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Abstract：
OBJECTIVE: To explore hub genes for glaucoma based on bioinformatics analysis and an experimental model verification.
METHODS: In the Gene Expression Omnibus (GEO) database, the GSE25812 and GSE26299 datasets were selected to analyze differentially expressed genes (DEGs) by the GEO2R tool. Through bioinformatics analysis, 9 hub genes were identified. Receiver operating characteristic (ROC) curves and principal component analysis (PCA) were performed to verify whether the hub gene can distinguish glaucoma from normal eyes. The mouse model of glaucoma was constructed, and the real-time reverse transcriptase-polymerase chain reaction (RT-qPCR) assay was performed to detect the expression levels of hub genes in glaucoma.
RESULTS: There were 128 overlapping DEGs in the GSE25812 and GSE26299 datasets, mainly involved in intracellular signalling, cell adhesion molecules and the Ras signalling pathway. A total of 9 hub genes were screened out, including GNAL, BGN, ETS2, FCGP4, MAPK10, MMP15, STAT1, TSPAN8, and VCAM1. The area under the curve (AUC) values of 9 hub genes were greater than 0.8. The PC1 axle could provide a 70.5% interpretation rate to distinguish glaucoma from normal eyes. In the ocular tissues of glaucoma in the mice model, the expression of BGN, ETS2, FCGR4, STAT1, TSPAN8, and VCAM1 was increased, while the expression of GNAL, MAPK10, and MMP15 was decreased.
CONCLUSIONS: Nine hub genes in glaucoma are identified, which may provide new biomarkers and therapeutic targets for glaucoma.
METHODS: In the Gene Expression Omnibus (GEO) database, the GSE25812 and GSE26299 datasets were selected to analyze differentially expressed genes (DEGs) by the GEO2R tool. Through bioinformatics analysis, 9 hub genes were identified. Receiver operating characteristic (ROC) curves and principal component analysis (PCA) were performed to verify whether the hub gene can distinguish glaucoma from normal eyes. The mouse model of glaucoma was constructed, and the real-time reverse transcriptase-polymerase chain reaction (RT-qPCR) assay was performed to detect the expression levels of hub genes in glaucoma.
RESULTS: There were 128 overlapping DEGs in the GSE25812 and GSE26299 datasets, mainly involved in intracellular signalling, cell adhesion molecules and the Ras signalling pathway. A total of 9 hub genes were screened out, including GNAL, BGN, ETS2, FCGP4, MAPK10, MMP15, STAT1, TSPAN8, and VCAM1. The area under the curve (AUC) values of 9 hub genes were greater than 0.8. The PC1 axle could provide a 70.5% interpretation rate to distinguish glaucoma from normal eyes. In the ocular tissues of glaucoma in the mice model, the expression of BGN, ETS2, FCGR4, STAT1, TSPAN8, and VCAM1 was increased, while the expression of GNAL, MAPK10, and MMP15 was decreased.
CONCLUSIONS: Nine hub genes in glaucoma are identified, which may provide new biomarkers and therapeutic targets for glaucoma.
摘要：
目的：基于生物信息学分析和实验模型验证，探索青光眼的枢纽基因。
方法：在基因表达综合（GEO）数据库中，选择GSE25812和GSE26299数据集,通过GEO2R工具分析差异表达基因(DEGs).通过生物信息学分析,鉴定了9个hub基因。进行受试者工作特征（ROC）曲线和主成分分析（PCA），以验证hub基因是否可以区分青光眼与正常眼。建立青光眼小鼠模型,实时逆转录酶-聚合酶链反应(RT-qPCR)检测中心基因在青光眼中的表达水平。
结果：在GSE25812和GSE26299数据集中有128个重叠的DEG，主要参与细胞内信号传导，细胞粘附分子和Ras信号通路。共筛选出9个hub基因,包括GNAL,BGN,ETS2、FCGP4、MAPK10、MMP15、STAT1、TSPAN8和VCAM1。9个hub基因的曲线下面积(AUC)值大于0.8。PC1轴可以提供70.5％的解释率，以区分青光眼与正常眼。在小鼠模型的青光眼眼组织中，BGN的表达，ETS2，FCGR4，STAT1，TSPAN8和VCAM1增加，而GNAL的表达，MAPK10和MMP15降低。
结论：确定了9个青光眼中心基因，这可能为青光眼提供新的生物标志物和治疗靶点。
方法：在基因表达综合（GEO）数据库中，选择GSE25812和GSE26299数据集,通过GEO2R工具分析差异表达基因(DEGs).通过生物信息学分析,鉴定了9个hub基因。进行受试者工作特征（ROC）曲线和主成分分析（PCA），以验证hub基因是否可以区分青光眼与正常眼。建立青光眼小鼠模型,实时逆转录酶-聚合酶链反应(RT-qPCR)检测中心基因在青光眼中的表达水平。
结果：在GSE25812和GSE26299数据集中有128个重叠的DEG，主要参与细胞内信号传导，细胞粘附分子和Ras信号通路。共筛选出9个hub基因,包括GNAL,BGN,ETS2、FCGP4、MAPK10、MMP15、STAT1、TSPAN8和VCAM1。9个hub基因的曲线下面积(AUC)值大于0.8。PC1轴可以提供70.5％的解释率，以区分青光眼与正常眼。在小鼠模型的青光眼眼组织中，BGN的表达，ETS2，FCGR4，STAT1，TSPAN8和VCAM1增加，而GNAL的表达，MAPK10和MMP15降低。
结论：确定了9个青光眼中心基因，这可能为青光眼提供新的生物标志物和治疗靶点。
