Li Wenyang, Zhao Xiang, Fu Jinxing, Cheng Lei
Department of Neurosurgery, The Affiliated Hospital of Qingdao University, Qingdao, 266000, China.
Department of Neurosurgery, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China.
Heliyon. 2024 Jul 10;10(14):e34432. doi: 10.1016/j.heliyon.2024.e34432. eCollection 2024 Jul 30.
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.
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.
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.
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.
烟雾病（MMD）以慢性脑血管病变为特征，是一种罕见但具有重大临床挑战的疾病，其死亡率和致残率较高。尽管进行了大量研究，但驱动其发病机制的确切生物标志物仍不清楚。
从基因表达综合数据库（GEO）中检索GSE189993和GSE141022的表达模式，以获取烟雾病患者样本与对照条件下样本之间的差异表达基因（DEG）。采用最小绝对收缩和选择算子（LASSO）、带递归特征消除的支持向量机（SVM-RFE）和随机森林（RF）算法来识别与烟雾病相关的候选诊断基因。随后，这些候选基因在一个独立队列（GSE157628）中进行验证。最终利用CMAP数据库预测与烟雾病相关的药物用于临床转化。
共识别出240个差异表达基因。功能富集分析揭示了烟雾病队列中胆固醇代谢途径、沙门氏菌感染途径和同种异体移植排斥途径的富集。EPDR1、DENND3和NCSTN成为识别出的烟雾病诊断生物标志物。最终利用CMAP数据库筛选出治疗烟雾病最有前景的十种药物化合物。最后，通过体外实验验证后，确定EPDR1、DENND3和NCSTN为关键基因。
EPDR1、DENND3和NCSTN已成为烟雾病潜在的新型生物标志物。T淋巴细胞、中性粒细胞、树突状细胞、自然杀伤细胞和浆细胞的参与可能在烟雾病的发病机制和进展中起关键作用。
