariate oncogene-pair expressions in metabolic prostate cancer phenotypes. This is primarily due to the lack of a suitable statistical method to analyze the data in the presence of oncogene interactions and within-metabolite-set correlations.

We analyzed data on the expressions of phosphorylated AKT1 and MYC and the concentrations of 228 metabolites from 60 human prostate tumor samples and 16 normal tissue samples. The metabolomic data allowed us to study not only the measurement of individual metabolites, which can exhibit a dynamic range, but the enriched phenotypes in terms of "metabolite sets" that come from known metabolic pathways. We studied 71 metabolite sets defined by KEGG annotation. We used a modification of linear combination test (LCT) for multiple continuous outcomes to find associations between metabolite sets and oncogenic expressions, after accounting for the correlation between AKT1 and MYC expressions and the correlation between metabolites in a metabolite set. The LCT performance was evaluated using a simulation study.

Through a comprehensive analytical method, our study linked oncogenomics and metabolomics data from patients to improve our understanding of the interconnected mechanisms underlying prostate cancer. This study showed that dysregulations of AKT1 and MYC significantly alter the metabolic pathways activated by nonglucose nutrient sources and their downstream targets. Our findings highlighted the role of MYC as the leading, but not the only, oncogene in prostate oncogenesis. In our simulation study, the LCT performed better than the known alternative method, gene-set enrichment analysis (GSEA).

Our study offers a solution for linking genomics and metabolomics, working directly with multiple continuous and correlated measurements.