Differential network centrality analysis identifies signatures in tumor-educated platelets

Tumor-educated platelets (TEPs) represent a promising non-invasive source of cancer identification. However, existing transcriptomic approaches often rely on hundreds or thousands of genes, limiting interpretability and clinical translatability. We propose a network-based strategy to identify compact and biologically meaningful gene signatures derived from topological rewiring between healthy and cancer conditions. Results: We constructed Spearman correlation networks from TEP RNA-seq data and quantified condition-specific topological changes using differential degree and betweenness centrality. Genes exhibiting consistent local and global rewiring were selected as candidates. The method was applied to gliomas, non-small cell lung cancer (NSCLC), and breast cancer (BrCa). The resulting genes were highly compact (29, 26, and 20 genes, respectively). PERMANOVA analyses confirmed that selected genes captured significant structural differences between groups, independent of dispersion effects. Across multiple classifiers and several independent external datasets, the proposed genes achieved superior or competitive predictive performance relative to larger DEG-based models, while substantially reducing dimensionality. Functional enrichment highlighted coherent cancer-related programs, including WNT/beta-catenin signalling in gliomas and translational machinery in NSCLC. Downstream analyses in gliomas further suggested a putative lncRNA/miRNA--FKBP5 regulatory axis linked to immune evasion mechanisms. Overall, differential centrality-based rewiring enables compact, interpretable, and generalizable TEP-derived biomarker panels across cancers.