PDF(Pubmed)
Abstract：
UNASSIGNED: Cuproptosis is a copper-dependent cell death that is connected to the development and immune response of multiple diseases. However, the function of cuproptosis in the immune characteristics of sepsis remains unclear.
UNASSIGNED: We obtained two sepsis datasets (GSE9960 and GSE134347) from the GEO database and classified the raw data with R packages. Cuproptosis-related genes were manually curated, and differentially expressed cuproptosis-related genes (DECuGs) were identified. Afterwards, we applied enrichment analysis and identified key DECuGs by performing machine learning techniques. Then, the immune cell infiltrations and correlation between DECuGs and immunocyte features were created by the CIBERSORT algorithm. Subsequently, unsupervised hierarchical clustering analysis was performed based on key DECuGs. We then constructed a ceRNA network based on key DECuGs by using multi-step computational strategies and predicted potential drugs in the DrugBank database. Finally, the role of these key genes in immune cells was validated at the single-cell RNA level between septic patients and healthy controls.
UNASSIGNED: Overall, 16 DECuGs were obtained, and most of them had lower expression levels in sepsis samples. Afterwards, we obtained six key DECuGs by performing machine learning. Then, the LIPT1-T-cell CD4 memory resting was the most positively correlated DECuG-immunocyte pair. Subsequently, two different subclusters were identified by six DECuGs. Bioinformatics analysis revealed that there were different immune characteristics between the two subclusters. Moreover, we identified the key lncRNA OIP5-AS1 within the ceRNA network and obtained 4 drugs that may represent novel drugs for sepsis. Finally, these key DECuGs were statistically significantly dysregulated in another validation set and showed a major distribution in monocytes, T cells, B cells, NK cells and platelets at the single-cell RNA level.
UNASSIGNED: These findings suggest that cuproptosis might promote the progression of sepsis by affecting the immune system and metabolic dysfunction, which provides a new direction for understanding potential pathogenic processes and therapeutic targets in sepsis.
UNASSIGNED: We obtained two sepsis datasets (GSE9960 and GSE134347) from the GEO database and classified the raw data with R packages. Cuproptosis-related genes were manually curated, and differentially expressed cuproptosis-related genes (DECuGs) were identified. Afterwards, we applied enrichment analysis and identified key DECuGs by performing machine learning techniques. Then, the immune cell infiltrations and correlation between DECuGs and immunocyte features were created by the CIBERSORT algorithm. Subsequently, unsupervised hierarchical clustering analysis was performed based on key DECuGs. We then constructed a ceRNA network based on key DECuGs by using multi-step computational strategies and predicted potential drugs in the DrugBank database. Finally, the role of these key genes in immune cells was validated at the single-cell RNA level between septic patients and healthy controls.
UNASSIGNED: Overall, 16 DECuGs were obtained, and most of them had lower expression levels in sepsis samples. Afterwards, we obtained six key DECuGs by performing machine learning. Then, the LIPT1-T-cell CD4 memory resting was the most positively correlated DECuG-immunocyte pair. Subsequently, two different subclusters were identified by six DECuGs. Bioinformatics analysis revealed that there were different immune characteristics between the two subclusters. Moreover, we identified the key lncRNA OIP5-AS1 within the ceRNA network and obtained 4 drugs that may represent novel drugs for sepsis. Finally, these key DECuGs were statistically significantly dysregulated in another validation set and showed a major distribution in monocytes, T cells, B cells, NK cells and platelets at the single-cell RNA level.
UNASSIGNED: These findings suggest that cuproptosis might promote the progression of sepsis by affecting the immune system and metabolic dysfunction, which provides a new direction for understanding potential pathogenic processes and therapeutic targets in sepsis.
摘要：
■角化是一种铜依赖性细胞死亡，与多种疾病的发展和免疫反应有关。然而,在脓毒症的免疫特征中，角化功能仍不清楚。
■我们从GEO数据库中获得了两个败血症数据集（GSE9960和GSE134347），并用R包对原始数据进行了分类。人工筛选与角化相关的基因，并鉴定了差异表达的角化相关基因（DECuGs）。之后,我们应用了富集分析，并通过执行机器学习技术确定了关键的DECuG。然后，通过CIBERSORT算法建立免疫细胞浸润和DECuG与免疫细胞特征之间的相关性。随后，基于关键DECuG进行无监督层次聚类分析。然后，我们通过使用多步计算策略和DrugBank数据库中预测的潜在药物，构建了基于关键DECuG的ceRNA网络。最后,这些关键基因在免疫细胞中的作用在脓毒症患者和健康对照组的单细胞RNA水平得到验证.
■总的来说，获得16个DECuG，大多数在脓毒症样本中的表达水平较低。之后,我们通过执行机器学习获得了六个关键的DECuG。然后，LIPT1-T细胞的CD4记忆静息状态是DECuG-免疫细胞对中最正相关的。随后，通过六个DECuG识别出两个不同的亚簇。生物信息学分析表明，两个亚簇之间存在不同的免疫特征。此外，我们在ceRNA网络中鉴定了关键的lncRNAOIP5-AS1,并获得了4种可能代表脓毒症新药的药物.最后,这些关键的DECuG在另一个验证集中在统计学上显著失调，并在单核细胞中显示出主要分布，T细胞,B细胞,NK细胞和血小板在单细胞RNA水平。
■这些研究结果表明，细胞凋亡可能通过影响免疫系统和代谢功能障碍来促进脓毒症的进展，为了解脓毒症的潜在致病过程和治疗靶点提供了新的方向。
■我们从GEO数据库中获得了两个败血症数据集（GSE9960和GSE134347），并用R包对原始数据进行了分类。人工筛选与角化相关的基因，并鉴定了差异表达的角化相关基因（DECuGs）。之后,我们应用了富集分析，并通过执行机器学习技术确定了关键的DECuG。然后，通过CIBERSORT算法建立免疫细胞浸润和DECuG与免疫细胞特征之间的相关性。随后，基于关键DECuG进行无监督层次聚类分析。然后，我们通过使用多步计算策略和DrugBank数据库中预测的潜在药物，构建了基于关键DECuG的ceRNA网络。最后,这些关键基因在免疫细胞中的作用在脓毒症患者和健康对照组的单细胞RNA水平得到验证.
■总的来说，获得16个DECuG，大多数在脓毒症样本中的表达水平较低。之后,我们通过执行机器学习获得了六个关键的DECuG。然后，LIPT1-T细胞的CD4记忆静息状态是DECuG-免疫细胞对中最正相关的。随后，通过六个DECuG识别出两个不同的亚簇。生物信息学分析表明，两个亚簇之间存在不同的免疫特征。此外，我们在ceRNA网络中鉴定了关键的lncRNAOIP5-AS1,并获得了4种可能代表脓毒症新药的药物.最后,这些关键的DECuG在另一个验证集中在统计学上显著失调，并在单核细胞中显示出主要分布，T细胞,B细胞,NK细胞和血小板在单细胞RNA水平。
■这些研究结果表明，细胞凋亡可能通过影响免疫系统和代谢功能障碍来促进脓毒症的进展，为了解脓毒症的潜在致病过程和治疗靶点提供了新的方向。
