Identification of immune microenvironment subtypes and signature genes for Alzheimer’s disease diagnosis and risk prediction based on explainable machine learning
BackgroundUsing interpretable machine learning, we sought to define the immune microenvironment subtypes and distinctive genes in AD.MethodsssGSEA, LASSO regression, and WGCNA algorithms were used to evaluate immune state in AD patients. To predict the fate of AD and identify distinctive genes, six...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-12-01
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| Series: | Frontiers in Immunology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2022.1046410/full |
| _version_ | 1811301556507639808 |
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| author | Yongxing Lai Yongxing Lai Peiqiang Lin Fan Lin Fan Lin Manli Chen Chunjin Lin Chunjin Lin Xing Lin Xing Lin Lijuan Wu Lijuan Wu Mouwei Zheng Mouwei Zheng Jianhao Chen |
| author_facet | Yongxing Lai Yongxing Lai Peiqiang Lin Fan Lin Fan Lin Manli Chen Chunjin Lin Chunjin Lin Xing Lin Xing Lin Lijuan Wu Lijuan Wu Mouwei Zheng Mouwei Zheng Jianhao Chen |
| author_sort | Yongxing Lai |
| collection | DOAJ |
| description | BackgroundUsing interpretable machine learning, we sought to define the immune microenvironment subtypes and distinctive genes in AD.MethodsssGSEA, LASSO regression, and WGCNA algorithms were used to evaluate immune state in AD patients. To predict the fate of AD and identify distinctive genes, six machine learning algorithms were developed. The output of machine learning models was interpreted using the SHAP and LIME algorithms. For external validation, four separate GEO databases were used. We estimated the subgroups of the immunological microenvironment using unsupervised clustering. Further research was done on the variations in immunological microenvironment, enhanced functions and pathways, and therapeutic medicines between these subtypes. Finally, the expression of characteristic genes was verified using the AlzData and pan-cancer databases and RT-PCR analysis.ResultsIt was determined that AD is connected to changes in the immunological microenvironment. WGCNA revealed 31 potential immune genes, of which the greenyellow and blue modules were shown to be most associated with infiltrated immune cells. In the testing set, the XGBoost algorithm had the best performance with an AUC of 0.86 and a P-R value of 0.83. Following the screening of the testing set by machine learning algorithms and the verification of independent datasets, five genes (CXCR4, PPP3R1, HSP90AB1, CXCL10, and S100A12) that were closely associated with AD pathological biomarkers and allowed for the accurate prediction of AD progression were found to be immune microenvironment-related genes. The feature gene-based nomogram may provide clinical advantages to patients. Two immune microenvironment subgroups for AD patients were identified, subtype2 was linked to a metabolic phenotype, subtype1 belonged to the immune-active kind. MK-866 and arachidonyltrifluoromethane were identified as the top treatment agents for subtypes 1 and 2, respectively. These five distinguishing genes were found to be intimately linked to the development of the disease, according to the Alzdata database, pan-cancer research, and RT-PCR analysis.ConclusionThe hub genes associated with the immune microenvironment that are most strongly associated with the progression of pathology in AD are CXCR4, PPP3R1, HSP90AB1, CXCL10, and S100A12. The hypothesized molecular subgroups might offer novel perceptions for individualized AD treatment. |
| first_indexed | 2024-04-13T07:12:09Z |
| format | Article |
| id | doaj.art-32568adaa07c4c1ba9039cf19483fec7 |
| institution | Directory Open Access Journal |
| issn | 1664-3224 |
| language | English |
| last_indexed | 2024-04-13T07:12:09Z |
| publishDate | 2022-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Immunology |
| spelling | doaj.art-32568adaa07c4c1ba9039cf19483fec72022-12-22T02:56:51ZengFrontiers Media S.A.Frontiers in Immunology1664-32242022-12-011310.3389/fimmu.2022.10464101046410Identification of immune microenvironment subtypes and signature genes for Alzheimer’s disease diagnosis and risk prediction based on explainable machine learningYongxing Lai0Yongxing Lai1Peiqiang Lin2Fan Lin3Fan Lin4Manli Chen5Chunjin Lin6Chunjin Lin7Xing Lin8Xing Lin9Lijuan Wu10Lijuan Wu11Mouwei Zheng12Mouwei Zheng13Jianhao Chen14Department of Geriatric Medicine, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou, Fujian, ChinaFujian Provincial Center for Geriatrics, Fujian Provincial Hospital, Fuzhou, Fujian, ChinaDepartment of Neurology, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou, Fujian, ChinaDepartment of Geriatric Medicine, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou, Fujian, ChinaFujian Provincial Center for Geriatrics, Fujian Provincial Hospital, Fuzhou, Fujian, ChinaDepartment of Neurology, Fujian Medical University Union Hospital, Fuzhou, Fujian, ChinaDepartment of Geriatric Medicine, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou, Fujian, ChinaFujian