216 MOLECULAR MEDICINE REPORTS 20: 216-224, 2019 Identification of pathogenic genes and transcription factors in glaucoma JIE FENG and JING XU Department of Ophthalmology, The First People's Hospital of Jining, Jining, Shandong 272011, P.R. China Received March 20, 2018; Accepted April 3, 2019 DOI: 10.3892/mmr.2019.10236 Abstract. Glaucoma is a group of eye diseases characterized Introduction by alterations in the contour of the optic nerve head, with corresponding visual field defects and progressive loss of Glaucoma is a widely known, multi-factorial disease, which retinal ganglion cells. The present study aimed to identify the may result in apoptosis of retinal ganglion cells. According key genes and upstream regulators in glaucoma. To screen the to the World Health Organization, glaucoma is the second pathogenic genes involved in glaucoma, an integrated analysis principal cause of blindness and the most common cause of was performed by using the microarray datasets in glaucoma irreversible blindness in the world (1,2). In glaucoma, the ante- derived from the Gene Expression Omnibus (GEO) database. rior and posterior segments of the eye are affected, and serious The functional annotation and potential pathways of differen- damage may be detected in the trabecular meshwork (3). tially expressed genes (DEGs) were additionally examined by Oxidative stress is considered to be responsible for the molec- Gene Ontology (GO) and Kyoto Encyclopedia of Genes and ular damage in the anterior chamber. Primary open-angle Genomes (KEGG) enrichment analyses. A glaucoma‑specific glaucoma (POAG) is the most common type of glaucoma, transcriptional regulatory network was constructed to identify accounting for 60-70% all glaucoma (4). A candidate protein crucial transcriptional factors that target the DEGs in glau- that may be associated with POAG is myocilin (MYOC), coma. From two GEO datasets, 1,935 DEGs (951 upregulated encoded by the MYOC gene. MYOC mutations are common and 984 downregulated genes) between glaucoma and normal in patients with POAG with high levels of intraocular pressure controls were identified. GO and KEGG analyses identified that (IOP) (5,6). Additionally, mutations in optineurin were identi- ‘eye development’ [false discovery rate (FDR)=0.00415533] fied in patients with POAG (7). Previous studies suggested and ‘visual perception’ (FDR=0.00713283) were significantly that an abnormal expression of serine/threonine-protein enriched pathways for DEGs. The expression of lipocalin 2 kinase TBK1 is a cause of normal-tension glaucoma (8-10). (LCN2), monoamine oxidase A (MAOA), hemoglobin Furthermore, a previous study suggested that the calcium subunit β (HBB), paired box 6 (PAX6), fibronectin (FN1) and load-activated calcium channel was involved in glaucoma and cAMP responsive element binding protein 1 (CREB1) were that cyclin-dependent kinase 4 inhibitor B antisense RNA 1 demonstrated to be involved in the pathogenesis of glaucoma. was upregulated in the retina of a rat model of glaucoma (11). In conclusion, LCN2, MAOA, HBB, PAX6, FN1 and CREB1 However, even substantial decreases in IOP are not able may serve roles in glaucoma, regulated by PAX4, solute carrier to prevent the development and progression of glaucoma in family 22 member 1, hepatocyte nuclear factor 4 α and ELK1, a number of clinical cases (12). Glaucoma-associated cell ETS transcription factor. These data may contribute to the death is primarily caused by apoptosis, which is triggered development of novel potential biomarkers, reveal the under- by oxidative stress via mitochondrial damage, inflammation, lying pathogenesis and additionally identify novel therapeutic endothelial dysregulation and dysfunction, and hypoxia (13). targets for glaucoma. In general, glaucoma is not preventable; however, the vast majority of patients may maintain useful visual function for life if they have early detection and appropriate treatment (14). Therefore, for the prevention of glaucoma, emphasis must be placed on early detection, and early diagnosis and treatment. The rapid development and application of high-throughput sequencing technology has provided a comprehensive and rapid Correspondence to: Ms. Jing Xu, Department of Ophthalmology, The First People's Hospital of Jining, 6 Jiankang Road, Jining, analytical method for the study of the pathogenesis of glaucoma, Shandong 272011, P.R. China and provide novel ideas for the future treatment of glaucoma (15). E‑mail: [email protected] The present study aimed to analyze high-throughput transcrip- tome data from tissue samples of patients with glaucoma and a Key words: glaucoma, transcription factors, differentially expressed normal control group. The data was used in bioinformatics anal- genes, integrated analysis