Introduction: Glioblastoma (GBM) is the most prevalent form of primary malignant brain tumor. Glioblastoma stem cells (GSCs) are characterized by their ability to self-renew and the capacity to initiate tumors. GSCs could be a factor for the low effectiveness of cancer therapies and for the short relapse time. For the efficient targeting of GSCs, a comprehensive understanding of their biological mechanisms is indispensable. Microarray technology has been revolutionizing gene expression profiling in recent years, enabling high-throughput analysis of transcriptomic changes across various conditions. By identifying differentially expressed genes, we can gain insights into the underlying molecular mechanisms driving these changes. However, to fully comprehend the impact of these genes, systems biology approaches can be employed to investigate their interactions within biological networks. In this study, we aimed to identify key genes involved in GSCs using microarray data, differential gene expression (DEGs) analysis, and systems biology tools.
Methods: We applied publicly available microarray data from Gene Expression Omnibus (GEO) to assess DEGs of GSE253400 by using Transcriptome Analysis Console (TAC) and normalized data of three stem-like state samples and three differentiated state samples of NCH421k cell line to identify significantly upregulated genes. Subsequently, to further narrow down the most biologically significant genes, the result of overexpressed genes was used to construct a protein-protein interaction (PPI) network using the STRING database. The constructed network was visualized using Cytoscape. Degree, Betweenness Centrality, Closeness Centrality and EigenVector unDir were the four topological measures that were taken into account in this research. Based on several topological evaluations of PPI-network, the greatest percentage of interactions was used to pick the highest-ranked key genes.
Results: From dataset with NCBI accession ID GSE253400, we have identified from 48226 total genes, 728 genes were upregulated and 256 genes were downregulated with fold change >2 or <-2 and P-value< 0.05 as filter criteria in this assessment. We selected upregulated genes with fold change>3 (304 genes) for the PPI network analysis. Using the four topological measures, we detected the top eight key genes that are CXCL10, ICAM1, CD44, TGFB1, FOS, AGT, SERPINE, SPP1 as the essential nodes within the interaction network.
Conclusion: In conclusion, our integrative approach, which combined microarray-based differential gene expression analysis with PPI-network assessment disclosed eight key genes CXCL10, ICAM1, CD44, TGFB1, FOS, AGT, SERPNE, SPP1 as key regulators in GSC differentiation pathways. These findings suggest potential targets for therapeutic intervention and provide a foundation for future research into the molecular mechanisms underlying Glioblastoma. Nevertheless, the findings require experimental validation could lead to significant advancements in treatment plans against Glioblastoma.