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System biology approach to identify the novel biomarkers in glioblastoma multiforme tumors by using computational analysis

نهج بيولوجيا النظام لتحديد المؤشرات الحيوية الجديدة في أورام الورم الأرومي الدبقي متعدد الأشكال باستخدام التحليل الحسابي
Authors: Safar M. Alqahtani; Ali Altharawi; Alhumaidi Alabbas; Faisal Ahmad; Faisal Ahmad; Hassan Ayaz; Asia Nawaz; +2 Authors

System biology approach to identify the novel biomarkers in glioblastoma multiforme tumors by using computational analysis

Abstract

Introduction: The most common primary brain tumor in adults is glioblastoma multiforme (GBM), accounting for 45.2% of all cases. The characteristics of GBM, a highly aggressive brain tumor, include rapid cell division and a propensity for necrosis. Regretfully, the prognosis is extremely poor, with only 5.5% of patients surviving after diagnosis.Methodology: To eradicate these kinds of complicated diseases, significant focus is placed on developing more effective drugs and pinpointing precise pharmacological targets. Finding appropriate biomarkers for drug discovery entails considering a variety of factors, including illness states, gene expression levels, and interactions between proteins. Using statistical techniques like p-values and false discovery rates, we identified differentially expressed genes (DEGs) as the first step in our research for identifying promising biomarkers in GBM. Of the 132 genes, 13 showed upregulation, and only 29 showed unique downregulation. No statistically significant changes in the expression of the remaining genes were observed.Results: Matrix metallopeptidase 9 (MMP9) had the greatest degree in the hub biomarker gene identification, followed by (periostin (POSTN) at 11 and Hes family BHLH transcription factor 5 (HES5) at 9. The significance of the identification of each hub biomarker gene in the initiation and advancement of glioblastoma multiforme was brought to light by the survival analysis. Many of these genes participate in signaling networks and function in extracellular areas, as demonstrated by the enrichment analysis.We also identified the transcription factors and kinases that control proteins in the proteinprotein interactions (PPIs) of the DEGs.Discussion: We discovered drugs connected to every hub biomarker. It is an appealing therapeutic target for inhibiting MMP9 involved in GBM. Molecular docking investigations indicated that the chosen complexes (carmustine, lomustine, marimastat, and temozolomide) had high binding affinities of −6.3, −7.4, −7.7, and −8.7 kcal/mol, respectively, the mean root-mean-square deviation (RMSD) value for the carmustine complex and marimastat complex was 4.2 Å and 4.9 Å, respectively, and the lomustine and temozolomide complex system showed an average RMSD of 1.2 Å and 1.6 Å, respectively. Additionally, high stability in root-mean-square fluctuation (RMSF) analysis was observed with no structural conformational changes among the atomic molecules. Thus, these in silico investigations develop a new way for experimentalists to target lethal diseases in future.

Keywords

Pulmonary and Respiratory Medicine, FOS: Computer and information sciences, Mechanisms and Implications of Ferroptosis in Cancer, Bioinformatics, protein–protein interactions, RM1-950, Gene Set Enrichment Analysis, Cancer research, Gene, Computational biology, Biochemistry, Genetics and Molecular Biology, Health Sciences, Genetics, Molecular Biology, Biology, Pharmacology, glioblastoma, Life Sciences, Biomarker, molecular dynamic simulations, Downregulation and upregulation, Analysis of Gene Interaction Networks, FOS: Biological sciences, gene expression, biomarker, Medicine, Therapeutics. Pharmacology, 14-3-3 Proteins: Structure, Function, and Regulation, Transcription factor, Glioblastoma

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
5
Top 10%
Average
Top 10%
Green
gold