Construction and analysis of protein-protein interaction networks based on proteomics data of prostate cancer

Abstract

Currently, using human prostate cancer (PCa) tissue samples to conduct proteomics research has generated a large amount of data; however, only a very small amount has been thoroughly investigated. In this study, we manually carried out the mining of the full text of proteomics literature that involved comparisons between PCa and normal or benign tissue and identified 41 differentially expressed proteins verified or reported more than 2 times from different research studies. We regarded these proteins as seed proteins to construct a protein-protein interaction (PPI) network. The extended network included one giant network, which consisted of 1,264 nodes connected via 1,744 edges, and 3 small separate components. The backbone network was then constructed, which was derived from key nodes and the subnetwork consisting of the shortest path between seed proteins. Topological analyses of these networks were conducted to identify proteins essential for the genesis of PCa. Solute carrier family 2 (facilitated glucose transporter), member 4 (SLC2A4) had the highest closeness centrality located in the center of each network, and the highest betweenness centrality and largest degree in the backbone network. Tubulin, beta 2C (TUBB2C) had the largest degree in the giant network and subnetwork. In addition, using module analysis of the whole PPI network, we obtained a densely connected region. Functional annotation indicated that the Ras protein signal transduction biological process, mitogen-activated protein kinase (MAPK), neurotrophin and the gonadotropin-releasing hormone (GnRH) signaling pathway may play an important role in the genesis and development of PCa. Further investigation of the SLC2A4, TUBB2C proteins, and these biological processes and pathways may therefore provide a potential target for the diagnosis and treatment of PCa.

Molecular Genetic Analysis of Human Endometrial Mesenchymal Stem Cells That Survived Sublethal Heat Shock

“…The number of edges per node characterizing the number of interacting proteins is termed a degree. Nodes with the highest degree are defined as hubs [ 37 ]. The degree is a fundamental parameter that is usually adopted to evaluate the nodes in a network for the identification of evolutionary conserved causal regulators in modular organization and networks [ 38 , 39 ].…”

Structural Studies of Predicted Ligand Binding Sites and Molecular Docking Analysis of Slc2a4 as a Therapeutic Target for the Treatment of Cancer

“…Although, the exact mode of action between Slc2a4 and cancer formation, progression and metastasis as not been fully understood. The uncontrolled growth and proliferation of cancer cells need a constant supply of metabolic energy, and glycolysis is one of the main biochemical process that characterised tumour cells, and the glycolytic breakdown of glucose is initiated by the transport of glucose, a rate-limiting process which is mediated by GLUT [ 35 ]. Moreover, an increase in GLUT upregulation in malignant cells has been linked with the overexpression of GLUT proteins, which supply steady metabolic energy to cancer cells [ 35 ].…”

Identifying potential entry inhibitors for emerging Nipah virus by molecular docking and chemical-protein interaction network

“…Since directly interacting proteins are known to be involved in similar biological processes to regulate particular disease (Hartwell, Hopfield, Leibler, & Murray, 1999), initial interactions were extended to include those nodes from HPRD that directly interact with drug-target proteins resulting in an extended drug-target interaction network comprising of 287 nodes and 1155 edges. This methodology of extending the neighborhood of protein interactions has known to be effective for analyzing complex traits and diseases (Ran et al, 2013) (C. Chen et al, 2016. This extended drug-target network was integrated to NiV-human protein-protein interaction network (93 nodes and 126 edges) (Martinez-Gil et al, 2017) on the basis of common interaction pair across respective datasets yielding a DPI network ( Figure 6A) Table represents residue closeness values, inferred from protein structure 2VSM, and sequence conservation scores for all docking-identified ligand-binding residues.…”