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

EBioMedicine

Nan Ji¹  Liwei Zhang²  Yuxin Yin²  Guangxi Wang²  Huajie Song²  Juntuo Zhou³  Zhe Zhang³  Chunyuan Yang⁴  Yang Zhang⁴  Yan Jin⁴  Yuyao Yuan⁴ 

[1]Institute of Systems Biomedicine, Department of Pathology, School of Basic Medical Sciences, Peking-Tsinghua Center for Life Sciences, Peking University Health Science Center, Beijing 100191, China

[2]Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, National Clinical Research Center for Neurological Diseases, Beijing 100070, China

[3]Institute of Precision Medicine, Peking University Shenzhen Hospital, Shenzhen 518036, China

[4]Institute of Systems Biomedicine, Department of Pathology, School of Basic Medical Sciences, Peking-Tsinghua Center for Life Sciences, Peking University Health Science Center, Beijing 100191, China

关键词: Malignant brain gliomas;    Machine learning;    SVM;    Plasma biomarker;    Lipidomics;   

DOI  :  

来源: DOAJ

【 摘 要 】

Summary: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 model was further evaluated in an independent validation cohort (n=920) enrolled from multiple medical centers. Findings: A panel of 11 plasma lipids was identified as candidate biomarkers with an accuracy of 0.999. The diagnostic model developed achieved a high performance in distinguishing MBGs patients from normal controls with an area under the receiver-operating characteristic curve (AUC) of 0.9877 and 0.9869 in the training and test cohorts, respectively. In the validation cohort, the 11 lipid panel still achieved an accuracy of 0.9641 and an AUC of 0.9866. Interpretation: The present study demonstrates the applicability and robustness of utilizing a machine learning algorithm to analyze lipidomic data for efficient and reliable biomarker screening. The 11 lipid biomarkers show great potential for the non-invasive diagnosis of MBGs with high throughput. Funding: A full list of funding bodies that contributed to this study can be found in the Acknowledgments section.

【 授权许可】

Unknown