PDF(Pubmed)
Abstract：
This study aims to analyze Coronavirus Disease 2019 (COVID-19)-associated copper-death genes using the Gene Expression Omnibus (GEO) dataset and machine learning, exploring their immune microenvironment correlation and underlying mechanisms. Utilizing GEO, we analyzed the GSE217948 dataset with control samples. Differential expression analysis identified 16 differentially expressed copper-death genes, and Cell type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) quantified immune cell infiltration. Gene classification yielded two copper-death clusters, with Weighted Gene Co-expression Network Analysis (WGCNA) identifying key module genes. Machine learning models (random forest, Support Vector Machine (SVM), Generalized Linear Model (GLM), eXtreme Gradient Boosting (XGBoost)) selected 6 feature genes validated by the GSE213313 dataset. Ferredoxin 1 (FDX1) emerged as the top gene, corroborated by Area Under the Curve (AUC) analysis. Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) revealed enriched pathways in T cell receptor, natural killer cytotoxicity, and Peroxisome Proliferator-Activated Receptor (PPAR). We uncovered differentially expressed copper-death genes and immune infiltration differences, notably CD8 T cells and M0 macrophages. Clustering identified modules with potential implications for COVID-19. Machine learning models effectively predicted COVID-19 risk, with FDX1\'s pivotal role validated. FDX1\'s high expression was associated with immune pathways, suggesting its role in COVID-19 pathogenesis. This comprehensive approach elucidated COVID-19-related copper-death genes, their immune context, and risk prediction potential. FDX1\'s connection to immune pathways offers insights into COVID-19 mechanisms and therapy.
摘要：
这项研究旨在使用基因表达综合（GEO）数据集和机器学习分析2019年冠状病毒病（COVID-19）相关的铜死亡基因，探索它们的免疫微环境相关性和潜在机制。利用GEO,我们分析了GSE217948数据集和对照样本.差异表达分析确定了16个差异表达的铜死亡基因，和通过估计RNA转录物的相对子集(CIBERSORT)量化的免疫细胞浸润的细胞类型鉴定。基因分类产生了两个铜死亡簇，使用加权基因共表达网络分析(WGCNA)识别关键模块基因。机器学习模型(随机森林、支持向量机(SVM)广义线性模型(GLM)，极限梯度增强（XGBoost））选择了通过GSE213313数据集验证的6个特征基因。铁氧还蛋白1（FDX1）成为顶级基因，曲线下面积(AUC)分析证实。基因集富集分析（GSEA）和基因集变异分析（GSVA）揭示了T细胞受体中的富集途径，自然杀伤细胞毒性，和过氧化物酶体增殖物激活受体(PPAR)。我们发现差异表达的铜死亡基因和免疫浸润差异，特别是CD8T细胞和M0巨噬细胞。聚类确定了对COVID-19有潜在影响的模块。机器学习模型有效地预测了COVID-19风险，FDX1的关键角色已验证。FDX1的高表达与免疫途径有关，提示其在COVID-19发病机制中的作用。这种综合方法阐明了COVID-19相关的铜死亡基因，他们的免疫环境，和风险预测潜力。FDX1与免疫途径的联系提供了对COVID-19机制和治疗的见解。
