PLASMA PROTEOMICS AND MACHINE LEARNING FOR NOVEL BIOMARKER DISCOVERY IN ADULT MALIGNANT GLIOMA

Abstract AIMS Plasma is a valuable source for identifying non-invasive biomarkers, and when combined with an examination of the highly dynamic proteome, it has potential to lead identification novel biomarkers in glioma. The aim this study was uncover plasma-based protein adult malignant METHOD Mass spectrometry-based proteomics tandem mass tag (TMT) labelling plasma from healthy individuals gliomas performed. A differential abundance analysis carried out identify proteins that were deregulated primary compared individuals. Machine learning employed diagnostic biomarker panel. RESULTS When comparing gliomas, several high low abundant found be deregulated. To improve accuracy ability detect machine led development classifier, which performed accuracy, specificity, sensitivity. CONCLUSIONS discovery once validated, may facilitate earlier diagnosis glioma patients, thereby reduce time-to-treatment.