Hub Genes, Possible Pathways and Predicted Drugs in Hereditary Gingival Fibromatosis by Bioinformatics Analysis.

Rong Xia Yang, Fan Shi, Shu Ning Du, Xin Yu Luo, Wan Qing Wang, Zhi Lu Yuan, Dong Chen
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Abstract

Objective: To explore potential pathogenic processes and possible treatments using unbiased and reliable bioinformatic tools.

Methods: Gene expression profiles of control and hepatocyte growth factor (HGF) samples were downloaded from CNP0000995. Analysis of differentially expressed genes (DEGs) was conducted using R software (version 4.2.1, R Foundation, Vienna, Austria). Functional enrichment analyses were performed using the Gene Ontology (GO), Kyoto Encyclopaedia of Genes and Genomes (KEGG) and Gene Set Enrichment Analysis (GSEA) databases, then the proteinprotein interaction (PPI) network was constructed to screen the top 10 hub genes. Finally, five genes related to cell junctions were selected to build gene-miRNA interactions and predict small-molecule drugs.

Results: A total of 342 downregulated genes and 188 upregulated genes were detected. Candidate pathways include the extracellular matrix (ECM) receptor interaction pathway, the TGF-β signalling pathway and the cell adhesion molecule (CAM) pathway, which were discovered through KEGG and GSEA enrichment studies. GO analyses revealed that these DEGs were significantly enriched in cell adhesion, the adherens junction and focal adhesion. Five hub genes (CDH1, SNAP25, RAC2, APOE and ITGB4) associated with cell adhesion were identified through PPI analysis. Finally, the gene-miRNA regulatory network identified three target miRNAs: hsa-miR-7110-5p, hsa-miR-149-3p and hsa-miR-1207-5p. Based on the gene expression profile, the small-molecule drugs zebularine, ecuronium and prostratin were selected for their demonstrated binding activity when docked with the mentioned molecules.

Conclusion: This study offered some novel insights into molecular pathways and identified five hub genes associated with cell adhesion. Based on these hub genes, three potential therapeutic miRNAs and small-molecule drugs were predicted, which are expected to provide guidance for the treatment of patients with HGF.

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通过生物信息学分析研究遗传性牙龈纤维瘤病的枢纽基因、可能途径和预测药物。
目的利用无偏见、可靠的生物信息学工具探索潜在的致病过程和可能的治疗方法:从 CNP0000995 下载对照组和肝细胞生长因子(HGF)样本的基因表达谱。使用 R 软件(4.2.1 版,R 基金会,奥地利维也纳)对差异表达基因(DEGs)进行分析。利用基因本体(GO)、京都基因组百科全书(KEGG)和基因组富集分析(GSEA)数据库进行了功能富集分析,然后构建了蛋白质相互作用(PPI)网络,筛选出前10个枢纽基因。最后,筛选出5个与细胞连接相关的基因,以建立基因-miRNA相互作用并预测小分子药物:结果:共检测到 342 个下调基因和 188 个上调基因。候选通路包括细胞外基质(ECM)受体相互作用通路、TGF-β 信号通路和细胞粘附分子(CAM)通路。GO分析表明,这些DEGs在细胞粘附、粘附连接和局灶粘附中明显富集。通过 PPI 分析,确定了与细胞粘附相关的五个枢纽基因(CDH1、SNAP25、RAC2、APOE 和 ITGB4)。最后,基因-miRNA调控网络确定了三个靶miRNA:hsa-miR-7110-5p、hsa-miR-149-3p和hsa-miR-1207-5p。根据基因表达谱,选择了小分子药物zebularine、ecuronium和prostratin,因为它们与上述分子对接时具有明显的结合活性:本研究为分子通路提供了一些新见解,并确定了与细胞粘附相关的五个枢纽基因。结论:本研究提供了分子通路的一些新见解,并确定了与细胞粘附相关的五个枢纽基因,根据这些枢纽基因预测了三个潜在的治疗性 miRNA 和小分子药物,有望为治疗 HGF 患者提供指导。
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