Predictive serum biomarkers of patients with cerebral infarction

作者：Yan Kong, Yuqing Feng, Yating Lu, Shi-sui Feng, Zheng Huang, Qianyi Wang, Huimin Huang, Ling Xue, Zhiheng Su, Yue Guo · 发表于：Neurological Research · 年份：2021 · DOI：10.1080/01616412.2021.1987055 · 被引用次数：9 · 研究领域：Metabolomics and Mass Spectrometry Studies、Acute Ischemic Stroke Management、Neurological Disease Mechanisms and Treatments

OBJECTIVES: Stroke is the third most common cause of death and also causes seizures and disability. Biomarkers are abnormal signal indicators at the biological level that are present before the organism is seriously affected and are more sensitive to early diagnosis than are traditional imaging methods. Early diagnosis of stroke can prevent the progression of the disease. However, there are currently no widely accepted biomarkers for stroke that have been applied clinically. METHODS: A serum metabonomics method based on ultra-high-performance liquid chromatography-quadrupole-time of flight tandem mass spectrometry (UPLC-Q-TOF/MS) was used to identify potential biomarkers and metabolic pathways of cerebral infarction. The receiver-operating characteristic (ROC) curve was used to verify the diagnostic and classification abilities of the biomarkers, and a support vector machine (SVM) model was developed for the prediction of cerebral infarction. RESULTS: Principal component analysis revealed a clear separation between the normal and cerebral infarction groups. A total of 13 potential serum biomarkers were identified, which were mainly involved in linoleic acid metabolism; phenylalanine, tyrosine, and tryptophan biosynthesis; tyrosine metabolism; arachidonic acid metabolism; and fatty acid biosynthesis. The ROC curve analysis showed that the potential biomarkers had high specificity and sensitivity for the diagnosis of cerebral infarction. The SVM model had good diagnostic abilit...