Comprehensive analysis of hypoxia-related genes in diagnosis and immune infiltration in acute myocardial infarction: based on bulk and single-cell RNA sequencing data.

IF 3.9 3区 生物学 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY
Frontiers in Molecular Biosciences Pub Date : 2024-08-21 eCollection Date: 2024-01-01 DOI:10.3389/fmolb.2024.1448705
Guoqing Liu, Wang Liao, Xiangwen Lv, Miaomiao Zhu, Xingqing Long, Jian Xie
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引用次数: 0

Abstract

Background: Hypoxia has been found to cause cellular dysfunction and cell death, which are essential mechanisms in the development of acute myocardial infarction (AMI). However, the impact of hypoxia-related genes (HRGs) on AMI remains uncertain.

Methods: The training dataset GSE66360, validation dataset GSE48060, and scRNA dataset GSE163956 were downloaded from the GEO database. We identified hub HRGs in AMI using machine learning methods. A prediction model for AMI occurrence was constructed and validated based on the identified hub HRGs. Correlations between hub HRGs and immune cells were explored using ssGSEA analysis. Unsupervised consensus clustering analysis was used to identify robust molecular clusters associated with hypoxia. Single-cell analysis was used to determine the distribution of hub HRGs in cell populations. RT-qPCR verified the expression levels of hub HRGs in the human cardiomyocyte model of AMI by oxygen-glucose deprivation (OGD) treatment in AC16 cells.

Results: Fourteen candidate HRGs were identified by differential analysis, and the RF model and the nomogram based on 8 hub HRGs (IRS2, ZFP36, NFIL3, TNFAIP3, SLC2A3, IER3, MAFF, and PLAUR) were constructed, and the ROC curves verified its good prediction effect in training and validation datasets (AUC = 0.9339 and 0.8141, respectively). In addition, the interaction between hub HRGs and smooth muscle cells, immune cells was elucidated by scRNA analysis. Subsequently, the HRG pattern was constructed by consensus clustering, and the HRG gene pattern verified the accuracy of its grouping. Patients with AMI could be categorized into three HRG subclusters, and cluster A was significantly associated with immune infiltration. The RT-qPCR results showed that the hub HRGs in the OGD group were significantly overexpressed.

Conclusion: A predictive model of AMI based on HRGs was developed and strongly associated with immune cell infiltration. Characterizing patients for hypoxia could help identify populations with specific molecular profiles and provide precise treatment.

基于大量和单细胞 RNA 测序数据,全面分析急性心肌梗死诊断和免疫浸润中的缺氧相关基因。
背景:研究发现,缺氧会导致细胞功能障碍和细胞死亡,这是急性心肌梗死(AMI)发生的重要机制。然而,缺氧相关基因(HRGs)对急性心肌梗死的影响仍不确定:我们从 GEO 数据库下载了训练数据集 GSE66360、验证数据集 GSE48060 和 scRNA 数据集 GSE163956。我们利用机器学习方法确定了 AMI 中的枢纽 HRGs。根据识别出的中心HRGs构建并验证了AMI发生的预测模型。利用ssGSEA分析探讨了中心HRGs与免疫细胞之间的相关性。无监督共识聚类分析用于识别与缺氧相关的稳健分子集群。单细胞分析用于确定中枢 HRGs 在细胞群中的分布。通过氧-葡萄糖剥夺(OGD)处理 AC16 细胞,RT-qPCR 验证了 AMI 人心肌细胞模型中枢 HRGs 的表达水平:结果:通过差异分析确定了14个候选HRGs,并构建了基于8个枢纽HRGs(IRS2、ZFP36、NFIL3、TNFAIP3、SLC2A3、IER3、MAFF和PLAUR)的RF模型和提名图,ROC曲线验证了其在训练和验证数据集中的良好预测效果(AUC分别为0.9339和0.8141)。此外,还通过 scRNA 分析阐明了枢纽 HRG 与平滑肌细胞、免疫细胞之间的相互作用。随后,通过共识聚类构建了HRG模式,HRG基因模式验证了其分组的准确性。AMI患者可分为三个HRG亚群,其中A群与免疫浸润显著相关。RT-qPCR结果显示,OGD组的中枢HRGs明显过表达:结论:基于HRGs建立的AMI预测模型与免疫细胞浸润密切相关。对缺氧患者进行特征描述有助于识别具有特定分子特征的人群并提供精确治疗。
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来源期刊
Frontiers in Molecular Biosciences
Frontiers in Molecular Biosciences Biochemistry, Genetics and Molecular Biology-Biochemistry
CiteScore
7.20
自引率
4.00%
发文量
1361
审稿时长
14 weeks
期刊介绍: Much of contemporary investigation in the life sciences is devoted to the molecular-scale understanding of the relationships between genes and the environment — in particular, dynamic alterations in the levels, modifications, and interactions of cellular effectors, including proteins. Frontiers in Molecular Biosciences offers an international publication platform for basic as well as applied research; we encourage contributions spanning both established and emerging areas of biology. To this end, the journal draws from empirical disciplines such as structural biology, enzymology, biochemistry, and biophysics, capitalizing as well on the technological advancements that have enabled metabolomics and proteomics measurements in massively parallel throughput, and the development of robust and innovative computational biology strategies. We also recognize influences from medicine and technology, welcoming studies in molecular genetics, molecular diagnostics and therapeutics, and nanotechnology. Our ultimate objective is the comprehensive illustration of the molecular mechanisms regulating proteins, nucleic acids, carbohydrates, lipids, and small metabolites in organisms across all branches of life. In addition to interesting new findings, techniques, and applications, Frontiers in Molecular Biosciences will consider new testable hypotheses to inspire different perspectives and stimulate scientific dialogue. The integration of in silico, in vitro, and in vivo approaches will benefit endeavors across all domains of the life sciences.
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