Evidence map›Paper›PMID 41158793›Full record

ArticleClinical, cosmetic and investigational dermatology2025

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

Hengxi Zeng, Zijie Luo, Weicheng Tian, Jiyuan Xiang, Wenxin Liao, Lin Cao, Chenting Zhang, Xia Wang

Abstract read
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Article in Clinical, cosmetic and investigational dermatology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Hengxi Zeng *Department of Dermatology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, People's Republic of China.
Zijie Luo *The First Clinical College, Guangzhou Medical University, Guangzhou, People's Republic of China.
Weicheng Tian *Urology Key Laboratory of Guangdong Province, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, People's Republic of China.
Jiyuan XiangDepartment of Dermatology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, People's Republic of China.
Wenxin LiaoDepartment of Dermatology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, People's Republic of China.
Lin CaoThe First Clinical College, Guangzhou Medical University, Guangzhou, People's Republic of China.
Chenting ZhangState Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou, People's Republic of China.
Xia WangDepartment of Dermatology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

bioinformaticsimmune cell infiltrationmachine learningoxidative stressperiodontitisvitiligo

Identifiers

PMID41158793
PMCPMC12558089

What Socratic holds

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.