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BioMed Research International
Volume 2019, Article ID 3907195, 8 pages
https://doi.org/10.1155/2019/3907195
Research Article

Ranking Cancer Proteins by Integrating PPI Network and Protein Expression Profiles

1Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
2Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, Michigan 48109-2029, USA

Correspondence should be addressed to Jing Li; nc.ude.utjs@il.gnij

Received 26 July 2018; Revised 6 December 2018; Accepted 12 December 2018; Published 6 January 2019

Academic Editor: Rosaria Scudiero

Copyright © 2019 Jie Ren et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Proteomics, the large-scale analysis of proteins, is contributing greatly to understanding gene function in the postgenomic era. However, disease protein ranking using shotgun proteomics data has not been fully evaluated. In this study, we prioritized disease-related proteins by integrating the protein-protein interaction (PPI) network and protein differential expression profiles from colon and rectal cancer (CRC) or breast cancer (BC) proteomics. We applied Local Ranking (LR) and Global Ranking (GR) methods in network with three kinds of protein sets as a priori knowledge, which were known disease proteins (KDPs) that were collected from the Online Mendelian Inheritance in Man (OMIM) database, differentially expressed proteins (DEPs), and the collection of KDPs and their direct neighborhood with differential expression (eKDPs). The cross-validations showed that GR method outperformed LR method while using eKDPs as the initial training showed significantly higher accuracy compared to using the other two a priori sets. And then we validated the top ranked proteins using RNAi-based loss-of-function screens in the DepMap database. The results showed that 75% of top 20 proteins in CRC are necessary for tumor survival. In summary, the network-based Global Ranking with protein differential expression can efficiently prioritize cancer-related proteins and discover new candidate cancer genes or proteins.