Identification of novel markers for neuroblastoma immunoclustering using machine learning
BackgroundDue to the unique heterogeneity of neuroblastoma, its treatment and prognosis are closely related to the biological behavior of the tumor. However, the effect of the tumor immune microenvironment on neuroblastoma needs to be investigated, and there is a lack of biomarkers to reflect the co...
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Frontiers Media S.A.
2024-11-01
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| Series: | Frontiers in Immunology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2024.1446273/full |
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| author | Longguo Zhang Longguo Zhang Huixin Li Huixin Li Fangyan Sun Fangyan Sun Qiuping Wu Qiuping Wu Leigang Jin Leigang Jin Aimin Xu Aimin Xu Jiarui Chen Ranyao Yang Ranyao Yang Ranyao Yang |
| author_facet | Longguo Zhang Longguo Zhang Huixin Li Huixin Li Fangyan Sun Fangyan Sun Qiuping Wu Qiuping Wu Leigang Jin Leigang Jin Aimin Xu Aimin Xu Jiarui Chen Ranyao Yang Ranyao Yang Ranyao Yang |
| author_sort | Longguo Zhang |
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| description | BackgroundDue to the unique heterogeneity of neuroblastoma, its treatment and prognosis are closely related to the biological behavior of the tumor. However, the effect of the tumor immune microenvironment on neuroblastoma needs to be investigated, and there is a lack of biomarkers to reflect the condition of the tumor immune microenvironment.MethodsThe GEO Database was used to download transcriptome data (both training dataset and test dataset) on neuroblastoma. Immunity scores were calculated for each sample using ssGSEA, and hierarchical clustering was used to categorize the samples into high and low immunity groups. Subsequently, the differences in clinicopathological characteristics and treatment between the different groups were examined. Three machine learning algorithms (LASSO, SVM-RFE, and Random Forest) were used to screen biomarkers and synthesize their function in neuroblastoma.ResultsIn the training set, there were 362 samples in the immunity_L group and 136 samples in the immunity_H group, with differences in age, MYCN status, etc. Additionally, the tumor microenvironment can also affect the therapeutic response of neuroblastoma. Six characteristic genes (BATF, CXCR3, GIMAP5, GPR18, ISG20, and IGHM) were identified by machine learning, and these genes are associated with multiple immune-related pathways and immune cells in neuroblastoma.ConclusionsBATF, CXCR3, GIMAP5, GPR18, ISG20, and IGHM may serve as biomarkers that reflect the conditions of the immune microenvironment of neuroblastoma and hold promise in guiding neuroblastoma treatment. |
| format | Article |
| id | doaj-art-13cc29a86f174eba877839c6d94a5f6e |
| institution | OA Journals |
| issn | 1664-3224 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Immunology |
| spelling | doaj-art-13cc29a86f174eba877839c6d94a5f6e2025-08-20T02:17:59ZengFrontiers Media S.A.Frontiers in Immunology1664-32242024-11-011510.3389/fimmu.2024.14462731446273Identification of novel markers for neuroblastoma immunoclustering using machine learningLongguo Zhang0Longguo Zhang1Huixin Li2Huixin Li3Fangyan Sun4Fangyan Sun5Qiuping Wu6Qiuping Wu7Leigang Jin8Leigang Jin9Aimin Xu10Aimin Xu11Jiarui Chen12Ranyao Yang13Ranyao Yang14Ranyao Yang15State Key Laboratory of Pharmaceutical Biotechnology, The University of Hong Kong, Hong Kong, Hong Kong SAR, ChinaDepartment of Medicine, The University of Hong Kong, Hong Kong, Hong Kong SAR, ChinaState Key Laboratory of Pharmaceutical Biotechnology, The University of Hong Kong, Hong Kong, Hong Kong SAR, ChinaDepartment of Medicine, The University of Hong Kong, Hong Kong, Hong Kong SAR, ChinaState Key Laboratory of Pharmaceutical Biotechnology, The University of Hong Kong, Hong Kong, Hong Kong SAR, ChinaDepartment of Medicine, The University of Hong Kong, Hong Kong, Hong Kong SAR, ChinaState Key Laboratory of