Dataset Information

MetaNetwork Enhances Biological Insights from Quantitative Proteomics Differences by Combining Clustering and Enrichment Analyses

ABSTRACT: Interpreting proteomics data remains challenging due to the large number of proteins that are quantified by modern mass spectrometry methods. Weighted gene correlation network analysis (WGCNA) can identify groups of biologically related proteins using only protein intensity values by constructing protein correlation networks. However, WGCNA is not widespread in proteomic analyses due to challenges in implementing workflows. To facilitate adoption of WGCNA by the proteomics field, we created MetaNetwork, an open-sourced, R-based application to perform sophisticated WGCNA workflows with no coding skill requirements for the end user. We demonstrate MetaNetwork’s utility by employing it to identify groups of proteins associated with prostate cancer from a proteomics analysis of tumor and adjacent normal tissue samples. We found a decrease in epithelial cell-type specific biomarkers, a known hallmark of prostate tumors. We further identified changes in module eigenproteins indicative of dysregulation in protein translation and trafficking pathways. These results demonstrate the value of using MetaNetwork to improve the biological interpretation of quantitative proteomics experiments for experiments including fifteen or more samples.

INSTRUMENT(S): LTQ Orbitrap Velos

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Prostate Gland, Prostate Cancer Cell

DISEASE(S): Prostate Cancer

SUBMITTER: Austin Carr

LAB HEAD: Lloyd M. Smith

PROVIDER: PXD030784 | Pride | 2022-01-27

REPOSITORIES: Pride

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			Action	DRS
	05-14-20_151T_rep1_frac1.raw	Raw
	05-14-20_151T_rep1_frac2.raw	Raw
	05-14-20_151T_rep1_frac3.raw	Raw
	05-14-20_151T_rep1_frac4.raw	Raw
	05-14-20_151T_rep1_frac5.raw	Raw

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MetaNetwork Enhances Biological Insights from Quantitative Proteomics Differences by Combining Clustering and Enrichment Analyses.

Carr Austin V AV Frey Brian L BL Scalf Mark M Cesnik Anthony J AJ Rolfs Zach Z Pike Kyndal A KA Yang Bing B Keller Mark P MP Jarrard David F DF Shortreed Michael R MR Smith Lloyd M LM

Journal of proteome research 20220124 2

Interpreting proteomics data remains challenging due to the large number of proteins that are quantified by modern mass spectrometry methods. Weighted gene correlation network analysis (WGCNA) can identify groups of biologically related proteins using only protein intensity values by constructing protein correlation networks. However, WGCNA is not widespread in proteomic analyses due to challenges in implementing workflows. To facilitate the adoption of WGCNA by the proteomics field, we created ...[more]

PMID: 35073098

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