Identification of Oxidative Stress-Related Shared Biomarkers in Vitiligo and Periodontitis: A Bioinformatics and Machine Learning Study

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 workCorrespondence: 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 [email protected]: 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