Based on multiple machine learning to identify the ENO2 as diagnosis biomarkers of glaucoma

Proteomic Analysis of Aqueous Humor in Central Retinal Artery Occlusion: Unveiling Novel Insights Into Disease Pathophysiology

Purpose

Central retinal artery occlusion (CRAO) is an ocular emergency that results from acute blockage of the blood supply to the retina and leads to a sudden vision loss. Other forms of ischemic retinopathies include diabetic retinopathy (DR), which involves chronic retinal ischemia and remains the leading cause of blindness in working-age adults. This study is the first to conduct a proteomic analysis of aqueous humor (AH) from patients with CRAO with a comparative analysis using vitreous humor (VH) samples from patients with DR.

Methods

AH samples were collected from 10 patients with CRAO undergoing paracentesis and 10 controls undergoing cataract surgery. VH samples were collected from 10 patients with DR and 10 non-diabetic controls undergoing pars plana vitrectomy (PPV). Samples were analyzed using mass spectrometry.

Results

Compared with controls, AH levels of 36 differentially expressed proteins (DEPs) were identified in patients with CRAO. Qiagen Ingenuity Pathway Analysis (IPA) revealed 11 proteins linked to ophthalmic diseases. Notably, enolase 2, a glycolysis enzyme isoform primarily expressed in neurons, was upregulated, suggesting neuronal injury and enzyme release. Additionally, clusterin, a protective glycoprotein, was downregulated. ELISA was conducted to confirm proteomics data. VH samples from patients with DR exhibited changes in a distinct set of proteins, including ones previously reported in the literature.

Conclusions

The study provides novel insights into CRAO pathophysiology with multiple hits identified. Proteomic results differed between DR and CRAO studies, likely due to the different pathophysiology and disease duration.

Translational Relevance

This is the first proteomic analysis of CRAO AH, with the potential to identify future therapeutic targets.

Artificial intelligence in glaucoma: opportunities, challenges, and future directions

Artificial intelligence (AI) has shown excellent diagnostic performance in detecting various complex problems related to many areas of healthcare including ophthalmology. AI diagnostic systems developed from fundus images have become state-of-the-art tools in diagnosing retinal conditions and glaucoma as well as other ocular diseases. However, designing and implementing AI models using large imaging data is challenging. In this study, we review different machine learning (ML) and deep learning (DL) techniques applied to multiple modalities of retinal data, such as fundus images and visual fields for glaucoma detection, progression assessment, staging and so on. We summarize findings and provide several taxonomies to help the reader understand the evolution of conventional and emerging AI models in glaucoma. We discuss opportunities and challenges facing AI application in glaucoma and highlight some key themes from the existing literature that may help to explore future studies. Our goal in this systematic review is to help readers and researchers to understand critical aspects of AI related to glaucoma as well as determine the necessary steps and requirements for the successful development of AI models in glaucoma.

A new gene-scoring method for uncovering novel glaucoma-related genes using non-negative matrix factorization based on RNA-seq data

Early diagnosis and treatment of glaucoma are challenging. The discovery of glaucoma biomarkers based on gene expression data could potentially provide new insights for early diagnosis, monitoring, and treatment options of glaucoma. Non-negative Matrix Factorization (NMF) has been widely used in numerous transcriptome data analyses in order to identify subtypes and biomarkers of different diseases; however, its application in glaucoma biomarker discovery has not been previously reported. Our study applied NMF to extract latent representations of RNA-seq data from BXD mouse strains and sorted the genes based on a novel gene scoring method. The enrichment ratio of the glaucoma-reference genes, extracted from multiple relevant resources, was compared using both the classical differentially expressed gene (DEG) analysis and NMF methods. The complete pipeline was validated using an independent RNA-seq dataset. Findings showed our NMF method significantly improved the enrichment detection of glaucoma genes. The application of NMF with the scoring method showed great promise in the identification of marker genes for glaucoma.

Identification of Potential Biomarkers for Group I Pulmonary Hypertension Based on Machine Learning and Bioinformatics Analysis

A number of processes and pathways have been reported in the development of Group I pulmonary hypertension (Group I PAH); however, novel biomarkers need to be identified for a better diagnosis and management. We employed a robust rank aggregation (RRA) algorithm to shortlist the key differentially expressed genes (DEGs) between Group I PAH patients and controls. An optimal diagnostic model was obtained by comparing seven machine learning algorithms and was verified in an independent dataset. The functional roles of key DEGs and biomarkers were analyzed using various in silico methods. Finally, the biomarkers and a set of key candidates were experimentally validated using patient samples and a cell line model. A total of 48 key DEGs with preferable diagnostic value were identified. A gradient boosting decision tree algorithm was utilized to build a diagnostic model with three biomarkers, PBRM1, CA1, and TXLNG. An immune-cell infiltration analysis revealed significant differences in the relative abundances of seven immune cells between controls and PAH patients and a correlation with the biomarkers. Experimental validation confirmed the upregulation of the three biomarkers in Group I PAH patients. In conclusion, machine learning and a bioinformatics analysis along with experimental techniques identified PBRM1, CA1, and TXLNG as potential biomarkers for Group I PAH.