转录组分析结合孟德尔随机化筛选与糖尿病视网膜病变因果相关的生物标志物。
Transcriptome analysis combined with Mendelian randomization screening for biomarkers causally associated with diabetic retinopathy.
机构信息
Department of Ophthalmology, The Second Affiliated Hospital of Kunming Medical University, Kunming, China.
Department of Ophthalmology, Dali Bai Autonomous Prefecture People's Hospital, Dali, China.
出版信息
Front Endocrinol (Lausanne). 2024 Jul 3;15:1410066. doi: 10.3389/fendo.2024.1410066. eCollection 2024.
BACKGROUND
Diabetic retinopathy (DR) is considered one of the most severe complications of diabetes mellitus, but its pathogenesis is still unclear. We hypothesize that certain genes exert a pivotal influence on the progression of DR. This study explored biomarkers for the diagnosis and treatment of DR through bioinformatics analysis.
METHODS
Within the GSE221521 and GSE189005 datasets, candidate genes were acquired from intersections of genes obtained using WGCNA and DESeq2 packages. Mendelian randomization (MR) analysis selected candidate biomarkers exhibiting causal relationships with DR. Receiver Operating Characteristic (ROC) analysis determined the diagnostic efficacy of biomarkers, the expression levels of biomarkers were verified in the GSE221521 and GSE189005 datasets, and a nomogram for diagnosing DR was constructed. Enrichment analysis delineated the roles and pathways associated with the biomarkers. Immune infiltration analysis analyzed the differences in immune cells between DR and control groups. The miRNet and networkanalyst databases were then used to predict the transcription factors (TFs) and miRNAs, respectively, of biomarkers. Finally, RT-qPCR was used to verify the expression of the biomarkers .
RESULTS
MR analysis identified 13 candidate biomarkers that had causal relationships with DR. The ROC curve demonstrated favorable diagnostic performance of three biomarkers (, , and ) for DR, and their expression trends were consistent across GSE221521 and GSE189005 datasets. The calibration curves and ROC curves indicated good predictive performance of the nomogram. The biomarkers were enriched in pathways of immune, cancer, amino acid metabolism, and oxidative phosphorylation. Ten immune cell lines showed notable disparities between the DR and control groups. Among them, effector memory CD8+ T cells, plasmacytoid dendritic cells, and activated CD4+ T cells exhibited good correlation with biomarker expression. The TF-mRNA-miRNA network suggested that hsa-mir-92a-3p, , and play important roles in biomarker targeting for DR. RT-qPCR results also demonstrated a notably high expression of in patients with DR, whereas notably low expression of .
CONCLUSION
, , and were identified as biomarkers for DR. The study findings provide novel insights into the pathogenesis of DR.
背景
糖尿病视网膜病变（DR）被认为是糖尿病最严重的并发症之一，但发病机制尚不清楚。我们假设某些基因对 DR 的进展有重要影响。本研究通过生物信息学分析探讨了用于 DR 诊断和治疗的生物标志物。
方法
在 GSE221521 和 GSE189005 数据集内，使用 WGCNA 和 DESeq2 包获取基因交集以获取候选基因。孟德尔随机分析（MR）选择与 DR 具有因果关系的候选生物标志物。受试者工作特征（ROC）分析确定生物标志物的诊断效能，在 GSE221521 和 GSE189005 数据集内验证生物标志物的表达水平，并构建用于诊断 DR 的列线图。富集分析描绘了与生物标志物相关的作用和途径。免疫浸润分析分析了 DR 组和对照组之间免疫细胞的差异。然后使用 miRNet 和 networkanalyst 数据库分别预测生物标志物的转录因子（TF）和 miRNA。最后，使用 RT-qPCR 验证生物标志物的表达。
结果
MR 分析确定了 13 个与 DR 具有因果关系的候选生物标志物。ROC 曲线表明，三种生物标志物（、和）对 DR 具有良好的诊断性能，其在 GSE221521 和 GSE189005 数据集内的表达趋势一致。校准曲线和 ROC 曲线表明列线图具有良好的预测性能。生物标志物富集在免疫、癌症、氨基酸代谢和氧化磷酸化途径中。10 种免疫细胞系显示 DR 组和对照组之间存在明显差异。其中，效应记忆 CD8+T 细胞、浆细胞样树突状细胞和活化的 CD4+T 细胞与生物标志物表达具有良好的相关性。TF-mRNA-miRNA 网络表明 hsa-mir-92a-3p、和在 DR 生物标志物靶向治疗中发挥重要作用。RT-qPCR 结果还表明，DR 患者中显著高表达，而显著低表达。
结论
、和被确定为 DR 的生物标志物。本研究结果为 DR 的发病机制提供了新的见解。
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