A bioinformatics approach to identifying the biomarkers and pathogenesis of major depressive disorder combined with acute myocardial infarction

Cheng Yu; Fengjun Zhang; Lili Zhang; Jiajing Li; Saixue Tang; Xuejun Li; Min Peng; Qiong Zhao; Xiuli Zhu

A bioinformatics approach to identifying the biomarkers and pathogenesis of major depressive disorder combined with acute myocardial infarction

Am J Transl Res. 2023 Feb 15;15(2):932-948. eCollection 2023.

Authors

Cheng Yu¹, Fengjun Zhang², Lili Zhang², Jiajing Li¹, Saixue Tang³, Xuejun Li¹, Min Peng⁴, Qiong Zhao⁴, Xiuli Zhu⁵

Affiliations

¹ Department of Traditional Chinese Medicine Classics, Shandong University of Traditional Chinese Medicine Affiliated Hospital Jinan, Shandong, China.
² College of Acupuncture and Massage, Shandong University of Traditional Chinese Medicine Jinan, Shandong, China.
³ First Clinical School of Medicine, Shandong University of Traditional Chinese Medicine Jinan, Shandong, China.
⁴ Department of Traditional Chinese Medicine, Shandong Provincial Hospital Affiliated to Shandong First Medical University Jinan, Shandong, China.
⁵ Department of Radiation Oncology and Shandong Province Key Laboratory of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences Jinan, Shandong, China.

PMID: 36915729
PMCID: PMC10006793

Abstract

This study investigated the pathogenesis of major depressive disorder (MDD) and acute myocardial infarction (AMI) using bioinformatics. We analyzed MDD and AMI (MDD-AMI) datasets provided by the Gene Expression Omnibus (GEO) database for genes common to MDD and AMI using GEO2R and weighted gene co-expression network analysis (WGCNA). We also performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses, and we used Disease Ontology (DO) analysis to identify a) the pathways through which genes function and b) comorbidities. We also created a protein-protein interaction (PPI) network using the STRING database to identify the hub genes and biomarkers. NetworkAnalyst 3.0 was used to construct a transcription factor (TF) gene regulatory network. We also identified relevant complications and potential drug candidates. The 27 genes common to MDD and AMI were enriched in the pathways regulating TFs and mediating immunity and inflammation. The hub genes in the PPI network included TLR2, HP, ICAM1, LCN2, LTF, VCAN, S100A9 and NFKBIA. Key TFs were KLF9, KLF11, ZNF24, and ZNF580. Cardiovascular, pancreatic, and skeletal diseases were common complications. Hydrocortisone, simvastatin, and estradiol were candidate treatment drugs. Identification of these genes and their pathways may provide new targets for further research on the pathogenesis, biomarkers, and treatment of MDD-AMI. Together our results suggested that TLR2 and VCAN might be the key genes associated with MDD complicated by AMI.

Keywords: Major depression disorder; acute myocardial infarction; bioinformatics analysis; biomarkers; pathogenesis.