Identification of molecular subgroups in major depressive disorder based on the gene expression profiles

Abstract

Depression, also known as major depressive disorder (MDD), is characterized by significant and persistent MDD. The pathogenesis of MDD is complex; some molecular markers have been repeatedly reported in MDD studies, hopefully making them reliable indicators of MDD. This study aimed to explore the correlation between each subtype of MDD. MDD data sets and the corresponding clinical data were collected using GEO database. Consensus cluster analysis was used to classify the MDD cases, using the WGCNA (Weighted gene co-expression network analysis) modules were identified using the dynamic tree cutting segmentation module and clinical traits, respectively. Based on the relationship between the color differentiation module and clinical characteristics, GSEA (Gene Set Enrichment Analysis) was used to screen the genes with a higher degree of enrichment in the differentially expressed upregulated genes between each subgroup and the normal control, and to analyze their molecular biological function and the related pathways. We divided the MDD cases into three subgroups, the WGCNA analysis based on the subtype-specific characteristics showed that six WGCNA modules were correlated with the clinical characteristics. GSEA was used to screen the expression of MARCKS, TAAR1, and ITGB1, among other molecular markers. The main metabolic pathways in which these molecular markers participate include axon guidance, bacterial invasion of epithelial cells, prion infection, and pyrimidine metabolism. Cases from different subgroups may have their specific or dominant gene expression patterns, and that the biomarker levels can help assess the severity of MDD, predict outcomes, or guide clinical treatment.