Kai Huang1, Xinying Zhang2, Jiahao Duan1, Ruting Wang1, Zifeng Wu1, Chun Yang3, Ling Yang4. 1. Department of Cardiology, The Third Affiliated Hospital of Soochow University, Changzhou 213003, China. 2. Department of Anesthesiology and Perioperative Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China. 3. Department of Anesthesiology and Perioperative Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China. Electronic address: chunyang@njmu.edu.cn. 4. Department of Cardiology, The Third Affiliated Hospital of Soochow University, Changzhou 213003, China. Electronic address: linda_yl@sina.com.
Abstract
BACKGROUND: Heart failure (HF) and depression are common disorders that markedly compromise quality of life and impose a great financial burden on the society. Although increasing evidence has supported the closely linkage between the two disorders, the comorbidity mechanisms remain to be fully illuminated. We performed a bioinformatics network analysis to understand potential diagnostic biomarkers and therapeutic targets for HF comorbided with depression. METHODS: We downloaded the datasets of HF and depression from the Gene Expression Omnibus (GEO) database and constructed co-expression networks by Weighted Gene Co-Expression Network Analysis (WGCNA) to identify key modules. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed on the common genes existing in the HF and depression related modules. Then, we employed the STRING database to construct the protein-protein interaction (PPI) network and detected the hub genes in the network. Finally, we validated the expression difference of hub genes from additional datasets of HF and depression. RESULTS: Functional enrichment analysis indicated that platelet activation, chemokine signaling and focal adhesion were probably involved in HF comorbided with depression. PPI network construction indicated that HF comorbided with depression is likely related to 5 hub genes, including STAT4, CD83, CX3CR1, COL1A2, and SH2D1B. In validated datasets, STAT4 and COL1A2 were especially involved in the comorbidity of HF and depression. CONCLUSION: Our work indicated a total of 5 hub genes including STAT4, CD83, CX3CR1, COL1A2, and SH2D1B, in which STAT4 and COL1A2 especially underlie the comorbidity mechanisms of HF and depression. These shared pathways might provide new targets for further mechanistic studies of the pathogenesis and treatment of HF and depression.
BACKGROUND: Heart failure (HF) and depression are common disorders that markedly compromise quality of life and impose a great financial burden on the society. Although increasing evidence has supported the closely linkage between the two disorders, the comorbidity mechanisms remain to be fully illuminated. We performed a bioinformatics network analysis to understand potential diagnostic biomarkers and therapeutic targets for HF comorbided with depression. METHODS: We downloaded the datasets of HF and depression from the Gene Expression Omnibus (GEO) database and constructed co-expression networks by Weighted Gene Co-Expression Network Analysis (WGCNA) to identify key modules. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed on the common genes existing in the HF and depression related modules. Then, we employed the STRING database to construct the protein-protein interaction (PPI) network and detected the hub genes in the network. Finally, we validated the expression difference of hub genes from additional datasets of HF and depression. RESULTS: Functional enrichment analysis indicated that platelet activation, chemokine signaling and focal adhesion were probably involved in HF comorbided with depression. PPI network construction indicated that HF comorbided with depression is likely related to 5 hub genes, including STAT4, CD83, CX3CR1, COL1A2, and SH2D1B. In validated datasets, STAT4 and COL1A2 were especially involved in the comorbidity of HF and depression. CONCLUSION: Our work indicated a total of 5 hub genes including STAT4, CD83, CX3CR1, COL1A2, and SH2D1B, in which STAT4 and COL1A2 especially underlie the comorbidity mechanisms of HF and depression. These shared pathways might provide new targets for further mechanistic studies of the pathogenesis and treatment of HF and depression.