Advances in Clinical and Experimental Medicine

Title abbreviation: Adv Clin Exp Med
JCR Impact Factor (IF) – 1.736
5-Year Impact Factor – 2.135
Index Copernicus  – 168.52
MEiN – 70 pts

ISSN 1899–5276 (print)
ISSN 2451-2680 (online)
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Advances in Clinical and Experimental Medicine

2020, vol. 29, nr 6, June, p. 649–659

doi: 10.17219/acem/121918

Publication type: original article

Language: English

License: Creative Commons Attribution 3.0 Unported (CC BY 3.0)

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Weighted gene co-expression network analysis to investigate the key genes implicated in global brain ischemia/reperfusion injury in rats

Dingying Ma1,A,C, Jun Qiao2,A,B, Qiang Qu3,B,F, Fei He4,A,D, Wenhua Chen3,4,D,E,F, Bo Yu4,C,D,F

1 Department of Rehabilitation Medicine, Ningbo No. 9 Hospital, China

2 Department of Rehabilitation, Shanghai Second Rehabilitation Hospital, China

3 Department of Rehabilitation, School of International Medical Technology, Shanghai Sanda University, China

4 Department of Rehabilitation Medicine, Shanghai General Hospital, Shanghai Jiaotong University, China


Background. Ischemia/reperfusion (I/R) refers to situations where blood is perfused into ischemic or hypoxic tissues, potentially resulting in an inflammatory response and oxidative injury.
Objectives. This study was conducted to explore the pathogenesis of I/R injury.
Material and Methods. GSE82146 was extracted from the Gene Expression Omnibus, consisting of 15 complete global brain ischemia (CGBI) reperfusion hippocampus samples and 12 non-ischemic control (NIC) hippocampus samples. The differentially expressed genes (DEGs) between the CGBI and NIC samples were selected using LIMMA package, and were then analyzed with weighted gene co-expression network analysis (WGCNA). Using DAVID software, the DEGs in significant modules were run through enrichment analysis. The DEGs in significant modules were merged, and then a protein–protein interaction (PPI) network was built for them using Cytoscape software. After miRNAs and transcription factors (TFs) were predicted for the DEGs using the WebGestalt tool, a TF-miRNA-target regulatory network was built using Cytoscape software. Furthermore, quantitative real-time polymerase chain reaction (qRT-PCR) analysis was conducted to detect the levels of key genes.
Results. There were 390 DEGs in the CGBI samples. Based on WGCNA, brown and turquoise modules were screened as CGBI-associated modules. In the PPI network, key nodes HSP90AA1 and HSPA5 were able to interact with each other. In the regulatory network, MYC, HSF1 and miR-22 had higher degree values. Moreover, HSPA5 was targeted by MYC in the regulatory network. In addition, upregulated HSPB1 and HMOX1, as well as downregulated NR4A2, were confirmed with qRT-PCR analysis.
Conclusion. HSPB1, HMOX1 and NR4A2 were the key genes correlated with I/R injury. Additionally, HSP90AA1, HSPA5, MYC, HSF1, and miR-22 might be related to the pathogenesis of I/R injury.

Key words

ischemia/reperfusion, differentially expressed genes, regulatory network, protein–protein interaction network, weighted gene co-expression network analysis

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