Abstract

Keratoconus (KC) is an ectatic cornea disease with high prevalence and asymptomatic at early stage, leading to decreased visual acuity and even cornea transplantation. However, the etiology mechanism of keratoconus is still poorly understood. Oxidative stress (OS) and extracellular matrix (ECM) remodeling play critical roles in keratoconus development. Here, based on keratoconus datasets from GEO database, we obtained 454 differentially expressed genes (DEGs), which were further intersected with oxidative stress (OS) and extracellular matrix (ECM) genesets from MSigDB database. A total of 17 OS- and ECM-related DEGs (OEDEGs) were identified. Feature genes were screened by least absolute shrinkage and selection operator (LASSO) and support vector machine recursive feature elimination (SVM-RFE) algorithms, and a six-gene (COL1A1, CYP1B1, MMP3, HMOX1, FOS and GDF15) classification model was developed utilizing Logistic regression (LR), Support Vector Machine (SVM) and Na?ve Bayes (NB) algorithms respectively, which was further verified in internal and external cohort. Subsequently, a predictive nomogram was constructed for KC patients. Six signature genes showed a strong correlation with the infiltration level of macrophages M1, neutrophils and eosinophils. Additionally, in vitro qRT-PCR validated the decreased expression of signature genes in either keratoconus clinical samples or human cornea epithelial (HCE) cells grown on soft hydrogel substrate. Finally, we revealed that CYP1B1 and GDF15 regulate cellular proliferation and response to oxidative stress. In conclusion, the developed combined OS and ECM gene signature showed excellent performance for keratoconus prediction, providing beneficial perspectives for keratoconus pathogenesis.