An immunogenic cell death-related regulators classification patterns and immune microenvironment infiltration characterization in intracranial aneurysm based on machine learning
BackgroundImmunogenic Cell Death (ICD) is a novel way to regulate cell death and can sufficiently activate adaptive immune responses. Its role in immunity is still emerging. However, the involvement of ICD in Intracranial Aneurysms (IA) remains unclear. This study aimed to identify biomarkers associ...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-09-01
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| Series: | Frontiers in Immunology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2022.1001320/full |
| _version_ | 1828234920191852544 |
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| author | Mirzat Turhon Mirzat Turhon Aierpati Maimaiti Dilmurat Gheyret Aximujiang Axier Nizamidingjiang Rexiati Kaheerman Kadeer Riqing Su Zengliang Wang Xiaohong Chen Xiaojiang Cheng Yisen Zhang Yisen Zhang Maimaitili Aisha |
| author_facet | Mirzat Turhon Mirzat Turhon Aierpati Maimaiti Dilmurat Gheyret Aximujiang Axier Nizamidingjiang Rexiati Kaheerman Kadeer Riqing Su Zengliang Wang Xiaohong Chen Xiaojiang Cheng Yisen Zhang Yisen Zhang Maimaitili Aisha |
| author_sort | Mirzat Turhon |
| collection | DOAJ |
| description | BackgroundImmunogenic Cell Death (ICD) is a novel way to regulate cell death and can sufficiently activate adaptive immune responses. Its role in immunity is still emerging. However, the involvement of ICD in Intracranial Aneurysms (IA) remains unclear. This study aimed to identify biomarkers associated with ICDs and determine the relationship between them and the immune microenvironment during the onset and progression of IAMethodsThe IA gene expression profiles were obtained from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) in IA were identified and the effects of the ICD on immune microenvironment signatures were studied. Techniques like Lasso, Bayes, DT, FDA, GBM, NNET, RG, SVM, LR, and multivariate analysis were used to identify the ICD gene signatures in IA. A consensus clustering algorithm was used for conducting the unsupervised cluster analysis of the ICD patterns in IA. Furthermore, enrichment analysis was carried out for investigating the various immune responses and other functional pathways. Along with functional annotation, the weighted gene co-expression network analysis (WGCNA), protein-protein interaction (PPI) network and module construction, identification of the hub gene, and co-expression analysis were also carried out.ResultsThe above techniques were used for establishing the ICD gene signatures of HMGB1, HMGN1, IL33, BCL2, HSPA4, PANX1, TLR9, CLEC7A, and NLRP3 that could easily distinguish IA from normal samples. The unsupervised cluster analysis helped in identifying three ICD gene patterns in different datasets. Gene enrichment analysis revealed that the IA samples showed many differences in pathways such as the cytokine-cytokine receptor interaction, regulation of actin cytoskeleton, chemokine signaling pathway, NOD-like receptor signaling pathway, viral protein interaction with the cytokines and cytokine receptors, and a few other signaling pathways compared to normal samples. In addition, the three ICD modification modes showed obvious differences in their immune microenvironment and the biological function pathways. Eight ICD-regulators were identified and showed meaningful associations with IA, suggesting they could severe as potential prognostic biomarkers.ConclusionsA new gene signature for IA based on ICD features was created. This signature shows that the ICD pattern and the immune microenvironment are closely related to IA and provide a basis for optimizing risk monitoring, clinical decision-making, and developing novel treatment strategies for patients with IA. |
| first_indexed | 2024-04-12T20:04:30Z |
| format | Article |
| id | doaj.art-499099b8a92840fdbbce403e48009a56 |
| institution | Directory Open Access Journal |
| issn | 1664-3224 |
| language | English |
| last_indexed | 2024-04-12T20:04:30Z |
| publishDate | 2022-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Immunology |
| spelling | doaj.art-499099b8a92840fdbbce403e48009a562022-12-22T03:18:26ZengFrontiers Media S.A.Frontiers in Immunology1664-32242022-09-011310.3389/fimmu.2022.10013201001320An immunogenic cell death-related regulators classification patterns and immune microenvironment infiltration characterization in intracranial aneurysm based on machine learningMirzat Turhon0Mirzat Turhon1Aierpati Maimaiti2Dilmurat Gheyret3Aximujiang Axier4Nizamidingjiang Rexiati5Kaheerman Kadeer6Riqing Su7Zengliang Wang8Xiaohong Chen9Xiaojiang Cheng10Yisen Zhang11Yisen Zhang12Maimaitili Aisha13Department of Neurointerventional Surgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, ChinaDepartment of Neurointerventional Surgery, Beijing Tiantan hospital, Capital Medical University, Beijing, ChinaDepartment of Neurosurgery, Neurosurgery Centre, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, ChinaDepartment of Neurosurgery, Neurosurgery Centre, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, ChinaDepartment of Neurosurgery, Neurosurgery Centre, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, ChinaDepartment of Neurosurgery, Neurosurgery Centre, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, ChinaDepartment of Neurosurgery, Neurosurgery