Dataset Information

Gene Expression Profiling of the Optic Nerve Head of Patients with Primary Open-Angle Glaucoma.

ABSTRACT: Background. The pressure-induced axonal injury of the vulnerable ONH has led many researchers to view glaucoma from the perspective of the genetic basis of the angle of the ONH. However, genetic studies on POAG from this perspective are limited. Methods. Microarray dataset GSE45570 of the ONH of healthy individuals and POAG patients were downloaded from the Gene Expression Omnibus. After screening for the DEGs using the limma package, enrichment analysis was performed using DAVID. The DEG interaction network was constructed using cancer spider at BioProfiling.de. Thereafter, DEG-related TFs were predicted using TRANSFAC, and TF-DEG regulatory networks were visualized using Cytoscape. Results. Thirty-one DEGs were identified including 11 upregulated and 20 downregulated DEGs. Thereafter, gene ontology terms of nucleosome assembly, sensory perception and cognition, and pathway of signaling by GPCR were found to be enriched among the DEGs. Furthermore, DEG interaction and TF-DEG networks were constructed. NEUROD1 was present in both the DEG network and the TF-DEG network as the node with the highest degree and was predicted as a marker gene in the ONH of patients with POAG. Conclusion. NEUROD1 may contribute greatly to the ONH of patients with POAG and was found to be involved in eye development and diseases.

SUBMITTER: Wang X

PROVIDER: S-EPMC5397728 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Publications

Gene Expression Profiling of the Optic Nerve Head of Patients with Primary Open-Angle Glaucoma.

Wang Xinrong X Gong Ke K Li Haiyan H Wang Congyi C Qu Chaoyi C Li Hui H

Journal of ophthalmology 20170405

<i>Background</i>. The pressure-induced axonal injury of the vulnerable ONH has led many researchers to view glaucoma from the perspective of the genetic basis of the angle of the ONH. However, genetic studies on POAG from this perspective are limited. <i>Methods</i>. Microarray dataset GSE45570 of the ONH of healthy individuals and POAG patients were downloaded from the Gene Expression Omnibus. After screening for the DEGs using the limma package, enrichment analysis was performed using DAVID. ...[more]

PMID: 28484645

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Project description:PurposePrimary open-angle glaucoma (POAG) is one of the leading causes of irreversible blindness in the United States and worldwide. Although deep learning methods have been proposed to diagnose POAG, these methods all used a single image as input. Contrastingly, glaucoma specialists typically compare the follow-up image with the baseline image to diagnose incident glaucoma. To simulate this process, we proposed a Siamese neural network, POAGNet, to detect POAG from optic disc photographs.DesignThe POAGNet, an algorithm for glaucoma diagnosis, is developed using optic disc photographs.ParticipantsThe POAGNet was trained and evaluated on 2 data sets: (1) 37 339 optic disc photographs from 1636 Ocular Hypertension Treatment Study (OHTS) participants and (2) 3684 optic disc photographs from the Sequential fundus Images for Glaucoma (SIG) data set. Gold standard labels were obtained using reading center grades.MethodsWe proposed a Siamese network model, POAGNet, to simulate the clinical process of identifying POAG from optic disc photographs. The POAGNet consists of 2 side outputs for deep supervision and uses convolution to measure the similarity between 2 networks.Main outcome measuresThe main outcome measures are the area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity.ResultsIn POAG diagnosis, extensive experiments show that POAGNet performed better than the best state-of-the-art model on the OHTS test set (area under the curve [AUC] 0.9587 versus 0.8750). It also outperformed the baseline models on the SIG test set (AUC 0.7518 versus 0.6434). To assess the transferability of POAGNet, we also validated the impact of cross-data set variability on our model. The model trained on OHTS achieved an AUC of 0.7490 on SIG, comparable to the previous model trained on the same data set. When using the combination of SIG and OHTS for training, our model achieved superior AUC to the single-data model (AUC 0.8165 versus 0.7518). These demonstrate the relative generalizability of POAGNet.ConclusionsBy simulating the clinical grading process, POAGNet demonstrated high accuracy in POAG diagnosis. These results highlight the potential of deep learning to assist and enhance clinical POAG diagnosis. The POAGNet is publicly available on https://github.com/bionlplab/poagnet.

Dataset Information

Gene Expression Profiling of the Optic Nerve Head of Patients with Primary Open-Angle Glaucoma.

Publications

Gene Expression Profiling of the Optic Nerve Head of Patients with Primary Open-Angle Glaucoma.

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