Weighted gene co-expression network analysis identifies FCER1G as a key gene associated with diabetic kidney disease

Shanshan Liu; Cuili Wang; Huiying Yang; Tingting Zhu; Hong Jiang; Jianghua Chen

doi:10.21037/atm-20-1087

Weighted gene co-expression network analysis identifies FCER1G as a key gene associated with diabetic kidney disease

Ann Transl Med. 2020 Nov;8(21):1427. doi: 10.21037/atm-20-1087.

Authors

Shanshan Liu^{1

2

3

4

5}, Cuili Wang^{1

2

3

4

5}, Huiying Yang⁶, Tingting Zhu^{1

2

3

4

5}, Hong Jiang^{1

2

3

4

5}, Jianghua Chen^{1

2

3

4

5}

Affiliations

¹ Kidney Disease Center, the First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China.
² Kidney Disease Immunology Laboratory, the Third Grade Laboratory, State Administration of Traditional Chinese Medicine of PR China, Hangzhou, China.
³ Key Laboratory of Multiple Organ Transplantation, Ministry of Health, Hangzhou, China.
⁴ Key Laboratory of Nephropathy, Zhejiang Province, Hangzhou, China.
⁵ Institute of Nephropathy, Zhejiang University, Hangzhou, China.
⁶ Department of Nephrology, Sir Run Run Shaw Hospital, College of Medicine, Zhejiang University, Hangzhou, China.

Abstract

Background: Diabetic kidney disease (DKD) is the primary cause of end-stage renal disease. However, the pathogenesis of DKD remains unclarified, and there is an urgent need for improved treatments. Recently, many crucial genes closely linked to the molecular mechanism underlying various diseases were discovered using weighted gene co-expression network analysis.

Methods: We used a gene expression omnibus series dataset GSE104948 with 12 renal glomerular DKD tissue samples and 18 control samples obtained from the gene expression omnibus database and performed weighted gene co-expression network analysis. After obtaining the trait-related modules, gene ontology and Kyoto encyclopedia of genes and genomes enrichment analyses of the modules were conducted and the key gene associated with DKD was selected from the top two most significant gene ontology terms using the maximal clique centrality method. Finally, we verified the key gene using protein-protein interaction analysis, additional datasets, and explored the relationship between the key gene and DKD renal function using the Nephroseq v5 online database.

Results: Among the 10 gene co-expression modules identified, the darkorange2 and red modules were highly related to DKD and the normal biological process, respectively. Majority of the genes in the darkorange2 module were related to immune and inflammatory responses, and potentially related to the progression of DKD due to their abnormal up-regulation. After performing sub-network analysis of the genes extracted from the top two most significant gene ontology terms and calculating the maximal clique centrality values of each gene, FCER1G, located at the center of the protein-protein interaction network, was identified as a key gene related to DKD. Furthermore, gene expression omnibus validation in additional datasets also showed that FCER1G was overexpressed in DKD compared with normal tissues. Finally, Pearson's correlation analysis between the expression of FCER1G and DKD renal function revealed that the abnormal upregulation of FCER1G was related to diabetic glomerular lesions.

Conclusions: Our study demonstrated for the first time that FCER1G is a crucial gene associated with the pathogenesis of DKD.

Keywords: Diabetic kidney disease (DKD); FCER1G; key gene; pathogenesis; weighted gene co-expression network analysis (WGCNA).