糖尿病视网膜病变中巨噬细胞极化和线粒体相关生物标志物的鉴定
Identification of macrophage polarisation and mitochondria-related biomarkers in diabetic retinopathy.
作者信息
Liu Weifeng, Tong Bin, Xiong Jian, Zhu Yanfang, Lu Hongwei, Xu Haonan, Yang Xi, Wang Feifei, Yu Peng, Hu Yunwei
机构信息
Ophthalmic Center, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, Jiangxi, China.
School of Ophthalmology and Optometry, Jiangxi Medical College, Nanchang University, Nanchang, 330006, Jiangxi, China.
出版信息
J Transl Med. 2025 Jan 6;23(1):23. doi: 10.1186/s12967-024-06038-1.
BACKGROUND
The activation of macrophages or microglia in patients' whole body or local eyes play significant roles in diabetic retinopathy (DR). Mitochondrial function regulates the inflammatory polarization of macrophages. Therefore, the common mechanism of mitochondrial related genes (MRGs) and macrophage polarisation related genes (MPRGs) in DR is explored in our study to illustrate the pathophysiology of DR.
METHODS
In this study, using common transcriptome data, differentially expressed genes (DEGs) were firstly analysed for GSE221521, while module genes related to MPRGs were obtained by weighted gene co-expression network analysis (WGCNA), intersections of DEGs with MRGs were taken, intersections of DEGs with module genes of the MPRGs were taken. After that, correlation analyses were performed to obtain candidate genes. Key genes were obtained by Mendelian randomisation (MR) analysis, then biomarkers were obtained by machine learning combined with receiver operating characteristic (ROC) and expression validation between DR and control cohorts in GSE221521 and GSE160306 to obtain biomarkers. Finally, biomarkers were subjected to immune infiltration analysis, gene set enrichment analysis (GSEA), and gene-gene interaction (GGI) analysis.
RESULTS
A number of 784 of DEGs were taken to intersect with 1136 MRGs and 782 MPRGs, respectively, after which 89 genes with correlation were taken as candidate genes. MR analysis yielded 13 key genes with clear causal links to DR. The expression trends of PTAR1 and SLC25A34 were consistent and notable between DR cohort and control cohort in GSE221521 and GSE160306. So PTAR1 and SLC25A34 were used as biomarkers. Immune infiltration analysis showed that activated NK cell and Monocyte were notably different between DR cohort and control cohorts, and PTAR1 showed the strongest positive correlations with activated NK cell. Both biomarkers were enriched in lysosome and insulin signaling pathway. The GGI network showed that biomarkers associated with prenyltransferase activity and prenylation function.
CONCLUSION
This study identified two biomarkers (PTAR1 and SLC25A34) which explore the pathogenesis of DR and provide reference targets for drug development.
背景
患者全身或局部眼部巨噬细胞或小胶质细胞的激活在糖尿病视网膜病变（DR）中起重要作用。线粒体功能调节巨噬细胞的炎症极化。因此，本研究探讨了糖尿病视网膜病变中线粒体相关基因（MRGs）和巨噬细胞极化相关基因（MPRGs）的共同机制，以阐明糖尿病视网膜病变的病理生理学。
方法
在本研究中，利用常见的转录组数据，首先对GSE221521分析差异表达基因（DEGs），通过加权基因共表达网络分析（WGCNA）获得与MPRGs相关的模块基因，取DEGs与MRGs的交集，取DEGs与MPRGs模块基因的交集。之后，进行相关性分析以获得候选基因。通过孟德尔随机化（MR）分析获得关键基因，然后通过机器学习结合受试者工作特征（ROC）以及GSE221521和GSE160306中DR队列与对照队列之间的表达验证获得生物标志物。最后，对生物标志物进行免疫浸润分析、基因集富集分析（GSEA）和基因-基因相互作用（GGI）分析。
结果
分别取784个DEGs与1136个MRGs和782个MPRGs进行交集，之后将89个具有相关性的基因作为候选基因。MR分析产生了13个与DR有明确因果关系的关键基因。在GSE221521和GSE160306中，DR队列与对照队列之间PTAR1和SLC25A34的表达趋势一致且显著。因此，PTAR1和SLC25A34被用作生物标志物。免疫浸润分析表明，DR队列与对照队列之间活化的自然杀伤细胞和单核细胞有显著差异, 并且PTAR1与活化的自然杀伤细胞显示出最强的正相关。两种生物标志物均富集于溶酶体和胰岛素信号通路。GGI网络显示生物标志物与异戊二烯转移酶活性和异戊二烯化功能相关联。
结论
本研究鉴定了两种生物标志物（PTAR1和SLC-25A34），其探索了糖尿病视网膜病变的发病机制并为药物开发提供了参考靶点。
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