Integrated Gene Expression Data-Driven Identification of Molecular Signatures, Prognostic Biomarkers, and Drug Targets for Glioblastoma.

IF 2.6 3区 生物学 Q3 BIOTECHNOLOGY & APPLIED MICROBIOLOGY BioMed Research International Pub Date : 2024-08-16 eCollection Date: 2024-01-01 DOI:10.1155/2024/6810200
Md Wasim Alom, Md Delowar Kobir Jibon, Md Omar Faruqe, Md Siddikur Rahman, Farzana Akter, Aslam Ali, Md Motiur Rahman
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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.

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综合基因表达数据驱动胶质母细胞瘤分子特征、预后生物标志物和药物靶点的鉴定。
胶质母细胞瘤(GBM)是一种高发且致命的脑肿瘤,死亡率很高,尤其是在成年人中。尽管进行了广泛的研究,但对其发展的潜在机制仍然知之甚少。计算分析为探索 GBM 的潜在预后生物标志物、药物靶点和治疗药物提供了一种强有力的方法。在这项研究中,我们利用基因表达总库(GEO)数据库中的三个基因表达数据集来识别与 GBM 进展相关的差异表达基因(DEGs)。我们的目标是发现与 GBM 发病机制有关的关键分子角色以及靶向治疗的潜在途径。对基因表达数据集的分析显示,共有 78 个常见 DEGs 可能与 GBM 的进展有关。通过进一步研究,我们发现了九个枢纽 DEGs,它们在蛋白-蛋白相互作用(PPI)网络中高度相互关联,表明它们在 GBM 生物学中的核心作用。通过基因本体(GO)和通路富集分析,我们了解了受这些 DEGs 影响的生物过程和免疫通路。在已鉴定的九个 DEGs 中,生存分析表明 GMFG 表达的增加与 GBM 患者生存率的降低相关,这表明 GMFG 有可能成为 GBM 的预后生物标志物和预防靶点。此外,分子对接和 ADMET 分析还从美国国立卫生研究院(NIH)的临床研究中发现了两种与 GMFG 蛋白相互作用的化合物。此外,100 纳秒分子动力学(MD)模拟评估了构象变化和结合强度。我们的研究强调了 GMFG 作为 GBM 预后生物标志物和治疗靶点的潜力。对 GMFG 及其相关通路的鉴定为我们深入了解驱动 GBM 进展的分子机制提供了宝贵的线索。此外,确定与 GMFG 有潜在相互作用的候选化合物为开发靶向疗法提供了令人兴奋的可能性。不过,还需要进一步的实验室实验来验证 GMFG 在 GBM 发病机制中的作用,并评估针对该分子的潜在治疗药物的疗效。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
BioMed Research International
BioMed Research International BIOTECHNOLOGY & APPLIED MICROBIOLOGY-MEDICINE, RESEARCH & EXPERIMENTAL
CiteScore
6.70
自引率
0.00%
发文量
1942
审稿时长
19 weeks
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.
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