利用加权基因共表达网络分析和机器学习识别非泵式冠状动脉旁路移植手术中麻醉诱导的心血管生物标记物

Jinxiu Hou, Jing Li
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摘要

背景:本研究旨在筛选麻醉诱导的锌指蛋白相关基因生物标志物,以预测离泵冠状动脉旁路移植术(OPCABG)期间的心血管功能。方法来自 GSE4386 的基因表达数据包括 20 份麻醉后和 20 份麻醉前心房组织样本。在 UniProt 数据库中搜索锌指蛋白相关基因(ZFPRGs),确定麻醉诱导的差异表达基因(DEGs),使用加权基因共表达网络分析(WGCNA)筛选枢纽基因,并使用三种机器学习算法进一步筛选心血管生物标记物。诊断准确性采用提名图模型进行评估。基因组富集分析用于分析生物标记物所富集的通路。建立了微RNA(miRNA)-mRNA-转录因子(TF)调控网络,以探索这些生物标志物的潜在调控机制。利用比较毒物基因组学数据库(CTD)预测了与疾病相关的药物。研究结果在麻醉前组和麻醉后组之间共筛选出1102个与心脏保护相关的DEGs。此外,还根据 WGCNA 获得了 1095 个枢纽基因,并从 UniProt 数据库下载了 2274 个 ZFPRG。经过 Venn 分析和机器学习,ZNF420、RNF135 和 BNC2 被选为 OPCABG 期间与心脏保护相关的锌指生物标记物。接收者操作特征曲线(ROC)和提名图模型证实了这三个心脏保护生物标志物的诊断价值和准确性。通路富集分析表明,ZNF420参与了细胞周期和三羧酸循环。RNF135 和 BNC2 则富集在氧化磷酸化途径中。在构建的 miRNA-mRNA-TF 网络中,miR-182-5p 和 miR-16-5p 同时调控三个心脏保护生物标志物。结论利用OPCABG样本鉴定了三种与心脏保护相关的锌指蛋白生物标志物(ZNF420、RNF135和BNC2)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Identification of Anesthetic-Induced Cardiovascular Biomarkers in Off-Pump Coronary Artery Bypass Grafting Surgery Using Weighted Gene Co-Expression Network Analysis and Machine Learning
Background: This study aimed to select anesthesia-induced zinc finger protein-related gene biomarkers that predict cardiovascular function during off-pump coronary artery bypass grafting (OPCABG). Methods: Gene expression data from GSE4386 included 20 post-anesthesia and 20 pre-anesthesia atrial tissue samples. Zinc finger protein-related genes (ZFPRGs) were searched in the UniProt database and anesthesia-induced differentially expressed genes (DEGs) were identified Weighted gene co-expression network analysis (WGCNA) was used to screen hub genes, and three machine learning algorithms were used to further screen for cardiovascular biomarkers. Diagnostic accuracy was evaluated using a nomogram model. Gene set enrichment analysis was used to analyze the pathways enriched by the biomarkers. A microRNA (miRNA)-mRNA-transcription factor (TF) regulatory network was established to explore the potential regulatory mechanisms of these biomarkers. Disease-related drugs were predicted using the Comparative Toxicogenomics Database (CTD). Results: A total of 1102 cardioprotection-related DEGs were selected between the pre- and post-anesthesia groups. Additionally, 1095 hub genes were obtained based on WGCNA, and 2274 ZFPRGs were downloaded from the UniProt database. After Venn analysis and machine learning, ZNF420, RNF135, and BNC2 were selected as cardioprotection-related zinc finger biomarkers during OPCABG. Receiver operating characteristic (ROC) curves and nomogram models confirmed the diagnostic value and accuracy of the three cardioprotective biomarkers. Pathway enrichment analysis revealed that ZNF420 is involved in the cell cycle and the tricarboxylic acid cycle. RNF135 and BNC2 were enriched in the oxidative phosphorylation pathway. In the constructed miRNA-mRNA-TF network, miR-182-5p and miR-16-5p simultaneously regulated three cardioprotective biomarkers. Conclusion: Three cardioprotection-related zinc finger protein biomarkers (ZNF420, RNF135, and BNC2) were identified using OPCABG samples.
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