Jing-Fan Gao,* Yue-Yan Dong,* Xin Jin, Li-Jun Dai, Jing-Rao Wang, Hong Zhang

Eye Hospital, The First Affiliated Hospital of Harbin Medical University, Harbin, 150001, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Hong Zhang, Department of Eye Hospital, The First Affiliated Hospital of Harbin Medical University, Harbin, 150001, People’s Republic of China, Email zhanghong@hrbmu.edu.cn

Objective: Keratoconus is a commonly progressive and blinding corneal disorder. Iron metabolism and oxidative stress play crucial roles in both keratoconus and ferroptosis. However, the association between keratoconus and ferroptosis is currently unclear. This study aimed to analyze and verify the role of ferroptosis-related genes (FRGs) in the pathogenesis of keratoconus through bioinformatics.
Methods: We first obtained keratoconus-related datasets and FRGs. Then, the differentially expressed FRGs (DE-FRGs) associated with keratoconus were screened through analysis, followed by analysis of their biological functions. Subsequently, the LASSO and SVM-RFE algorithms were used to screen for diagnostic biomarkers. GSEA was performed to explore the potential functions of the marker genes. Finally, the associations between these biomarkers and immune cells were analyzed. qRT‒PCR was used to detect the expression of these biomarkers in corneal tissues.
Results: A total of 39 DE-FRGs were screened, and functional enrichment analysis revealed that the DE-FRGs were closely related to apoptosis, oxidative stress, and the immune response. Then, using multiple algorithms, 6 diagnostic biomarkers were selected, and the ROC curve was used to verify their risk prediction ability. In addition, based on CIBERSORT analysis, alterations in the immune microenvironment of keratoconus patients might be associated with H19, GCH1, CHAC1, and CDKN1A. Finally, qRT‒PCR confirmed that the expression of H19 and CHAC1 was elevated in the keratoconus group.
Conclusion: This study identified 6 DE-FRGs, 4 of which were associated with immune infiltrating cells, and established a diagnostic model with predictive value for keratoconus.

Keywords: keratoconus, ferroptosis, immune infiltration, machine learning