Prophylactic intraoperative tranexamic acid administration and postoperative blood loss after transapical aortic valve implantation

Weighted gene co-expression identification of CDKN1A as a hub inflammation gene following cardiopulmonary bypass in children with congenital heart disease

Background

Congenital heart disease (CHD) is the most common type of birth defect. Most patients with CHD require surgery, and cardiopulmonary bypass (CPB) is the most common surgery performed.

Methods

The present study utilized weighted gene co-expression network analysis (WGCNA) to identify key inflammation genes after CPB for CHD. The GSE132176 dataset was downloaded from the Gene Expression Omnibus(GEO) database for WGCNA to identify the modules closely related to clinical traits. Disease enrichment, functional annotation and pathway enrichment were performed on genes in the module closely related to clinical traits using Enrichr and Metascape. Immune infiltration analysis was also performed on the training dataset using CIBERSORT. Finally, we identified hub genes using high gene significance (GS), high module members (MMs) and Cytoscape, and we verified the hub genes using an independent dataset and Western blot analysis.

Results

WGCNA showed that the brown module with 461 genes had the highest correlation to CHD after CPB. Functional annotation and pathway enrichment analysis were performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses, which showed that genes in the brown module were enriched in inflammation-related pathways. In the disease enrichment analysis, genes in the brown module were enriched for inflammatory diseases. After the 30 most highly associated brown intramodular genes were screened, a protein-protein interaction network was constructed using the STRING online analysis website. The protein-protein interaction results were then calculated using 12 algorithms in the cytoHubba plugin of Cytoscape software. The final result showed that CDKN1A was the fundamental gene of post-CPB for CHD. Using another independent validation dataset (GSE12486), we confirmed that CDKN1A was significantly differentially expressed between preoperative and postoperative CPB (Wilcoxon, P = 0.0079; T-test, P = 0.006). In addition, CDKN1A expression was elevated in eosinophils, neutrophils, memory CD4 T cells and activated mast cells. Western blot analysis showed that the expression of CDKN1A protein was significantly higher postoperative CPB than preoperative CPB. Moreover, CDKN1A was mainly related to inflammation.

Conclusion

In summary, we found a relationship between CDKN1A and inflammation after CPB for congenital heart disease by WGCNA, experiments and various bioinformatics methods. Thus, CDKN1A maybe serve as a biomarker or therapeutic target for accurate diagnosis and treatment of inflammation after CPB in the future.