Identification of Ferroptosis-Related Genes in Heart Failure Induced by Transverse Aortic Constriction

Abstract

Jian Jun Gu,1,2,* Tian Jian Du,1,2,* Li Na Zhang,3,* Jing Zhou,4 Xiang Gu,2 Ye Zhu2 1Department of Cardiology, Institute of Translational Medicine, Medical College, Yangzhou University, Yangzhou, Jiangsu, People’s Republic of China; 2Department of Cardiology, Northern Jiangsu People’s Hospital, Yangzhou, Jiangsu, People’s Republic of China; 3Department of Cardiology, The Affiliated Hospital of Yangzhou University, Yangzhou University, Yangzhou, Jiangsu, People’s Republic of China; 4Key Laboratory of Animal Breeding Reproduction and Molecular Design for Jiangsu Province, College of Animal Science and Technology, Yangzhou University, Yangzhou, Jiangsu, People’s Republic of China*These authors contributed equally to this workCorrespondence: Ye Zhu; Xiang Gu, Department of Cardiology, Northern Jiangsu People’s Hospital, 98 Nantong West Road, Yangzhou, Jiangsu, People’s Republic of China, Email [email protected]; [email protected]: Heart failure (HF) is a common clinical syndrome due to ventricular dysfunction and is a major cause of mortality worldwide. Ferroptosis, marked by excessive iron-dependent lipid peroxidation, is closely related to HF. Therefore, the purpose of this study is to explore and validate ferroptosis-related markers in HF by bioinformatics analysis and animal experiments validation.Materials and Methods: The gene expression profiles (GSE36074) of murine transverse aortic constriction (TAC) were obtained from the Gene Expression Omnibus (GEO); From the FerrDb database, ferroptosis-related genes (FRGs) were identified. Using GEO2R, differential expressed genes (DEGs) were screened. An overlapping analysis was conducted among DEGs and FRGs to identify ferroptosis-related DEGs (FRDEGs). We then performed clustering, functional enrichment analysis, and protein-protein interaction (PPI) analyses. In addition, the key FRDEGs were extracted by cytoHubba plugin and the networks of transcription factors (TFs)-key FRDEGs and microRNA-key FRDEGs were constructed. Lastly, the key FRDEGs were carried by quantitative reverse transcription PCR (RT-qPCR) and immunohistochemistry (IHC).Results: Fifty-nine FRGs showing significantly different expression were identified from a total of 1918 DEGs in mice heart by transverse aortic constriction. GO and KEGG functional enrichment analysis revealed that these 59 ferroptosis-related DEGs mostly associated with positive regulation of apoptotic process, FoxO signaling pathway, VEGF signaling pathway, Apoptosis, Ferroptosis. Five key FRDEGs (Mapk14, Hif1a, Ddit3, Tlr4 and Ptgs2) were identified using PPI networks; Based on TFs-key FRDEGs networks, we found that Mapk14, Hif1a, Tlr4 and Ptgs2 were regulated by 3, 4, 5, and 29 TFs, respectively; however, Ddit3 was not regulated by any TF; By analyzing the miRNA–key FRDEGs networks, we found that 39, 74, 11, 28, and 18 miRNAs targets regulate the expression of Mapk14, Hif1a, Ddit3, Tlr4 and Ptgs2, respectively. Lastly, five key FRDEGs were validated at the mRNA and protein levels by RT-qPCR and IHC, which were in line with our bioinformatics analysis.Conclusion: Our findings reveal that Mapk14, Hif1a, Ddit3, Tlr4 and Ptgs2 may be involved in the development of HF through regulating ferroptosis and as potential targets for HF.Keywords: heart failure, transverse aortic constriction, ferroptosis, bioinformatics, key genes

Keywords

• heart failure
• transverse aortic constriction
• ferroptosis
• bioinformatics
• key genes