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Ranking Cancer Proteins by Integrating PPI Network and Protein Expression Profiles.

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 prot...

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Published in:BioMed Research International pp. 1 - 9
Main Authors: Ren, Jie, Shang, Lulu, Wang, Qing, Li, Jing
Format: equations & formulas research tables/charts Journal Article
Published: Wiley-Blackwell 1/6/2019
Online Access:View this record in EBSCOhost
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      jtl: BioMed Research International
      issn: 23146133
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      dt: 1/6/2019
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      pub: Wiley-Blackwell
      place: Malden, Massachusetts
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        133947400
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        133947400
        10.1155/2019/3907195
        133947400
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        atl: Ranking Cancer Proteins by Integrating PPI Network and Protein Expression Profiles.
      aug:
        au:
          Ren, Jie
          Shang, Lulu
          Wang, Qing
          Li, Jing
        affil: Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
      sug:
        subj:
          Colorectal Neoplasms
          Breast Neoplasms
          Oncogenes
          Gene Expression
          Proteomics
          Metabolic Networks and Pathways
          Human
          Validity
      ab: 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.
      pubtype: Academic Journal
      doctype:
        equations & formulas
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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