Machine learning-based identification of novel hub genes associated with oxidative stress in lupus nephritis: implications for diagnosis and therapeutic targets
Background Lupus nephritis (LN) is a complication of SLE characterised by immune dysfunction and oxidative stress (OS). Limited options exist for LN. We aimed to identify LN-related OS, highlighting the need for non-invasive diagnostic and therapeutic approaches.Methods LN-differentially expressed g...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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BMJ Publishing Group
2024-04-01
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| Series: | Lupus Science and Medicine |
| Online Access: | https://lupus.bmj.com/content/11/1/e001126.full |
| _version_ | 1797201491739541504 |
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| author | Wei Liu Ye Zhang Yu Zhuang Xiaoyan He Huiqiong Zeng Xiaodong Yan Qianwen Qiu |
| author_facet | Wei Liu Ye Zhang Yu Zhuang Xiaoyan He Huiqiong Zeng Xiaodong Yan Qianwen Qiu |
| author_sort | Wei Liu |
| collection | DOAJ |
| description | Background Lupus nephritis (LN) is a complication of SLE characterised by immune dysfunction and oxidative stress (OS). Limited options exist for LN. We aimed to identify LN-related OS, highlighting the need for non-invasive diagnostic and therapeutic approaches.Methods LN-differentially expressed genes (DEGs) were extracted from Gene Expression Omnibus datasets (GSE32591, GSE112943 and GSE104948) and Molecular Signatures Database for OS-associated DEGs (OSEGs). Functional enrichment analysis was performed for OSEGs related to LN. Weighted gene co-expression network analysis identified hub genes related to OS-LN. These hub OSEGs were refined as biomarker candidates via least absolute shrinkage and selection operator. The predictive value was validated using receiver operating characteristic (ROC) curves and nomogram for LN prognosis. We evaluated LN immune cell infiltration using single-sample gene set enrichment analysis and CIBERSORT. Additionally, gene set enrichment analysis explored the functional enrichment of hub OSEGs in LN.Results The study identified four hub genes, namely STAT1, PRODH, TXN2 and SETX, associated with OS related to LN. These genes were validated for their diagnostic potential, and their involvement in LN pathogenesis was elucidated through ROC and nomogram. Additionally, alterations in immune cell composition in LN correlated with hub OSEG expression were observed. Immunohistochemical analysis reveals that the hub gene is most correlated with activated B cells and CD8 T cells. Finally, we uncovered that the enriched pathways of OSEGs were mainly involved in the PI3K-Akt pathway and the Janus kinase-signal transducer and activator of transcription pathway.Conclusion These findings contribute to advancing our understanding of the complex interplay between OS, immune dysregulation and molecular pathways in LN, laying a foundation for the identification of potential diagnostic biomarkers and therapeutic targets. |
| first_indexed | 2024-04-24T07:48:24Z |
| format | Article |
| id | doaj.art-f0ea55603b2745f4bedf0013efce8cb5 |
| institution | Directory Open Access Journal |
| issn | 2053-8790 |
| language | English |
| last_indexed | 2024-04-24T07:48:24Z |
| publishDate | 2024-04-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | Lupus Science and Medicine |
| spelling | doaj.art-f0ea55603b2745f4bedf0013efce8cb52024-04-18T18:15:07ZengBMJ Publishing GroupLupus Science and Medicine2053-87902024-04-0111110.1136/lupus-2023-001126Machine learning-based identification of novel hub genes associated with oxidative stress in lupus nephritis: implications for diagnosis and therapeutic targetsWei Liu0Ye Zhang1Yu Zhuang2Xiaoyan He3Huiqiong Zeng4Xiaodong Yan5Qianwen Qiu6Department of Ophthalmology, The Third Hospital Affiliated to the Third Military Medical University Department of Ophthalmology, Chongqing, ChinaDepartment of Rehabilitation Medicine, Shanghai Sixth People`s Hospital, Shanghai, ChinaFudan University School of Nursing, Shanghai, ChinaDepartment of Pharmacy, Jiangjin Central Hospital of Chongqing, Chongqing, ChinaTraditional Chinese Medicine Department of Immunology, Women & Children Health Institute Futian Shenzhen, Shenzhen, ChinaSchool of Basic Medical Sciences, Yunnan University of Chinese Medicine, Kunming, ChinaTraditional Chinese Medicine Department of Immunology, Women & Children Health Institute Futian Shenzhen, Shenzhen, ChinaBackground Lupus nephritis (LN) is a complication of SLE characterised by immune dysfunction and oxidative stress (OS). Limited options exist for LN. We aimed to identify LN-related OS, highlighting the need for non-invasive diagnostic and therapeutic approaches.Methods LN-differentially expressed genes (DEGs) were extracted from Gene Expression Omnibus datasets (GSE32591, GSE112943 and GSE104948) and Molecular Signatures Database for OS-associated DEGs (OSEGs). Functional enrichment analysis was performed for OSEGs related to LN. Weighted gene co-expression network analysis identified hub genes related to OS-LN. These hub OSEGs were refined as biomarker candidates via least absolute shrinkage and selection operator. The predictive value was validated using receiver operating characteristic (ROC) curves and nomogram for LN prognosis. We evaluated LN immune cell infiltration using single-sample gene set enrichment analysis and CIBERSORT. Additionally, gene set enrichment analysis explored the functional enrichment of hub OSEGs in LN.Results The study identified four hub genes, namely STAT1, PRODH, TXN2 and SETX, associated with OS related to LN. These genes were validated for their diagnostic potential, and their involvement in LN pathogenesis was elucidated through ROC and nomogram. Additionally, alterations in immune cell composition in LN correlated with hub OSEG expression were observed. Immunohistochemical analysis reveals that the hub gene is most correlated with activated B cells and CD8 T cells. Finally, we uncovered that the enriched pathways of OSEGs were mainly involved in the PI3K-Akt pathway and the Janus kinase-signal transducer and activator of transcription pathway.Conclusion These findings contribute to advancing our understanding of the complex interplay between OS, immune dysregulation and molecular pathways in LN, laying a foundation for the identification of potential diagnostic biomarkers and therapeutic targets.https://lupus.bmj.com/content/11/1/e001126.full |
| spellingShingle | Wei Liu Ye Zhang Yu Zhuang Xiaoyan He Huiqiong Zeng Xiaodong Yan Qianwen Qiu Machine learning-based identification of novel hub genes associated with oxidative stress in lupus nephritis: implications for diagnosis and therapeutic targets Lupus Science and Medicine |
| title | Machine learning-based identification of novel hub genes associated with oxidative stress in lupus nephritis: implications for diagnosis and therapeutic targets |
| title_full | Machine learning-based identification of novel hub genes associated with oxidative stress in lupus nephritis: implications for diagnosis and therapeutic targets |
| title_fullStr | Machine learning-based identification of novel hub genes associated with oxidative stress in lupus nephritis: implications for diagnosis and therapeutic targets |
| title_full_unstemmed | Machine learning-based identification of novel hub genes associated with oxidative stress in lupus nephritis: implications for diagnosis and therapeutic targets |
| title_short | Machine learning-based identification of novel hub genes associated with oxidative stress in lupus nephritis: implications for diagnosis and therapeutic targets |
| title_sort | machine learning based identification of novel hub genes associated with oxidative stress in lupus nephritis implications for diagnosis and therapeutic targets |
| url | https://lupus.bmj.com/content/11/1/e001126.full |
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