Open medicine (Warsaw, Poland) · 2025
Development and validation of potential molecular subtypes and signatures of ocular sarcoidosis based on autophagy-related gene analysis.
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Wu Zixuan, Long Xi, Tan Kang, Yao Xiaolei, Peng Qinghua
Аннотация
BACKGROUND: Sarcoidosis is characterized by the proliferation of noncaseating granulomas and presents as a complex chronic inflammatory disease. Autophagy plays a crucial role in the initiation, progression, and treatment resistance of various cancers. Despite the recognized importance of autophagy, the involvement of autophagy-related genes (ARGs) in the pathophysiology of ocular sarcoidosis (OS) remains largely unexplored.
METHODS: We intersected differentially expressed genes with a curated list of 177 ARGs to identify candidates potentially involved in OS. Advanced methodologies, including GSEA and GSVA, were employed to explore the biological functions. Further refinement using Lasso regression and SVM-RFE allowed for the identification of key hub genes and the assessment of their diagnostic potential for OS.
RESULTS: Our investigation identified 11 ARGs (DRAM1, SOGA1, ATG16L2, FYCO1, ATG7, ATG12, ATG14, KIAA0226, KIAA1324, KIAA1324L, and KIAA0226L) closely associated with OS. Functional analyses revealed their involvement in processes such as extracellular stimulus, response to nutrient levels, and positive regulation of catabolic process. Importantly, the diagnostic capabilities of these ARGs demonstrated significant efficacy in distinguishing OS from unaffected states.
CONCLUSIONS: Through rigorous bioinformatics analyses, this study identifies 11 ARGs as novel biomarker candidates for OS, elucidating their potential roles in the disease's pathogenesis.
METHODS: We intersected differentially expressed genes with a curated list of 177 ARGs to identify candidates potentially involved in OS. Advanced methodologies, including GSEA and GSVA, were employed to explore the biological functions. Further refinement using Lasso regression and SVM-RFE allowed for the identification of key hub genes and the assessment of their diagnostic potential for OS.
RESULTS: Our investigation identified 11 ARGs (DRAM1, SOGA1, ATG16L2, FYCO1, ATG7, ATG12, ATG14, KIAA0226, KIAA1324, KIAA1324L, and KIAA0226L) closely associated with OS. Functional analyses revealed their involvement in processes such as extracellular stimulus, response to nutrient levels, and positive regulation of catabolic process. Importantly, the diagnostic capabilities of these ARGs demonstrated significant efficacy in distinguishing OS from unaffected states.
CONCLUSIONS: Through rigorous bioinformatics analyses, this study identifies 11 ARGs as novel biomarker candidates for OS, elucidating their potential roles in the disease's pathogenesis.