首页|Taiyuan Central Hospital of Shanxi Medical University Reports Findings in Atrial Fibrillation (Identification of common mechanisms and biomarkers of atrial fibr illation and heart failure based on machine learning)
Taiyuan Central Hospital of Shanxi Medical University Reports Findings in Atrial Fibrillation (Identification of common mechanisms and biomarkers of atrial fibr illation and heart failure based on machine learning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Heart Disorders and Di seases - Atrial Fibrillation is the subject of a report. According to news repor ting out of Taiyuan, People’s Republic of China, by NewsRx editors, research sta ted, “Atrial fibrillation (AF) is the most common arrhythmia. Heart failure (HF) is a disease caused by heart dysfunction.” Our news journalists obtained a quote from the research from the Taiyuan Central Hospital of Shanxi Medical University, “The prevalence of AF and HF were progre ssively increasing over time. The coexistence of AF and HF presents a significa nt therapeutic challenge. In order to provide new ideas for the diagnosis of AF and HF, it is necessary to carry out biomarker related studies. The training set and validation set data of AF and HF patient samples were downloaded from the G EO database, ‘limma’ was used to compare the differences in gene expression leve ls between the disease group and the normal group to screen for differentially e xpressed genes (DEGs). Weighted correlation network analysis (WGCNA) identified the modules with the highest positive correlation with AF and HF. Functional enr ichment and PPI network construction of key genes were carried out. Biomarkers w ere screened by machine learning. The infiltration of immune cells in AF and HF groups was evaluated by R-packet ‘CIBERSORT’. The miRNA network was constructed and potential therapeutic agents for biomarker genes were predicted through the drugbank database. Through WGCNA analysis, it was found that the modules most po sitively correlated with AF and HF were MEturquoise (r = 0.21, P value = 0.09) a nd MEbrown (r = 0.62, P value = 8e-12), respectively. We screened 25 genes that were highly correlated with both AF and HF. Lasso regression analysis results sh owed 7 and 20 core genes in AF and HF groups, respectively. The top 20 important genes in AF and HF groups were obtained as core genes by RF model analysis. Fou r biomarkers were obtained after the intersection of core genes in four groups, namely, GLUL, NCF2, S100A12, and SRGN. The diagnostic efficacy of four genes in AF validation sets was good (AUC: GLUL 0.76, NCF2 0.64, S100A12 0.68, and SRGN 0 .76), as well as in the HF validation set (AUC: GLUL 0.76, NCF2 0.84, S100A12 0. 92, and SRGN 0.68). The highest correlation with neutrophils was observed for GL UL, NCF2, and S100A12, while SRGN exhibited the strongest correlation with T cel ls CD4 memory resting in the AF group. GLUL, NCF2, S100A12, and SRGN were most a ssociated with neutrophils in the HF group. A total of 101 miRNAs were predicted by four genes, and GLUL, NCF2, and S100A12 predicted a total of 10 potential th erapeutic agents. We identified four biological markers that are highly correlat ed with AF and HF, namely, GLUL, NCF2, S100A12, and SRGN.”
TaiyuanPeople’s Republic of ChinaAsi aAtrial FibrillationBiomarkersCardiac ArrhythmiasCardiologyCardiovascu lar Diseases and ConditionsCyborgsDiagnostics and ScreeningDrugs and Thera piesEmerging TechnologiesGeneticsHealth and MedicineHeart DiseaseHeart Disorders and DiseasesHeart FailureMachine Learning
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