{"title":"Integrated Gene Expression Data-Driven Identification of Molecular Signatures, Prognostic Biomarkers, and Drug Targets for Glioblastoma.","authors":"Md Wasim Alom, Md Delowar Kobir Jibon, Md Omar Faruqe, Md Siddikur Rahman, Farzana Akter, Aslam Ali, Md Motiur Rahman","doi":"10.1155/2024/6810200","DOIUrl":null,"url":null,"abstract":"<p><p>Glioblastoma (GBM) is a highly prevalent and deadly brain tumor with high mortality rates, especially among adults. Despite extensive research, the underlying mechanisms driving its progression remain poorly understood. Computational analysis offers a powerful approach to explore potential prognostic biomarkers, drug targets, and therapeutic agents for GBM. In this study, we utilized three gene expression datasets from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs) associated with GBM progression. Our goal was to uncover key molecular players implicated in GBM pathogenesis and potential avenues for targeted therapy. Analysis of the gene expression datasets revealed a total of 78 common DEGs that are potentially involved in GBM progression. Through further investigation, we identified nine hub DEGs that are highly interconnected in protein-protein interaction (PPI) networks, indicating their central role in GBM biology. Gene Ontology (GO) and pathway enrichment analyses provided insights into the biological processes and immunological pathways influenced by these DEGs. Among the nine identified DEGs, survival analysis demonstrated that increased expression of GMFG correlated with decreased patient survival rates in GBM, suggesting its potential as a prognostic biomarker and preventive target for GBM. Furthermore, molecular docking and ADMET analysis identified two compounds from the NIH clinical collection that showed promising interactions with the GMFG protein. Besides, a 100 nanosecond molecular dynamics (MD) simulation evaluated the conformational changes and the binding strength. Our study highlights the potential of GMFG as both a prognostic biomarker and a therapeutic target for GBM. The identification of GMFG and its associated pathways provides valuable insights into the molecular mechanisms driving GBM progression. Moreover, the identification of candidate compounds with potential interactions with GMFG offers exciting possibilities for targeted therapy development. However, further laboratory experiments are required to validate the role of GMFG in GBM pathogenesis and to assess the efficacy of potential therapeutic agents targeting this molecule.</p>","PeriodicalId":9007,"journal":{"name":"BioMed Research International","volume":null,"pages":null},"PeriodicalIF":2.6000,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11343637/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"BioMed Research International","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1155/2024/6810200","RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/1 0:00:00","PubModel":"eCollection","JCR":"Q3","JCRName":"BIOTECHNOLOGY & APPLIED MICROBIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Glioblastoma (GBM) is a highly prevalent and deadly brain tumor with high mortality rates, especially among adults. Despite extensive research, the underlying mechanisms driving its progression remain poorly understood. Computational analysis offers a powerful approach to explore potential prognostic biomarkers, drug targets, and therapeutic agents for GBM. In this study, we utilized three gene expression datasets from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs) associated with GBM progression. Our goal was to uncover key molecular players implicated in GBM pathogenesis and potential avenues for targeted therapy. Analysis of the gene expression datasets revealed a total of 78 common DEGs that are potentially involved in GBM progression. Through further investigation, we identified nine hub DEGs that are highly interconnected in protein-protein interaction (PPI) networks, indicating their central role in GBM biology. Gene Ontology (GO) and pathway enrichment analyses provided insights into the biological processes and immunological pathways influenced by these DEGs. Among the nine identified DEGs, survival analysis demonstrated that increased expression of GMFG correlated with decreased patient survival rates in GBM, suggesting its potential as a prognostic biomarker and preventive target for GBM. Furthermore, molecular docking and ADMET analysis identified two compounds from the NIH clinical collection that showed promising interactions with the GMFG protein. Besides, a 100 nanosecond molecular dynamics (MD) simulation evaluated the conformational changes and the binding strength. Our study highlights the potential of GMFG as both a prognostic biomarker and a therapeutic target for GBM. The identification of GMFG and its associated pathways provides valuable insights into the molecular mechanisms driving GBM progression. Moreover, the identification of candidate compounds with potential interactions with GMFG offers exciting possibilities for targeted therapy development. However, further laboratory experiments are required to validate the role of GMFG in GBM pathogenesis and to assess the efficacy of potential therapeutic agents targeting this molecule.
期刊介绍:
BioMed Research International is a peer-reviewed, Open Access journal that publishes original research articles, review articles, and clinical studies covering a wide range of subjects in life sciences and medicine. The journal is divided into 55 subject areas.