Identification of Key Gene Modules and Hub Genes of Hypertension Based on WGCNA Algorithm

Background: Hypertension is a chronic disease with high morbidity and high mortality in the world. Its pathogenesis is complicated and its molecular mechanism has not been fully elucidated, which seriously threatens human life and health. The purpose of this paper was to the molecular study of hypertension, explore candidate biomarkers affecting the occurrence of hypertension from the perspective of weighted network, and provide the theoretical and practical basis for the prevention and treatment of hypertension. Materials and methods: The hypertension gene expression dataset of GSE75360 were downloaded from the Gene Expression Omnibus (GEO). The “limma” package of R was utilized to screen the differentially expressed genes (DEGs) between the sample group with and without high blood pressure. Next, Weight Gene co-expression Network Analysis (WGCNA) algorithm was used to establish a co-expression network of the DEGs and to detect hypertension-related gene modules. And DAVID was utilized to perform Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG). Finally, we proposed the hierarchical fusion method to screen hub genes. Results: We identified 2 key gene modules that were significantly associated with hypertension, named Mlightcyan and Mgreenyellow. In addition, 18 hub genes (RPS28, LOC730288/RPS28P6, LOC645968/ RPS3AP25, LOC727826/RPS11P5, RPL21, LOC653079/ RPL36P14, LOC441743/RPL23AP5, LOC651453/RPL36P14, LPPR2, NSMCE4A, FKBP1A, RAB5C, MAN2B1, FURIN, TBXAS1, RPS6KA4, PARN, LOC642489/FKBP1C) relating to hypertension were identified form the two key gene modules. Conclusions: In this study, we identified two key gene modules and 18 hub genes, which were associated with the mechanisms of hypertension. These findings will provide references that improve the understanding of the pathogenesis of hypertension. The hub genes might can serve as therapeutic targets for diagnosis of hypertension in the future.

[1]  Kun Lu,et al.  The Alternative Splicing Landscape of Brassica napus Infected with Leptosphaeria maculans , 2019, Genes.

[2]  Lei Chen,et al.  WGCNA Application to Proteomic and Metabolomic Data Analysis. , 2017, Methods in enzymology.

[3]  Steve Horvath,et al.  WGCNA: an R package for weighted correlation network analysis , 2008, BMC Bioinformatics.

[4]  E. Scott,et al.  Endothelial HIF signaling regulates pulmonary fibrosis-associated pulmonary hypertension. , 2016, American journal of physiology. Lung cellular and molecular physiology.

[5]  E. Benjamin,et al.  Specific Inflammatory Stimuli Lead to Distinct Platelet Responses in Mice and Humans , 2015, PloS one.

[6]  P. Shannon,et al.  Cytoscape: a software environment for integrated models of biomolecular interaction networks. , 2003, Genome research.

[7]  R. Anjos,et al.  Programming of Essential Hypertension: What Pediatric Cardiologists Need to Know , 2015, Pediatric Cardiology.

[8]  Jinhui Liu,et al.  ITLNI identified by comprehensive bioinformatic analysis as a hub candidate biological target in human epithelial ovarian cancer , 2019, Cancer management and research.

[9]  The Gene Ontology Consortium,et al.  The Gene Ontology Resource: 20 years and still GOing strong , 2018, Nucleic Acids Res..

[10]  Yogesh K. Dwivedi,et al.  Co-expression network modeling identifies key long non-coding RNA and mRNA modules in altering molecular phenotype to develop stress-induced depression in rats , 2019, Translational Psychiatry.

[11]  Bin Zhang,et al.  Defining clusters from a hierarchical cluster tree: the Dynamic Tree Cut package for R , 2008, Bioinform..

[12]  Hiroyuki Ogata,et al.  KEGG: Kyoto Encyclopedia of Genes and Genomes , 1999, Nucleic Acids Res..

[13]  Q. Lan,et al.  Identification of key gene modules for human osteosarcoma by co-expression analysis , 2018, World Journal of Surgical Oncology.

[14]  Jiang He,et al.  The global epidemiology of hypertension , 2020, Nature Reviews Nephrology.

[15]  Ke Yin,et al.  Using weighted gene co-expression network analysis to identify key modules and hub genes in tongue squamous cell carcinoma , 2019, Medicine.

[16]  L. Ghiadoni,et al.  Endothelium‐dependent contractions and endothelial dysfunction in human hypertension , 2009, British journal of pharmacology.

[17]  Pingping Zheng,et al.  LncRNAs related key pathways and genes in ischemic stroke by weighted gene co-expression network analysis (WGCNA). , 2020, Genomics.

[18]  Zhi Chen,et al.  PDGF Promotes the Warburg Effect in Pulmonary Arterial Smooth Muscle Cells via Activation of the PI3K/AKT/mTOR/HIF-1α Signaling Pathway , 2017, Cellular Physiology and Biochemistry.

[19]  K. Gripp,et al.  Diamond–Blackfan anemia with mandibulofacial dystostosis is heterogeneous, including the novel DBA genes TSR2 and RPS28 , 2014, American journal of medical genetics. Part A.

[20]  Nan-fang Li,et al.  Associations between genetic variations in the FURIN gene and hypertension , 2010, BMC Medical Genetics.