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基于加权基因共表达网络分析鉴定与胶质瘤组织学分级和预后相关的枢纽基因 GRIN1。

Identification of Hub Gene GRIN1 Correlated with Histological Grade and Prognosis of Glioma by Weighted Gene Coexpression Network Analysis.

机构信息

Department of Neurosurgery, Shengjing Hospital, China Medical University, Shenyang, Liaoning, China.

Department of Neurobiology, School of Life Science, China Medical University, Shenyang, Liaoning, China.

出版信息

Biomed Res Int. 2021 Nov 19;2021:4542995. doi: 10.1155/2021/4542995. eCollection 2021.

Abstract

The function of glutamate ionotropic receptor NMDA type subunit 1 (GRIN1) in neurodegenerative diseases has been widely reported; however, its role in the occurrence of glioma remains less explored. We obtained clinical data and transcriptome data from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). Hub gene's expression differential analysis and survival analysis were conducted by browsing the Gene Expression Profiling Interactive Analysis (GEPIA) database, Human Protein Atlas database, and LOGpc database. We conducted a variation analysis of datasets obtained from GEO and TCGA and performed a weighted gene coexpression network analysis (WGCNA) using the R programming language (3.6.3). Kaplan-Meier (KM) analysis was used to calculate the prognostic value of GRIN1. Finally, we conducted Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Using STRING, we constructed a protein-protein interaction (PPI) network. Cytoscape software, a prerequisite of visualizing core genes, was installed, and CytoHubba detected the 10 most tumor-related core genes. We identified 185 differentially expressed genes (DEGs). GO and KEGG enrichment analyses illustrated that the identified DEGs are imperative in different biological functions and ascertained the potential pathways in which the DEGs may be enriched. The overall survival calculated by KM analysis showed that patients with lower expression of GRIN1 had worse prognoses than patients with higher expression of GRIN1 ( = 0.004). The GEPIA and LOGpc databases were used to verify the expression difference of GRIN1 among GBM, LGG, and normal brain tissues. Ultimately, immunohistochemical assay results showed that GRIN1 was detected in normal tissue and not in the tumor specimens. Our results highlight a potential target for glioma treatment and will further our understanding of the molecular mechanisms underlying the treatment of glioma.

摘要

谷氨酸离子型受体 NMDA 型亚基 1(GRIN1)在神经退行性疾病中的功能已被广泛报道;然而,其在胶质瘤发生中的作用仍较少被探索。我们从基因表达综合数据库(GEO)和癌症基因组图谱(TCGA)中获得了临床数据和转录组数据。通过浏览基因表达谱交互式分析(GEPIA)数据库、人类蛋白质图谱数据库和 LOGpc 数据库,对基因进行差异表达分析和生存分析。我们对从 GEO 和 TCGA 获得的数据集进行了变异分析,并使用 R 编程语言(3.6.3)进行了加权基因共表达网络分析(WGCNA)。使用 Kaplan-Meier(KM)分析计算 GRIN1 的预后价值。最后,我们进行了基因本体论(GO)和京都基因与基因组百科全书(KEGG)富集分析。使用 STRING,我们构建了一个蛋白质-蛋白质相互作用(PPI)网络。安装了可视化核心基因的 Cytoscape 软件,并使用 CytoHubba 检测到 10 个最与肿瘤相关的核心基因。我们鉴定了 185 个差异表达基因(DEGs)。GO 和 KEGG 富集分析表明,所鉴定的 DEGs 在不同的生物学功能中具有重要作用,并确定了 DEGs 可能富集的潜在途径。KM 分析计算的总生存率表明,GRIN1 表达较低的患者预后比 GRIN1 表达较高的患者差(=0.004)。GEPIA 和 LOGpc 数据库用于验证 GRIN1 在 GBM、LGG 和正常脑组织中的表达差异。最终,免疫组织化学检测结果表明,GRIN1 在正常组织中被检测到,而在肿瘤标本中未被检测到。我们的结果突出了治疗胶质瘤的潜在靶点,并将进一步加深我们对胶质瘤治疗分子机制的理解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f8f/8626183/a6e7ecefb593/BMRI2021-4542995.001.jpg

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