Screening of key biomarkers and immune infiltration in Pulmonary Arterial Hypertension via integrated bioinformatics analysis

ABSTRACT

This study aimed to screen key biomarkers and investigate immune infiltration in pulmonary arterial hypertension (PAH) based on integrated bioinformatics analysis. The Gene Expression Omnibus (GEO) database was used to download three mRNA expression profiles comprising 91 PAH lung specimens and 49 normal lung specimens. Three mRNA expression datasets were combined, and differentially expressed genes (DEGs) were obtained. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses and the protein-protein interaction (PPI) network of DEGs were performed using the STRING and DAVID databases, respectively. The diagnostic value of hub gene expression in PAH was also analyzed. Finally, the infiltration of immune cells in PAH was analyzed using the CIBERSORT algorithm. Total 182 DEGs (117 upregulated and 65 downregulated) were identified, and 15 hub genes were screened. These 15 hub genes were significantly associated with immune system functions such as myeloid leukocyte migration, neutrophil migration, cell chemotaxis, Toll-like receptor signaling pathway, and NF-κB signaling pathway. A 7-gene-based model was constructed and had a better diagnostic value in identifying PAH tissues compared with normal controls. The immune infiltration profiles of the PAH and normal control samples were significantly different. High proportions of resting NK cells, activated mast cells, monocytes, and neutrophils were found in PAH samples, while high proportions of resting T cells CD4 memory and Macrophages M1 cell were found in normal control samples. Functional enrichment of DEGs and immune infiltration analysis between PAH and normal control samples might help to understand the pathogenesis of PAH.

KEYWORDS:

• Bioinformatics analysis
• differentially expressed genes (degs)
• pulmonary arterial hypertension (pah)
• immune infiltration
• immune system function

Acknowledgements

We thanked the authors who built up these datasets (GSE15197, GSE113439, and GSE117261). Their work provided convenience for this article greatly. We would like to thank Editage (www.editage.cn) for English language editing.

Author contributions

(I) Conception and design: Yu Zeng, Nanhong Li, Zhenzhen Zheng, Riken Chen, Junfen Cheng, and Cheng Hong.

(II) Administrative support: Cheng Hong and Junfen Cheng.

(III) Provision of study materials or patients: Yu Zeng, Nanhong Li, Zhenzhen Zheng, Riken Chen, Wang Liu, Jinru Zhu and Mingqing Zeng.

(IV) Collection and assembly of data: Yu Zeng, Nanhong Li, Zhenzhen Zheng, Min Peng, Wang Liu, and Jinru Zhu.

(V) Data analysis and interpretation: Yu Zeng, Nanhong Li, Zhenzhen Zheng, Riken Chen, Junfen Cheng, and Cheng Hong.

(VI) Manuscript writing: Yu Zeng, and Nanhong Li.

(VII) Final approval of manuscript: All authors.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Supplementary material

Supplemental data for this article can be accessed here.

Additional information

Funding

The work was supported by the Natural Science Foundation of Guangdong Province (2021A1515011373).