Identification of lysosome-related hub genes as potential biomarkers and immune infiltrations of moyamoya disease by multiple bioinformatics methods and machine-learning strategies.

Background: Moyamoya disease (MMD), characterized by chronic cerebrovascular pathology, poses a rare yet significant clinical challenge, associated with elevated rates of mortality and disability. Despite intensive research endeavors, the exact biomarkers driving its pathogenesis remain enigmatic. Methods: The expression patterns of GSE189993 and GSE141022 were retrieved from the Gene Expression Omnibus (GEO) repository to procure differentially expressed genes (DEGs) between samples afflicted with MMD and those under control conditions. The Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine with Recursive Feature Elimination (SVM-RFE), and Random Forest (RF) algorithms were employed for identifying candidate diagnostic genes associated with MMD. Subsequently, these candidate genes underwent validation in an independent cohort (GSE157628). The CMAP database was ultimately employed to forecast drugs pertinent to MMD for clinical translation. Results: A collective of 240 DEGs were discerned. Functional enrichment scrutiny unveiled the enrichment of the cholesterol metabolism pathway, salmonella infection pathway, and allograft rejection pathway within the MMD cohort. EPDR1, DENND3, and NCSTN emerged as discerned diagnostic biomarkers for MMD. The CMAP database was ultimately employed to scrutinize the ten most auspicious pharmaceutical compounds for managing MMD. Finally, after validation through in vitro experiments, EPDR1, DENND3, and NCSTN were identified as the key genes. Conclusion: EPDR1, DENND3, and NCSTN have emerged as potential novel biomarkers for MMD. The involvement of T lymphocytes, neutrophilic granulocytes, dendritic cells, natural killer cells, and plasma cells could be pivotal in the pathogenesis and advancement of MMD.