DECIPHERING KEY HUB GENES IN MEDULLOBLASTOMA: INTEGRATIVE ANALYSIS OF TRANSCRIPTOMIC DATASETS REVEALS POTENTIAL BIOMARKERS AND THERAPEUTIC TARGETS
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Keywords
Medulloblastoma, Core hub genes, ROC curve, GEO2R
Abstract
Introduction: Medulloblastoma (MB) is the most common malignant brain tumor in children, accounting for about 20% of all pediatric brain tumors. Originating in the cerebellum or posterior fossa, MB poses significant treatment challenges despite advances in surgical, radiation, and chemotherapeutic interventions. Understanding the molecular underpinnings of MB is crucial for developing targeted therapies and improving diagnostic tools.
Methods: This study utilized the GSE42656 dataset from the GEO database to identify differentially expressed genes (DEGs) and hub genes associated with MB. Using the limma package, we screened the top 50 DEGs and constructed a protein-protein interaction (PPI) network via the STRING database. Central nodes and potential key regulators were identified. Cytoscape software was employed to determine hub genes using the degree method, and their diagnostic efficacy was evaluated using ROC curve analysis.
Results: The PPI network analysis highlighted central nodes such as GABRG2, STXBP1, and DLG4, suggesting their pivotal roles in MB. Notable genes like MAP4, RBFOX1, and NPTN, though less connected, were also significant. Clusters of interactions involved genes such as CLTA, PRKCE, and AASS, indicating potential pathways or complexes. Hub genes identified included AASS, CAMKB2, MAP4, and SLC12A5, all central due to their high connectivity. AASS showed high expression in MB samples, while MAP4 and SLC12A5 exhibited low expression. CAMKB2 displayed varying expression levels, indicating complex regulatory dynamics. ROC curve analysis demonstrated high diagnostic efficiency for these hub genes, with significant AUC values, highlighting their potential as biomarkers.
Conclusion: This study provides a comprehensive analysis of the genetic basis of MB, identifying key DEGs and hub genes with significant diagnostic potential. These findings contribute to understanding MB pathogenesis and underscore the importance of integrating genomic data with bioinformatics tools to uncover critical molecular mechanisms, offering promising targets for future research and therapeutic development.
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Muhammad Afzal
Department of Biochemistry, Faisalabad Medical University, Faisalabad, Pakistan
Zahid Iqbal Rajput
Department of Microbiology, Shaheed Benazir Bhutto University of Veterinary & Animal Sciences, Sakrand, Pakistan
Sobia Mumtaz
Livestock and Dairy Development Department Punjab, Pakistan
Kashif Prince
Department of Medicine, Cholistan University of Veterinary & Animal Sciences, Bahawalpur, Pakistan
Shahid Moin Qureshi
Department of Human Anatomy/Histology & Embryology, Hebei North University, Hebei, P.R. China
Hafiz Shehzad Muzammil
Department of Human Nutrition and Food Technology, Faculty of Allied Health Sciences, Superior University, Lahore, Pakistan
Muhammad Junaid
Department of Dairy Technology, Faculty of Animal Production and Technology, University of Veterinary and Animal Sciences, Lahore, Pakistan
Dilshad Rashid
Para Veterinary Institute Layyah, sub campus, University of Veterinary and Animal Science, Lahore, Pakistan
Umair Younas
Department of Livestock Management, Faculty of Animal Production and Technology (FAPT), Cholistan University of Veterinary and Animal Sciences, Bahawalpur, Pakistan
Maryam Saddiqa
Department of Livestock & Dairy Development Punjab, Pakistan
Muhammad Arif Rizwan
Institute of Continuing Education and Extension, Cholistan University of Veterinary and Animal Sciences, Bahawalpur, Pakistan