Bioinformatics analysis of a TF-miRNA-lncRNA regulatory network in major depressive disorder

• Published in: Psychiatry research 2021-05, Vol.299, p.113842-113842, Article 113842
• Main Authors: Bian, Qinglai, Chen, Jianbei, Wu, Jiajia, Ding, Fengmin, Li, Xiaojuan, Ma, Qingyu, Zhang, Liqing, Zou, Xiaojuan, Chen, Jiaxu
• Format: Article
• Language: English
• Subjects: Bioinformatics analysis; Major depressive disorder; Regulatory network
• ISSN: 0165-1781; 1872-7123
• DOI: 10.1016/j.psychres.2021.113842

•A protein–protein interaction (PPI) network of the most significant module and a TF-miRNA-lncRNA regulatory network of MDD were constructed.•Five hub genes in the PPI network and 10 genes in the TF-miRNA-lncRNA regulatory network with high degree values were identified, which may provide new insights for the investigation of key pathways, diagnostic bio-markers, and therapeutic targets of MDD. Major depressive disorder (MDD) is a highly prevalent disease and one of the main causes of disability worldwide. Although many studies have partially revealed the occurrence and development process of MDD, the pathogeny and molecular mechanisms are not fully understood. Weighted gene coexpression network analysis (WGCNA) was used to explore the co-expression modules and hub genes in MDD. A protein–protein interaction (PPI) network of the most significant module and a TF-miRNA-lncRNA regulatory network of MDD were constructed using bioinformatics analysis tools. A KEGG pathway and gene ontology (GO) functional enrichment analysis of the genes in the significant module was performed using DAVID. Five hub genes in the PPI network and 10 genes in the TF-miRNA-lncRNA regulatory network with high degree values were identified, which may provide new insights for the investigation of key pathways, diagnostic bio-markers, and therapeutic targets of MDD. This study brings a novel perspective and provides valuable information to explore the molecular mechanism of MDD.