Serum CD133-Associated Proteins Identified by Machine Learning Are Connected to Neural Development, Cancer Pathways, and 12-Month Survival in Glioblastoma.

Simple Summary: Glioblastoma (GBM) is an aggressive form of brain cancer characterized by its poor prognosis due to its resistance and recurrence capabilities. Liquid biopsies have been increasingly utilized when studying GBM as they provide extensive amounts of data at multiple time points without the invasiveness of tissue samples. We used a large-scale proteomic panel from the serum of GBM patients collected before chemoradiation therapy (CRT) to study the connections of CD133, a protein recognized as involved in GBM resistance. We used a novel machine learning process to identify 24 proteins associated with CD133 and 12-month survival, successfully grouping patients into risk groups based on protein profiles. Our results identify a potentially harmful protein profile while revealing important serum associations of CD133. These identified proteins are possible prognostic indicators and treatment entry points with heightened value because they can be frequently traced and monitored. Glioma is the most prevalent type of primary central nervous system cancer, while glioblastoma (GBM) is its most aggressive variant, with a median survival of only 15 months when treated with maximal surgical resection followed by chemoradiation therapy (CRT). CD133 is a potentially significant GBM biomarker. However, current clinical biomarker studies rely on invasive tissue samples. These make prolonged data acquisition impossible, resulting in increased interest in the use of liquid biopsies. Our study, analyzed 7289 serum proteins from 109 patients with pathology-proven GBM obtained prior to CRT using the aptamer-based SOMAScan® proteomic assay technology. We developed a novel methodology that identified 24 proteins linked to both serum CD133 and 12-month overall survival (OS) through a multi-step machine learning (ML) analysis. These identified proteins were subsequently subjected to survival and clustering evaluations, categorizing patients into five risk groups that accurately predicted 12-month OS based on their protein profiles. Most of these proteins are involved in brain function, neural development, and/or cancer biology signaling, highlighting their significance and potential predictive value. Identifying these proteins provides a valuable foundation for future serum investigations as validation of clinically applicable GBM biomarkers can unlock immense potential for diagnostics and treatment monitoring. [ABSTRACT FROM AUTHOR]