Network-based integrated analysis of omics data reveal novel players of TGF-β1-induced EMT in human peritoneal mesothelial cells View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2019-12

AUTHORS

Soo Min Han, Hye-Myung Ryu, Jinjoo Suh, Kong-Joo Lee, Soon-Youn Choi, Sangdun Choi, Yong-Lim Kim, Joo Young Huh, Hunjoo Ha

ABSTRACT

Long-term peritoneal dialysis is associated with progressive fibrosis of the peritoneum. Epithelial-mesenchymal transition (EMT) of mesothelial cells is an important mechanism involved in peritoneal fibrosis, and TGF-β1 is considered central in this process. However, targeting currently known TGF-β1-associated pathways has not proven effective to date. Therefore, there are still gaps in understanding the mechanisms underlying TGF-β1-associated EMT and peritoneal fibrosis. We conducted network-based integrated analysis of transcriptomic and proteomic data to systemically characterize the molecular signature of TGF-β1-stimulated human peritoneal mesothelial cells (HPMCs). To increase the power of the data, multiple expression datasets of TGF-β1-stimulated human cells were employed, and extended based on a human functional gene network. Dense network sub-modules enriched with differentially expressed genes by TGF-β1 stimulation were prioritized and genes of interest were selected for functional analysis in HPMCs. Through integrated analysis, ECM constituents and oxidative stress-related genes were shown to be the top-ranked genes as expected. Among top-ranked sub-modules, TNFAIP6, ZC3H12A, and NNT were validated in HPMCs to be involved in regulation of E-cadherin, ZO-1, fibronectin, and αSMA expression. The present data shows the validity of network-based integrated analysis in discovery of novel players in TGF-β1-induced EMT in peritoneal mesothelial cells, which may serve as new prognostic markers and therapeutic targets for peritoneal dialysis patients. More... »

PAGES

1497

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  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1038/s41598-018-37101-9

    DOI

    http://dx.doi.org/10.1038/s41598-018-37101-9

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1111950445

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/30728376


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