Novel Gene Signatures for Prostate Cancer Detection: Network Centrality-based Screening with Experimental Validation

Abstract

Prostate cancer (PCa) is a kind of malignant tumor with high incidence among males worldwide. The identification of novel biomarker signatures is, therefore of clinical significance for PCa precision medicine. It has been acknowledged that the breaking of stability and vulnerability in biological network provides important clues for cancer biomarker discovery. In this study, a bioinformatics model by characterizing the centrality of nodes in PCa-specific protein-protein interaction (PPI) network was proposed and applied to identify novel gene signatures for PCa detection. Compared with traditional methods, this model integrated degree, closeness and betweenness centrality as the criterion for Hub gene prioritization. The identified biomarkers were validated based on receiver-operating characteristic evaluation, qRT-PCR experimental analysis and literature-guided functional survey. Four genes, i.e., MYOF, RBFOX3, OCLN, and CDKN1C, were screened with average AUC ranging from 0.79 to 0.87 in the predicted and validated datasets for PCa diagnosis. Among them, MYOF, RBFOX3, and CDKN1C were observed to be down-regulated whereas OCLN was over-expressed in PCa groups. The in vitro qRT-PCR experiment using cell line samples convinced the potential of identified genes as novel biomarkers for PCa detection. Biological process and pathway enrichment analysis suggested the underlying role of identified biomarkers in mediating PCa-related genes and pathways including TGF-β, Hippo, MAPK signaling during PCa occurrence and progression. Novel gene signatures were screened as candidate biomarkers for PCa detection based on topological characterization of PCa-specific PPI network. More clinical validation using human samples will be performed in future work.