Xie Peiyun, Yuan Bowei, Gu Zhanhao, Li Rong, Chen Ding
Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China.
Affiliated Qingyuan Hospital, Qingyuan People's Hospital, Guangzhou Medical University, Qingyuan 511518, Guangdong, China.
J Ophthalmol. 2025 Jun 1;2025:7107888. doi: 10.1155/joph/7107888. eCollection 2025.
Keratoconus (KC) can lead to severe vision loss, impacting daily life. The etiology of KC is not yet clear, and early diagnosis and treatment are crucial for prognosis. This study aimed to explore basement membrane (BM)-related gene signatures for the diagnosis and therapy of KC and provide novel insights into its pathogenesis. Based on the public datasets GSE112155 and GSE151631 in the GEO database, we obtained the differentially expressed genes (DEGs) of KC and downloaded BM-related genes based on the GeneCards database. Through a combination of bioinformatics methods, primarily weighted gene coexpression network analysis (WGCNA) and machine learning such as random forest (RF) and support vector machine (SVM), BM-related genes were identified as biomarkers for KC diagnosis. Subsequently, we further validated these findings using unsupervised clustering analysis, nomogram, and ROC curve analysis. Through the analysis of two KC-related datasets, 227 DEGs were screened out and intersected with BM-related genes to obtain 195 intersecting genes. By applying WGCNA and two machine learning algorithms, we identified four key genes, namely, CRY2, RNF19B, PPP1R18, and PFKFB3. These genes were significantly expressed in the normal control group. According to the ROC analysis, all four genes demonstrated excellent diagnostic performance in internal validation, with AUC values all exceeding 0.8. In external validation, CRY2, RNF19B, and PPP1R18 showed good predictive performance, each with AUC values greater than 0.6. Unsupervised clustering and nomogram also supported the good diagnostic capabilities of these genes. In addition, unsupervised clustering analysis also indicated that these four genes were mainly distributed in subtype A of KC. Immune infiltration analysis and functional enrichment analysis further suggested that immune inflammation, metabolism, and apoptosis were also involved in KC. Using bioinformatics analysis, we found three novel hub genes, CRY2, RNF19B, and PPP1R18, which are beneficial for the diagnosis and therapy of KC.
圆锥角膜（KC）可导致严重视力丧失，影响日常生活。KC的病因尚不清楚，早期诊断和治疗对预后至关重要。本研究旨在探索与基底膜（BM）相关的基因特征用于KC的诊断和治疗，并为其发病机制提供新见解。基于基因表达综合数据库（GEO数据库）中的公共数据集GSE112155和GSE151631，我们获得了KC的差异表达基因（DEGs），并基于基因卡片数据库下载了与BM相关的基因。通过综合生物信息学方法，主要是加权基因共表达网络分析（WGCNA）和机器学习如随机森林（RF）和支持向量机（SVM），与BM相关的基因被鉴定为KC诊断的生物标志物。随后，我们使用无监督聚类分析、列线图和ROC曲线分析进一步验证了这些发现。通过对两个与KC相关的数据集进行分析，筛选出227个DEGs，并与BM相关基因进行交集，得到195个交集基因。通过应用WGCNA和两种机器学习算法，我们鉴定出四个关键基因，即CRY2、RNF19B、PPP1R18和PFKFB3。这些基因在正常对照组中显著表达。根据ROC分析，这四个基因在内部验证中均表现出优异的诊断性能，AUC值均超过0.8。在外部验证中，CRY2、RNF19B和PPP1R18表现出良好的预测性能，AUC值均大于0.6。无监督聚类和列线图也支持这些基因具有良好的诊断能力。此外，无监督聚类分析还表明这四个基因主要分布在KC的A型亚型中。免疫浸润分析和功能富集分析进一步表明免疫炎症、代谢和凋亡也参与了KC。通过生物信息学分析，我们发现了三个新的枢纽基因CRY2、RNF19B和PPP1R18，它们对KC的诊断和治疗有益。
