Depression currently affects 4% of the world’s population; it is associated with disability in 11% of the global population. Moreover, there are limited resources to treat depression effectively. Therefore, we aimed to identify a promising novel therapeutic target for depression using bioinformatic analysis. The GSE54568, GSE54570, GSE87610, and GSE92538 gene expression data profiles were retrieved from the Gene Expression Omnibus (GEO) database. We prepared the four GEO profiles for differential analysis, protein–protein interaction (PPI) network construction, and weighted gene co-expression network analysis (WGCNA). Gene Ontology functional enrichment and Kyoto Encyclopedia of Genes and Genomes metabolic pathway analyses were conducted to determine the key functions of the corresponding genes. Additionally, we performed correlation analyses of the hub genes with transcription factors, immune genes, and N6-methyladenosine (m6A) genes to reveal the functional landscape of the core genes associated with depression. Compared with the control samples, the depression samples contained 110 differentially expressed genes (DEGs), which comprised 56 downregulated and 54 upregulated DEGs. Moreover, using the WGCNA and PPI clustering analysis, the blue module and cluster 1 were found to be significantly correlated with depression. GTF2F2 was the only common gene identified using the differential analysis and WGCNA; thus, it was used as the hub gene. According to the enrichment analyses, GTF2F2 was predominantly involved in the cell cycle and JAK-STAT, PI3K-Akt, and p53 signaling pathways. Furthermore, differential and correlation analyses revealed that 9 transcription factors, 12 immune genes, and 2 m6A genes were associated with GTF2F2 in depression samples. GTF2F2 may serve as a promising diagnostic biomarker and treatment target of depression, and this study provides a novel perspective and valuable information to explore the molecular mechanism of depression.