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

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 MetaNetwork, an open-source, 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 proteomic analysis of tumor and adjacent normal tissue samples.
We found a decrease in cytoskeleton-related protein expression, 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 with 15 or more samples.