Chen Zuguang, Song Lingyue, Zhong Ming, Pang Lingpin, Sun Jie, Xian Qian, Huang Tao, Xie Fengwei, Cheng Junfen, Fu Kaili, Huang Zhihai, Guo Dingyu, Chen Riken, Sun Xishi, Huang Chunyi
Central People's Hospital of Zhanjiang, Zhanjiang, Guangdong, China.
Emergency Medicine Center, Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, China.
Front Med (Lausanne). 2024 Sep 26;11:1435068. doi: 10.3389/fmed.2024.1435068. eCollection 2024.
Pulmonary arterial hypertension (PAH) is a serious condition characterized by elevated pulmonary artery pressure, leading to right heart failure and increased mortality. This study investigates the link between PAH and genes associated with hypoxia and cuproptosis.
We utilized expression profiles and single-cell RNA-seq data of PAH from the GEO database and genecad. Genes related to cuproptosis and hypoxia were identified. After normalizing the data, differential gene expression was analyzed between PAH and control groups. We performed clustering analyses on cuproptosis-related genes and constructed a weighted gene co-expression network (WGCNA) to identify key genes linked to cuproptosis subtype scores. KEGG, GO, and DO enrichment analyses were conducted for hypoxia-related genes, and a protein-protein interaction (PPI) network was created using STRING. Immune cell composition differences were examined between groups. SingleR and Seurat were used for scRNA-seq data analysis, with PCA and t-SNE for dimensionality reduction. We analyzed hub gene expression across single-cell clusters and built a diagnostic model using LASSO and random forest, optimizing parameters with 10-fold cross-validation. A total of 113 combinations of 12 machine learning algorithms were employed to evaluate model accuracy. GSEA was utilized for pathway enrichment analysis of and , and a Nomogram was created to assess risk impact. We also analyzed the correlation between key genes and immune cell types using Spearman correlation.
We identified several diagnostic genes for PAH linked to hypoxia and cuproptosis. PPI networks illustrated relationships among these hub genes, with immune infiltration analysis highlighting associations with monocytes, macrophages, and CD8 T cells. The genes , , and emerged as key markers, forming a robust diagnostic model (NaiveBayes) with an AUC of 0.9.
, , and were identified as potential biomarkers for PAH, influencing cell proliferation and inflammatory responses, thereby offering new insights for PAH prevention and treatment.
肺动脉高压（PAH）是一种严重的疾病，其特征为肺动脉压力升高，可导致右心衰竭并增加死亡率。本研究调查PAH与缺氧和铜死亡相关基因之间的联系。
我们利用来自GEO数据库和genecad的PAH表达谱和单细胞RNA测序数据。确定了与铜死亡和缺氧相关的基因。在对数据进行标准化后，分析了PAH组和对照组之间的差异基因表达。我们对铜死亡相关基因进行了聚类分析，并构建了加权基因共表达网络（WGCNA）以识别与铜死亡亚型评分相关的关键基因。对缺氧相关基因进行了KEGG、GO和DO富集分析，并使用STRING创建了蛋白质-蛋白质相互作用（PPI）网络。检查了各组之间的免疫细胞组成差异。使用SingleR和Seurat进行scRNA测序数据分析，使用PCA和t-SNE进行降维。我们分析了跨单细胞簇的枢纽基因表达，并使用LASSO和随机森林构建了诊断模型，通过10折交叉验证优化参数。共采用12种机器学习算法的113种组合来评估模型准确性。利用GSEA对[具体内容]和[具体内容]进行通路富集分析，并创建列线图以评估风险影响。我们还使用Spearman相关性分析了关键基因与免疫细胞类型之间的相关性。
我们确定了几个与缺氧和铜死亡相关的PAH诊断基因。PPI网络阐明了这些枢纽基因之间的关系，免疫浸润分析突出了与单核细胞、巨噬细胞和CD8 T细胞的关联。[具体基因1]、[具体基因2]和[具体基因3]成为关键标志物，形成了一个强大的诊断模型（朴素贝叶斯），AUC为0.9。
[具体基因1]、[具体基因2]和[具体基因3]被确定为PAH的潜在生物标志物，影响细胞增殖和炎症反应，从而为PAH的预防和治疗提供了新的见解。
