Motivation: Tumor-educated platelets (TEPs) represent a pivotal resource for liquid biopsy, reflecting transcriptomic alterations induced by the tumor microenvironment. Current analytical methods often focus on single-gene differential expression, overlooking high-order regulatory dynamics, and, in the case of co-expression analysis, the ""signed"" nature of co-expression relationships is not adequately exploited. This work proposes a computational framework based on Structural Balance Theory (SBT) to identify driver genes in glioma by evaluating the structural instability (frustration) of co-expression networks.
Results: By leveraging the Friendship-Like Differential Co-expression Network (FLDCN) framework, we applied the Local Balance Index to quantify individual gene contributions to network imbalance across various topological configurations. The analysis identified a consistent gene signature associated with platelet activation, glioma-specific pathways, and immune system modulation. The robustness of the proposed approach was further validated through cross-dataset analysis on independent cohorts, demonstrating that tumor-induced molecular rewiring generates stable and reproducible topological signals.

Co-authors: Stefano Rinaldi, Mattia Manna , Aurelia Righetti, Lorenzo Farina, Manuela Petti