Integrated Analysis of Glioblastoma: Unravelling Molecular Signatures across Diverse Datasets for Enhanced Diagnosis

first_page Download PDF settings Order Article Reprints Font Type: Arial Georgia Verdana Font Size: Aa Aa Aa Line Spacing:    Column Width:    Background: Open AccessAbstract Integrated Analysis of Glioblastoma: Unravelling Molecular Signatures across Diverse Datasets for Enhanced Diagnosis † by Ezenwalie Ifechukwudelu EzenwalieEzenwalie Ifechukwudelu Ezenwalie SciProfiles Scilit Preprints.org Google Scholar Bioinformatics Department, Teesside University, Middlesbrough TS1 3BX, UK † Presented at the 3rd International Electronic Conference on Biomolecules, 23–25 April 2024; Available online: https://sciforum.net/event/IECBM2024. Proceedings 2024, 103(1), 19; https://doi.org/10.3390/proceedings2024103019 Published: 12 April 2024 Download keyboard_arrow_down Download PDF Download PDF with Cover Download XML Download Epub Versions Notes Keywords: Glioblastoma Multiforme; differentially expressed genes; PPI network; functional enrichment; molecular signatures; microarray; RNA-Seq; bioinformatics 1. BackgroundGlioblastoma Multiforme (GBM) presents significant diagnostic and therapeutic challenges due to its complex molecular pathogenesis. Current diagnostic methods often fail to detect early molecular signatures critical for timely intervention. This study integrates microarray and RNA-Seq datasets from both serum and tissue samples to explore differentially expressed genes (DEGs) and protein–protein interaction networks with the aim of identifying robust biomarkers and understanding the molecular underpinnings of GBM. 2. MethodsUtilizing microarray datasets (GSE116520 and GSE90604) and RNA-Seq datasets (GSE165595 and GSE228512) from both brain tissue and serum samples, this study conducted integrative differential gene expression analysis using limma and DESeq2 packages. Functional annotation and gene ontology analyses were performed using DAVID and ShinyGO tools. Protein–protein interaction (PPI) networks were constructed using the STRING database and analysed via Cytoscape to identify central hub genes. 3. ResultsThe analysis and cross-technology validation highlighted 1051 common DEGs across tissue datasets where 87 were upregulated and 255 were downregulated. Notably, three genes, MAST3, ADAM11, and PTPRK, were consistent across tissue and serum datasets, suggesting their utility as non-invasive biomarkers. Functional annotation identified critical biological processes and pathways disrupted in GBM, such as cell division, angiogenesis, and cell adhesion. The PPI network analysis identified central hub genes, offering insights into the molecular interactions contributing to the pathophysiology of GBM. 4. ConclusionsThis study underscores a complex network of molecular interactions pivotal to the pathophysiology of GBM. The identified DEGs and pathways provide a foundation for developing diagnostic panels and therapeutic targets, emphasizing the need for further research to translate these biomarkers from bench to bedside. FundingThis research received no external funding.Institutional Review Board StatementNot applicable.Informed Consent StatementNot applicable.Data Availability StatementAll data used in this study can be found in Gene Expression Omnibus (GEO) database.Conflicts of InterestThe authors declare no conflict of interest.Disclaimer/Publisher's Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. © 2024 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Share and Cite MDPI and ACS Style Ezenwalie, E.I. Integrated Analysis of Glioblastoma: Unravelling Molecular Signatures across Diverse Datasets for Enhanced Diagnosis. Proceedings 2024, 103, 19. https://doi.org/10.3390/proceedings2024103019 AMA Style Ezenwalie EI. Integrated Analysis of Glioblastoma: Unravelling Molecular Signatures across Diverse Datasets for Enhanced Diagnosis. Proceedings. 2024; 103(1):19. https://doi.org/10.3390/proceedings2024103019 Chicago/Turabian Style Ezenwalie, Ezenwalie Ifechukwudelu. 2024. "Integrated Analysis of Glioblastoma: Unravelling Molecular Signatures across Diverse Datasets for Enhanced Diagnosis" Proceedings 103, no. 1: 19. https://doi.org/10.3390/proceedings2024103019 Article Metrics No No Article Access Statistics Multiple requests from the same IP address are counted as one view.