Multi-omics analysis predicts fibronectin 1 as a prognostic biomarker in glioblastoma multiforme

Glioblastoma (GBM) is one of the most malignant and intractable central nervous system tumors with high recurrence, low survival rate, and poor prognosis. Despite the advances of aggressive, multimodal treatment, a successful treatment strategy is still elusive, often leading to therapeutic resistan...

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Bibliographic Details
Main Authors: Apu, M.N.H (Author), Kabir, F. (Author)
Format: Article
Language:English
Published: Academic Press Inc. 2022
Subjects:
Online Access:View Fulltext in Publisher
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001 10.1016-j.ygeno.2022.110378
008 220706s2022 CNT 000 0 und d
020 |a 08887543 (ISSN) 
245 1 0 |a Multi-omics analysis predicts fibronectin 1 as a prognostic biomarker in glioblastoma multiforme 
260 0 |b Academic Press Inc.  |c 2022 
856 |z View Fulltext in Publisher  |u https://doi.org/10.1016/j.ygeno.2022.110378 
520 3 |a Glioblastoma (GBM) is one of the most malignant and intractable central nervous system tumors with high recurrence, low survival rate, and poor prognosis. Despite the advances of aggressive, multimodal treatment, a successful treatment strategy is still elusive, often leading to therapeutic resistance and fatality. Thus, it is imperative to search for and identify novel markers critically associated with GBM pathogenesis to improve the existing trend of diagnosis, prognosis, and treatment. Seven publicly available GEO microarray datasets containing 409 GBM samples were integrated and further data mining was conducted using several bioinformatics tools. A total of 209 differentially expressed genes (DEGs) were identified in the GBM tissue samples compared to the normal brains. Gene Ontology (GO) enrichment analysis of the DEGs revealed association of the upregulates genes with extracellular matrix (ECM), conceivably contributing to the invasive nature of GBM while downregulated DEGs were found to be predominantly related to neuronal processes and structures. Alongside, Kyoto Encyclopedia of Genes and Genomes (KEGG) and Reactome pathway analyses described the involvement of the DEGs with various crucial contributing pathways (PI3K-Akt signaling pathway, p53 signaling pathway, insulin secretion, etc.) in GBM progression and pathogenesis. Protein-protein interaction (PPI) network containing 879 nodes and 1237 edges revealed 3 significant modules and consecutive KEGG pathway analysis of these modules showed a significant connection to gliomagenesis. Later, 10 hub genes were screened out based on degree and their expressions were externally validated. Surprisingly, only fibronectin 1 (FN1) high expression appeared to be related to poor prognosis. Subsequently, 109 transcription factors and 211 miRNAs were detected to be involved with the hub genes where FN1 demonstrated the highest number of interactions. Considering its high connectivity and potential prognostic value FN1 could be a novel biomarker providing new insights into the prognosis and treatment for GBM, although experimental validation is required. © 2022 The Authors 
650 0 4 |a Bioinformatics 
650 0 4 |a Differentially expressed genes 
650 0 4 |a Fibronectin 1 
650 0 4 |a Glioblastoma multiforme 
650 0 4 |a Prognosis 
700 1 0 |a Apu, M.N.H.  |e author 
700 1 0 |a Kabir, F.  |e author 
773 |t Genomics