Department of Ophthalmology, The Second Hospital of Jilin University, Changchun, 130041 Jilin Province, China.
Comput Math Methods Med. 2022 Jul 15;2022:8030243. doi: 10.1155/2022/8030243. eCollection 2022.
Primary open-angle glaucoma (POAG) is the most common type of glaucoma. The potential influence of some DEGs on the progression of POAG was still incomplete. In this study, we integrated transcriptome data with clinical data to investigate the relationship between them in POAG patients.
The gene expression profile (GSE27276) from Gene Expression Omnibus (GEO) was used to identify DEGs. The LIMMA package of R was used to identify the DEGs (Diboun et al., 2006). The adjusted values (adj value) were calculated instead to avoid the appearance of false-positive results. Genes with |log fold change (FC)| larger than 1 and adj value < 0.01 were taken as DEGs between PH and PC samples. GO (Gene Ontology) function and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analyses of the DEGs were performed. Protein-protein interactions (PPIs) of the DEGs were constructed.
A total of 182 DEGs were identified through our analysis, of which 119 genes were upregulated and 63 genes were downregulated. GO enrichment analysis illustrated that these DEGs were mostly enriched into haptoglobin binding, antioxidant activity, and organic acid binding. KEGG enrichment analysis illustrated that these DEGs were mostly enriched into infection. The most significant module was identified by MCODE consists of 8 DEGs, and BCL11A is the seeded gene. The second most significant module consists of 5 DEGs, and IL1RN is the seeded gene.
Our results demonstrate the potential influence of some DEGs on the progression of POAG, providing a comprehensive bioinformatics analysis of the pathogenesis, which may contribute to future investigation into the molecular mechanisms and biomarkers.
原发性开角型青光眼(POAG)是最常见的青光眼类型。一些差异表达基因(DEGs)对 POAG 进展的潜在影响尚不完全清楚。在这项研究中,我们整合了转录组数据和临床数据,以研究它们在 POAG 患者中的关系。
我们使用来自基因表达综合数据库(GEO)的基因表达谱数据集(GSE27276)来识别 DEGs。R 语言中的 LIMMA 包用于识别 DEGs(Diboun 等人,2006 年)。使用调整后的 P 值(adj 值)来避免出现假阳性结果。选择 |log 倍数变化(FC)| 大于 1 且 adj 值<0.01 的基因作为 PH 和 PC 样本之间的 DEGs。对 DEGs 进行了 GO(基因本体论)功能和 KEGG(京都基因与基因组百科全书)通路富集分析。构建了 DEGs 的蛋白质-蛋白质相互作用(PPI)网络。
通过分析共确定了 182 个 DEGs,其中 119 个基因上调,63 个基因下调。GO 富集分析表明,这些 DEGs 主要富集到触珠蛋白结合、抗氧化活性和有机酸结合。KEGG 富集分析表明,这些 DEGs 主要富集到感染。MCODE 识别到的最显著模块由 8 个 DEGs 组成,BCL11A 是种子基因。第二显著模块由 5 个 DEGs 组成,IL1RN 是种子基因。
我们的结果表明,一些 DEGs 可能对 POAG 的进展有潜在影响,为发病机制提供了全面的生物信息学分析,可能有助于进一步研究分子机制和生物标志物。