Mutational and expressional similarities among paraganglioma, low-grade glioma, and glioblastoma: A comprehensive clustering approach to central nervous system tumors

Abstract

AIM: To compare central nervous system (CNS) tumors, such as paraganglioma, low-grade glioma (LGG), and glioblastoma (GBM), in terms of driver genes and gene expression, and to investigate the roles of common driver genes and genes with altered expression in cellular proliferation mechanisms and their interactions. MATERIAL and METHODS: Mutation datasets for pheochromocytoma/paraganglioma, LGG, and GBM from The Cancer Genome Atlas (TCGA) database were used for driver gene prediction. Six datasets from the Gene Expression Omnibus (GEO) database were used for differential gene expression analysis. A hybrid approach combining clustering and computational biology methods was applied to identify driver genes. Gene expression analyses were repeated for two gene expression datasets for each tumor type, and the intersection of the results was taken. Protein interaction analyses, overall survival analyses, and carcinogenesis-related functional analyses were performed on the common driver genes and the genes with the most significant changes in expression. RESULTS: ATRX, NF1, MUC16, and TTN were identified as driver gene candidates for all three tumor types. FSTL5, GABRG2, VSNL1, and LPL were found to be the genes with the most altered expression across all tumor types. Our findings suggest that, while CNS tumors with similar symptoms share molecular features, they can be more accurately differentiated through detailed investigation of the expression and mutation burden of the identified genes. This may also help accelerate the treatment planning process. CONCLUSION: This study confirms that paraganglioma, LGG, and GBM may share common mutational and expressional gene patterns. The identified genes may serve as potential therapeutic targets in the treatment of glial and neuroendocrine tumors.