Weighted gene co-expression network analysis identifies specific modules and hub genes related to subsyndromal symptomatic depression. (7th February 2020)

• Record Type: Journal Article
• Authors: Geng, Ruijie; Li, Zezhi; Yu, Shunying; Yuan, Chengmei; Hong, Wu; Wang, Zuowei; Wang, Qingzhong; Yi, Zhenghui; Fang, Yiru
• Is Part Of: World journal of biological psychiatry. Volume 21:Number 2(2020)
• Page Start: 102
• Page End: 110
• Publication Date: 2020-02-07
• Subjects: SSD -- WGCNA -- hub gene -- inflammatory response -- type II diabetes mellitus; Biological psychiatry -- Periodicals; Biological Psychiatry -- Periodicals; 616.89
• DOI: 10.1080/15622975.2018.1548782
• Languages: English
• ISSNs: 1562-2975

Abstract

Abstract: Objectives: The identification of the potential molecule targets for subsyndromal symptomatic depression (SSD) is critical for improving the effective clinical treatment on the mental illness. In the current study, we mined the genome-wide expression profiling and investigated the novel biological pathways associated with SSD. Methods: Expression of differentially expressed genes ( DEGs) were analysed with microarrays of blood tissue cohort of eight SSD patients and eight healthy subjects. The gene co-expression is calculated by WGCNA, an R package software. The function of the genes was annotated by gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Results: We identified 11 modules from the 9, 427 DEGs. Three co-expression modules (blue, cyan and red) showed striking correlation with the phenotypic trait between SSD and healthy controls. Gene ontology and KEGG pathway analysis demonstrated that the function of these three modules was enriched with the pathway of inflammatory response and type II diabetes mellitus. Finally, three hub genes, NT5DC1, SGSM2 and MYCBP, were identified from the blue module as significant genes. Conclusions: This first blood gene expression study in SSD observed distinct patterns between cases and controls which may provide novel insight into understanding the molecular mechanisms of SSD.