AUTHOR=Wu Jiahe , Cai Huanhuan , Lei Zhe , Li Chenze , Hu Yushuang , Zhang Tong , Zhu Haoyan , Lu Yi , Cao Jianlei , Hu Xiaorong 

TITLE=Expression pattern and diagnostic value of ferroptosis-related genes in acute myocardial infarction

JOURNAL=Frontiers in Cardiovascular Medicine

VOLUME=Volume 9 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2022.993592

DOI=10.3389/fcvm.2022.993592

ISSN=2297-055X

ABSTRACT=Background: Ferroptosis is a form of regulatory cell death (RCD) caused by iron-dependent lipid peroxidation. The role of ferroptosis in the process of acute myocardial infarction (AMI) remains unclear and requires further study. Therefore, it is helpful to use transcriptomics to identify ferroptosis related genes (FRGs) involved in AMI and explore their mechanisms.
Methods: The AMI-related microarray datasets GSE66360 and GSE61144 were obtained from the Gene Expression Omnibus (GEO) database. Enrichment analysis and protein-protein interaction (PPI) analysis were performed for the genes with common significant differential expression (CoDEGs) in these two datasets. The FRGs were obtained from the FerrDb V2 and the differentially expressed FRGs were used for receiver operating characteristic (ROC) analysis to identify potential biomarkers. The expression of these FRGs was verified using external dataset GSE60993 and GSE775. Finally, the expression of these FRGs was further verified in myocardial hypoxia model.
Results: A total of 131 CoDEGs were identified and these genes were mainly enriched in the terms of “inflammatory response”, “immune response”, “plasma membrane”, “receptor activity”, “protein homodimerization activity”, “calcium ion binding”, “Phagosome”, “Cytokine-cytokine receptor interaction” and “Toll-like receptor signaling pathway”. The top 7 hub genes ITGAM, S100A12, S100A9, TLR2, TLR4, TLR8 and TREM1 were identified from the PPI network. 45 and 14 FRGs were identified in GSE66360 and GSE61144, respectively. FRGs ACSL1, ATG7, CAMKK2,  GABARAPL1, KDM6B, LAMP2, PANX2, PGD, PTEN, SAT1, STAT3, TLR4 and ZFP36 were significantly differentially expressed in external dataset GSE60993 with AUC≥0.7. Finally, ALOX5, CAMKK2, KDM6B, LAMP2, PTEN, PTGS2 and ULK1 were identified as biomarkers of AMI based on the time-gradient transcriptome dataset GSE775 of AMI in mice and the cellular hypoxia model.
Conclusion: In this study, based on the existing datasets, we identified differentially expressed FRGs in blood samples from patients with AMI and further validated these FRGs in the mouse time-gradient transcriptome dataset of AMI and the cellular hypoxia model. This study explored the mechanism of FRGs involved in AMI, providing clues for the accurate diagnosis of AMI and the selection of new therapeutic targets.