A novel signature combing cuproptosis- and ferroptosis-related genes in sepsis-induced cardiomyopathy.

Cardiac dysfunction caused by sepsis, usually termed sepsis-induced cardiomyopathy (SIC), is one of the most serious complications of sepsis, and ferroptosis can play a key role in this disease. In this study, we identified key cuproptosis- and ferroptosis-related genes involved in SIC and further explored drug candidates for the treatment of SIC. The GSE79962 gene expression profile of SIC patients was downloaded from the Gene Expression Omnibus database (GEO). The data was used to identify differentially expressed genes (DEGs) and to perform weighted correlation network analysis (WGCNA). Furthermore, Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were conducted. Then, gene set enrichment analysis (GSEA) was applied to further analyze pathway regulation, with an adjusted -value <0.05 and a false discovery rate (FDR) <0.25. Ferroptosis-related genes were obtained from the FerrDb V2 database, and cuproptosis-related genes were obtained from the literature. We constructed a novel signature (CRF) by combing cuproptosis-related genes with ferroptosis-related genes using the STRING website. The SIC hub genes were obtained by overlapping DEGs, WGCNA-based hub genes and CRF genes, and receiver operating characteristic (ROC) curve analysis was used to determine the diagnostic value of hub genes. A transcription factor-microRNA-hub gene network was also constructed based on the miRnet database. Finally, potential therapeutic compounds for SIC were predicted based on the Drug Gene Interaction Database. We identified 173 DEGs in SIC patients. Four hub modules and 411 hub genes were identified by WGCNA. A total of 144 genes were found in the CRF. Then, POR, SLC7A5 and STAT3 were identified as intersecting hub genes and their diagnostic values were confirmed with ROC curves. Drug screening identified 15 candidates for SIC treatment. We revealed that the cuproptosis- and ferroptosis-related genes, POR, SLC7A5 and STAT3, were significantly correlated with SIC and we also predicted therapeutic drugs for these targets. The findings from this study will make contributions to the development of treatments for SIC.