An Integrated Analysis of Tumor Purity of Common Central Nervous System Tumors in Children Based on Machine Learning Methods
Background: Tumor purity is defined as the proportion of cancer cells in the tumor tissue, and its effects on molecular genetics, the immune microenvironment, and the prognosis of children’s central nervous system (CNS) tumors are under-researched.Methods: We applied random forest machine learning,...
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Frontiers Media S.A.
2021
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oai:doaj.org-article:362a997897724957843f32eb2199605b2021-12-03T05:59:02ZAn Integrated Analysis of Tumor Purity of Common Central Nervous System Tumors in Children Based on Machine Learning Methods1664-802110.3389/fgene.2021.707802https://doaj.org/article/362a997897724957843f32eb2199605b2021-12-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fgene.2021.707802/fullhttps://doaj.org/toc/1664-8021Background: Tumor purity is defined as the proportion of cancer cells in the tumor tissue, and its effects on molecular genetics, the immune microenvironment, and the prognosis of children’s central nervous system (CNS) tumors are under-researched.Methods: We applied random forest machine learning, the InfiniumPurify algorithm, and the ESTIMATE algorithm to estimate the tumor purity of every child’s CNS tumor sample in several published pediatric CNS tumor sample datasets from Gene Expression Omnibus (GEO), aiming to perform an integrated analysis on the tumor purity of children’s CNS tumors.Results: Only the purity of CNS tumors in children based on the random forest (RF) machine learning method was normally distributed. In addition, the children’s CNS tumor purity was associated with primary clinical pathological and molecular indicators. Enrichment analysis of biological pathways related to the purity of medulloblastoma (MB) revealed some classical signaling pathways associated with MB biology and development-related pathways. According to the correlation analysis between MB purity and the immune microenvironment, three immune-related genes, namely, CD8A, CXCR2, and TNFRSF14, were negatively related to MB purity. In contrast, no significant correlation was detected between immunotherapy-associated markers, such as PD-1, PD-L1, and CTLA4; most infiltrating immune cells; and MB purity. In the tumor purity–related survival analysis of MB, ependymoma (EPN), and children’s high-grade glioma, we discovered a minor effect of tumor purity on the survival of the aforementioned pediatric patients with CNS tumors.Conclusion: Our purity pediatric pan-CNS tumor analysis provides a deeper understanding and helps with the clinical management of pediatric CNS tumors.Jian YangJiajia WangShuaiwei TianQinhua WangYang ZhaoBaocheng WangLiangliang CaoZhuangzhuang LiangHeng ZhaoHao LianJie MaFrontiers Media S.A.articlepediatriccentral nervous system tumorsmedulloblastomatumor puritymachine learningGeneticsQH426-470ENFrontiers in Genetics, Vol 12 (2021) |
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DOAJ |
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DOAJ |
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EN |
| topic |
pediatric central nervous system tumors medulloblastoma tumor purity machine learning Genetics QH426-470 |
| spellingShingle |
pediatric central nervous system tumors medulloblastoma tumor purity machine learning Genetics QH426-470 Jian Yang Jiajia Wang Shuaiwei Tian Qinhua Wang Yang Zhao Baocheng Wang Liangliang Cao Zhuangzhuang Liang Heng Zhao Hao Lian Jie Ma An Integrated Analysis of Tumor Purity of Common Central Nervous System Tumors in Children Based on Machine Learning Methods |
| description |
Background: Tumor purity is defined as the proportion of cancer cells in the tumor tissue, and its effects on molecular genetics, the immune microenvironment, and the prognosis of children’s central nervous system (CNS) tumors are under-researched.Methods: We applied random forest machine learning, the InfiniumPurify algorithm, and the ESTIMATE algorithm to estimate the tumor purity of every child’s CNS tumor sample in several published pediatric CNS tumor sample datasets from Gene Expression Omnibus (GEO), aiming to perform an integrated analysis on the tumor purity of children’s CNS tumors.Results: Only the purity of CNS tumors in children based on the random forest (RF) machine learning method was normally distributed. In addition, the children’s CNS tumor purity was associated with primary clinical pathological and molecular indicators. Enrichment analysis of biological pathways related to the purity of medulloblastoma (MB) revealed some classical signaling pathways associated with MB biology and development-related pathways. According to the correlation analysis between MB purity and the immune microenvironment, three immune-related genes, namely, CD8A, CXCR2, and TNFRSF14, were negatively related to MB purity. In contrast, no significant correlation was detected between immunotherapy-associated markers, such as PD-1, PD-L1, and CTLA4; most infiltrating immune cells; and MB purity. In the tumor purity–related survival analysis of MB, ependymoma (EPN), and children’s high-grade glioma, we discovered a minor effect of tumor purity on the survival of the aforementioned pediatric patients with CNS tumors.Conclusion: Our purity pediatric pan-CNS tumor analysis provides a deeper understanding and helps with the clinical management of pediatric CNS tumors. |
| format |
article |
| author |
Jian Yang Jiajia Wang Shuaiwei Tian Qinhua Wang Yang Zhao Baocheng Wang Liangliang Cao Zhuangzhuang Liang Heng Zhao Hao Lian Jie Ma |
| author_facet |
Jian Yang Jiajia Wang Shuaiwei Tian Qinhua Wang Yang Zhao Baocheng Wang Liangliang Cao Zhuangzhuang Liang Heng Zhao Hao Lian Jie Ma |
| author_sort |
Jian Yang |
| title |
An Integrated Analysis of Tumor Purity of Common Central Nervous System Tumors in Children Based on Machine Learning Methods |
| title_short |
An Integrated Analysis of Tumor Purity of Common Central Nervous System Tumors in Children Based on Machine Learning Methods |
| title_full |
An Integrated Analysis of Tumor Purity of Common Central Nervous System Tumors in Children Based on Machine Learning Methods |
| title_fullStr |
An Integrated Analysis of Tumor Purity of Common Central Nervous System Tumors in Children Based on Machine Learning Methods |
| title_full_unstemmed |
An Integrated Analysis of Tumor Purity of Common Central Nervous System Tumors in Children Based on Machine Learning Methods |
| title_sort |
integrated analysis of tumor purity of common central nervous system tumors in children based on machine learning methods |
| publisher |
Frontiers Media S.A. |
| publishDate |
2021 |
| url |
https://doaj.org/article/362a997897724957843f32eb2199605b |
| work_keys_str_mv |
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