Identification of differentially expressed genes associated with coronary in-stent restenosis by integrated bioinformatics approaches

Meijuan Chen; Yaoshan Dun; Wenliang Zhang; Suixin Liu

doi:10.21037/apm-21-2681

Identification of differentially expressed genes associated with coronary in-stent restenosis by integrated bioinformatics approaches

Ann Palliat Med. 2022 Jun;11(6):1940-1953. doi: 10.21037/apm-21-2681. Epub 2022 Feb 9.

Authors

Meijuan Chen¹, Yaoshan Dun², Wenliang Zhang³, Suixin Liu³

Affiliations

¹ Division of Cardiac Rehabilitation, Department of Physical Medicine & Rehabilitation, Xiangya Hospital of Central South University, Changsha, China.
² Division of Cardiac Rehabilitation, Department of Physical Medicine & Rehabilitation, Xiangya Hospital of Central South University, Changsha, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital of Central South University, Changsha, China; Division of Preventive Cardiology, Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA.
³ Division of Cardiac Rehabilitation, Department of Physical Medicine & Rehabilitation, Xiangya Hospital of Central South University, Changsha, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital of Central South University, Changsha, China.

PMID: 35144391
DOI: 10.21037/apm-21-2681

Abstract

Background: In-stent restenosis (ISR) is a common complication following percutaneous coronary intervention (PCI). Designing ISR-specific therapeutic targets is important for optimizing coronary stenosis therapy. This study aims to find new potential key genes related to ISR and the usefulness of differentially expressed genes (DEGs) as diagnostic biomarkers for ISR.

Methods: Microarray datasets of peripheral blood was downloaded from the Gene Expression Omnibus (GEO) public database (GSE46560, n=11). The DEGs between the ISR group and the control were obtained through the limma package analysis of the R language. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were used to obtain the related signal pathways. STRING database was used to constructed a protein-to-protein interaction network with DEGs. Networkanalyst database was used to predict the target miRNA of the target gene. The area under the curve (AUC) value obtained from receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic effectiveness of the hub gene in distinguishing ISR patients from normal individuals.

Results: A total of 154 DEGs were identified between ISR group and control group, including 57 up-regulated and 97 down-regulated DEGs. GO enrichment analysis showed that genes were enriched in homeostasis, cell morphology and cell connection. KEGG pathway analysis shows that genes are mainly involved in human papillomavirus infection, salmonella infection, human T-cell leukemia virus type 1 infection, axon guidance and MAPK signaling pathway. CLTA, CAT, STAT5A, CD300A, CA1, NCF2, HBQ1, AHSP, SLC4A1 and EPB42 were identified as 10 hub genes. A total of 151 target miRNAs of 7 DEGs were obtained, and 149 mRNA-miRNA pairs were identified. ROC curve indicates that CA1, STAT5A and HBQ1 have high diagnostic value in ISR.

Conclusions: CA1, STAT5A and HBQ1 are identified as specific related genes for ISR patients. These genes may provide new targets for the early identification and treatment of ISR.

Keywords: In-stent restenosis (ISR); bioinformatics analysis; biomarker; differentially expressed genes.

MeSH terms

Blood Proteins
Computational Biology
Coronary Restenosis*
Gene Expression Profiling
Humans
MicroRNAs* / genetics
Molecular Chaperones / genetics
Percutaneous Coronary Intervention* / adverse effects

Substances

AHSP protein, human
Blood Proteins
MicroRNAs
Molecular Chaperones