Centre, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, ChinaDepartment of Neurosurgery, Neurosurgery Centre, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, ChinaDepartment of Neurosurgery, Neurosurgery Centre, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, ChinaDepartment of Neurosurgery, Neurosurgery Centre, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, ChinaDepartment of Neurosurgery, Neurosurgery Centre, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, ChinaDepartment of Neurointerventional Surgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, ChinaDepartment of Neurointerventional Surgery, Beijing Tiantan hospital, Capital Medical University, Beijing, ChinaDepartment of Neurosurgery, Neurosurgery Centre, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, ChinaBackgroundImmunogenic Cell Death (ICD) is a novel way to regulate cell death and can sufficiently activate adaptive immune responses. Its role in immunity is still emerging. However, the involvement of ICD in Intracranial Aneurysms (IA) remains unclear. This study aimed to identify biomarkers associated with ICDs and determine the relationship between them and the immune microenvironment during the onset and progression of IAMethodsThe IA gene expression profiles were obtained from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) in IA were identified and the effects of the ICD on immune microenvironment signatures were studied. Techniques like Lasso, Bayes, DT, FDA, GBM, NNET, RG, SVM, LR, and multivariate analysis were used to identify the ICD gene signatures in IA. A consensus clustering algorithm was used for conducting the unsupervised cluster analysis of the ICD patterns in IA. Furthermore, enrichment analysis was carried out for investigating the various immune responses and other functional pathways. Along with functional annotation, the weighted gene co-expression network analysis (WGCNA), protein-protein interaction (PPI) network and module construction, identification of the hub gene, and co-expression analysis were also carried out.ResultsThe above techniques were used for establishing the ICD gene signatures of HMGB1, HMGN1, IL33, BCL2, HSPA4, PANX1, TLR9, CLEC7A, and NLRP3 that could easily distinguish IA from normal samples. The unsupervised cluster analysis helped in identifying three ICD gene patterns in different datasets. Gene enrichment analysis revealed that the IA samples showed many differences in pathways such as the cytokine-cytokine receptor interaction, regulation of actin cytoskeleton, chemokine signaling pathway, NOD-like receptor signaling pathway, viral protein interaction with the cytokines and cytokine receptors, and a few other signaling pathways compared to normal samples. In addition, the three ICD modification modes showed obvious differences in their immune microenvironment and the biological function pathways. Eight ICD-regulators were identified and showed meaningful associations with IA, suggesting they could severe as potential prognostic biomarkers.ConclusionsA new gene signature for IA based on ICD features was created. This signature shows that the ICD pattern and the immune microenvironment are closely related to IA and provide a basis for optimizing risk monitoring, clinical decision-making, and developing novel treatment strategies for patients with IA.https://www.frontiersin.org/articles/10.3389/fimmu.2022.1001320/fullimmunogenic cell deathintracranial aneurysmimmune microenvironmentrisk signaturemachine learning |
| spellingShingle | Mirzat Turhon Mirzat Turhon Aierpati Maimaiti Dilmurat Gheyret Aximujiang Axier Nizamidingjiang Rexiati Kaheerman Kadeer Riqing Su Zengliang Wang Xiaohong Chen Xiaojiang Cheng Yisen Zhang Yisen Zhang Maimaitili Aisha An immunogenic cell death-related regulators classification patterns and immune microenvironment infiltration characterization in intracranial aneurysm based on machine learning Frontiers in Immunology immunogenic cell death intracranial aneurysm immune microenvironment risk signature machine learning |
| title | An immunogenic cell death-related regulators classification patterns and immune microenvironment infiltration characterization in intracranial aneurysm based on machine learning |
| title_full | An immunogenic cell death-related regulators classification patterns and immune microenvironment infiltration characterization in intracranial aneurysm based on machine learning |
| title_fullStr | An immunogenic cell death-related regulators classification patterns and immune microenvironment infiltration characterization in intracranial aneurysm based on machine learning |
| title_full_unstemmed | An immunogenic cell death-related regulators classification patterns and immune microenvironment infiltration characterization in intracranial aneurysm based on machine learning |
| title_short | An immunogenic cell death-related regulators classification patterns and immune microenvironment infiltration characterization in intracranial aneurysm based on machine learning |
| title_sort | immunogenic cell death related regulators classification patterns and immune microenvironment infiltration characterization in intracranial aneurysm based on machine learning |
| topic | immunogenic cell death intracranial aneurysm immune microenvironment risk signature machine learning |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2022.1001320/full |
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