Yan Zhang, Yihong Huang, Maosheng Guo, Wanzhu Chen, Yuyu Wu
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引用次数: 0
Abstract
Objective: The aim of this study was to reveal the biological functionalities associated with endoplasmic reticulum stress (ERS)-related genes (ERSGs) in the context of diabetic retinopathy (DR).
Methods: Differentially expressed genes (DEGs) within the DR group and the Control group were identified and then integrated with ERSGs. Gene Ontology (GO) and Gene Set Enrichment Analysis (GSEA) methodologies were used to investigate potential biological mechanisms. A diagnostic model for ERS and a nomogram were formulated based on biomarkers selected through the Least Absolute Shrinkage and Selection Operator method. The diagnostic efficacy of this model was thoroughly evaluated. ERS-associated subtypes were identified, and the Single-Sample GSEA (ssGSEA) and CIBERSORT algorithms were used to assess immune infiltration.
Results: We identified 10 ERS-related DEGs (ERSRDEGs) within the DR Group. Subsequently, a diagnostic model was constructed based on 5 ERS genes, namely CCND1, IGFBP2, TLR4, TXNIP, and VIM. The validation analysis demonstrated the commendable diagnostic performance of the model. Analysis of the ssGSEA immune characteristics revealed a positive correlation in the DR group between myeloid-derived suppressor cells (MDSC), regulatory T cells (Tregs), and CCND1 TXNIP. Furthermore, a significant negative correlation was observed between central memory CD4 T cells and CCND1. In the context of CIBERSORT, the results indicated a positive correlation between macrophages and IGFBP2, as well as Tregs and IGFBP2 in the DR group. Notably, a conspicuous negative correlation was identified between resting mast cells and IGFBP2.
Conclusion: The present study provides novel diagnostic biomarkers for DR from an ERS perspective.
研究目的本研究旨在揭示糖尿病视网膜病变（DR）背景下内质网应激（ERS）相关基因（ERSGs）的生物学功能：方法：鉴定 DR 组和对照组中的差异表达基因（DEGs），然后将其与 ERSGs 整合。采用基因本体（GO）和基因组富集分析（GSEA）方法研究潜在的生物学机制。通过最小绝对缩减和选择操作者方法筛选出的生物标志物为基础，建立了 ERS 诊断模型和提名图。对该模型的诊断效果进行了全面评估。确定了ERS相关亚型，并使用单样本GSEA（ssGSEA）和CIBERSORT算法评估了免疫浸润：结果：我们在 DR 组中发现了 10 个 ERS 相关 DEGs（ERSRDEGs）。随后，基于 5 个 ERS 基因（即 CCND1、IGFBP2、TLR4、TXNIP 和 VIM）构建了诊断模型。验证分析表明，该模型的诊断性能值得称赞。对ssGSEA免疫特征的分析表明，在DR组中，髓源性抑制细胞（MDSC）、调节性T细胞（Tregs）和CCND1 TXNIP之间存在正相关。此外，还观察到中心记忆 CD4 T 细胞与 CCND1 之间存在明显的负相关。在 CIBERSORT 中，结果显示在 DR 组中，巨噬细胞与 IGFBP2 以及 Tregs 与 IGFBP2 呈正相关。值得注意的是，静止肥大细胞与 IGFBP2 之间存在明显的负相关：本研究从 ERS 的角度为 DR 提供了新的诊断生物标志物。
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