IMR Press / RCM / Volume 23 / Issue 2 / DOI: 10.31083/j.rcm2302052
Open Access Original Research
Construction of a pyroptosis-related classifier for risk prediction of acute myocardial infarction
Kehang Guo1,2,†Zewei Zhuo2,3,†Pengfei Chen4,†Huihuan Wu1,2Qi Yang2Jingwei Li2Rui Jiang2,3Qiuxian Mao5,*Hao Chen1,2,*Weihong Sha1,2,*
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1 School of Medicine, South China University of Technology, 510030 Guangzhou, Guangdong, China
2 Department of Gastroenterology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 510080 Guangzhou, Guangdong, China
3 School of Bioscience and Bioengineering, South China University of Technology, 510006 Guangzhou, Guangdong, China
4 Department of Laboratory Medicine, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, 510060 Guangzhou, Guangdong, China
5 Prenatal Diagnostic Department, Guangdong Second Provincial General Hospital, 510317 Guangzhou, Guangdong, China
*Correspondence: maoqx@gd2h.org.cn (Qiuxian Mao); chenhao@gdph.org.cn (Hao Chen); shaweihong@gdph.org.cn (Weihong Sha)
These authors contributed equally.
Academic Editors: Brian Tomlinson and Takatoshi Kasai
Rev. Cardiovasc. Med. 2022, 23(2), 52; https://doi.org/10.31083/j.rcm2302052
Submitted: 20 December 2021 | Revised: 13 January 2022 | Accepted: 21 January 2022 | Published: 9 February 2022
(This article belongs to the Special Issue State-of-the-Art Cardiovascular Medicine in Asia 2021)
Copyright: © 2022 The Author(s). Published by IMR Press.
This is an open access article under the CC BY 4.0 license.
Abstract

Background: Acute myocardial infarction (AMI) is a common cardiovascular disease that has a high mortality. Pyroptosis is a programmed cell death mediated by inflammasome. It remains to be clarified on the expression pattern and risk predictive role of pyroptosis-related genes in AMI. Methods: The gene expression data were extracted from the Gene Expression Omnibus (GEO), and pyroptosis-related genes were obtained from published articles. Pyroptosis-related differential expressed genes were selected between normal and AMI samples and then we explored their immune infiltration level using CIBERSORT. Univariate Cox and LASSO regression were applied to establish a classifier based on pyroptosis-related genes. ROC analysis was utilized to evaluate the classifier. Results: In this study, we obtained 20 pyroptosis-related genes which showed differential expression in AMI and normal samples. Among the differential expressed genes, GZMB was significantly positively associated with activated NK cells (R = 0.71, p < 0.01), while NLRP3 exhibited a negative correlation with resting NK cells (R = –0.66, p < 0.01). 9 genes (NLRP9, GSDMD, CASP8, AIM2, GPX4, NOD1, NOD2, SCAF11, GSDME) were eventually identified as a predictive risk classifier for AMI patients. With the classifier, patients at high and low risk could be discriminated. Further external validation showed the high accuracy of the classifier (AUC = 0.75). Conclusions: Pyroptosis-related genes are closely related to immune infiltration in AMI, and a 9-gene classifier has good performance in predicting the risk of AMI with high accuracy, which could provide a new way for targeted treatment in AMI.

Keywords
acute myocardial infarction
pyroptosis
immune infiltration
risk prediction
classifier
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