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已发表论文
白癜风与牙周炎氧化应激相关共享生物标志物的鉴定：一项生物信息学与机器学习研究
Authors Zeng H, Luo Z, Tian W, Xiang J, Liao W, Cao L, Zhang C, Wang X
Received 9 June 2025
Accepted for publication 1 October 2025
Published 23 October 2025 Volume 2025:18 Pages 2719—2737
DOI https://doi.org/10.2147/CCID.S545677
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Prof. Dr. Rungsima Wanitphakdeedecha
Hengxi Zeng,1,* Zijie Luo,2,* Weicheng Tian,3,* Jiyuan Xiang,1 Wenxin Liao,1 Lin Cao,2 Chenting Zhang,4 Xia Wang1
1Department of Dermatology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, People’s Republic of China; 2The First Clinical College, Guangzhou Medical University, Guangzhou, People’s Republic of China; 3Urology Key Laboratory of Guangdong Province, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, People’s Republic of China; 4State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Xia Wang, Department of Dermatology, The First Affiliated Hospital of Guangzhou Medical University, 28 Datansha Island, Qiaozhong Middle Road, Liwan District, Guangzhou, Guangdong, 510120, People’s Republic of China, Email 13632292064@163.com
Background: Oxidative stress is associated with both vitiligo and periodontitis, but the detailed pathogenesis requires further elucidation. Evidence suggests a connection between periodontitis and autoimmune as well as chronic inflammatory skin diseases. The objective of this study is to investigate shared biomarkers related to oxidative stress in periodontitis and vitiligo using an integrated approach of bioinformatics and machine learning.
Methods: Data for periodontitis and vitiligo were downloaded from the NCBI GEO public database. After batch effect removal, differentially expressed genes (DEGs) were identified and combined with weighted gene co-expression network analysis (WGCNA) to pinpoint shared genes. Pathway enrichment analysis using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) was conducted for the shared genes. We identified hub genes with least absolute shrinkage and selection operator (LASSO) regression and Support Vector Machine (SVM) machine learning algorithms. Finally, the ssGSEA method was used to analyze the level of immune cell infiltration.
Results: Ninety-three shared genes between periodontitis and vitiligo were identified, with GO and KEGG enrichment analyses revealing a significant association with oxidative stress. Through machine learning algorithms, PTGS2, CCL5, and PRDX4 were identified as hub genes serving as shared biomarkers for oxidative stress in both diseases. Furthermore, immune cell infiltration revealed that periodontitis and vitiligo share similar immune infiltration patterns.
Conclusion: Our study has identified PTGS2, CCL5, and PRDX4 as key biomarkers for vitiligo and periodontitis, two diseases linked by similar immune infiltration patterns. These biomarkers offer new diagnostic insights and potential therapeutic targets.
Keywords: vitiligo, periodontitis, oxidative stress, bioinformatics, machine learning, immune cell infiltration
1Department of Dermatology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, People’s Republic of China; 2The First Clinical College, Guangzhou Medical University, Guangzhou, People’s Republic of China; 3Urology Key Laboratory of Guangdong Province, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, People’s Republic of China; 4State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Xia Wang, Department of Dermatology, The First Affiliated Hospital of Guangzhou Medical University, 28 Datansha Island, Qiaozhong Middle Road, Liwan District, Guangzhou, Guangdong, 510120, People’s Republic of China, Email 13632292064@163.com
Background: Oxidative stress is associated with both vitiligo and periodontitis, but the detailed pathogenesis requires further elucidation. Evidence suggests a connection between periodontitis and autoimmune as well as chronic inflammatory skin diseases. The objective of this study is to investigate shared biomarkers related to oxidative stress in periodontitis and vitiligo using an integrated approach of bioinformatics and machine learning.
Methods: Data for periodontitis and vitiligo were downloaded from the NCBI GEO public database. After batch effect removal, differentially expressed genes (DEGs) were identified and combined with weighted gene co-expression network analysis (WGCNA) to pinpoint shared genes. Pathway enrichment analysis using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) was conducted for the shared genes. We identified hub genes with least absolute shrinkage and selection operator (LASSO) regression and Support Vector Machine (SVM) machine learning algorithms. Finally, the ssGSEA method was used to analyze the level of immune cell infiltration.
Results: Ninety-three shared genes between periodontitis and vitiligo were identified, with GO and KEGG enrichment analyses revealing a significant association with oxidative stress. Through machine learning algorithms, PTGS2, CCL5, and PRDX4 were identified as hub genes serving as shared biomarkers for oxidative stress in both diseases. Furthermore, immune cell infiltration revealed that periodontitis and vitiligo share similar immune infiltration patterns.
Conclusion: Our study has identified PTGS2, CCL5, and PRDX4 as key biomarkers for vitiligo and periodontitis, two diseases linked by similar immune infiltration patterns. These biomarkers offer new diagnostic insights and potential therapeutic targets.
Keywords: vitiligo, periodontitis, oxidative stress, bioinformatics, machine learning, immune cell infiltration