Provincial Center for Geriatrics, Fujian Provincial Hospital, Fuzhou, Fujian, ChinaDepartment of Geriatric Medicine, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou, Fujian, ChinaFujian Provincial Center for Geriatrics, Fujian Provincial Hospital, Fuzhou, Fujian, ChinaDepartment of Geriatric Medicine, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou, Fujian, ChinaFujian Provincial Center for Geriatrics, Fujian Provincial Hospital, Fuzhou, Fujian, ChinaDepartment of Geriatric Medicine, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou, Fujian, ChinaFujian Provincial Center for Geriatrics, Fujian Provincial Hospital, Fuzhou, Fujian, ChinaDepartment of Rehabilitation Medicine, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou, Fujian, ChinaBackgroundUsing interpretable machine learning, we sought to define the immune microenvironment subtypes and distinctive genes in AD.MethodsssGSEA, LASSO regression, and WGCNA algorithms were used to evaluate immune state in AD patients. To predict the fate of AD and identify distinctive genes, six machine learning algorithms were developed. The output of machine learning models was interpreted using the SHAP and LIME algorithms. For external validation, four separate GEO databases were used. We estimated the subgroups of the immunological microenvironment using unsupervised clustering. Further research was done on the variations in immunological microenvironment, enhanced functions and pathways, and therapeutic medicines between these subtypes. Finally, the expression of characteristic genes was verified using the AlzData and pan-cancer databases and RT-PCR analysis.ResultsIt was determined that AD is connected to changes in the immunological microenvironment. WGCNA revealed 31 potential immune genes, of which the greenyellow and blue modules were shown to be most associated with infiltrated immune cells. In the testing set, the XGBoost algorithm had the best performance with an AUC of 0.86 and a P-R value of 0.83. Following the screening of the testing set by machine learning algorithms and the verification of independent datasets, five genes (CXCR4, PPP3R1, HSP90AB1, CXCL10, and S100A12) that were closely associated with AD pathological biomarkers and allowed for the accurate prediction of AD progression were found to be immune microenvironment-related genes. The feature gene-based nomogram may provide clinical advantages to patients. Two immune microenvironment subgroups for AD patients were identified, subtype2 was linked to a metabolic phenotype, subtype1 belonged to the immune-active kind. MK-866 and arachidonyltrifluoromethane were identified as the top treatment agents for subtypes 1 and 2, respectively. These five distinguishing genes were found to be intimately linked to the development of the disease, according to the Alzdata database, pan-cancer research, and RT-PCR analysis.ConclusionThe hub genes associated with the immune microenvironment that are most strongly associated with the progression of pathology in AD are CXCR4, PPP3R1, HSP90AB1, CXCL10, and S100A12. The hypothesized molecular subgroups might offer novel perceptions for individualized AD treatment.https://www.frontiersin.org/articles/10.3389/fimmu.2022.1046410/fullalzheimer’s diseaseimmune microenvironmentcharacteristic genesmachine learningimmune subtypes |
| spellingShingle | Yongxing Lai Yongxing Lai Peiqiang Lin Fan Lin Fan Lin Manli Chen Chunjin Lin Chunjin Lin Xing Lin Xing Lin Lijuan Wu Lijuan Wu Mouwei Zheng Mouwei Zheng Jianhao Chen Identification of immune microenvironment subtypes and signature genes for Alzheimer’s disease diagnosis and risk prediction based on explainable machine learning Frontiers in Immunology alzheimer’s disease immune microenvironment characteristic genes machine learning immune subtypes |
| title | Identification of immune microenvironment subtypes and signature genes for Alzheimer’s disease diagnosis and risk prediction based on explainable machine learning |
| title_full | Identification of immune microenvironment subtypes and signature genes for Alzheimer’s disease diagnosis and risk prediction based on explainable machine learning |
| title_fullStr | Identification of immune microenvironment subtypes and signature genes for Alzheimer’s disease diagnosis and risk prediction based on explainable machine learning |
| title_full_unstemmed | Identification of immune microenvironment subtypes and signature genes for Alzheimer’s disease diagnosis and risk prediction based on explainable machine learning |
| title_short | Identification of immune microenvironment subtypes and signature genes for Alzheimer’s disease diagnosis and risk prediction based on explainable machine learning |
| title_sort | identification of immune microenvironment subtypes and signature genes for alzheimer s disease diagnosis and risk prediction based on explainable machine learning |
| topic | alzheimer’s disease immune microenvironment characteristic genes machine learning immune subtypes |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2022.1046410/full |
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