Tumor-educated platelets (TEPs) are circulating blood cells implicated as central players in the systemic and local responses to tumor growth, thus altering their RNA profile. To date, some studies have shown that the TEPs transcriptome can be used for a less invasive cancer diagnosis. The objective of this study is to propose a procedure that can identify a set of key genes with diagnostic value for glioblastoma multiforme (GBM). To identify these key genes, we analyzed TEPs RNA-seq data of healthy subjects and GBM patients from three different public datasets (one of them used as main dataset and the others as test sets). We performed differential expression analysis (DEA) and differential co-expression (DCE) network analysis. Specifically, leveraging the main dataset, we first performed DEA analysis to identify differentially expressed genes (DEGs) and then used these genes to construct and analyze the differential co-expression network. From those networks, we extracted centrality metrics and local clustering coefficient to identify key nodes, hence the more suitable genes for diagnostic purposes. Then we tested those key genes on the other two datasets. Our findings shows that genes identified by betweenness centrality exhibit superior diagnostic power compared to: DEGs, other gene sets identified through other metrics and random sets of differentially expressed genes.