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Background: Vitiligo is a common depigmentation disorder that affects approximately 1–2% of the global population. Oxidative stress plays a crucial role in its pathogenesis, yet systematic identification of oxidative stress-related biomarkers remains limited. This study aimed to identify and validate oxidative stress-related biomarkers for vitiligo and to construct a risk prediction model using bioinformatics, machine learning, and Mendelian randomization approaches.
Results: Through integration of high-throughput sequencing data, 1,581 differentially expressed genes were identified, of which 42 intersected with known oxidative stress-related genes. Three machine learning algorithms converged on five candidate genes. Real-time polymerase chain reaction validation in paired vitiligo lesional and non-lesional skin tissues confirmed the significant upregulation of ATOX1, STAT1, and PDCD10 and downregulation of FXN (p < 0.05). A risk prediction nomogram based on these four genes achieved high accuracy in the training dataset (area under the curve = 1.00) and an independent validation dataset (area under the curve = 0.93). PDCD10 demonstrated the strongest individual discriminatory performance (area under the curve = 0.820) in external validation. Mendelian randomization analysis indicated that ATOX1, STAT1, and PDCD10 had odds ratios greater than 1, suggesting a potential trend toward increased vitiligo risk, although the associations did not reach statistical significance.
Conclusions: This study identified ATOX1, STAT1, FXN, and PDCD10 as oxidative stress-related biomarkers for vitiligo. The four-gene risk prediction model demonstrated promising accuracy, with potential implications for early detection and personalized treatment strategies, pending further validation in larger cohorts.
References
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