Imagine a grand treasure hunt where two rare gems, Carotid Atherosclerosis and Parkinson’s Disease, are hidden deep in the vast neuroscience database. With their shared risk factors and frequent coexistence, scientists set out on a quest to unlock the secret code connecting these disorders. Armed with the power of bioinformatics analysis, they dove into genetic databases to reveal common differentially expressed genes (DEGs) that form the foundation of their discovery. By constructing protein-protein interaction networks and pinpointing hub genes through advanced regression techniques, they unraveled six key players that hold the key to understanding the shared molecular mechanisms between Carotid Atherosclerosis and Parkinson’s Disease. These remarkable findings were not the end of the expedition – the genes proved to be valuable diagnostic markers as well. High-flying ROC curves confirmed their efficiency in diagnosing both disorders. But wait, there’s more! The scientists also stumbled upon familiar infiltrators – activated B cells, effector memory CD8+ T cells, and macrophages – present in both disorders. The presence of these immune cells suggests an intriguing link between inflammation and disease progression. Beyond this remarkable journey lies a vast realm of experimental studies eagerly waiting to explore further depths and uncover the holistic tapestry connecting these neurological disorders.

BackgroundCerebrovascular disease (CVD) related to atherosclerosis and Parkinson’s disease (PD) are two prevalent neurological disorders. They share common risk factors and frequently occur together. The aim of this study is to investigate the association between atherosclerosis and PD using genetic databases to gain a comprehensive understanding of underlying biological mechanisms.MethodsThe gene expression profiles of atherosclerosis (GSE28829 and GSE100927) and PD (GSE7621 and GSE49036) were downloaded from the Gene Expression Omnibus (GEO) database. After identifying the common differentially expressed genes (DEGs) for these two disorders, we constructed protein-protein interaction (PPI) networks and functional modules, and further identified hub genes using Least Absolute Shrinkage and Selection Operator (LASSO) regression. The diagnostic effectiveness of these hub genes was evaluated using Receiver Operator Characteristic Curve (ROC) analysis. Furthermore, we used single sample gene set enrichment analysis (ssGSEA) to analyze immune cell infiltration and explored the association of the identified hub genes with infiltrating immune cells through Spearman’s rank correlation analysis in R software.ResultsA total of 50 shared DEGs, with 36 up-regulated and 14 down-regulated genes, were identified through the intersection of DEGs of atherosclerosis and PD. Using LASSO regression, we identified six hub genes, namely C1QB, CD53, LY96, P2RX7, C3, and TNFSF13B, in the lambda.min model, and CD14, C1QB, CD53, P2RX7, C3, and TNFSF13B in the lambda.1se model. ROC analysis confirmed that both models had good diagnostic efficiency for atherosclerosis datasets GSE28829 (lambda.min AUC = 0.99, lambda.1se AUC = 0.986) and GSE100927 (lambda.min AUC = 0.922, lambda.1se AUC = 0.933), as well as for PD datasets GSE7621 (lambda.min AUC = 0.924, lambda.1se AUC = 0.944) and GSE49036 (lambda.min AUC = 0.894, lambda.1se AUC = 0.881). Furthermore, we found that activated B cells, effector memory CD8 + T cells, and macrophages were the shared correlated types of immune cells in both atherosclerosis and PD.ConclusionThis study provided new sights into shared molecular mechanisms between these two disorders. These common hub genes and infiltrating immune cells offer promising clues for further experimental studies to explore the common pathogenesis of these disorders.

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