Toward Novel Diagnostics for Primary Open-Angle Glaucoma? An Association Study of Polymorphic Variation in Ras Homolog Family Member (A, B, C, D) Genes RHOA, RHOB, RHOC, and RHOD

Identifying Hub Genes for Glaucoma based on Bulk RNA Sequencing Data and Multi-machine Learning Models

AIMS

The aims of this study were to determine hub genes in glaucoma through multiple machine learning algorithms.

BACKGROUND

Glaucoma has afflicted many patients for many years, with excessive pressure in the eye continuously damaging the nervous system and leading to severe blindness. An effective molecular diagnostic method is currently lacking.

OBJECTIVE

The present study attempted to reveal the molecular mechanism and gene regulatory network of hub genes in glaucoma, followed by an attempt to reveal the drug-gene-disease network regulated by hub genes.

METHODS

A microarray sequencing dataset (GSE9944) was obtained through the Gene Expression Omnibus database. The differentially expressed genes in Glaucoma were identified. Based on these genes, we constructed three machine learning models for feature training, Random Forest model (RF), Least absolute shrinkage and selection operator regression model (LASSO), and Support Vector Machines model (SVM). Meanwhile, Weighted Gene Co-Expression Network Analysis (WGCNA) was performed for GSE9944 expression profiles to identify Glaucoma-related genes. The overlapping genes in the four groups were considered as hub genes of Glaucoma. Based on these genes, we also constructed a molecular diagnostic model of Glaucoma. In this study, we also performed molecular docking analysis to explore the gene-drug network targeting hub genes. In addition, we evaluated the immune cell infiltration landscape in Glaucoma samples and normal samples by applying CIBERSORT method.

RESULTS

8 hub genes were determined: ATP6V0D1, PLEC, SLC25A1, HRSP12, PKN1, RHOD, TMEM158 and GSN. The diagnostic model showed excellent diagnostic performance (area under the curve=1). GSN might positively regulate T cell CD4 naïve as well as negatively regulate T cell regulation (Tregs). In addition, we constructed gene-drug networks in an attempt to explore novel therapeutic agents for Glaucoma.

CONCLUSION

Our results systematically determined 8 hub genes and established a molecular diagnostic model that allowed the diagnosis of Glaucoma. Our study provided a basis for future systematic studies of Glaucoma pathogenesis.

Insights into the regulatory molecules involved in glaucoma pathogenesis

Glaucoma is an important cause of irreversible blindness, characterized by optic nerve anomalies. Increased intraocular pressure (IOP) and aging are major risk factors. Retinal ganglion cells and trabecular meshwork cells are certainly involved in the etiology of glaucoma. Glaucoma is usually a complex disease, and various genes and functions may contribute to its etiology. Among these may be genes that encode regulatory molecules. In this review, regulatory molecules including 18 transcription factors (TFs), 195 microRNAs (miRNAs), 106 long noncoding RNAs (lncRNAs), and two circular RNAs (circRNAs) that are reasonable candidates for having roles in glaucoma pathogenesis are described. The targets of the regulators are reported. Glaucoma-related features including apoptosis, stress responses, immune functions, ECM properties, IOP, and eye development are affected by the targeted genes. The targeted genes that are frequently targeted by multiple regulators most often affect apoptosis and the related features of cell death and cell survival. BCL2, CDKN1A, and TP53 are among the frequent targets of three types of glaucoma-relevant regulators, TFs, miRNAs, and lncRNAs. TP53 was itself identified as a glaucoma-relevant TF. Several of the glaucoma-relevant TFs are themselves among frequent targets of regulatory molecules, which is consistent with existence of a complex network involved in glaucoma pathogenesis.

Identification of genes involved in glaucoma pathogenesis using combined network analysis and empirical studies

Glaucoma is a leading cause of blindness. We aimed in this study to identify genes that may make subtle and cumulative contributions to glaucoma pathogenesis. To this end, we identified molecular interactions and pathways that include transcription factors (TFs) FOXC1, PITX2, PAX6 and NFKB1 and various microRNAs including miR-204 known to have relevance to trabecular meshwork (TM) functions and/or glaucoma. TM tissue is involved in glaucoma pathogenesis. In-house microarray transcriptome results and data sources were used to identify target genes of the regulatory molecules. Bioinformatics analyses were done to filter TM and glaucoma relevant genes. These were submitted to network-creating softwares to define interactions, pathways and a network that would include the genes. The network was stringently scrutinized and minimized, then expanded by addition of microarray data and data on TF and microRNA-binding sites. Selected features of the network were confirmed by empirical studies such as dual luciferase assays, real-time PCR and western blot experiments and apoptosis assays. MYOC, WDR36, LTPBP2, RHOA, CYP1B1, OPA1, SPARC, MEIS2, PLEKHG5, RGS5, BBS5, ALDH1A1, NOMO2, CXCL6, FMNL2, ADAMTS5, CLOCK and DKK1 were among the genes included in the final network. Pathways identified included those that affect ECM properties, IOP, ciliary body functions, retinal ganglion cell viability, apoptosis, focal adhesion and oxidative stress response. The identification of many genes potentially involved in glaucoma pathology is consistent with its being a complex disease. The inclusion of several known glaucoma-related genes validates the approach used.