Integrated bioinformatics analysis and experimental animal models identify a robust biomarker and its correlation with the immune microenvironment in pulmonary arterial hypertension.

Background: 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. Methods: 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. Results: 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. Conclusion: 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.

Integrated Bioinformatics Analysis Identifies Robust Biomarkers and Its Correlation With Immune Microenvironment in Nonalcoholic Fatty Liver Disease.

Objective: Nonalcoholic fatty liver disease (NAFLD) is a serious threat to human health worldwide. In this study, the aim is to analyze diagnosis biomarkers in NAFLD and its relationship with the immune microenvironment based on bioinformatics analysis. Methods: We downloaded microarray datasets (GSE48452 and GSE63067) from the Gene Expression Omnibus (GEO) database for screening differentially expressed genes (DEGs). The hub genes were screened by a series of machine learning analyses, such as support vector machine (SVM), least absolute shrinkage and selection operator (LASSO), and weighted gene co-expression network analysis (WGCNA). It is worth mentioning that we used the gene enrichment analysis to explore the driver pathways of NAFLD occurrence. Subsequently, the aforementioned genes were validated by external datasets (GSE66676). Moreover, the CIBERSORT algorithm was used to estimate the proportion of different types of immune cells. Finally, the Spearman analysis was used to verify the relationship between hub genes and immune cells. Results: Hub genes (CAMK1D, CENPV, and TRHDE) were identified. In addition, we found that the pathogenesis of NAFLD is mainly related to nutrient metabolism and the immune system. In correlation analysis, CENPV expression had a strong negative correlation with resting memory CD4 T cells, and TRHDE expression had a strong positive correlation with naive B cells. Conclusion: CAMK1D, CENPV, and TRHDE play regulatory roles in NAFLD. In particular, CENPV and TRHDE may regulate the immune microenvironment by mediating resting memory CD4 T cells and naive B cells, respectively, and thus influence disease progression.