Pharmaceutical Biotechnology, The University of Hong Kong, Hong Kong, Hong Kong SAR, ChinaDepartment of Medicine, The University of Hong Kong, Hong Kong, Hong Kong SAR, ChinaState Key Laboratory of Pharmaceutical Biotechnology, The University of Hong Kong, Hong Kong, Hong Kong SAR, ChinaDepartment of Medicine, The University of Hong Kong, Hong Kong, Hong Kong SAR, ChinaState Key Laboratory of Pharmaceutical Biotechnology, The University of Hong Kong, Hong Kong, Hong Kong SAR, ChinaDepartment of Medicine, The University of Hong Kong, Hong Kong, Hong Kong SAR, ChinaGuangdong Provincial Key Laboratory of Food, Nutrition and Health, and Department of Nutrition, School of Public Health, Sun Yat-sen University, Guangzhou, ChinaState Key Laboratory of Pharmaceutical Biotechnology, The University of Hong Kong, Hong Kong, Hong Kong SAR, ChinaDepartment of Medicine, The University of Hong Kong, Hong Kong, Hong Kong SAR, ChinaDepartment of Clinical Pharmacy, Jining First People’s Hospital, Shandong First Medical University, Jining, ChinaBackgroundDue to the unique heterogeneity of neuroblastoma, its treatment and prognosis are closely related to the biological behavior of the tumor. However, the effect of the tumor immune microenvironment on neuroblastoma needs to be investigated, and there is a lack of biomarkers to reflect the condition of the tumor immune microenvironment.MethodsThe GEO Database was used to download transcriptome data (both training dataset and test dataset) on neuroblastoma. Immunity scores were calculated for each sample using ssGSEA, and hierarchical clustering was used to categorize the samples into high and low immunity groups. Subsequently, the differences in clinicopathological characteristics and treatment between the different groups were examined. Three machine learning algorithms (LASSO, SVM-RFE, and Random Forest) were used to screen biomarkers and synthesize their function in neuroblastoma.ResultsIn the training set, there were 362 samples in the immunity_L group and 136 samples in the immunity_H group, with differences in age, MYCN status, etc. Additionally, the tumor microenvironment can also affect the therapeutic response of neuroblastoma. Six characteristic genes (BATF, CXCR3, GIMAP5, GPR18, ISG20, and IGHM) were identified by machine learning, and these genes are associated with multiple immune-related pathways and immune cells in neuroblastoma.ConclusionsBATF, CXCR3, GIMAP5, GPR18, ISG20, and IGHM may serve as biomarkers that reflect the conditions of the immune microenvironment of neuroblastoma and hold promise in guiding neuroblastoma treatment.https://www.frontiersin.org/articles/10.3389/fimmu.2024.1446273/fullbiomarkertumor microenvironmentimmunoclusteringmachine learningneuroblastoma |
| spellingShingle | Longguo Zhang Longguo Zhang Huixin Li Huixin Li Fangyan Sun Fangyan Sun Qiuping Wu Qiuping Wu Leigang Jin Leigang Jin Aimin Xu Aimin Xu Jiarui Chen Ranyao Yang Ranyao Yang Ranyao Yang Identification of novel markers for neuroblastoma immunoclustering using machine learning Frontiers in Immunology biomarker tumor microenvironment immunoclustering machine learning neuroblastoma |
| title | Identification of novel markers for neuroblastoma immunoclustering using machine learning |
| title_full | Identification of novel markers for neuroblastoma immunoclustering using machine learning |
| title_fullStr | Identification of novel markers for neuroblastoma immunoclustering using machine learning |
| title_full_unstemmed | Identification of novel markers for neuroblastoma immunoclustering using machine learning |
| title_short | Identification of novel markers for neuroblastoma immunoclustering using machine learning |
| title_sort | identification of novel markers for neuroblastoma immunoclustering using machine learning |
| topic | biomarker tumor microenvironment immunoclustering machine learning neuroblastoma |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2024.1446273/full |
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