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Identification of Inflammatory Gene in the Congenital Heart Surgery Patients following Cardiopulmonary Bypass via the Way of WGCNA and Machine Learning Algorithms
Liang Cai, Bingdong Zhang

TL;DR
This study identifies key genes involved in inflammation after heart surgery using gene analysis and machine learning.
Contribution
The study introduces a novel combination of WGCNA and machine learning to identify hub genes linked to CPB-induced inflammation.
Findings
21 key module genes were identified through DEG and WGCNA analysis.
Four hub genes (TRAF3IP2-AS1, PPARGC1B, CD4, and PDLIM5) were confirmed using LASSO and SVM-RFE.
The hub genes are associated with inflammation and have 215 potential therapeutic agents.
Abstract
Performing cardiopulmonary bypass (CPB) to reduce ischemic injury during surgery is a common approach to cardiac surgery. However, this procedure can lead to systemic inflammation and multiorgan dysfunction. Therefore, elucidating the molecular mechanisms of CPB-induced inflammatory cytokine release is essential as a critical first step in identifying new targets for therapeutic intervention. The GSE143780 dataset which is mRNA sequencing from total circulating leukocytes of the neonatorum was downloaded from the Gene Expression Omnibus (GEO) database. A total of 21 key module genes were obtained by analyzing the intersection of differentially expressed gene (DEG) and gene coexpression network analysis (WGCNA), and then, 4 genes (TRAF3IP2-AS1, PPARGC1B, CD4, and PDLIM5) were further confirmed after the least absolute shrinkage and selection operator (LASSO) and support vector machine…
Genes, proteins, chemicals, diseases, species, mutations and cell lines named across the full text — each resolved to its canonical identifier and authoritative record.
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Taxonomy
TopicsCancer-related molecular mechanisms research · Signaling Pathways in Disease · Macrophage Migration Inhibitory Factor
