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Mol Cell Proteomics. 2017 Jan;16(1):121-134. doi: 10.1074/mcp.M116.060301. Epub 2016 Nov 11.

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

Author information

1
From the ‡Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee 37232.
2
§Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, Texas 77030.
3
¶Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas 77030.
4
‖Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142.
5
**Department of Pathology, Johns Hopkins Medical Institutions, Baltimore, Maryland 21205.
6
‡‡Department of Medicine, Baylor College of Medicine, Houston, Texas 77030.
7
§§Department of Internal Medicine, Washington University School of Medicine, St. Louis, Missouri 63110.
8
¶¶Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352.
9
‖‖University of North Carolina at Chapel Hill, 130 Mason Farm Road, Chapel Hill, North Carolina 27599.
10
Clinical Research Division, Fred Hutchinson Cancer Research Center, 1100 Eastlake Avenue East, Seattle, Washington 98109.
11
Office of Cancer Clinical Proteomics Research, National Cancer Institute, Bethesda, Maryland 20892.
12
Department of Biochemistry, Vanderbilt University, Nashville, Tennessee 37232.
13
Jim Ayers Institute for Precancer Detection and Diagnosis, Vanderbilt-Ingram Cancer Center, Nashville, Tennessee 37232.
14
From the ‡Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee 37232; bing.zhang@vanderbilt.edu.

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.

PMID:
27836980
PMCID:
PMC5217778
DOI:
10.1074/mcp.M116.060301
[Indexed for MEDLINE]
Free PMC Article

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