Weighted gene co-expression network analysis of methylated genes in burn scar tissue

Guo Peng

doi:10.3760/cma.j.cn501120-20200311-00150

Volume 37 Issue 12

Dec. 2021

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Article Navigation > CHINESE JOURNAL OF BURNS AND WOUNDS > 2021 > 37(12): 1185-1190

Bian Huining, Lai Wen, Zheng Shaoyi, et al. Analysis of liver damage and reactivation of hepatitis B virus in hepatitis B surface antigen positive patients after extremely severe burn injury[J]. Chin j Burns, 2015, 31(4): 244-247. Doi: 10.3760/cma.j.issn.1009-2587.2015.04.002

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Guo P.Weighted gene co-expression network analysis of methylated genes in burn scar tissue[J].Chin J Burns,2021,37(12):1185-1190.DOI: 10.3760/cma.j.cn501120-20200311-00150.

Citation:

Guo P.Weighted gene co-expression network analysis of methylated genes in burn scar tissue[J].Chin J Burns,2021,37(12):1185-1190.DOI: 10.3760/cma.j.cn501120-20200311-00150.

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Weighted gene co-expression network analysis of methylated genes in burn scar tissue

doi: 10.3760/cma.j.cn501120-20200311-00150

Guo Peng^,

Department of Plastic Surgery, the Fourth Hospital of Hebei Medical University, Shijiazhuang 050011, China

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Corresponding author: Guo Peng, Email: 277051445@163.com
Received Date: 2020-03-11

Abstract

Abstract

Objective To investigate the methylated genes in burn scar tissue by weighted gene co-expression network analysis (WGCNA), and to discover molecular markers and therapeutic targets of scar formation. Methods An observational research method was used. Datasets were downloaded from the National Center for Biotechnology Information Gene Expression Omnibus Database of America. The GSE136906 (n=6) and GSE137134 (n=6) datasets in the same batch were screened out for mRNA sequencing and methylation sequencing respectively, and the dataset GSE108110 (n=9) was incorporated into support vector machine and modeling analysis. The Limma software package was used to identify the differentially expressed genes and differentially methylated genes between scar tissue after burn and normal tissue. WGCNA was used to select the module with strong correlation with clinical features of scar tissue and large number of genes. Functional enrichment analysis of the genes in the module was performed to find genes with abnormal methylation. The receiver operating characteristic (ROC) curve was used to judge diagnostic efficacy of genes with abnormal methylation for scar, and support vector machine (SVM) was used to verify. Results A total of 10 modules were identified, and the brown module with large number of genes was highly correlated to burn scar tissue formation. The genes in the brown module were mainly concentrated in "regulation of androgen receptor signaling pathway", "cytokine-cytokine receptor interaction", "positive regulation of insulin secretion", and so on. The model showed 35 genes with abnormal methylation status. The ROC curve (area under the curve>0.9) and SVM modeling (accuracy=93.3%) indicated that CCR2, LMO7, STEAP4, NNAT, and TCF7L2 genes had good diagnostic performance for scar. Conclusions CCR2, LMO7, STEAP4, NNAT, and TCF7L2 can be used as potential targets for burn scar treatment.
- Burns,
- Cicatrix,
- Methylation,
- Weighted gene co-expression network,
- CCR2,
- LMO7

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Weighted gene co-expression network analysis of methylated genes in burn scar tissue

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Weighted gene co-expression network analysis of methylated genes in burn scar tissue

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