Metabolic detection of malignant brain gliomas through plasma lipidomic analysis and support vector machine-based machine learning.

ABSTRACT:

Background

Most malignant brain gliomas (MBGs) are associated with dismal outcomes, mainly due to their late diagnosis. Current diagnostic methods for MBGs are based on imaging and histological examination, which limits their early detection. Here, we aimed to identify reliable plasma lipid biomarkers for non-invasive diagnosis for MBGs.

Methods

Untargeted lipidomic analysis was firstly performed using a discovery cohort (n=107). The data were processed by a support vector machine (SVM)-based discriminating model to retrieve a panel of candidate biomarkers. Then, a targeted quantification method was developed, and the SVM-based diagnostic model was constructed using a training cohort (n=750) and tested using a test cohort (n=225). Finally, the performance of the diagnostic m

SUBMITTER: Zhou J 

PROVIDER: S-EPMC9189781 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature