Comparison of molecular signatures in large-scale protein interaction networks in normal and cancer conditions of brain, cervix, lung, ovary and prostate

Abstract

Cancer, the disease of intricateness, has remained beyond our complete perception so far. Network systems biology (termed NSB) is one of the most recent approaches to understand the unsolved problems of cancer development. From this perspective, differential protein networks (PINs) have been developed based on the expression and interaction data of brain, cervix, lung, ovary and prostate for normal and cancer conditions. Differential expression database GeneHub-GEPIS and interaction database STRING were applied for primary data retrieval. Cytoscape platform and related plugins named network analyzer; MCODE and ModuLand were used for visualization of complex networks and subsequent analysis. Significant differences were observed among different common network parameters between normal and cancer states. Moreover, molecular complex numbers and overlapping modularization found to be varying significantly between normal and cancerous tissues. The number of the ranked molecular complex and the nodes involved in the overlapping modules were meaningfully higher in cancer condition. We identified 79 commonly up-regulated and 6 down-regulated proteins in all five tissues. Number of nodes, edges; multi edge node pair, and average number of neighbor are found with significant fluctuations in case of cervix and ovarian tissues. Cluster analysis showed that the association of Myc and Cdk4 proteins is very close with other proteins within the network. Cervix and ovarian tissue showed higher increment of the molecular complex number and overlapping module network during cancer in comparison to normal state. The differential molecular signatures identified from the work can be studied further to understand the cancer signaling process, and potential therapeutic and detection approach.