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Mol Cell Proteomics DOI:10.1074/mcp.M116.060301

Proteome Profiling Outperforms Transcriptome Profiling for Coexpression Based Gene Function Prediction.

Publication TypeJournal Article
Year of Publication2017
AuthorsWang, J, Ma, Z, Carr, SA, Mertins, P, Zhang, H, Zhang, Z, Chan, DW, Ellis, MJC, R Townsend, R, Smith, RD, McDermott, JE, Chen, X, Paulovich, AG, Boja, ES, Mesri, M, Kinsinger, CR, Rodriguez, H, Rodland, KD, Liebler, DC, Zhang, B
JournalMol Cell Proteomics
Volume16
Issue1
Pages121-134
Date Published2017 Jan
ISSN1535-9484
Abstract

Coexpression of mRNAs under multiple conditions is commonly used to infer cofunctionality of their gene products despite well-known limitations of this "guilt-by-association" (GBA) approach. Recent advancements in mass spectrometry-based proteomic technologies have enabled global expression profiling at the protein level; however, whether proteome profiling data can outperform transcriptome profiling data for coexpression based gene function prediction has not been systematically investigated. Here, we address this question by constructing and analyzing mRNA and protein coexpression networks for three cancer types with matched mRNA and protein profiling data from The Cancer Genome Atlas (TCGA) and the Clinical Proteomic Tumor Analysis Consortium (CPTAC). Our analyses revealed a marked difference in wiring between the mRNA and protein coexpression networks. Whereas protein coexpression was driven primarily by functional similarity between coexpressed genes, mRNA coexpression was driven by both cofunction and chromosomal colocalization of the genes. Functionally coherent mRNA modules were more likely to have their edges preserved in corresponding protein networks than functionally incoherent mRNA modules. Proteomic data strengthened the link between gene expression and function for at least 75% of Gene Ontology (GO) biological processes and 90% of KEGG pathways. A web application Gene2Net (http://cptac.gene2net.org) developed based on the three protein coexpression networks revealed novel gene-function relationships, such as linking ERBB2 (HER2) to lipid biosynthetic process in breast cancer, identifying PLG as a new gene involved in complement activation, and identifying AEBP1 as a new epithelial-mesenchymal transition (EMT) marker. Our results demonstrate that proteome profiling outperforms transcriptome profiling for coexpression based gene function prediction. Proteomics should be integrated if not preferred in gene function and human disease studies.

DOI10.1074/mcp.M116.060301
Pubmed

http://www.ncbi.nlm.nih.gov/pubmed/27836980?dopt=Abstract

Alternate JournalMol. Cell Proteomics
PubMed ID27836980
PubMed Central IDPMC5217778
Grant ListU24 CA160034 / CA / NCI NIH HHS / United States
U24 CA159988 / CA / NCI NIH HHS / United States
U24 CA160036 / CA / NCI NIH HHS / United States
U24 CA160019 / CA / NCI NIH HHS / United States
U24 CA160035 / CA / NCI NIH HHS / United States