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

Lü, Feng; Hu, Feng; Qiu, Bai‐Quan; Zou, Hong-Peng; Xu, Jianjun

doi:10.3389/fgene.2022.929293

Cited by 7 publications

(1 citation statement)

References 56 publications

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“…Studies have shown that it is necessary to combine clinical, electrocardiographic, hemodynamic parameters and echocardiography and other methods to fully diagnose SCM [27]. Previous studies have identified novel biomarkers in SCM by differential analysis and protein-protein interaction network analysis [28]. For the first time, our study combined difference analysis, WGCNA and machine learning algorithms to screen potential biomarkers of SCM.…”

Section: Discussionmentioning

confidence: 99%

Integrated Bioinformatics Analysis Reveals APEX1 as a Potential Biomarker for Septic Cardiomyopathy

2023

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A severe threat to human health is septic cardiomyopathy (SCM), a condition with high morbidity and fatality rates throughout the world. However, effective treatment targets are still lacking. Therefore, it is necessary and urgent to find new therapeutic targets of SCM. Methods. We obtained gene chip datasets GSE79962, GSE53007 and GSE13205 from the GEO database. After data normalization, GSE79962 was used as the training set and screened for differentially expressed genes (DEGs). Then, the module genes most related to SCM were identified via weighted gene co-expression network analysis (WGCNA). The potential target genes of SCM were obtained by intersection of DEGs and WGCNA module genes. We further performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) function and pathway enrichment analyses on these genes. In addition, potential biomarkers were screened using machine learning algorithms and receiver operating characteristic (ROC) curve analysis. Gene Set Enrichment Analysis (GSEA) was then used to explore the mechanisms underlying the involvement of potential biomarkers. Finally, we validated the obtained potential biomarkers in test sets (GSE53007 and GSE13205). Results. A total of 879 DEGs were obtained by differential expression analysis. WGCNA generated 2939 module genes significantly associated with SCM. The intersection of the two results produced 479 potential target genes. Enrichment analysis showed that these genes were involved in the positive regulation of protein kinase A signaling, histone deacetylase activity and T cell receptor signaling pathway, etc. Then, the results of machine learning algorithm and ROC analysis revealed that NEIL3, APEX1, KCNJ14 and TKTL1 had good diagnostic efficacy. GSEA results showed that these genes involved in signaling pathways mainly enriched in base excision repair and glycosaminoglycan biosynthesis pathways, etc. Notably, APEX1 was significantly up-regulated in the SCM groups of the two test sets and the AUC (area under curve) > 0.85. Conclusions. Our study identified NEIL3, APEX1, KCNJ14 and TKTL1 may play important roles in the pathogenesis of SCM through integrated bioinformatics analysis, and APEX1 may be a novel biomarker with great potential in the clinical diagnosis and treatment of SCM in the future.

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Section: Discussionmentioning

confidence: 99%