This research utilised a multiple approach, including a transcriptome-wide differential gene expression analysis, protein-protein interaction analysis, and clinical data analysis, to determine what the molecular signatures of glioblastoma multiforme (GBM) were.
Year: 2026
Type: preprint
Abstract: Glioblastoma (GBM) is the most aggressive primary malignant brain tumor in adults. It has a high degree of molecular diversity and has a median survival of approximately 15 months, even after being treated using multiple methods. A comprehensive study of the transcriptome of GBM can help identify both biomarkers and potential therapeutic options.The objective of this study was to identify differentially expressed genes (DEGs) in glioblastoma multiforme (GBM) compared to normal brain tissues; to characterize the biological functions of these DEGs; to create a protein-protein interaction (PPI) network to determine which DEGs are most relevant as "hub" genes; and to assess the potential for clinical use of these hub genes.Methods: We used RNA sequence data from the TCGA-GBM dataset to analyze the expression patterns of genes. We identified differentially expressed genes using DESeq2. To identify biological processes or pathways that may be involved in glioblastoma development, we performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses on the list of differentially expressed genes using clusterProfiler. In order to create a protein-protein interaction (PPI) network, we obtained PPI information from STRING and then analyzed the network properties (degree, betweenness, and closeness) in igraph. We combined the RNA sequence data with each patient's clinical history and evaluated whether differences in gene expression were associated with patient survival. Specifically, we used Kaplan–Meier survival curves (log-rank test) and Cox proportional hazard regression models with Benjamini-Hochberg false discovery rate correction to assess the relationship between differential gene expression and patient survival.Results: From 48,293 tested genes, there were 16,402 that showed differential expression, with 8,583 being up-regulated and 7,819 down-regulated [confirm this after applying the low-count filters and thresholds]. The protein-protein interaction (PPI) network was composed of 190 nodes and 1,398 edges. The most highly connected hub genes included CDK1, BUB1, CCNA2, AURKB, DLGAP5, BUB1B, CCNB2, BIRC5, TOP2A and CENPA; each of these is involved in cell-cycle and/or mitotic regulation. None of the four studied hub genes (KIF20A, CDK1, BIRC5, CENPA) was significantly associated with overall survival (log-rank p = 0.52-0.79) in the 152 patients for whom we had matching gene expression and survival data. In addition, no gene met the FDR threshold of 0.05 in our Cox regression analysis.Conclusion: The transcriptional reprogramming in GBM is extensive, with a very tight connection between proliferation and mitosis. Although the "hub" genes identified as being involved in these processes are potential targets for future research into their mechanism(s), we did not find any prognostic value associated with them within our study population; therefore, further validation will be needed using additional, independent data sets.
Source: Figshare
Author: Munaza Sabahat
Language: English
Subfield: Oncology
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W7225644358
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