Beyond Differential Expression: Network Centrality measures reveal Tissue-Specific Signatures in Tumor-Educated Platelets

Objective Tumor-educated platelets (TEPs) are circulating blood components that play a central role in both systemic and local responses to tumor growth, leading to alterations in their RNA profiles. Recent studies have demonstrated that the TEP transcriptome can be leveraged for minimally invasive cancer diagnostics. In a previous analysis based on TEPs transcriptomic data, we highlighted the diagnostic potential of a specific set of key genes for glioblastoma multiforme (GBM). In that study, we applied network-based approaches to reduce an initial set of approximately 57736 genes to a subset of 42 candidates (99,93% reduction). These genes were subsequently validated through enrichment analysis, and a binary classifier trained to distinguish between healthy individuals and cancer patients achieved strong performance. In the present work, we extend this analytical framework to four additional cancer types—breast cancer, colorectal cancer, non-small cell lung cancer and pancreatic adenocarcinoma to investigate whether the key genes identified in GBM are tissue-specific or shared across different tumor types. Our objective is to demonstrate that the key genes identified for GBM differ from those associated with other cancer types, thereby suggesting that a network-based approach captures biologically meaningful, tissue-specific signals rather than noise, even when the data are derived from peripheral liquid biopsies, thus reinforcing the validity of TEPs based approaches. Methods To identify key genes, we employed a combination of differential expression analysis (DEA) and differential co-expression (DCE) network analysis. Initially, DEA was performed to detect differentially expressed genes (DEGs). These genes were then used to construct differential co-expression networks, which were subsequently analyzed to identify key nodes based on centrality metrics. This pipeline was applied independently to five different cancer types, resulting in a distinct set of key genes for each condition. Then we performed a comparative analysis across these gene sets to determine which genes were tissue-specific. Finally, enrichment analysis was conducted on the tissue specific gene subsets to further validate their biological relevance. Results The analysis of key genes across all conditions and centrality metrics provides evidence that the network based approach identifies tissue specific genes. In contrast, the DEGs based approach does not recover tissue-specific signals and identifies largely overlapping gene sets across different cancer types. More specifically, for each condition, more than 70% of the key genes identified using betweenness, closeness, and degree centrality were tissue-specific. In contrast, for DEGs based selection, more than 70% of the genes were shared across conditions. Conclusion The results suggest that the network approach is effective in identifying tissue specific gene signatures, indicating its potential to uncover biologically relevant markers in cancer transcriptomic data, also the identification of tissue specific genes through a peripheral liquid biopsy (TEPs based) further supports the method.