Establishment and analysis of artificial neural network diagnosis model for coagulation-related molecular subgroups in coronary artery disease - Zheng, Biwei; Li, Yujing; Xiong, Guoliang (Frontiers in Genetics, 2024)
Background: Coronary artery disease (CAD) is the most widespread common type of cardiovascular disease and the main reason cause significant morbidity and mortalityfor mortality globally. Abnormal coagulation cascade function is one of the potential high-risk factors in CAD patients, but the research on the molecular mechanism of coagulation function in CAD is still limited.We clustered and categorized 352 CAD instances paitents based on the expression patterns of coagulation-related genes (CRGs), and then we explored the molecular and immunological variations across the subgroups to reveal the underlying biological characteristics of CAD patients. The feature genes between CRG-subgroups were further identified using a random forest model (RF) and least absolute shrinkage and selection operator (LASSO) regression, and an artificial neural network prediction model was constructed.Results: CAD patients could be divided into the C1 and C2 CRG-subgroups, with the C1 subgroup highly enriched in immune-related signaling pathways. The differential expressed genes between the two CRG-subgroups (DE-CRGs) were primarily enriched in signaling pathways connected to signal transduction and energy metabolism. Subsequently, 10 feature DE-CRGs were identified by RF and LASSO. We constructed a novel artificial neural network model using these 10 genes and evaluated and validated its diagnostic performance on a public dataset.Conclusions: Diverse molecular subgroups of CAD patients may each have a unique gene expression pattern. We may identify subgroups using a few feature genes, providing a theoretical basis for the precise treatment of CAD patients with different molecular subgroups.