Zhu Haoyan, Li Mengyao, Wu Jiahe, Yan Liqiu, Xiong Wei, Hu Xiaorong, Lu Zhibing, Li Chenze, Cai Huanhuan
Department of Cardiology, Zhongnan Hospital of Wuhan University, Wuhan, China.
Institute of Myocardial Injury and Repair, Wuhan University, Wuhan, China.
PeerJ. 2024 Dec 4;12:e18591. doi: 10.7717/peerj.18591. eCollection 2024.
Acute myocardial infarction (AMI) is one of the most serious cardiovascular diseases. Apoptosis is a type of programmed cell death that causes DNA degradation and chromatin condensation. The role of apoptosis in AMI progression remains unclear.
Three AMI-related microarray datasets (GSE48060, GSE66360 and GSE97320) were obtained from the Gene Expression Omnibus database and combined for further analysis. Differential expression analysis and enrichment analysis were performed on the combined dataset to identify differentially expressed genes (DEGs). Apoptosis-related genes (ARGs) were screened through the intersection of genes associated with apoptosis in previous studies and DEGs. The expression pattern of ARGs was studied on the basis of their raw expression data. Three machine learning algorithms, Least Absolute Shrinkage and Selection Operator (LASSO), support vector machine-recursive feature elimination (SVM-RFE), and Random Forest (RF) were utilized to screen crucial genes in these ARGs. Immune infiltration was estimated by single sample gene set enrichment analysis (ssGSEA). Corresponding online databases were used to predict miRNAs, transcription factors (TFs) and therapeutic agents of crucial genes. A nomogram clinical prediction model of the crucial genes was constructed and evaluated. The Mendelian randomization analysis was employed to investigate whether there is a causal relationship between apoptosis and AMI. Finally, an AMI mouse model was established, and apoptosis in the hearts of AMI mice was assessed TUNEL staining. qRT-PCR was employed to validate these crucial genes in the hearts of AMI mice. The external dataset GSE59867 was used for further validating the crucial genes.
Fifteen ARGs (GADD45A, DDIT3, FEZ1, PMAIP1, IER3, IFNGR1, CDKN1A, GNA15, IL1B, EREG, BCL10, JUN, EGR3, GADD45B, and CD14) were identified. Six crucial genes (CDKN1A, BCL10, PMAIP1, IL1B, GNA15, and CD14) were screened from ARGs by machine learning. A total of 102 miRNAs, 13 TFs and 23 therapeutic drugs were predicted targeting these crucial genes. The clinical prediction model of the crucial genes has shown good predictive capability. The Mendelian randomization analysis demonstrated that apoptosis is a risk factor for AMI. Lastly, the expression of CDKN1A, CD14 and IL1B was verified in the AMI mouse model and external dataset.
In this study, ARGs were screened by machine learning algorithms, and verified by qRT-PCR in the AMI mouse model. Finally, we demonstrated that CDKN1A, CD14 and IL1B were the crucial genes involved in apoptosis in AMI. These genes may provide new target for the recognition and intervention of apoptosis in AMI.
急性心肌梗死（AMI）是最严重的心血管疾病之一。细胞凋亡是一种程序性细胞死亡，可导致DNA降解和染色质浓缩。细胞凋亡在AMI进展中的作用尚不清楚。
从基因表达综合数据库中获取三个与AMI相关的微阵列数据集（GSE48060、GSE66360和GSE97320）并合并以进行进一步分析。对合并后的数据集进行差异表达分析和富集分析，以鉴定差异表达基因（DEG）。通过先前研究中与细胞凋亡相关的基因与DEG的交集筛选细胞凋亡相关基因（ARG）。根据ARG的原始表达数据研究其表达模式。利用三种机器学习算法，即最小绝对收缩和选择算子（LASSO）、支持向量机递归特征消除（SVM-RFE）和随机森林（RF）来筛选这些ARG中的关键基因。通过单样本基因集富集分析（ssGSEA）估计免疫浸润。使用相应的在线数据库预测关键基因的miRNA、转录因子（TF）和治疗药物。构建并评估关键基因的列线图临床预测模型。采用孟德尔随机化分析来研究细胞凋亡与AMI之间是否存在因果关系。最后，建立AMI小鼠模型，并通过TUNEL染色评估AMI小鼠心脏中的细胞凋亡。采用qRT-PCR验证AMI小鼠心脏中的这些关键基因。外部数据集GSE59867用于进一步验证关键基因。
鉴定出15个ARG（GADD45A、DDIT3、FEZ1、PMAIP1、IER3、IFNGR1、CDKN1A、GNA15、IL1B、EREG、BCL10、JUN、EGR3、GADD45B和CD14）。通过机器学习从ARG中筛选出6个关键基因（CDKN1A、BCL10、PMAIP1、IL1B、GNA15和CD14）。共预测了靶向这些关键基因的102个miRNA、13个TF和23种治疗药物。关键基因的临床预测模型显示出良好的预测能力。孟德尔随机化分析表明细胞凋亡是AMI的一个危险因素。最后，在AMI小鼠模型和外部数据集中验证了CDKN1A、CD14和IL1B的表达。
在本研究中，通过机器学习算法筛选出ARG，并在AMI小鼠模型中通过qRT-PCR进行验证。最后，我们证明CDKN1A、CD14和IL1B是参与AMI细胞凋亡的关键基因。这些基因可能为识别和干预AMI中的细胞凋亡提供新的靶点。
