Septic cardiomyopathy (SC) is a rare and harmful cardiovascular disease with decreased left ventricular (LV) output and multiple organ failure, which poses a serious threat to human life. Despite the advances in SC, its diagnostic basis and treatment methods are limited, and the specific diagnostic biomarkers and its candidate regulatory targets have not yet been fully established. In this study, the GSE79962 gene expression profile was retrieved, with 20 patients with SC and 11 healthy donors as control. Weighted gene coexpression network analysis (WGCNA) was employed to investigate gene modules that were strongly correlated with clinical phenotypes. Blue module was found to be most significantly related to SC. Moreover, Gene Ontology (GO) functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed on the coexpression genes in blue module and showed that it was associated with metabolic pathways, oxidative phosphorylation, and cardiac muscle contraction. Furthermore, a total of 10 hub genes NDUFB5, TIMMDC1, VDAC3, COQ10A, MRPL16 (mitochondrial ribosomal protein L16), C3orf43, TMEM182, DLAT, NDUFA8, and PDHB (pyruvate dehydrogenase E1 beta subunit) in the blue module were identified at transcriptional level and further validated at translational level in myocardium of an lipopolysaccharide-induced septic cardiac dysfunction mouse model. Overall, the results of quantitative real-time polymerase chain reaction were consistent with most of the microarray analysis results. Intriguingly, we observed that the highest change was NDUFB5, TIMMDC1, and VDAC3. These identified and validated genes provided references that would advance the understanding of molecular mechanisms of SC. Taken together, using WGCNA, the hub genes NDUFB5, TIMMDC1, and VDAC3 might serve as potential biomarkers for diagnosis and/or therapeutic targets for precise treatment of SC in the future.
Keywords: differentially expressed genes; functional enrichment analysis; septic cardiomyopathy; weighted gene coexpression network analysis.