Document Type : Research Articles
Authors
1
Department of Medical Genetics and Molecular Medicine, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
2
Department of Small Animal Clinical Sciences, Western College of Veterinary Medicine, University of Saskatchewan, 52Campus Drive, Saskatoon, SK S7N 5B4, Canada.
3
Proteomics Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
4
Proteomics Research Center, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Abstract
Background: Glioblastoma (GBM) is the most aggressive cancer in the central nervous system in glial cells. Finding novel biomarkers in GBM offers numerous advantages that can contribute to early detection, personalized treatment, improved patient outcomes, and advancements in cancer research and drug development. Integrating machine learning with RNAseq data in medicine holds significant potential for identifying novel biomarkers in various diseases, including cancer. Methods: Gene expression raw data was used to detect differentially expressed genes (DEGs) within a cohort of 532 GBM patients. The molecular pathway analysis, disease ontology, and protein-protein interactions of DEGs were assessed. Machine learning methods were performed to identify candidate genes. Survival curves were estimated using the Kaplan–Meier method and Cox proportional hazard to find prognostic biomarkers. Results: The molecular pathway analysis revealed that key dysregulated genes are in GPCRs, class A rhodopsin−like, MAPK signaling pathway, and calcium regulation in cardiac cells. Additionally, survival analysis showed that ten downregulated genes, including CPLX3, GPR162, LCNL1, SLC5A5, GPR61, GPR68, IL1RL2, HCRTR1, AIPL1, and SYTL1, and also ten upregulated genes, including C1orf92, CATSPER1, CCDC19, EPS8L1, FAIM3, FAM70B, FCN3, GPR157, IGFBP1, and MYBPH decreased the overall survival in GBM patients. Furthermore, the machine learning detected twenty genes, among which LRRTM2 and OPRL1 were candidates with high correlation coefficients. Conclusion: Our data suggest that genes belonging to G Protein-Coupled Receptors play a critical role in various aspects of glioblastoma progression and pathogenesis. Four members of GPCRs, including GPR162, GPR61, GPR68, and GPR157, can be considered prognostic biomarkers. Additionally, the combination of A2BP1 and GPR157 was reported as a diagnostic marker.
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