Comparative Analysis of Protein-Protein Interactions in Cancer-Associated Genes

作     者：Purnima Guda Sridar V. Chittur Chittibabu Guda 

作者机构：GenNYsis Center for Excellence in Cancer Genomics and Department of Epidemiology & Biostatistics State University of New York at AlbanyRensselaer NY 12144-3456 USA Center for Functional Genomics and Department of Biomedical Sciences State University of New York at AlbanyRensselaer NY 12144-3456 USA 

出 版 物：《Genomics, Proteomics & Bioinformatics》 (基因组蛋白质组与生物信息学报（英文版）)

年 卷 期：2009年第7卷第1期

页      面：25-36页

核心收录：

学科分类：0710[理学-生物学] 07[理学] 09[农学] 071007[理学-遗传学] 0901[农学-作物学] 090102[农学-作物遗传育种] 

基　　金：supported by the start-up funds to CG from SUNY-Albany partly by the Academic Research Enhancement Award(1R15GM080681-01) to CG from NIGMS/NIH 

主　　题：protein-protein interaction,cancer-associated genes GO similarity analysis cancer bioinformatics 

摘      要：Protein-protein interactions （PPIs） have been widely studied to understand the biological processes or molecular functions associated with different disease systems like cancer. While focused studies on individual cancers have generated valuable information, global and comparative analysis of datasets from different cancer types has not been done. In this work, we carried out bioinformatic analysis of PPIs corresponding to differentially expressed genes from microarrays of various tumor tissues （belonging to bladder, colon, kidney and thyroid cancers） and compared their associated biological processes and molecular functions （based on Gene Ontology terms）. We identified a set of processes or functions that are common to all these cancers, as well as those that are specific to only one or partial cancer types. Similarly, protein interaction networks in nucleic acid metabolism were compared to identify the common/specific clusters of proteins across different cancer types. Our results provide a basis for further experimental investigations to study protein interaction networks associated with cancer. The methodology developed in this work can also be applied to study similar disease systems.