yses to identify key transcription factors (TFs) associated with glaucoma, to examine the pathogenesis of glaucoma and provide a basis for the diagnosis of glaucoma and drug development. FENG and XU: PATHOGENIC GENES AND TFs IN GLAUCOMA 217 Materials and methods FACtor (TRANSFAC) website match tool (gene-regulation. com/pub/databases.html) was subsequently used to analyze Microarray expression profiling in Gene Expression Omnibus TFs capable of binding to the promoter region of the DEGs. (GEO). The GEO is the largest database of high-throughput TFs that exhibited altered expression in glaucoma with gene expression data that was developed and is maintained FDR<0.001 were selected. Position Weight Matrix scanning by the National Center for Biotechnology Information (16). was used to scan the human genome sequence to obtain the The GEO was searched to obtain gene expression profiling protein-coding genes that were regulated by the differentially studies of glaucoma subjects. The following key search terms expressed TFs. Following removal of redundant information, were used: [ʻglaucoma’ (Medical subject headings Terms) OR the glaucoma‑specific transcriptional regulatory network was ʻglaucoma’ (All Fields)] AND ʻHomo sapiens’ (porgn) AND constructed using Cytoscape software. ʻgse’ (Filter). The selection criteria were as follows: i) The selected dataset must include genome-wide mRNA tran- In silico validation of DEGs using GEO. The GEO database scriptome data; ii) the data was obtained from the trabecular (GSE9944) was used to validate the expression of selected meshwork tissue samples of glaucoma and normal control glaucoma DEGs. The expression levels of these genes were trabecular meshwork tissue samples; and iii) normalized and compared between the glaucoma cases and the normal group. raw datasets were considered. Following selection, two sets of The expression of five genes, cAMP responsive element GSE27276 (17) and GSE4316 (18) glaucoma mRNA data were binding protein 1 (CREB1), fibronectin 1 (FN1), keratin 19 obtained (19,20). (KRT19), lipocalin 2 (LCN2) and paired box 6 (PAX6) was detected, with the difference of expression levels presented as Identification of differentially expressed genes (DEGs) in box-plots. glaucoma compared with normal controls. Background correction was performed on the raw data. The normaliza- Results tion was performed using the Linear Models for Microarray (Limma version 3.30.13) Data package in R (21). Subsequently, Differential expression analysis of genes in glaucoma. The two-tailed Student’s t-tests were performed to calculate indi- probes corresponding to multiple genes were removed, and vidual P-values. Stouffer's test was used to merge individual the average gene expression to which multiple probes corre- P-values, and multiple comparison correction was performed sponded with was calculated. Finally, the intersection of using the Benjamini and Hochberg method to obtain the 15,757 genes was obtained. false discovery rate (FDR) (22). Genes with FDR<0.001 were A total of two gene expression microarray datasets selected as DEGs. Finally, the DEGs in glaucoma vs. normal (GSE27276 and GSE4316) were used for the analysis. were identified. Compared with the normal controls, 1,935 DEGs in glau- coma were obtained (P<0.05); among these, 951 genes were Functional annotation of DEGs. Gene Ontology (GO) (23) upregulated and 984 genes were downregulated. The top 40 and Kyoto Encyclopedia of Genes and Genomes (KEGG) (24) most significantly up‑ or downregulated genes are summa- pathway enrichment analysis were performed to detect rized in Table I. Among which, PAX6 (26), LCN2 (27), and the biological functions and potential pathways associated MAOA (28) were downregulated and were associated with with DEGs using GeneCoDis3 (http://genecodis.cnb.csic. glaucoma. The DEGs were screened for clustering analysis. es/analysis) as previously described (19). The GO functions of The heatmap produced by cluster analysis of the two sets of the DEGs were determined according to the three categories cDNA microarray data is presented in Fig. 1. of: ‘Biological process’; ‘molecular functions’; and ‘cellular component’. Pathway enrichment analysis was based on the Functional annotation. In Fig. 2, GO enrichment demonstrated KEGG database, as previously described (25). that the DEGs were significantly enriched the ‘Biological processes’ categories: ‘eye development’ (FDR=4.15x10-3); Protein‑protein interaction (PPI) network construction. In ‘visual perception’ (FDR=7.13x10-3); ‘negative regulation order to identify candidate genes involved in the formation of of insulin receptor signaling pathway’ (FDR=1.47x10-2); glaucoma, PPI networks of significant DEGs were constructed, the ‘Cellular components’ categories: