{"title":"系统性幼年类风湿关节炎中 Th2/Th17 细胞相关基因的综合特征:使用多种机器学习方法从孟德尔随机化和转录组数据中获取证据","authors":"Mei Wang, Jing Wang, Fei Lv, Aifeng Song, Wurihan Bao, Huiyun Li, Yongsheng Xu","doi":"10.2147/IJGM.S482288","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Growing research has demonstrated that alterations in Th2 and Th17 cell composition were linked to systemic juvenile rheumatoid arthritis (sJRA). Nevertheless, whether these associations indicate a causal link remains unclear, and the potential effects of Th2/Th17-related molecules have not been clarified.</p><p><strong>Methods: </strong>Mendelian randomization (MR) alongside transcriptome examination was implemented to ascertain the links between the Th2/Th17 cells and sJRA. Subsequently, we established an innovative machine learning (ML) framework encompassing 12 ML approaches and their 111 permutations to generate a unified Th2/Th17 classifier, which underwent verification across three separate cohorts. The hub Th2/Th17-related genes' level in the sJRA patients was substantiated via qRT-PCR. Lastly, the SHapley Additive exPlanations (SHAP) in conjunction with the XGBoost algorithm to pinpoint ideal Th2/Th17-linked genes.</p><p><strong>Results: </strong>Based on MR analyses of two sJRA GWAS, 2 immunophenotypes (lymphocyte and IgD+ B cell) were causally linked to sJRA. Based on IOBR algorithms, we revealed that lymphocyte Th2/Th17 proportion was markedly changed in sJRA from seven cohorts. WGCNA and differential analysis in two merged GEO cohorts identified 64 Th2/Th17-related genes. Based on the average AUC (0.844) and model stability in four cohorts, we converted 12 ML techniques into 111 combinations, from which we chose the optimal algorithm to generate an ML-derived diagnostic signature (Th2/Th17 classifier). qRT-PCR verified results. Moreover, immune cell infiltration and functional enrichment analysis suggested hub Th2/Th17-related gene potentially mediated sJRA onset. XGBoost algorithm and SHAP detected HRH2 as crucial genetic markers, which may be an important target for sJRA.</p><p><strong>Conclusion: </strong>A diagnostic model (Th2/Th17 classifier) via 111 ML algorithm combinations in six independent cohorts was generated and validated, which stands as an effective instrument for sJRA detection. The identification of essential immune components and molecular cascades, along with HRH2, could emerge as vital therapeutic targets for sJRA intervention, providing an enhanced understanding of its fundamental processes.</p>","PeriodicalId":14131,"journal":{"name":"International Journal of General Medicine","volume":"17 ","pages":"5973-5996"},"PeriodicalIF":2.1000,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11645899/pdf/","citationCount":"0","resultStr":"{\"title\":\"Comprehensive Characterization of Th2/Th17 Cells-Related Gene in Systemic Juvenile Rheumatoid Arthritis: Evidence from Mendelian Randomization and Transcriptome Data Using Multiple Machine Learning Approaches.\",\"authors\":\"Mei Wang, Jing Wang, Fei Lv, Aifeng Song, Wurihan Bao, Huiyun Li, Yongsheng Xu\",\"doi\":\"10.2147/IJGM.S482288\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Growing research has demonstrated that alterations in Th2 and Th17 cell composition were linked to systemic juvenile rheumatoid arthritis (sJRA). Nevertheless, whether these associations indicate a causal link remains unclear, and the potential effects of Th2/Th17-related molecules have not been clarified.</p><p><strong>Methods: </strong>Mendelian randomization (MR) alongside transcriptome examination was implemented to ascertain the links between the Th2/Th17 cells and sJRA. Subsequently, we established an innovative machine learning (ML) framework encompassing 12 ML approaches and their 111 permutations to generate a unified Th2/Th17 classifier, which underwent verification across three separate cohorts. The hub Th2/Th17-related genes' level in the sJRA patients was substantiated via qRT-PCR. Lastly, the SHapley Additive exPlanations (SHAP) in conjunction with the XGBoost algorithm to pinpoint ideal Th2/Th17-linked genes.</p><p><strong>Results: </strong>Based on MR analyses of two sJRA GWAS, 2 immunophenotypes (lymphocyte and IgD+ B cell) were causally linked to sJRA. Based on IOBR algorithms, we revealed that lymphocyte Th2/Th17 proportion was markedly changed in sJRA from seven cohorts. WGCNA and differential analysis in two merged GEO cohorts identified 64 Th2/Th17-related genes. Based on the average AUC (0.844) and model stability in four cohorts, we converted 12 ML techniques into 111 combinations, from which we chose the optimal algorithm to generate an ML-derived diagnostic signature (Th2/Th17 classifier). qRT-PCR verified results. Moreover, immune cell infiltration and functional enrichment analysis suggested hub Th2/Th17-related gene potentially mediated sJRA onset. XGBoost algorithm and SHAP detected HRH2 as crucial genetic markers, which may be an important target for sJRA.</p><p><strong>Conclusion: </strong>A diagnostic model (Th2/Th17 classifier) via 111 ML algorithm combinations in six independent cohorts was generated and validated, which stands as an effective instrument for sJRA detection. The identification of essential immune components and molecular cascades, along with HRH2, could emerge as vital therapeutic targets for sJRA intervention, providing an enhanced understanding of its fundamental processes.</p>\",\"PeriodicalId\":14131,\"journal\":{\"name\":\"International Journal of General Medicine\",\"volume\":\"17 \",\"pages\":\"5973-5996\"},\"PeriodicalIF\":2.1000,\"publicationDate\":\"2024-12-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11645899/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of General Medicine\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.2147/IJGM.S482288\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q2\",\"JCRName\":\"MEDICINE, GENERAL & INTERNAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of General Medicine","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.2147/IJGM.S482288","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"MEDICINE, GENERAL & INTERNAL","Score":null,"Total":0}
Comprehensive Characterization of Th2/Th17 Cells-Related Gene in Systemic Juvenile Rheumatoid Arthritis: Evidence from Mendelian Randomization and Transcriptome Data Using Multiple Machine Learning Approaches.
Background: Growing research has demonstrated that alterations in Th2 and Th17 cell composition were linked to systemic juvenile rheumatoid arthritis (sJRA). Nevertheless, whether these associations indicate a causal link remains unclear, and the potential effects of Th2/Th17-related molecules have not been clarified.
Methods: Mendelian randomization (MR) alongside transcriptome examination was implemented to ascertain the links between the Th2/Th17 cells and sJRA. Subsequently, we established an innovative machine learning (ML) framework encompassing 12 ML approaches and their 111 permutations to generate a unified Th2/Th17 classifier, which underwent verification across three separate cohorts. The hub Th2/Th17-related genes' level in the sJRA patients was substantiated via qRT-PCR. Lastly, the SHapley Additive exPlanations (SHAP) in conjunction with the XGBoost algorithm to pinpoint ideal Th2/Th17-linked genes.
Results: Based on MR analyses of two sJRA GWAS, 2 immunophenotypes (lymphocyte and IgD+ B cell) were causally linked to sJRA. Based on IOBR algorithms, we revealed that lymphocyte Th2/Th17 proportion was markedly changed in sJRA from seven cohorts. WGCNA and differential analysis in two merged GEO cohorts identified 64 Th2/Th17-related genes. Based on the average AUC (0.844) and model stability in four cohorts, we converted 12 ML techniques into 111 combinations, from which we chose the optimal algorithm to generate an ML-derived diagnostic signature (Th2/Th17 classifier). qRT-PCR verified results. Moreover, immune cell infiltration and functional enrichment analysis suggested hub Th2/Th17-related gene potentially mediated sJRA onset. XGBoost algorithm and SHAP detected HRH2 as crucial genetic markers, which may be an important target for sJRA.
Conclusion: A diagnostic model (Th2/Th17 classifier) via 111 ML algorithm combinations in six independent cohorts was generated and validated, which stands as an effective instrument for sJRA detection. The identification of essential immune components and molecular cascades, along with HRH2, could emerge as vital therapeutic targets for sJRA intervention, providing an enhanced understanding of its fundamental processes.
期刊介绍:
The International Journal of General Medicine is an international, peer-reviewed, open access journal that focuses on general and internal medicine, pathogenesis, epidemiology, diagnosis, monitoring and treatment protocols. The journal is characterized by the rapid reporting of reviews, original research and clinical studies across all disease areas.
A key focus of the journal is the elucidation of disease processes and management protocols resulting in improved outcomes for the patient. Patient perspectives such as satisfaction, quality of life, health literacy and communication and their role in developing new healthcare programs and optimizing clinical outcomes are major areas of interest for the journal.
As of 1st April 2019, the International Journal of General Medicine will no longer consider meta-analyses for publication.