Identification of Potential Biomarkers and Immune Infiltration Characteristics in Idiopathic Pulmonary Arterial Hypertension Using Bioinformatics Analysis

Objectives: Idiopathic pulmonary arterial hypertension (IPAH) is a rare but severe lung disorder, which may lead to heart failure and early mortality. However, little is known about the etiology of IPAH. Thus, the present study aimed to establish the differentially expressed genes (DEGs) between IPAH and normal tissues, which may serve as potential prognostic markers in IPAH. Furthermore, we utilized a versatile computational method, CIBERSORT to identify immune cell infiltration characteristics in IPAH. Materials and Methods: The GSE117261 and GSE48149 datasets were obtained from the Gene Expression Omnibus database. The GSE117261 dataset was adopted to screen DEGs between IPAH and the control groups with the criterion of vertical bar log2 fold change vertical bar >= 1, adjusted P < 0.05, and to further explore their potential biological functions via Gene Ontology analysis, Kyoto Encyclopedia of Genes and Genomes Pathway analysis, and Gene Set Enrichment Analysis. Moreover, the support vector machine (SVM)-recursive feature elimination and the least absolute shrinkage and selection operator regression model were performed jointly to identify the best potential biomarkers. Then we built a regression model based on these selected variables. The GSE48149 dataset was used as a validation cohort to appraise the diagnostic efficacy of the SVM classifier by receiver operating characteristic (ROC) analysis. Finally, immune infiltration was explored by CIBERSORT in IPAH. We further analyzed the correlation between potential biomarkers and immune cells. Results: In total, 75 DEGs were identified; 40 were downregulated, and 35 genes were upregulated. Functional enrichment analysis found a significantly enrichment in heme binding, inflammation, chemokines, cytokine activity, and abnormal glycometabolism. HBB, RNASE2, S100A9, and IL1R2 were identified as the best potential biomarkers with an area under the ROC curve (AUC) of 1 (95%CI = 0.937-1.000, specificity = 100%, sensitivity = 100%) in the discovery cohort and 1(95%CI = 0.805-1.000, specificity = 100%, sensitivity = 100%) in the validation cohort. Moreover, immune infiltration analysis by CIBERSORT showed a higher level of CD8+ T cells, resting memory CD4+ T cells, gamma delta T cells, M1 macrophages, resting mast cells, as well as a lower level of naive CD4+ T cells, monocytes, M0 macrophages, activated mast cells, and neutrophils in IPAH compared with the control group. In addition, HBB, RNASE2, S100A9, and IL1R2 were correlated with immune cells. Conclusion: HBB, RNASE2, S100A9, and IL1R2 were identified as potential biomarkers to discriminate IPAH from the control. There was an obvious difference in immune infiltration between patient with IPAH and normal groups.

Automated summary

HBB, RNASE2, S100A9, and IL1R2 were identified as potential biomarkers to discriminate IPAH from the control group. There was a significant difference in immune infiltration between patients with IPAH and the normal group, with higher levels of certain immune cells observed in IPAH patients.