Wei Chen, Zijing Li, Xiaoyan Zhou, Chunli Li, Yuting Lin
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引用次数: 0
Abstract
Purpose: Oxidative stress has long been recognized as a significant influence in the pathophysiology of age-related macular degeneration (AMD). Therefore there is a need to explore the relationship between oxidative stress-related biomarkers and AMD.
Methods: Based on Gene Expression Omnibus database-Gene Expression Omnibus Series (GSE)29801 and GSE135092 datasets, three machine learning methods were used to screen biomarkers. The Wilcoxon test was used to compare the percentage of immune cells in control and AMD samples. The causal relationship between biomarkers and AMD was explored in a series of Mendelian randomization (MR) analyses. Ultimately, the expression levels of biomarkers were validated by quantitative real-time polymerase chain reaction (qRT-PCR) in the simulated AMD cell model.
Results: A total of 16 differentially expressed oxidative stress-related genes (DE-OSRGs) were screened. Functional enrichment analysis indicated that DE-OSRGs participated in cellular senescence, cell cycle regulation, and PPAR signaling pathways. Machine learning methods were used to screen for five biomarkers (GFAP, Stearoyl-CoA desaturase [SCD], BCKDHB, GPX8, and MSRB2). The qRT-PCR results showed that the expression levels of five biomarkers were significantly different between the simulated AMD cell model and control groups. Spearman correlation analysis showed that GPX8 had the highest positive correlation with M2 macrophages (correlation coefficient [cor] = 0.36, P < 0.01), and SCD had a strong negative correlation with eosinophils (cor = -0.28, P < 0.05). MR results revealed that BCKDHB played a crucial role as a risk factor for AMD (odds ratio > 1, P < 0.05).
Conclusions: This study screened the biomarkers related to oxidative stress in AMD, providing a certain theoretical basis for the prevention and clinical diagnosis of AMD.
Translational relevance: Identifying biomarkers with diagnostic value for AMD could provide new understanding of its pathogenesis, and open up potential targets for clinical intervention.
期刊介绍:
Translational Vision Science & Technology (TVST), an official journal of the Association for Research in Vision and Ophthalmology (ARVO), an international organization whose purpose is to advance research worldwide into understanding the visual system and preventing, treating and curing its disorders, is an online, open access, peer-reviewed journal emphasizing multidisciplinary research that bridges the gap between basic research and clinical care. A highly qualified and diverse group of Associate Editors and Editorial Board Members is led by Editor-in-Chief Marco Zarbin, MD, PhD, FARVO.
The journal covers a broad spectrum of work, including but not limited to:
Applications of stem cell technology for regenerative medicine,
Development of new animal models of human diseases,
Tissue bioengineering,
Chemical engineering to improve virus-based gene delivery,
Nanotechnology for drug delivery,
Design and synthesis of artificial extracellular matrices,
Development of a true microsurgical operating environment,
Refining data analysis algorithms to improve in vivo imaging technology,
Results of Phase 1 clinical trials,
Reverse translational ("bedside to bench") research.
TVST seeks manuscripts from scientists and clinicians with diverse backgrounds ranging from basic chemistry to ophthalmic surgery that will advance or change the way we understand and/or treat vision-threatening diseases. TVST encourages the use of color, multimedia, hyperlinks, program code and other digital enhancements.