Juaiti Mukamengjiang, Feng Yilu, Tang Yiyang, Liang Benhui, Zha Lihuang, Yu Zaixin
Department of Cardiology, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, P.R. China.
National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, P.R. China.
Heliyon. 2024 Apr 16;10(8):e29587. doi: 10.1016/j.heliyon.2024.e29587. eCollection 2024 Apr 30.
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.
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.
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.
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.
肺动脉高压（PAH）对全球人类健康构成重大风险。本研究旨在通过运用先进的生物信息学技术，识别PAH的诊断生物标志物，并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（DEG），并对枢纽基因进行一系列机器学习分析。通过基因富集分析评估与PAH相关的通路后，使用外部数据集对鉴定出的基因进行验证，并在野百合碱（MCT）诱导的大鼠模型中得到证实。此外，采用三种算法估计各种免疫细胞类型的比例，并证实枢纽基因与免疫细胞之间的联系。
使用支持向量机（SVM）、套索回归（LASSO）和加权基因共表达网络分析（WGCNA），我们鉴定出七个枢纽基因，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，功能富集分析结果建立了PAH与代谢和免疫系统之间的联系。相关性评估表明，在MCP-counter算法中BPIFA1的表达与各种免疫细胞类型呈负相关，在单样本基因集富集分析（ssGSEA）算法中与巨噬细胞呈正相关，在CIBERSORT算法中与M1和M2巨噬细胞相关。
BPIFA1作为PAH的调节因子，有可能通过对M1和M2巨噬细胞的调节影响免疫微环境和疾病进展。
