Identification of crucial genes in abdominal aortic aneurysm by WGCNA

Siliang Chen; Dan Yang; Chuxiang Lei; Yuan Li; Xiaoning Sun; Mengyin Chen; Xiao Wu; Yuehong Zheng

doi:10.7717/peerj.7873

Identification of crucial genes in abdominal aortic aneurysm by WGCNA

PeerJ. 2019 Oct 8:7:e7873. doi: 10.7717/peerj.7873. eCollection 2019.

Authors

Siliang Chen¹, Dan Yang², Chuxiang Lei¹, Yuan Li¹, Xiaoning Sun¹, Mengyin Chen¹, Xiao Wu¹, Yuehong Zheng¹

Affiliations

¹ Department of Vascular Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, PR China.
² Department of Computational Biology and Bioinformatics, Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, PR China.

Abstract

Background: Abdominal aortic aneurysm (AAA) is the full thickness dilation of the abdominal aorta. However, few effective medical therapies are available. Thus, elucidating the molecular mechanism of AAA pathogenesis and exploring the potential molecular target of medical therapies for AAA is of vital importance.

Methods: Three expression datasets (GSE7084, GSE47472 and GSE57691) were downloaded from the Gene Expression Omnibus (GEO). These datasets were merged and then normalized using the "sva" R package. Differential expressed gene (DEG) analysis and weighted gene co-expression network analysis (WGCNA) were conducted. We compared the co-expression patterns between AAA and normal conditions, and hub genes of each functional module were identified. DEGs were mapped to co-expression network under AAA condition and a DEG co-expression network was generated. Crucial genes were identified using molecular complex detection (MCODE) (a plugin in Cytoscape).

Results: In our study, 6 and 10 gene modules were detected for the AAA and normal conditions, respectively, while 143 DEGs were screened. Compared to the normal condition, genes associated with immune response, inflammation and muscle contraction were clustered in three gene modules respectively under the AAA condition; the hub genes of the three modules were MAP4K1, NFIB and HPK1, respectively. A DEG co-expression network with 102 nodes and 303 edges was identified, and a hub gene cluster with 10 genes from the DEG co-expression network was detected. YIPF6, RABGAP1, ANKRD6, GPD1L, PGRMC2, HIGD1A, GMDS, MGP, SLC25A4 and FAM129A were in the cluster. The expression levels of these 10 genes showed potential diagnostic value.

Conclusion: Based on WGCNA, we detected 6 modules under the AAA condition and 10 modules in the normal condition. Hub genes of each module and hub gene clusters of the DEG co-expression network were identified. These genes may act as potential targets for medical therapy and diagnostic biomarkers. Further studies are needed to elucidate the detailed biological function of these genes in the pathogenesis of AAA.

Keywords: Abdominal aortic aneurysm; Hub gene cluster; WGCNA.

Grants and funding

This work was supported by the Natural Science Foundation of China (81770481 and 51890894), the Natural Science Foundation of Beijing (7172171) and the CAMS Innovation Fund for Medical Sciences (CIFMS, 2017-I2M-1-008). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.