Glioblastoma (GBM) is a fatal brain tumor with limited treatment options. O6-methylguanine-DNA-methyltransferase (MGMT) promoter methylation status is the central molecular biomarker linked to both the response to temozolomide, the standard chemotherapy drug employed for GBM, and to patient survival. However, MGMT status is captured on tumor tissue which, given the difficulty in acquisition, limits the use of this molecular feature for treatment monitoring. MGMT protein expression levels may offer additional insights into the mechanistic understanding of MGMT but, currently, they correlate poorly to promoter methylation. The difficulty of acquiring tumor tissue for MGMT testing drives the need for non-invasive methods to predict MGMT status. Feature selection aims to identify the most informative features to build accurate and interpretable prediction models. This study explores the new application of a combined feature selection (i.e., LASSO and mRMR) and the rank-based weighting method (i.e., MGMT ProFWise) to non-invasively link MGMT promoter methylation status and serum protein expression in patients with GBM. Our method provides promising results, reducing dimensionality (by more than 95%) when employed on two large-scale proteomic datasets (7k SomaScan^® panel and CPTAC) for all our analyses. The computational results indicate that the proposed approach provides 14 shared serum biomarkers that may be helpful for diagnostic, prognostic, and/or predictive operations for GBM-related processes, given further validation.

Glioblastoma multiforme (GBM) is the most common primary brain tumor, characterized by poor overall survival and near-universal recurrence due to rapid proliferation and treatment resistance. Inflammatory markers (i.e., cytokines and acute phase reactants [APRs]) have been studied in serum as potential biomarkers in GBM. However, the lack of serum data collected at multiple time points, particularly during treatment interventions, has limited the translation of these findings into clinical applications for monitoring treatment response. In this study, we analyzed a panel of 29 cultivated serum inflammatory markers employing serum from 109 individuals with pathology-proven GBM, collected both pre- and post-chemoradiation therapy (CRT). The goal was to determine the feasibility of measuring these serum markers in patient samples and to understand the effects of CRT on inflammatory biomarkers and signaling pathways. Among the proteins studied, albumin, C-reactive protein (CRP), glial fibrillary protein (GFAP), kininogen, interleukin (IL)-13, IL-1b, IL-10, Parkinson’s disease-1, vascular cell adhesion molecule 1 (VCAM-1), and tumor necrosis factor-alpha (TNF-α) were the most significantly altered following CRT. Of these, albumin, CRP, GFAP, IL-6, VCAM-1, and TNF-α emerged as the most relevant and promising serum biomarkers. Interaction analyses of baseline and post-CRT data identified IL-6 as a potential driver of signal alteration linked to APRs pathways and suggested its role as a mediator of tumor microenvironment reprogramming in conjunction with TNF-α and VCAM-1. The data revealed a balancing act of pro-apoptotic, anti-proliferative, and pro-viability pathways, with significant impacts on the signal transducer and activator of transcription 3 and nuclear factor kappa B pathways. Overall, the altered signature of the examined serum biomarkers suggests a decrease in the inflammatory response following GBM treatment. These findings underscore the need for further research, incorporating clinical outcomes and validation in larger datasets, to confirm the clinical applicability of these biomarkers.