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CeRNA network analysis and functional enrichment of salt sensitivity of blood pressure by weighted-gene co-expression analysis
Han Cao1; Han Qi2; Zheng Liu3; Wen-Juan Peng1; Chun-Yue Guo1; Yan-Yan Sun1; Christine Pao4; Yu-Tao Xiang5; Ling Zhang1
2019-09
Source PublicationPEERJ
ISSN2167-8359
AbstractBackground Salt sensitivity of blood pressure (SSBP) is an independent risk factor for cardiovascular disease. The pathogenic mechanisms of SSBP are still uncertain. This study aimed to construct the co-regulatory network of SSBP and data mining strategy based on the competitive endogenous RNA (ceRNA) theory. Methods LncRNA and mRNA microarray was performed to screen for candidate RNAs. Four criteria were used to select the potential differently expressed RNAs. The weighted correlation network analysis (WGCNA) package of R software and target miRNA and mRNA prediction online databases were used to construct the ceRNA co-regulatory network and discover the pathways related to SSBP. Gene ontology enrichment, gene set enrichment analysis (GSEA) and KEGG pathway analysis were performed to explore the functions of hub genes in networks. Results There were 274 lncRNAs and 36 mRNAs that differently expressed between salt-sensitive and salt-resistant groups (P < 0.05). Using WGCNA analysis, two modules were identified (blue and turquoise). The blue module had a positive relationship with salt-sensitivity (R = 0.7, P < 0.01), high-density lipoprotein (HDL) (R = 0.53, P = 0.02), and total cholesterol (TC) (R = 0.55, P = 0.01). The turquoise module was positively related with triglyceride (TG) (R = 0.8, P < 0.01) and low-density lipoprotein (LDL) (R = 0.54, P = 0.01). Furthermore, 84 ceRNA loops were identified and one loop may be of great importance for involving in pathogenesis of SSBP. KEGG analysis showed that differently expressed mRNAs were mostly enriched in the SSBP-related pathways. However, the enrichment results of GSEA were mainly focused on basic physical metabolic processes. Conclusion The microarray data mining process based on WGCNA co-expression analysis had identified 84 ceRNA loops that closely related with known SSBP pathogenesis. The results of our study provide implications for further understanding of the pathogenesis of SSBP and facilitate the precise diagnosis and therapeutics.
KeywordCompetitive Endogenous Rnas Gene Set Enrichment Analysis Long Non-coding Rnas Salt Sensitivity Of Blood Pressure Weighted Gene Co-expression Network Analysis
DOI10.7717/peerj.7534
Indexed BySCI
Language英语
WOS Research AreaScience & Technology - Other Topics
WOS SubjectMultidisciplinary Sciences
WOS IDWOS:000485672100001
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Cited Times [WOS]:2   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionFaculty of Health Sciences
Affiliation1.Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, and Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
2.The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders & the Advanced Innovation Center for Human Brain Protection, Beijing Anding Hospital, School of Mental Health, Capital Medical University, Beijing, China
3.Science Department, Peking University People’s Hospital, Beijing, China
4.Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America
5.Unit of Psychiatry, Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Macau, China
Recommended Citation
GB/T 7714
Han Cao,Han Qi,Zheng Liu,et al. CeRNA network analysis and functional enrichment of salt sensitivity of blood pressure by weighted-gene co-expression analysis[J]. PEERJ,2019.
APA Han Cao.,Han Qi.,Zheng Liu.,Wen-Juan Peng.,Chun-Yue Guo.,...&Ling Zhang.(2019).CeRNA network analysis and functional enrichment of salt sensitivity of blood pressure by weighted-gene co-expression analysis.PEERJ.
MLA Han Cao,et al."CeRNA network analysis and functional enrichment of salt sensitivity of blood pressure by weighted-gene co-expression analysis".PEERJ (2019).
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