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

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

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


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.

Original languageEnglish
Article number1497
JournalScientific Reports
Issue number1
StatePublished - 1 Dec 2019

Bibliographical note

Funding Information:
This study was supported by grants from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI) (HI15C0001 and HC15C1129), and National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning, Korea (2017R1A2B2005849, 2015R1C1A1A02037367, and 2016R1A2B4006575). The authors would like to thank Jaeho Jeong from Ewha Womans University, Seoul, Korea for the support on proteomic analysis.

Publisher Copyright:
© 2019, The Author(s).


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