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增殖性糖尿病视网膜病变中与视网膜新生血管相关的新型生物标志物的鉴定及功能分析

Identification and functional analysis of novel biomarkers related to retinal neovascularization in proliferative diabetic retinopathy.

作者信息

Zhang Shengnan, Song Wenqi, Huang Bingyao, Wang Tao, Sun Chao

机构信息

Department of Ophthalmology, Zibo Central Hospital, Zibo, China.

Sanitary Inspection Center, Zibo Center for Disease Control and Prevention, Zibo, China.

出版信息

Technol Health Care. 2025 May;33(3):1274-1287. doi: 10.1177/09287329241296705. Epub 2024 Dec 9.

DOI:10.1177/09287329241296705
PMID:40331545
Abstract

BackgroundProliferative diabetes retinopathy (PDR) seriously affects the vision of patients. Exploring the key genes of retinal neovascularization is crucial for developing new biomarkers and therapeutic targets.ObjectiveThis study aimed to identify key genes associated with retinal neovascularization in Proliferative Diabetic Retinopathy (PDR), intending to develop new biomarkers and therapeutic targets. This would further our understanding of the progression of diabetic retinopathy and improve patient prognosis.MethodsThe gene data from 36 diabetic retinopathy patient samples and 45 samples from healthy volunteers or diabetic patients were selected from the GEO DataSets (Gene Expression Omnibus), specifically datasets GSE102485 and GSE160310. Utilizing the SVA algorithm to merge datasets and the limma package in R to identify differentially expressed genes (DEGs), we conducted a bioinformatic analysis of diabetic retinopathy. Functional insights were gained through DAVID database analyses, while STRING database-derived Protein-Protein Interaction (PPI) networks visualized in Cytoscape provided further context. Key genes were identified through LASSO regression and SVM analyses, with ROC curves assessing their diagnostic value. Single gene set enrichment analysis (GSEA) enhanced our understanding of the perturbed biological processes and pathways, advancing knowledge of diabetic retinopathy at the genomic level.ResultsA rigorous bioinformatic analysis yielded a comprehensive list of 1139 differentially expressed genes (DEGs), of which six pivotal genes-KDM5D, AC007040.11, AC015688.3, NLRP2, GYPC, and TMSB4Y-were identified as central to the study. These six genes consistently demonstrated a high diagnostic accuracy, with each exhibiting an area under the receiver operating characteristic (ROC) curve (AUC) exceeding 0.75. Gene Ontology (GO) enrichment analysis elucidated their primary roles in intricate biological processes, including inflammatory and immune responses, T-cell activation, cell apoptosis, and angiogenesis. Moreover, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment revealed their involvement in crucial signaling cascades such as cytokine-cytokine receptor interactions, cell adhesion molecule pathways, PI3K-Akt signaling, and hematopoietic cell lineage, further substantiating their significance in the pathogenesis of diabetic retinopathy. In this research, we conducted a comprehensive gene expression analysis in patients with and without PDR, identifying differentially expressed genes, pivotal biomarkers, and critical pathways potentially involved in PDR progression. Our findings enhance understanding of PDR's molecular dynamics and offer potential avenues for improved prognostication and therapeutic intervention, despite limitations due to sample group heterogeneity.

摘要

背景

增殖性糖尿病视网膜病变(PDR)严重影响患者视力。探索视网膜新生血管形成的关键基因对于开发新的生物标志物和治疗靶点至关重要。

目的

本研究旨在确定增殖性糖尿病视网膜病变(PDR)中与视网膜新生血管形成相关的关键基因,以开发新的生物标志物和治疗靶点。这将加深我们对糖尿病视网膜病变进展的理解,并改善患者预后。

方法

从GEO数据集(基因表达综合数据库)中选取36例糖尿病视网膜病变患者样本以及45例健康志愿者或糖尿病患者样本的基因数据,具体为数据集GSE102485和GSE160310。利用SVA算法合并数据集,并使用R语言中的limma包来识别差异表达基因(DEG),我们对糖尿病视网膜病变进行了生物信息学分析。通过DAVID数据库分析获得功能见解,而在Cytoscape中可视化的STRING数据库衍生的蛋白质-蛋白质相互作用(PPI)网络提供了更多背景信息。通过LASSO回归和支持向量机(SVM)分析确定关键基因,并通过ROC曲线评估其诊断价值。单基因集富集分析(GSEA)加深了我们对受干扰的生物过程和信号通路的理解,在基因组水平上推进了对糖尿病视网膜病变的认识。

结果

经过严格的生物信息学分析,得出了一份包含1139个差异表达基因(DEG)的综合列表,其中六个关键基因——KDM5D、AC007040.11、AC015688.3、NLRP2、GYPC和TMSB4Y——被确定为该研究的核心。这六个基因始终表现出较高的诊断准确性,每个基因在受试者工作特征(ROC)曲线下的面积(AUC)均超过0.75。基因本体(GO)富集分析阐明了它们在复杂生物过程中的主要作用,包括炎症和免疫反应、T细胞活化、细胞凋亡和血管生成。此外,京都基因与基因组百科全书(KEGG)通路富集显示它们参与了关键信号级联反应,如细胞因子-细胞因子受体相互作用、细胞粘附分子通路、PI3K-Akt信号传导和造血细胞谱系,进一步证实了它们在糖尿病视网膜病变发病机制中的重要性。在本研究中,我们对有和没有PDR的患者进行了全面的基因表达分析,确定了差异表达基因、关键生物标志物以及可能参与PDR进展的关键通路。尽管由于样本组异质性存在局限性,但我们的发现增强了对PDR分子动力学的理解,并为改善预后和治疗干预提供了潜在途径。

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