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已发表论文

顺利获得学习算法鉴定和验证特应性皮炎线粒体潜在关键生物标志物

 

Authors Xu J , Pan X, Zhang M, Sun K, Li Z, Chen J

Received 12 December 2024

Accepted for publication 14 March 2025

Published 21 March 2025 Volume 2025:18 Pages 4291—4306

DOI http://doi.org/10.2147/JIR.S507085

Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 2

Editor who approved publication: Dr Tara Strutt

Junhao Xu,1,* Xinyu Pan,2,* Miao Zhang,2,* Kairong Sun,2 Zihan Li,1 Juan Chen2 

1The Second Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, 310053, People’s Republic of China; 2College of Basic Medical Science, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, 310053, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Juan Chen, Email cj906@zcmu.edu.cn

Purpose: Atopic dermatitis (AD) is a common inflammatory skin condition characterized by erythema and pruritus. Its precise pathogenesis remains unclear, though factors such as genetic predisposition, autoantigen response, allergen exposure, infections, and skin barrier dysfunction are involved. Research suggests a correlation between AD and mitochondrial dysfunction, as well as oxidative stress in skin tissues.
Methods:  Skin sample datasets related to AD (GSE36842, GSE120721, GSE16161, and GSE121212) were retrieved from the GEO database. Differential gene analysis identified differentially expressed genes (DEGs) in AD. Three potential biomarkers—COX17, ACOX2, and ADH1B—were identified using LASSO and Support Vector Machine (SVM) algorithms. These biomarkers were validated through ROC curve analysis, nomogram modeling, calibration curves, and real-time PCR. Immune infiltration analysis assessed correlations of the biomarkers. Additionally, single-cell analysis of the GSE153760 dataset identified nine cell clusters and confirmed expression patterns of the three hub genes.
Results: Differential analysis identified 150 upregulated and 367 downregulated genes. Enrichment analysis revealed significant pathways related to mitochondrial function, oxidative stress, and energy metabolism in skin samples from AD patients. Area under the curve (AUC) values for biomarkers COX17, ACOX2, and ADH1B were 1.000, 0.928, and 0.895, respectively, indicating strong predictive capacity. qPCR results showed COX17 was highly expressed in AD lesions, while ACOX2 and ADH1B were higher in normal skin, consistent with previous findings. Correlation analysis indicated ACOX2 and ADH1B were positively correlated with resting mast cells but negatively with activated T cells and NK cells, while COX17 showed a positive correlation with activated T cells and a negative correlation with resting mast cells.
Conclusion: This study suggests that the hub genes COX17, ACOX2, and ADH1B may serve as potential biomarkers in the pathogenesis of AD. These findings could provide insights for the treatment and prognosis of AD and related inflammatory skin conditions.

Keywords: atopic dermatitis, mitochondria, learning algorithm, biomarkers, oxidative stress, immune infiltration

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