Comparative molecular analysis of oral submucous fibrosis and other organ fibrosis based on weighted gene co-expression network analysis.
10.11817/j.issn.1672-7347.2022.220452
- Author:
Jun CHEN
1
,
2
,
3
,
4
;
Binjie LIU
1
,
2
,
3
,
5
;
Xiaoli XIE
1
,
2
,
3
,
5
;
Wenjie LI
1
,
2
,
3
,
6
Author Information
1. Hunan Key Laboratory of Oral Health Research
2. Hunan 3D Printing Engineering Research Center of Oral Care
3. Hunan Clinical Research Center of Oral Major Diseases and Oral Health
4. Xiangya School of Stomatology, Central South University, Changsha 410008. chenjun1222@csu.edu.cn.
5. Xiangya School of Stomatology, Central South University, Changsha 410008.
6. Xiangya School of Stomatology, Central South University, Changsha 410008. liwenj@csu.edu.cn.
- Publication Type:Journal Article
- Keywords:
fibrotic diseases;
immunity;
inflammation;
oral submucous fibrosis;
weighted gene co-expression network analysis
- MeSH:
Animals;
Oral Submucous Fibrosis/genetics*;
Gene Expression Profiling;
Inflammation;
Signal Transduction;
Fibrosis
- From:
Journal of Central South University(Medical Sciences)
2022;47(12):1663-1672
- CountryChina
- Language:English
-
Abstract:
OBJECTIVES:There is currently a lack of economic and suitable animal models that can accurately recapitulate the oral submucous fibrosis (OSF) disease state for indepth study. This is one of the primary reasons for the limited therapeutic methods available for OSF. Based on the underlying logic of pan-cancer analysis, this study systematically compares OSF and the other four types of organ fibrosis from the aspects of molecules, signaling pathways, biological processes, etc. A comprehensive analysis of the similarities and differences between OSF and other organ fibrosis is helpful for researchers to discover some general rules of fibrosis disease and may provide new ideas for studying OSF.
METHODS:Microarray data of the GSE64216, GSE76882, GSE171294, GSE92592, and GSE90051 datasets were downloaded from GEO. Differentially expressed mRNAs (DEmRNAs) of each type of fibrosis were identified by Limma package. Weighted gene co-expression network analysis (WGCNA) was used to identify each type of fibrosis-related module. The similarities and differences of each fibrosis-related-module genes were analyzed by function and pathway enrichment analysis.
RESULTS:A total of 6 057, 10 910, 27 990, 10 480, and 4 801 DEmRNAs were identified in OSF, kidney intestinal fibrosis (KIF), liver fibrosis (LF), idiopathic pulmonary fibrosis (IPF), and skin fibrosis (SF), respectively. By using WGCNA, each type of fibrosis-related module was identified. The co-expression networks for each type of fibrosis were constructed respectively. Except that KIF and LF have 5 common hub genes, other fibrotic diseases have no common hub genes with each other. The common pathways of OSF, KIF, LF, IPF, and SF mainly focus on immune-related pathways.
CONCLUSIONS:OSF and the other 4 types of fibrotic diseases are tissue- and organ-specific at the molecular level, but they share many common signaling pathways and biological processes, mainly in inflammation and immunity.
