Guo Qingbao, Xie Manli, Wang Xiaopeng, Han Cong, Gao Gan, Wang Qian-Nan, Li Jingjie, Duan Lian, Bao Xiangyang
Department of Neurosurgery, XI'AN NO.9 HOSPITAL, Shaanxi, 710054, China.
Department of Occupational Diseases, Xi'an Central Hospital, Shaanxi, 710003, China.
J Neuroinflammation. 2025 Apr 29;22(1):123. doi: 10.1186/s12974-025-03446-y.
Moyamoya disease (MMD) is a rare cerebrovascular disease in humans. Although early revascularization can improve symptoms, it cannot reverse the progression of the disease. The current diagnosis still relies on traditional a Digital Subtraction Angiography (DSA) examination, which is invasive and expensive, leading to delayed diagnosis and affecting treatment timing and patient prognosis. The ability to diagnose MMD early and develop personalized treatment plans can significantly improve the prognosis of patients. Here, we have introduced the research on MMD biomarkers. By integrating proteomics and metabolomics data, we have successfully identified over 1700 features from more than 60 serum samples collected at the onset of symptoms in MMD patients. We use multiple computational strategies to interpret complex information in serum, providing a comprehensive perspective for early diagnosis of MMD. Diagnostic ability of our biomarker is significantly better than previous studies, especially when used in combination. In the study of molecular mechanisms, we found that the ferroptosis pathway was significant disruption in MMD patients, which was also confirmed by transcriptomics data. Finally, we validated the metabolites and proteins associated with ferroptosis pathways, as well as the biomarkers screened by machine learning, using another independent MMD cohort. Our research provides important clues for the diagnosis of MMD, and this assay can identify MMD early, thereby promoting stronger monitoring and intervention.
烟雾病(MMD)是一种罕见的人类脑血管疾病。尽管早期血运重建可以改善症状,但无法逆转疾病的进展。目前的诊断仍依赖于传统的数字减影血管造影(DSA)检查,该检查具有侵入性且费用高昂,导致诊断延迟并影响治疗时机和患者预后。早期诊断MMD并制定个性化治疗方案的能力可以显著改善患者的预后。在此,我们介绍了关于MMD生物标志物的研究。通过整合蛋白质组学和代谢组学数据,我们成功地从MMD患者症状发作时收集的60多个血清样本中识别出1700多个特征。我们使用多种计算策略来解读血清中的复杂信息,为MMD的早期诊断提供全面的视角。我们的生物标志物的诊断能力明显优于以往的研究,尤其是联合使用时。在分子机制研究中,我们发现铁死亡途径在MMD患者中存在显著破坏,这也得到了转录组学数据的证实。最后,我们使用另一个独立的MMD队列验证了与铁死亡途径相关的代谢物和蛋白质,以及通过机器学习筛选出的生物标志物。我们的研究为MMD的诊断提供了重要线索,并且这种检测方法可以早期识别MMD,从而加强监测和干预。