Archives of Medical Science (Mar 2019)

Identification of key proteins and lncRNAs in hypertrophic cardiomyopathy by integrated network analysis

  • Xiaofeng Hu,
  • Guilin Shen,
  • Xiaoli Lu,
  • Guomin Ding,
  • Lishui Shen

DOI
https://doi.org/10.5114/aoms.2018.75593
Journal volume & issue
Vol. 15, no. 2
pp. 484 – 497

Abstract

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Introduction Hypertrophic cardiomyopathy (HCM), a genetically heterogeneous disorder of cardiac myocytes, is one of the main causes of sudden cardiac death of young people. However, the molecular mechanism involved in HCM has remained largely unclear. Of note, non-coding RNAs were reported to play an important role in human diseases. In this study, we focused on identifying differentially expressed long non-coding RNA (lncRNAs) and mRNAs in HCM by analyzing a public dataset (GSE36961). Material and methods We performed bioinformatics analysis to explore key pathways underlying HCM progression. Gene Ontology (GO) analysis was first performed to evaluate the potential roles of differentially expressed genes and lncRNAs in HCM. Moreover, protein–protein interaction (PPI) networks were constructed to reveal interactions among differentially expressed proteins. Specifically, co-expression networks were also constructed to identify hub lncRNAs in HCM. Results A total of 6147 mRNAs (p < 0.001) and 126 lncRNAs (p < 0.001) were found to be dysregulated in HCM. Gene Ontology (GO) analysis showed that these differentially expressed genes and lncRNAs were associated with metabolism, energy pathways, signal transduction, and cell communication. Moreover, TSPYL3, LOC401431, LOC158376, LOC606724, PDIA3P and LOH3CR2A (p < 0.001) were identified as key lncRNAs in HCM progression. Conclusions Taken together, our analysis revealed a series of lncRNAs and mRNAs that were differentially expressed in HCM and which were involved in HCM progression by regulating pathways, such as metabolism, energy pathways, signal transduction, and cell communication. This study will provide useful information to explore the mechanisms underlying HCM progression and to provide potential candidate biomarkers for diagnosis in HCM.

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