Identifying key genes in glaucoma based on a benchmarked dataset and the gene regulatory network

Abstract

The current study aimed to identify key genes in glaucoma based on a benchmarked dataset and gene regulatory network (GRN). Local and global noise was added to the gene expression dataset to produce a benchmarked dataset. Differentially-expressed genes (DEGs) between patients with glaucoma and normal controls were identified utilizing the Linear Models for Microarray Data (Limma) package based on benchmarked dataset. A total of 5 GRN inference methods, including Zscore, GeneNet, context likelihood of relatedness (CLR) algorithm, Partial Correlation coefficient with Information Theory (PCIT) and GEne Network Inference with Ensemble of Trees (Genie3) were evaluated using receiver operating characteristic (ROC) and precision and recall (PR) curves. The interference method with the best performance was selected to construct the GRN. Subsequently, topological centrality (degree, closeness and betweenness) was conducted to identify key genes in the GRN of glaucoma. Finally, the key genes were validated by performing reverse transcription-quantitative polymerase chain reaction (RT-qPCR). A total of 176 DEGs were detected from the benchmarked dataset. The ROC and PR curves of the 5 methods were analyzed and it was determined that Genie3 had a clear advantage over the other methods; thus, Genie3 was used to construct the GRN. Following topological centrality analysis, 14 key genes for glaucoma were identified, including IL6, EPHA2 and GSTT1 and 5 of these 14 key genes were validated by RT-qPCR. Therefore, the current study identified 14 key genes in glaucoma, which may be potential biomarkers to use in the diagnosis of glaucoma and aid in identifying the molecular mechanism of this disease.

The Wheat GENIE3 Network Provides Biologically-Relevant Information in Polyploid Wheat

“…This suggested that the relative performance of the GENIE3 network is data-dependent, highlighting the importance of testing the outputs of a GENIE3 network before utilizing it for de novo predictions. Since its introduction, the GENIE3 algorithm has been used to identify tissue-specific gene regulatory networks in maize ( Huang et al 2018 ) and key regulatory genes in glaucoma ( Chen et al 2017 ), as well as to study the drought response in sunflower ( Marchand et al 2014 ). Notably, previous studies have integrated the GENIE3 network predictions with ChIP-Seq and other proteomic and transcriptomic data and found that the GENIE3 predictions do correspond with independent biological datasets ( Walley et al 2016 ; Huang et al 2018 ).…”

Investigation of Key Signaling Pathways Associating miR-204 and Common Retinopathies

“…A study by Chen et al on glaucoma patients and healthy individuals, using bioinformatics analysis, introduced several key genes that were for the first time nominated as associated with glaucoma progression. They measured the expression of genes on tissue samples of patients and healthy individuals and confirmed genes [ 24 ]. Another study by Kwon et al showed that myocilin, a glaucoma-related gene, binds to ERBB2 receptors and activates downstream PI3K/Akt pathways involved in the maintenance and development of nerve cells and neuronal myelination.…”

Unified feature association networks through integration of transcriptomic and proteomic data

“…It is also important to point out that the dataset we have here, containing nearly 100 datapoints is actually somewhat small compared to other studies using network inference. While there have been several studies that use datasets of a size similar to ours or smaller [54–56] most studies contain many hundreds of samples. It will be of interest to determine how the choice of network inference method, particularly the use of GENIE3, may be affected by very large sample sizes.…”