Identification of novel biomarkers in septic cardiomyopathy via integrated bioinformatics analysis and experimental validation

Feng Lu; Feng Hu; Baiquan Qiu; Hongpeng Zou; Jianjun Xu

doi:10.3389/fgene.2022.929293

Identification of novel biomarkers in septic cardiomyopathy via integrated bioinformatics analysis and experimental validation

Front Genet. 2022 Jul 25:13:929293. doi: 10.3389/fgene.2022.929293. eCollection 2022.

Authors

Feng Lu¹, Feng Hu², Baiquan Qiu¹, Hongpeng Zou¹, Jianjun Xu¹

Affiliations

¹ Department of Cardiothoracic Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, China.
² Department of Cardiovascular Medicine, The Second Affiliated Hospital of Nanchang University, Nanchang, China.

Abstract

Purpose: Septic cardiomyopathy (SCM) is an important world public health problem with high morbidity and mortality. It is necessary to identify SCM biomarkers at the genetic level to identify new therapeutic targets and strategies. Method: DEGs in SCM were identified by comprehensive bioinformatics analysis of microarray datasets (GSE53007 and GSE79962) downloaded from the GEO database. Subsequently, bioinformatics analysis was used to conduct an in-depth exploration of DEGs, including GO and KEGG pathway enrichment analysis, PPI network construction, and key gene identification. The top ten Hub genes were identified, and then the SCM model was constructed by treating HL-1 cells and AC16 cells with LPS, and these top ten Hub genes were examined using qPCR. Result: STAT3, SOCS3, CCL2, IL1R2, JUNB, S100A9, OSMR, ZFP36, and HAMP were significantly elevated in the established SCM cells model. Conclusion: After bioinformatics analysis and experimental verification, it was demonstrated that STAT3, SOCS3, CCL2, IL1R2, JUNB, S100A9, OSMR, ZFP36, and HAMP might play important roles in SCM.

Keywords: biomarkers; differentially expressed genes; integrated bioinformatics analysis; sepsis; septic cardiomyopathy.