Identifying effective immune biomarkers in alopecia areata diagnosis based on machine learning methods
Abstract Background Alopecia areata (AA) is a common non-scarring hair loss disorder associated with autoimmune conditions. However, the pathobiology of AA is not well understood, and there is no targeted therapy available for AA. Methods In this study, differential gene expression analysis, immune...
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| Language: | English |
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BMC
2025-01-01
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| Series: | BMC Medical Informatics and Decision Making |
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| Online Access: | https://doi.org/10.1186/s12911-025-02853-8 |
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| author | Qingde Zhou Lan Lan Wei Wang Xinchang Xu |
| author_facet | Qingde Zhou Lan Lan Wei Wang Xinchang Xu |
| author_sort | Qingde Zhou |
| collection | DOAJ |
| description | Abstract Background Alopecia areata (AA) is a common non-scarring hair loss disorder associated with autoimmune conditions. However, the pathobiology of AA is not well understood, and there is no targeted therapy available for AA. Methods In this study, differential gene expression analysis, immune status assessment, weighted correlation network analysis (WGCNA), and functional enrichment analysis were performed to identify shared genes associated with both immunological response and AA. Machine learning methods were then used to identify three hub genes as potential diagnostic markers for AA. External validation was performed, and the correlation of hub genes with immune infiltration, immune checkpoint genes, and key marker genes and pathways were evaluated. Results Three hub genes were identified, which accurately predicted the progression of AA and the immune status. The hub genes were found to be diagnostic markers for AA with high predictive accuracy. External validation confirmed the efficacy of these markers in identifying AA patients. Conclusion Overall, the study provides a novel approach for the diagnosis, prevention, and treatment of AA. The findings could potentially lead to the development of targeted therapies for AA based on the identified hub genes. The study also highlights the potential of machine learning and bioinformatics analysis in identifying new biomarkers for autoimmune diseases. Graphical Abstract |
| format | Article |
| id | doaj-art-6408fd8453d244bba8ffcacc7fcdc8b5 |
| institution | Kabale University |
| issn | 1472-6947 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Informatics and Decision Making |
| spelling | doaj-art-6408fd8453d244bba8ffcacc7fcdc8b52025-01-19T12:26:05ZengBMCBMC Medical Informatics and Decision Making1472-69472025-01-0125111710.1186/s12911-025-02853-8Identifying effective immune biomarkers in alopecia areata diagnosis based on machine learning methodsQingde Zhou0Lan Lan1Wei Wang2Xinchang Xu3Department of Pharmacy, Hangzhou Third People’s Hospital, Hangzhou Third Hospital Affiliated to Zhejiang Chinese Medical UniversityZhejiang University School of MedicineDepartment of Pharmacy, Hangzhou Third People’s Hospital, Hangzhou Third Hospital Affiliated to Zhejiang Chinese Medical UniversityDepartment of Pharmacy, Hangzhou Third People’s Hospital, Hangzhou Third Hospital Affiliated to Zhejiang Chinese Medical UniversityAbstract Background Alopecia areata (AA) is a common non-scarring hair loss disorder associated with autoimmune conditions. However, the pathobiology of AA is not well understood, and there is no targeted therapy available for AA. Methods In this study, differential gene expression analysis, immune status assessment, weighted correlation network analysis (WGCNA), and functional enrichment analysis were performed to identify shared genes associated with both immunological response and AA. Machine learning methods were then used to identify three hub genes as potential diagnostic markers for AA. External validation was performed, and the correlation of hub genes with immune infiltration, immune checkpoint genes, and key marker genes and pathways were evaluated. Results Three hub genes were identified, which accurately predicted the progression of AA and the immune status. The hub genes were found to be diagnostic markers for AA with high predictive accuracy. External validation confirmed the efficacy of these markers in identifying AA patients. Conclusion Overall, the study provides a novel approach for the diagnosis, prevention, and treatment of AA. The findings could potentially lead to the development of targeted therapies for AA based on the identified hub genes. The study also highlights the potential of machine learning and bioinformatics analysis in identifying new biomarkers for autoimmune diseases. Graphical Abstracthttps://doi.org/10.1186/s12911-025-02853-8Alopecia areataImmune cellsBioinformaticsMachine learningDiagnosisBiomarkers |
| spellingShingle | Qingde Zhou Lan Lan Wei Wang Xinchang Xu Identifying effective immune biomarkers in alopecia areata diagnosis based on machine learning methods BMC Medical Informatics and Decision Making Alopecia areata Immune cells Bioinformatics Machine learning Diagnosis Biomarkers |
| title | Identifying effective immune biomarkers in alopecia areata diagnosis based on machine learning methods |
| title_full | Identifying effective immune biomarkers in alopecia areata diagnosis based on machine learning methods |
| title_fullStr | Identifying effective immune biomarkers in alopecia areata diagnosis based on machine learning methods |
| title_full_unstemmed | Identifying effective immune biomarkers in alopecia areata diagnosis based on machine learning methods |
| title_short | Identifying effective immune biomarkers in alopecia areata diagnosis based on machine learning methods |
| title_sort | identifying effective immune biomarkers in alopecia areata diagnosis based on machine learning methods |
| topic | Alopecia areata Immune cells Bioinformatics Machine learning Diagnosis Biomarkers |
| url | https://doi.org/10.1186/s12911-025-02853-8 |
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