PDF(Pubmed)
Abstract：
UNASSIGNED: Pulmonary arterial hypertension (PAH) represents a substantial global risk to human health. This study aims to identify diagnostic biomarkers for PAH and assess their association with the immune microenvironment through the utilization of sophisticated bioinformatics techniques.
UNASSIGNED: Based on two microarray datasets, differentially expressed genes (DEGs) were detected, and hub genes underwent a sequence of machine learning analyses. After pathways associated with PAH were assessed by gene enrichment analysis, the identified genes were validated using external datasets and confirmed in a monocrotaline (MCT)-induced rat model. In addition, three algorithms were employed to estimate the proportions of various immune cell types, and the link between hub genes and immune cells was substantiated.
UNASSIGNED: Using SVM, LASSO, and WGCNA, we identified seven hub genes, including (BPIFA1, HBA2, HBB, LOC441081, PI15, S100A9, and WIF1), of which only BPIFA1 remained stable in the external datasets and was validated in an MCT-induced rat model. Furthermore, the results of the functional enrichment analysis established a link between PAH and both metabolism and the immune system. Correlation assessment showed that BPIFA1 expression in the MCP-counter algorithm was negatively associated with various immune cell types, positively correlated with macrophages in the ssGSEA algorithm, and correlated with M1 and M2 macrophages in the CIBERSORT algorithm.
UNASSIGNED: BPIFA1 serves as a modulator of PAH, with the potential to impact the immune microenvironment and disease progression, possibly through its regulatory influence on both M1 and M2 macrophages.
UNASSIGNED: Based on two microarray datasets, differentially expressed genes (DEGs) were detected, and hub genes underwent a sequence of machine learning analyses. After pathways associated with PAH were assessed by gene enrichment analysis, the identified genes were validated using external datasets and confirmed in a monocrotaline (MCT)-induced rat model. In addition, three algorithms were employed to estimate the proportions of various immune cell types, and the link between hub genes and immune cells was substantiated.
UNASSIGNED: Using SVM, LASSO, and WGCNA, we identified seven hub genes, including (BPIFA1, HBA2, HBB, LOC441081, PI15, S100A9, and WIF1), of which only BPIFA1 remained stable in the external datasets and was validated in an MCT-induced rat model. Furthermore, the results of the functional enrichment analysis established a link between PAH and both metabolism and the immune system. Correlation assessment showed that BPIFA1 expression in the MCP-counter algorithm was negatively associated with various immune cell types, positively correlated with macrophages in the ssGSEA algorithm, and correlated with M1 and M2 macrophages in the CIBERSORT algorithm.
UNASSIGNED: BPIFA1 serves as a modulator of PAH, with the potential to impact the immune microenvironment and disease progression, possibly through its regulatory influence on both M1 and M2 macrophages.
摘要：
■肺动脉高压（PAH）对人类健康构成重大的全球风险。本研究旨在确定PAH的诊断生物标志物，并通过利用复杂的生物信息学技术评估它们与免疫微环境的关联。
■基于两个微阵列数据集，检测差异表达基因(DEGs),和集线器基因经历了一系列机器学习分析。在通过基因富集分析评估与PAH相关的途径后，使用外部数据集验证鉴定的基因,并在野百合碱(MCT)诱导的大鼠模型中证实.此外,使用三种算法来估计各种免疫细胞类型的比例，hub基因和免疫细胞之间的联系得到了证实。
■使用SVM，拉索,和WGCNA,我们确定了七个hub基因，包括(BPIFA1、HBA2、HBB、LOC441081、PI15、S100A9和WIF1),其中只有BPIFA1在外部数据集中保持稳定，并在MCT诱导的大鼠模型中得到验证。此外，功能富集分析的结果建立了PAH与代谢和免疫系统之间的联系。相关性评估显示,MCP-counter算法中BPIFA1的表达与各种免疫细胞类型呈负相关,在ssGSEA算法中与巨噬细胞呈正相关，并与CIBERSORT算法中的M1和M2巨噬细胞相关。
■BPIFA1用作PAH的调制器，有可能影响免疫微环境和疾病进展，可能通过其对M1和M2巨噬细胞的调节作用。
■基于两个微阵列数据集，检测差异表达基因(DEGs),和集线器基因经历了一系列机器学习分析。在通过基因富集分析评估与PAH相关的途径后，使用外部数据集验证鉴定的基因,并在野百合碱(MCT)诱导的大鼠模型中证实.此外,使用三种算法来估计各种免疫细胞类型的比例，hub基因和免疫细胞之间的联系得到了证实。
■使用SVM，拉索,和WGCNA,我们确定了七个hub基因，包括(BPIFA1、HBA2、HBB、LOC441081、PI15、S100A9和WIF1),其中只有BPIFA1在外部数据集中保持稳定，并在MCT诱导的大鼠模型中得到验证。此外，功能富集分析的结果建立了PAH与代谢和免疫系统之间的联系。相关性评估显示,MCP-counter算法中BPIFA1的表达与各种免疫细胞类型呈负相关,在ssGSEA算法中与巨噬细胞呈正相关，并与CIBERSORT算法中的M1和M2巨噬细胞相关。
■BPIFA1用作PAH的调制器，有可能影响免疫微环境和疾病进展，可能通过其对M1和M2巨噬细胞的调节作用。
