AUTHOR=Zeng Haowei , Liu Xiaoqin , Zhang Yushun 

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

JOURNAL=Frontiers in Cardiovascular Medicine

VOLUME=Volume 8 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2021.624714

DOI=10.3389/fcvm.2021.624714

ISSN=2297-055X

ABSTRACT=Objectives:The present study aimed to screen 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 identify DEGs between IPAH and the control groups with the criterion of adjusted P ＜ 0.05, |log2 fold change|≥ 1, 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 revealed that the DEGs were significantly enriched 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) in the discovery cohort and 1(95%CI=0.805-1.000) in the validation cohort. Moreover, immune infiltration analysis by CIBERSORT showed  a significant difference in immune cell infiltration between patients with IPAH and controls. 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 cell infiltration between the IPAH and control groups.