Identification of Hub Genes and Immune Infiltration in Coronary Artery Disease: A Risk Prediction Model.

IF 4.2 2区医学 Q2 IMMUNOLOGY

Journal of Inflammation Research Pub Date : 2024-11-11 eCollection Date: 2024-01-01 DOI:10.2147/JIR.S475639

Wenchao Xie, Wang Liao, Hongming Lin, Guanglin He, Zhaohai Li, Lang Li

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引用次数: 0

Abstract

Purpose: Our study aimed to establish a prediction model for coronary artery disease (CAD) that integrates immune infiltration and a gene expression signature.

Methods: 613 differentially expressed genes (DEGs) and 12 hub genes were screened via the GSE113079 dataset. The pathway enrichment analysis indicated that these genes (613 DEGs and 12 hub genes) were closely associated with the inflammatory and immune responses. Based on the differentially expressed miRNA (DEmiRNA)-DEG regulatory network and immune cell infiltration, the Lasso algorithm constructed a CAD risk prediction model containing the risk score and immune score. Then, ROC-AUC and polymerase chain reaction (PCR) were performed for validation.

Results: Six hub genes (PTGER1, PIK3R1, ADRA2A, CORT, CXCL12, and S1PR5) had a high distinguishing capability (AUC > 0.90). In addition, the miRNAs targeting 12 hub genes were predicted and intersected with the DEmiRNAs, and the DEmiRNA-DEG regulatory network was then constructed. Two LASSO models and a novel CAD risk prediction model were constructed through LASSO regression analysis, and they both accurately obtained the risk of CAD. The CAD risk prediction model shows good performance (AUC = 0.988). We also constructed a valid nomogram, and PCR results verified three downregulation hub genes and one upregulation gene in the CAD risk model.

Conclusion: We demonstrated the molecular mechanism of the hub genes in CAD and provided a valuable tool for predicting the risk of CAD.

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识别冠状动脉疾病的枢纽基因和免疫渗透：风险预测模型

目的：我们的研究旨在建立一个结合免疫浸润和基因表达特征的冠状动脉疾病（CAD）预测模型。通路富集分析表明，这些基因（613 个 DEGs 和 12 个中心基因）与炎症和免疫反应密切相关。基于差异表达 miRNA（DEmiRNA）-DEG 调控网络和免疫细胞浸润，Lasso 算法构建了一个包含风险评分和免疫评分的 CAD 风险预测模型。然后，进行 ROC-AUC 和聚合酶链反应（PCR）验证：结果：6个中心基因（PTGER1、PIK3R1、ADRA2A、CORT、CXCL12和S1PR5）具有较高的区分能力（AUC > 0.90）。此外，还预测了靶向 12 个枢纽基因的 miRNA，并将其与 DEmiRNA 相交，从而构建了 DEmiRNA-DEG 调控网络。通过 LASSO 回归分析，建立了两个 LASSO 模型和一个新型 CAD 风险预测模型，它们都能准确预测 CAD 的风险。CAD 风险预测模型显示出良好的性能（AUC = 0.988）。我们还构建了一个有效的提名图，PCR 结果验证了 CAD 风险模型中的三个下调枢纽基因和一个上调基因：结论：我们证明了枢纽基因在 CAD 中的分子机制，并为预测 CAD 风险提供了一种有价值的工具。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Inflammation Research Immunology and Microbiology-Immunology

CiteScore

6.10

自引率

2.20%

发文量

658

审稿时长

16 weeks

期刊介绍： An international, peer-reviewed, open access, online journal that welcomes laboratory and clinical findings on the molecular basis, cell biology and pharmacology of inflammation.