Research Paper Volume 15, Issue 5 pp 1394—1411
ACSL1, CH25H, GPCPD1, and PLA2G12A as the potential lipid-related diagnostic biomarkers of acute myocardial infarction
- 1 Department of Cardiology, Hunan Provincial People's Hospital, Changsha 410000, China
- 2 Department of Epidemiology, Hunan Provincial People's Hospital (The First Affiliated Hospital of Hunan Normal University), Changsha 410000, China
- 3 Clinical Medicine Research Center of Heart Failure of Hunan Province, Changsha 410000, China
- 4 The First Affiliated Hospital of Hunan Normal University (Hunan Provincial People's Hospital), Changsha 410000, China
- 5 Department of Emergency, Hunan Provincial People's Hospital, Changsha 410000, China
- 6 Clinical Data Center, Hunan Provincial People's Hospital, Changsha 410000, China
Received: October 24, 2022 Accepted: February 13, 2023 Published: February 24, 2023
https://doi.org/10.18632/aging.204542How to Cite
Copyright: © 2023 Liu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
Lipid metabolism plays an essential role in the genesis and progress of acute myocardial infarction (AMI). Herein, we identified and verified latent lipid-related genes involved in AMI by bioinformatic analysis. Lipid-related differentially expressed genes (DEGs) involved in AMI were identified using the GSE66360 dataset from the Gene Expression Omnibus (GEO) database and R software packages. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted to analyze lipid-related DEGs. Lipid-related genes were identified by two machine learning techniques: least absolute shrinkage and selection operator (LASSO) regression and support vector machine recursive feature elimination (SVM-RFE). The receiver operating characteristic (ROC) curves were used to descript diagnostic accuracy. Furthermore, blood samples were collected from AMI patients and healthy individuals, and real-time quantitative polymerase chain reaction (RT-qPCR) was used to determine the RNA levels of four lipid-related DEGs. Fifty lipid-related DEGs were identified, 28 upregulated and 22 downregulated. Several enrichment terms related to lipid metabolism were found by GO and KEGG enrichment analyses. After LASSO and SVM-RFE screening, four genes (ACSL1, CH25H, GPCPD1, and PLA2G12A) were identified as potential diagnostic biomarkers for AMI. Moreover, the RT-qPCR analysis indicated that the expression levels of four DEGs in AMI patients and healthy individuals were consistent with bioinformatics analysis results. The validation of clinical samples suggested that 4 lipid-related DEGs are expected to be diagnostic markers for AMI and provide new targets for lipid therapy of AMI.
Abbreviations
AMI: acute myocardial infarction; DEGs: differentially expressed genes; GEO: Gene Expression Omnibus; GO: gene ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; LASSO: Least Absolute Shrinkage and Selector Operation; SVM-RFE: support vector machine recursive feature elimination; ROC: receiver operating characteristic; RT-qPCR: real-time quantitative polymerase chain reaction; LDL: low-density lipoprotein; VLDL: very low-density lipoprotein; MACEs: major adverse cardiovascular events; ACSL1: Acyl-CoA Synthetase Long-Chain Family Member 1; PLBD1: Phospholipase B Domain Containing 1; CH25H: Cholesterol 25-Hydroxylase; ELOVL4: ELOVL Fatty Acid Elongase 4; TNFAIP8L2: TNF Alpha Induced Protein 8 Like 2; CYP2E1: Cytochrome P450 Family 2 Subfamily E Member 1; GPCPD1: Glycerophosphocholine Phosphodiesterase 1; PLA2G12A: Phospholipase A2 Group XIIA; AUC: area under the curve; CI: confidence interval; BMI: Body mass index; HDL-C: high-density lipoprotein cholesterol; LDL-C: low-density lipoprotein cholesterol; Apo: apolipoprotein; TC: total cholesterol; TG: triglyceride; CK: creatine kinase; CK-MB: creatine kinase-myocardial band.