Identification of potential biomarkers of inflammation-related genes for ischemic cardiomyopathy

Jianru Wang; Shiyang Xie; Yanling Cheng; Xiaohui Li; Jian Chen; Mingjun Zhu

doi:10.3389/fcvm.2022.972274

Identification of potential biomarkers of inflammation-related genes for ischemic cardiomyopathy

Front Cardiovasc Med. 2022 Aug 23:9:972274. doi: 10.3389/fcvm.2022.972274. eCollection 2022.

Authors

Jianru Wang^{1

2}, Shiyang Xie^{1

2}, Yanling Cheng¹, Xiaohui Li¹, Jian Chen^{3

4}, Mingjun Zhu¹

Affiliations

¹ Department of Cardiovascular, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China.
² Central Laboratory, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China.
³ Department of Vascular Disease, Shanghai TCM-Integrated Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
⁴ Institute of Vascular Anomalies, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China.

Abstract

Objective: Inflammation plays an important role in the pathophysiology of ischemic cardiomyopathy (ICM). We aimed to identify potential biomarkers of inflammation-related genes for ICM and build a model based on the potential biomarkers for the diagnosis of ICM.

Materials and methods: The microarray datasets and RNA-Sequencing datasets of human ICM were downloaded from the Gene Expression Omnibus database. We integrated 8 microarray datasets via the SVA package to screen the differentially expressed genes (DEGs) between ICM and non-failing control samples, then the differentially expressed inflammation-related genes (DEIRGs) were identified. The least absolute shrinkage and selection operator, support vector machine recursive feature elimination, and random forest were utilized to screen the potential diagnostic biomarkers from the DEIRGs. The potential biomarkers were validated in the RNA-Sequencing datasets and the functional experiment of the ICM rat, respectively. A nomogram was established based on the potential biomarkers and evaluated via the area under the receiver operating characteristic curve (AUC), calibration curve, decision curve analysis (DCA), and Clinical impact curve (CIC).

Results: 64 DEGs and 19 DEIRGs were identified, respectively. 5 potential biomarkers (SERPINA3, FCN3, PTN, CD163, and SCUBE2) were ultimately selected. The validation results showed that each of these five potential biomarkers showed good discriminant power for ICM, and their expression trends were consistent with the bioinformatics results. The results of AUC, calibration curve, DCA, and CIC showed that the nomogram demonstrated good performance, calibration, and clinical utility.

Conclusion: SERPINA3, FCN3, PTN, CD163, and SCUBE2 were identified as potential biomarkers associated with the inflammatory response to ICM. The proposed nomogram could potentially provide clinicians with a helpful tool to the diagnosis and treatment of ICM from an inflammatory perspective.

Keywords: bioinformatics analyses; biomarker; heart failure; inflammation-related genes; ischemic cardiomyopathy; nomogram.