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.
CONCLUSIONS: 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.
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.
CONCLUSIONS: 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.
摘要：
目的：使用无偏见和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。
方法:从CNP0000995下载对照和肝细胞生长因子(HGF)样品的基因表达谱。差异表达基因(DEGs)分析采用R软件(4.2.1版,RFoundation,维也纳,奥地利)。使用基因本体论(GO)进行功能富集分析，京都基因和基因组百科全书（KEGG）和基因集富集分析（GSEA）数据库，然后构建蛋白相互作用（PPI）网络，筛选前10个hub基因。最后,选择与细胞连接相关的五个基因来构建基因-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。候选途径包括细胞外基质（ECM）受体相互作用途径，TGF-β信号通路和细胞粘附分子（CAM）通路，这是通过KEGG和GSEA富集研究发现的。GO分析显示，这些DEGs在细胞粘附中显著富集，粘附连接和粘着斑。通过PPI分析鉴定了与细胞粘附相关的五个hub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,小分子药物zebularine，选择了与上述分子对接时证明的结合活性的埃库溴铵和prostratin。
结论：这项研究为分子途径提供了一些新的见解，并确定了与细胞粘附相关的五个hub基因。基于这些枢纽基因，预测了三种潜在的治疗性miRNA和小分子药物，有望为HGF患者的治疗提供指导。
方法:从CNP0000995下载对照和肝细胞生长因子(HGF)样品的基因表达谱。差异表达基因(DEGs)分析采用R软件(4.2.1版,RFoundation,维也纳,奥地利)。使用基因本体论(GO)进行功能富集分析，京都基因和基因组百科全书（KEGG）和基因集富集分析（GSEA）数据库，然后构建蛋白相互作用（PPI）网络，筛选前10个hub基因。最后,选择与细胞连接相关的五个基因来构建基因-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。候选途径包括细胞外基质（ECM）受体相互作用途径，TGF-β信号通路和细胞粘附分子（CAM）通路，这是通过KEGG和GSEA富集研究发现的。GO分析显示，这些DEGs在细胞粘附中显著富集，粘附连接和粘着斑。通过PPI分析鉴定了与细胞粘附相关的五个hub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,小分子药物zebularine，选择了与上述分子对接时证明的结合活性的埃库溴铵和prostratin。
结论：这项研究为分子途径提供了一些新的见解，并确定了与细胞粘附相关的五个hub基因。基于这些枢纽基因，预测了三种潜在的治疗性miRNA和小分子药物，有望为HGF患者的治疗提供指导。
