1 华中科技大学同济医学院附属武汉市中心医院药学部，武汉 430022
2 华中科技大学同济医学院附属武汉儿童医院(武汉市妇幼保健院)，妇女儿童健康研究所，武汉 430015
Identification of diagnostic markers for geriatric depression based on transcriptional regulatory network
CorrespondingAuthor: GAO Wenqi Email: email@example.com
Foundation: This work was supported by the National Natural Science Foundation (81903592), Wuhan Clinical Medical Research Project (WX20C15), and Wuhan Yellow Crane Talent, Outstanding Young Talents Program, China.
Objective: Geriatric depression (GD) is associated with higher risk of suicide and vascular dementia. The present study aims to explored diagnostic biomarkers of depression in the elderly based on gene transcriptional regulation network. Methods: The gene expression profile dataset GSE76826 was downloaded from Gene Expression Omnibus (GEO). R software was used to identify differentially expressed genes (DEGs) between GD and healthy controls. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs were carried out based on DAVID database. The STRING online bioinformatics tool performed regulatory network analysis of DEGs and constructed protein-protein interaction (PPI) networks. Cytoscape software was used to identify hub genes and search for transcription factor. pROC software package performed receiver operating characteristic (ROC) curve analysis to screen transcription factors for diagnostic biomarkers. Results: Compared with healthy controls, a total of 1 411 DEGs were differentially expressed in the GD samples. Four key modules were identified from the PPI constructed by DEGs. KEGG pathway enrichment analysis showed that DEGs were enrichment in cilium movement, antimicrobial humoral response, O-glycan processing, mucosal immune response, Myocardial transmembrane transporter activity, hormone biosynthetic process, neurotransmitter biosynthetic process, and drug metabolism-cytochrome P450 pathway. Fifteen transcription factors were obtained from the PPI network constructed by Cytoscape. ROC analysis revealeded that transcription factors LMO2 and CEBPB had high diagnostic efficiencies. Conclusion: Through integrated analysis of dataset GSE76826, transcription factors that may be potential diagnostic biomarkers of GD were obtained, which provides a new perspective for understanding the early diagnosis of GD.