Protein expression information of prostate infection based on data mining.

Abstract

In order to deeply explore the interaction between prostate cancer (PCa)-related proteins and to screen out effective targets for clinical practice, data mining of PCa proteomics literature is conducted, 41 differentially expressed seed proteins are identified, and a protein interaction network is constructed. The extended network consists of a mega network and three separate small parts, which are used to find key nodes and build a backbone network through connectivity screening. Topological analysis of these networks reveals that solute carrier family 2 (glucose transporter) member 4 (SLC2A4) and tubulin β-2C (TUBB2C) are centrally located in the protein interaction network. In addition, by using the module analysis, the dense connection area is found. Functional annotations indicate that the biological processes of Ras protein signaling, mitogen-activated protein kinase (MAPK), and neurotrophin and gonadotropin-releasing hormone (GnRH) signaling pathways play important roles in the pathogenesis of PCa. Therefore, further studies of SLC2A4 and TUBB2C proteins, and these biological processes and pathways may provide potential targets for the diagnosis and treatment of PCa.