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用于预测青光眼潜在生物标志物的表达谱分析:骨形态发生蛋白1、肌营养不良蛋白和GEM

Expression profile analysis to predict potential biomarkers for glaucoma: BMP1, DMD and GEM.

作者信息

Zhang Dao Wei, Zhang Shenghai, Wu Jihong

机构信息

Eye Institute, Eye and ENT Hospital, College of Medicine, Fudan University, Shanghai, China.

Shanghai Key Laboratory of Visual Impairment and Restoration, Science and Technology Commission of Shanghai Municipality, Shanghai, China.

出版信息

PeerJ. 2020 Sep 3;8:e9462. doi: 10.7717/peerj.9462. eCollection 2020.

Abstract

PURPOSE

Glaucoma is the second commonest cause of blindness. We assessed the gene expression profile of astrocytes in the optic nerve head to identify possible prognostic biomarkers for glaucoma.

METHOD

A total of 20 patient and nine normal control subject samples were derived from the GSE9944 (six normal samples and 13 patient samples) and GSE2378 (three normal samples and seven patient samples) datasets, screened by microarray-tested optic nerve head tissues, were obtained from the Gene Expression Omnibus (GEO) database. We used a weighted gene coexpression network analysis (WGCNA) to identify coexpressed gene modules. We also performed a functional enrichment analysis and least absolute shrinkage and selection operator (LASSO) regression analysis. Genes expression was represented by boxplots, functional geneset enrichment analyses (GSEA) were used to profile the expression patterns of all the key genes. Then the key genes were validated by the external dataset.

RESULTS

A total 8,606 genes and 19 human optic nerve head samples taken from glaucoma patients in the GSE9944 were compared with normal control samples to construct the co-expression gene modules. After selecting the most common clinical traits of glaucoma, their association with gene expression was established, which sorted two modules showing greatest correlations. One with the correlation coefficient is 0.56 ( = 0.01) and the other with the correlation coefficient is -0.56 ( = 0.01). Hub genes of these modules were identified using scatterplots of gene significance versus module membership. A functional enrichment analysis showed that the former module was mainly enriched in genes involved in cellular inflammation and injury, whereas the latter was mainly enriched in genes involved in tissue homeostasis and physiological processes. This suggests that genes in the green-yellow module may play critical roles in the onset and development of glaucoma. A LASSO regression analysis identified three hub genes: Recombinant Bone Morphogenetic Protein 1 gene (), Duchenne muscular dystrophy gene () and mitogens induced GTP-binding protein gene (). The expression levels of the three genes in the glaucoma group were significantly lower than those in the normal group. GSEA further illuminated that , and participated in the occurrence and development of some important metabolic progresses. Using the GSE2378 dataset, we confirmed the high validity of the model, with an area under the receiver operator characteristic curve of 85%.

CONCLUSION

We identified several key genes, including , and that may be involved in the pathogenesis of glaucoma. Our results may help to determine the prognosis of glaucoma and/or to design gene- or molecule-targeted drugs.

摘要

目的

青光眼是第二常见的致盲原因。我们评估了视神经乳头星形胶质细胞的基因表达谱,以确定青光眼可能的预后生物标志物。

方法

从基因表达综合数据库(GEO)中获取了来自GSE9944(6个正常样本和13个患者样本)和GSE2378(3个正常样本和7个患者样本)数据集的总共20例患者和9例正常对照受试者样本,这些样本通过微阵列检测的视神经乳头组织进行筛选。我们使用加权基因共表达网络分析(WGCNA)来识别共表达基因模块。我们还进行了功能富集分析和最小绝对收缩和选择算子(LASSO)回归分析。基因表达用箱线图表示,功能基因集富集分析(GSEA)用于描绘所有关键基因的表达模式。然后通过外部数据集对关键基因进行验证。

结果

将GSE9944中从青光眼患者获取的总共8606个基因和19个人类视神经乳头样本与正常对照样本进行比较,以构建共表达基因模块。在选择青光眼最常见的临床特征后,建立了它们与基因表达的关联,筛选出两个显示出最大相关性的模块。一个模块的相关系数为0.56(P = 0.01),另一个模块的相关系数为 - 0.56(P = 0.01)。使用基因显著性与模块成员关系的散点图确定了这些模块的核心基因。功能富集分析表明,前一个模块主要富集在参与细胞炎症和损伤的基因中,而后者主要富集在参与组织稳态和生理过程的基因中。这表明绿黄模块中的基因可能在青光眼的发生和发展中起关键作用。LASSO回归分析确定了三个核心基因:重组骨形态发生蛋白1基因(BMP1)、杜氏肌营养不良基因(DMD)和有丝分裂原诱导的GTP结合蛋白基因(MRG1)。青光眼组中这三个基因的表达水平明显低于正常组。GSEA进一步表明,BMP1、DMD和MRG1参与了一些重要代谢过程的发生和发展。使用GSE2378数据集,我们确认了该模型的高有效性,受试者工作特征曲线下面积为85%。

结论

我们鉴定了几个关键基因,包括BMP1、DMD和MRG1,它们可能参与青光眼的发病机制。我们的结果可能有助于确定青光眼的预后和/或设计基因或分子靶向药物。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5f26/7474882/4038a265179b/peerj-08-9462-g001.jpg

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