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Identification of Key Immune-Related Genes in the Treatment of Heart Failure After Myocardial Infarction with Empagliflozin Based on RNA-Seq.
Zhang, Pei; Wang, Tian-Yu; Luo, Zi-Yue; Ding, Jun-Can; Yang, Qiang; Hu, Peng-Fei.
Affiliation
  • Zhang P; Department of Cardiology, Sir Run Run Shaw Hospital, College of Medicine Zhejiang University, Hangzhou, Zhejiang Province, 310018, People's Republic of China.
  • Wang TY; Second Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, Zhejiang Province, 310053, People's Republic of China.
  • Luo ZY; Second Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, Zhejiang Province, 310053, People's Republic of China.
  • Ding JC; Second Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, Zhejiang Province, 310053, People's Republic of China.
  • Yang Q; Second Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, Zhejiang Province, 310053, People's Republic of China.
  • Hu PF; Department of Cardiology, the Second Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, Zhejiang Province, 310005, People's Republic of China.
J Inflamm Res ; 16: 4679-4696, 2023.
Article in En | MEDLINE | ID: mdl-37872957
Purpose: Heart failure is a serious complication after acute myocardial infarction (AMI). It is crucial to investigate the mechanism of action of empagliflozin in the treatment of heart failure. Methods: A total of 20 wild type (WT) male C57BL6/J mice were used to establish a model of heart failure after myocardial infarction and randomly divided into 2 groups: treatment group and control group. The treatment group was treated with empagliflozin, and the control group was treated with placebo. After 8 weeks of treatment, mouse heart tissues were collected for next generation sequencing. Bioinformatics methods were used to screen the key genes. Finally, the correlation between clinical data and gene expression was analyzed. Quantitative real-time polymerase chain reaction (qRT-PCR) was used to verify the expression of key genes. Results: A mouse model of heart failure was successfully constructed. By DEG analysis, a total of 740 DEGs in the treatment group vs the control group were obtained. Dendritic cells, granulocytes, follicular B, plasma cell, cDC1, cDC2, pDC and neutrophils were 8 different immune cells identified by immunoinfiltration analysis. Through WGCNA, the turquoise module with the highest correlation with the above differential immune cells was selected. One hundred and forty-two immune-related DEGs were obtained by taking intersection of the DEGs and the genes of the turquoise module. Col17a1 and Gria4 were finally screened out as key immune-related genes via PPI analysis and machine learning. Col17a1 was significantly up-regulated, while Gria4 was significantly down-regulated in the treatment group. At the same time, the expression level of Col17a1 was significantly correlated with left ventricular ejection fraction (LVEF), left ventricular fraction shortening (LVFS) and left ventricular internal dimension systole (LVIDs). Conclusion: Col17a1 and Gria4 are key immune-related genes of empagliflozin in the treatment of heart failure after myocardial infarction. This study provides a scientific basis for elucidating the mechanism of action of empagliflozin in treating heart failure after myocardial infarction.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: J Inflamm Res Year: 2023 Document type: Article Country of publication: New Zealand

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: J Inflamm Res Year: 2023 Document type: Article Country of publication: New Zealand