Identification of novel biomarkers in septic cardiomyopathy via integrated bioinformatics analysis and experimental validation - Lu, Feng; Hu, Feng; Qiu, Baiquan; Zou, Hongpeng; Xu, Jianjun (Frontiers in Genetics, 2022)
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, key gene identification. The top 10 Hub genes were identified, and then the SCM model was constructed by treating HL-1 cells and AC16 cells with LPS, and these top 10 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 may play important roles in SCM.