Elucidating gastric cancer mechanisms and therapeutic potential of Adociaquinone A targeting EGFR: A genomic analysis and Computer Aided Drug Design (CADD) approach

IF 5.3
Mariam Abdulaziz Alkhateeb, Nada H. Aljarba, Qudsia Yousafi, Fatima Anwar, Partha Biswas
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引用次数: 0

Abstract

Gastric cancer predominantly adenocarcinoma, accounts for over 85% of gastric cancer diagnoses. Current therapeutic options are limited, necessitating the discovery of novel drug targets and effective treatments. The Affymetrix gene expression microarray dataset (GSE64951) was retrieved from NCBI-GEO data normalization and DEGs identification was done by using R-Bioconductor package. Gene Ontology (GO) analysis of DEGs was performed using DAVID. The protein–protein interaction network was constructed by STRING database plugin in Cytoscape. Subclusters/modules of important interacting genes in main network were extracted by using MCODE. The hub genes from in the network were identified by using Cytohubba. The miRNet tool built a hub gene/mRNA-miRNA network and Kaplan–Meier-Plotter conducted survival analysis. AutoDock Vina and GROMACS MD simulations were used for docking and stability analysis of marine compounds against the 5CNN protein. Total 734 DEGs (507 up-regulated and 228 down-regulated) were identified. Differentially expressed genes (DEGs) were enriched in processes like cell–cell adhesion and ATP binding. Eight hub genes (EGFR, HSPA90AA1, MAPK1, HSPA4, PPP2CA, CDKN2A, CDC20, and ATM) were selected for further analysis. A total of 23 miRNAs associated with hub genes were identified, with 12 of them targeting PPP2CA. EGFR displayed the highest expression and hazard rate in survival analyses. The kinase domain of EGFR (PDBID: 5CNN) was chosen as the drug target. Adociaquinone A from Petrosia alfiani, docked with 5CNN, showed the lowest binding energy with stable interactions across a 50 ns MD simulation, highlighting its potential as a lead molecule against EGFR. This study has identified crucial DEGs and hub genes in gastric cancer, proposing novel therapeutic targets. Specifically, Adociaquinone A demonstrates promising potential as a bioactive drug against EGFR in gastric cancer, warranting further investigation. The predicted miRNA against the hub gene/proteins can also be used as potential therapeutic targets.

Abstract Image

阐明针对表皮生长因子受体的 Adociaquinone A 的胃癌机制和治疗潜力:基因组分析和计算机辅助药物设计 (CADD) 方法。
胃癌主要是腺癌,占胃癌诊断病例的 85% 以上。目前的治疗方案有限,因此需要发现新的药物靶点和有效的治疗方法。我们从 NCBI-GEO 数据归一化中检索了 Affymetrix 基因表达微阵列数据集(GSE64951),并使用 R-Bioconductor 软件包进行了 DEGs 鉴定。使用 DAVID 对 DEGs 进行基因本体(GO)分析。利用 Cytoscape 中的 STRING 数据库插件构建了蛋白质-蛋白质相互作用网络。使用 MCODE 提取主网络中重要相互作用基因的子群/模块。使用 Cytohubba 确定了网络中的中心基因。miRNet 工具构建了枢纽基因/mRNA-miRNA 网络,Kaplan-Meier-Plotter 进行了生存分析。AutoDock Vina 和 GROMACS MD 模拟用于海洋化合物与 5CNN 蛋白的对接和稳定性分析。共鉴定出 734 个 DEGs(507 个上调,228 个下调)。差异表达基因(DEGs)富集于细胞-细胞粘附和 ATP 结合等过程。研究人员选择了八个中心基因(表皮生长因子受体、HSPA90AA1、MAPK1、HSPA4、PPP2CA、CDKN2A、CDC20 和 ATM)进行进一步分析。共鉴定出 23 个与枢纽基因相关的 miRNA,其中 12 个靶向 PPP2CA。在生存分析中,表皮生长因子受体的表达量和危险率最高。表皮生长因子受体的激酶结构域(PDBID:5CNN)被选为药物靶点。与 5CNN 对接的 Petrosia alfiani 中的 Adociaquinone A 在 50 ns MD 模拟中显示出最低的结合能和稳定的相互作用,突出了其作为抗表皮生长因子受体的先导分子的潜力。这项研究发现了胃癌中关键的 DEGs 和枢纽基因,提出了新的治疗靶点。特别是,Adociaquinone A 具有作为抗胃癌表皮生长因子受体的生物活性药物的潜力,值得进一步研究。针对枢纽基因/蛋白预测的 miRNA 也可作为潜在的治疗靶点。
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来源期刊
CiteScore
11.50
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期刊介绍: The Journal of Cellular and Molecular Medicine serves as a bridge between physiology and cellular medicine, as well as molecular biology and molecular therapeutics. With a 20-year history, the journal adopts an interdisciplinary approach to showcase innovative discoveries. It publishes research aimed at advancing the collective understanding of the cellular and molecular mechanisms underlying diseases. The journal emphasizes translational studies that translate this knowledge into therapeutic strategies. Being fully open access, the journal is accessible to all readers.
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