Chen Wei, Li Zijing, Zhou Xiaoyan, Li Chunli, Lin Yuting
AierEye Hospital, Jinan University, Shenzhen Guangdong, China.
Department of Ophthalmology, The University of HongKong-Shenzhen Hospital, Shenzhen, Guangdong, China.
Transl Vis Sci Technol. 2025 Aug 1;14(8):42. doi: 10.1167/tvst.14.8.42.
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.
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.
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).
This study screened the biomarkers related to oxidative stress in AMD, providing a certain theoretical basis for the prevention and clinical diagnosis of AMD.
Identifying biomarkers with diagnostic value for AMD could provide new understanding of its pathogenesis, and open up potential targets for clinical intervention.
长期以来，氧化应激被认为在年龄相关性黄斑变性（AMD）的病理生理学中具有重要影响。因此，有必要探索氧化应激相关生物标志物与AMD之间的关系。
基于基因表达综合数据库-基因表达综合系列（GSE）29801和GSE135092数据集，使用三种机器学习方法筛选生物标志物。采用Wilcoxon检验比较对照组和AMD样本中免疫细胞的百分比。在一系列孟德尔随机化（MR）分析中探索生物标志物与AMD之间的因果关系。最终，通过定量实时聚合酶链反应（qRT-PCR）在模拟AMD细胞模型中验证生物标志物的表达水平。
共筛选出16个差异表达的氧化应激相关基因（DE-OSRGs）。功能富集分析表明，DE-OSRGs参与细胞衰老、细胞周期调控和PPAR信号通路。使用机器学习方法筛选出5种生物标志物（胶质纤维酸性蛋白[GFAP]、硬脂酰辅酶A去饱和酶[SCD]、支链α-酮酸脱氢酶E1亚基β[BCKDHB]、谷胱甘肽过氧化物酶8[GPX8]和甲硫氨酸亚砜还原酶B2[MSRB2]）。qRT-PCR结果显示，模拟AMD细胞模型与对照组之间5种生物标志物的表达水平存在显著差异。Spearman相关性分析表明，GPX8与M2巨噬细胞的正相关性最高（相关系数[cor]=0.36，P<0.01），SCD与嗜酸性粒细胞呈强负相关（cor=-0.28，P<0.05）。MR结果显示，BCKDHB作为AMD的危险因素发挥着关键作用（优势比>1，P<0.05）。
本研究筛选出了与AMD氧化应激相关的生物标志物，为AMD的预防和临床诊断提供了一定的理论依据。
鉴定具有AMD诊断价值的生物标志物可为其发病机制提供新的认识，并为临床干预开辟潜在靶点。
