Differential Co-Expression Networks of Tumor Educated Platelets Transcriptome for Glioblastoma Multiforme Diagnosis

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 two different public datasets (one used as the main dataset the other as test set). 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 this network, we extracted centrality metrics and local clustering coefficient to identify key nodes, hence the more suitable genes for diagnostic purposes. Then we tested these key genes on the other dataset. Our findings show that genes identified by betweenness centrality exhibit superior diagnostic power compared to: DEGs, gene sets identified through other metrics, and random sets of differentially expressed genes.