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

Zongjin Li, Changxin Song, Zeyu Jia, Dong Chen, Yan Liang
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Abstract

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.
基于WGCNA算法的高血压关键基因模块和枢纽基因鉴定
背景:高血压是世界范围内发病率高、死亡率高的慢性疾病。其发病机制复杂,分子机制尚未完全阐明,严重威胁着人类的生命和健康。本文旨在对高血压进行分子研究,从加权网络的角度探索影响高血压发生的候选生物标志物,为高血压的防治提供理论和实践依据。材料和方法:从gene expression Omnibus (GEO)下载GSE75360高血压基因表达数据集。利用R的“limma”包筛选高血压组和非高血压组之间的差异表达基因(DEGs)。接下来,采用体重基因共表达网络分析(Weight Gene co-expression Network Analysis, WGCNA)算法建立deg共表达网络,检测高血压相关基因模块。使用DAVID进行基因本体(GO)富集分析和京都基因与基因组百科全书(KEGG)。最后,我们提出了分层融合筛选枢纽基因的方法。结果:我们发现了2个与高血压显著相关的关键基因模块,分别为Mlightcyan和Mgreenyellow。此外,从两个关键基因模块中鉴定出18个与高血压相关的枢纽基因(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)。结论:在本研究中,我们发现了与高血压发病机制相关的2个关键基因模块和18个枢纽基因。这些发现将为进一步了解高血压的发病机制提供参考。枢纽基因可能成为未来高血压诊断的治疗靶点。
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