Introduction: Glaucoma, a principal cause of irreversible vision loss, is characterized by intricate optic neuropathy involving significant immune mechanisms. This study seeks to elucidate the molecular and immune complexities of glaucoma, aiming to improve our understanding of its pathogenesis.Methods: Gene expression profiles from glaucoma patients were analyzed to identify immune-related differentially expressed genes (DEGs). Techniques used were weighted gene co-expression network analysis (WGCNA) for network building, machine learning algorithms for biomarker identification, establishment of subclusters related to immune reactions, and single-sample gene set enrichment analysis (ssGSEA) to explore hub genes’ relationships with immune cell infiltration and immune pathway activation. Validation was performed using an NMDA-induced excitotoxicity model and RT-qPCR for hub gene expression measurement.Results: The study identified 409 DEGs differentiating healthy individuals from glaucoma patients, highlighting the immune response’s significance in disease progression. Immune cell infiltration analysis revealed elevated levels of activated dendritic cells, natural killer cells, monocytes, and immature dendritic cells in glaucoma samples. Three hub genes, CD40LG, TEK, and MDK, were validated as potential diagnostic biomarkers for high-risk glaucoma patients, showing increased expression in the NMDA-induced excitotoxicity model.Discussion: The findings propose the three identified immune-related genes (IRGs) as novel diagnostic markers for glaucoma, offering new insights into the disease's pathogenesis and potential therapeutic targets. The strong correlation between these IRGs and immune responses underscores the intricate role of immunity in glaucoma, suggesting a shift in the approach to its diagnosis and treatment.

中文翻译：

---

使用基因表达谱鉴定青光眼免疫相关生物标志物

简介：青光眼是不可逆视力丧失的主要原因，其特征是涉及重要免疫机制的复杂视神经病变。本研究旨在阐明青光眼的分子和免疫复杂性，旨在提高我们对其发病机制的了解。方法：分析青光眼患者的基因表达谱，以确定与免疫相关的差异表达基因（DEG）。使用的技术包括用于网络构建的加权基因共表达网络分析（WGCNA）、用于生物标志物识别的机器学习算法、建立与免疫反应相关的子簇以及用于探索中心基因与免疫反应相关的子簇的单样本基因集富集分析（ssGSEA）。免疫细胞浸润和免疫通路激活。使用 NMDA 诱导的兴奋性毒性模型和用于中心基因表达测量的 RT-qPCR 进行验证。结果：该研究确定了区分健康个体和青光眼患者的 409 个 DEG，强调了免疫反应在疾病进展中的重要性。免疫细胞浸润分析显示，青光眼样本中活化的树突状细胞、自然杀伤细胞、单核细胞和未成熟树突状细胞的水平升高。三个枢纽基因，CD40LG,泰克， 和MDK，被验证为高危青光眼患者的潜在诊断生物标志物，在 NMDA 诱导的兴奋性毒性模型中显示表达增加。讨论：研究结果提出三个已鉴定的免疫相关基因 (IRG) 作为青光眼的新型诊断标志物，提供了新的见解深入了解该疾病的发病机制和潜在的治疗靶点。这些 IRG 与免疫反应之间的强相关性强调了免疫在青光眼中的复杂作用，表明其诊断和治疗方法的转变。