Identifying Key Somatic Copy Number Alterations Driving Dysregulation of Cancer Hallmarks in Lower-Grade Glioma
Somatic copy-number alterations (SCNAs) are major contributors to cancer development that are pervasive and highly heterogeneous in human cancers. However, the driver roles of SCNAs in cancer are insufficiently characterized. We combined network propagation and linear regression models to design an...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-06-01
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| Series: | Frontiers in Genetics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2021.654736/full |
| _version_ | 1819103152158277632 |
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| author | Yao Zhou Shuai Wang Haoteng Yan Bo Pang Xinxin Zhang Lin Pang Yihan Wang Jinyuan Xu Jing Hu Yujia Lan Yanyan Ping |
| author_facet | Yao Zhou Shuai Wang Haoteng Yan Bo Pang Xinxin Zhang Lin Pang Yihan Wang Jinyuan Xu Jing Hu Yujia Lan Yanyan Ping |
| author_sort | Yao Zhou |
| collection | DOAJ |
| description | Somatic copy-number alterations (SCNAs) are major contributors to cancer development that are pervasive and highly heterogeneous in human cancers. However, the driver roles of SCNAs in cancer are insufficiently characterized. We combined network propagation and linear regression models to design an integrative strategy to identify driver SCNAs and dissect the functional roles of SCNAs by integrating profiles of copy number and gene expression in lower-grade glioma (LGG). We applied our strategy to 511 LGG patients and identified 98 driver genes that dysregulated 29 cancer hallmark signatures, forming 143 active gene-hallmark pairs. We found that these active gene-hallmark pairs could stratify LGG patients into four subtypes with significantly different survival times. The two new subtypes with similar poorest prognoses were driven by two different gene sets (one including EGFR, CDKN2A, CDKN2B, INFA8, and INFA5, and the other including CDK4, AVIL, and DTX3), respectively. The SCNAs of the two gene sets could disorder the same cancer hallmark signature in a mutually exclusive manner (including E2F_TARGETS and G2M_CHECKPOINT). Compared with previous methods, our strategy could not only capture the known cancer genes and directly dissect the functional roles of their SCNAs in LGG, but also discover the functions of new driver genes in LGG, such as IFNA5, IFNA8, and DTX3. Additionally, our method can be applied to a variety of cancer types to explore the pathogenesis of driver SCNAs and improve the treatment and diagnosis of cancer. |
| first_indexed | 2024-12-22T01:45:55Z |
| format | Article |
| id | doaj.art-3b3b9c251b174221baa8239c0a9fc53a |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-12-22T01:45:55Z |
| publishDate | 2021-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-3b3b9c251b174221baa8239c0a9fc53a2022-12-21T18:43:05ZengFrontiers Media S.A.Frontiers in Genetics1664-80212021-06-011210.3389/fgene.2021.654736654736Identifying Key Somatic Copy Number Alterations Driving Dysregulation of Cancer Hallmarks in Lower-Grade GliomaYao ZhouShuai WangHaoteng YanBo PangXinxin ZhangLin PangYihan WangJinyuan XuJing HuYujia LanYanyan PingSomatic copy-number alterations (SCNAs) are major contributors to cancer development that are pervasive and highly heterogeneous in human cancers. However, the driver roles of SCNAs in cancer are insufficiently characterized. We combined network propagation and linear regression models to design an integrative strategy to identify driver SCNAs and dissect the functional roles of SCNAs by integrating profiles of copy number and gene expression in lower-grade glioma (LGG). We applied our strategy to 511 LGG patients and identified 98 driver genes that dysregulated 29 cancer hallmark signatures, forming 143 active gene-hallmark pairs. We found that these active gene-hallmark pairs could stratify LGG patients into four subtypes with significantly different survival times. The two new subtypes with similar poorest prognoses were driven by two different gene sets (one including EGFR, CDKN2A, CDKN2B, INFA8, and INFA5, and the other including CDK4, AVIL, and DTX3), respectively. The SCNAs of the two gene sets could disorder the same cancer hallmark signature in a mutually exclusive manner (including E2F_TARGETS and G2M_CHECKPOINT). Compared with previous methods, our strategy could not only capture the known cancer genes and directly dissect the functional roles of their SCNAs in LGG, but also discover the functions of new driver genes in LGG, such as IFNA5, IFNA8, and DTX3. Additionally, our method can be applied to a variety of cancer types to explore the pathogenesis of driver SCNAs and improve the treatment and diagnosis of cancer.https://www.frontiersin.org/articles/10.3389/fgene.2021.654736/fullsomatic copy number alterationdriver genesrandom walk with restartcancer hallmarkLGGregression analysis |
| spellingShingle | Yao Zhou Shuai Wang Haoteng Yan Bo Pang Xinxin Zhang Lin Pang Yihan Wang Jinyuan Xu Jing Hu Yujia Lan Yanyan Ping Identifying Key Somatic Copy Number Alterations Driving Dysregulation of Cancer Hallmarks in Lower-Grade Glioma Frontiers in Genetics somatic copy number alteration driver genes random walk with restart cancer hallmark LGG regression analysis |
| title | Identifying Key Somatic Copy Number Alterations Driving Dysregulation of Cancer Hallmarks in Lower-Grade Glioma |
| title_full | Identifying Key Somatic Copy Number Alterations Driving Dysregulation of Cancer Hallmarks in Lower-Grade Glioma |
| title_fullStr | Identifying Key Somatic Copy Number Alterations Driving Dysregulation of Cancer Hallmarks in Lower-Grade Glioma |
| title_full_unstemmed | Identifying Key Somatic Copy Number Alterations Driving Dysregulation of Cancer Hallmarks in Lower-Grade Glioma |
| title_short | Identifying Key Somatic Copy Number Alterations Driving Dysregulation of Cancer Hallmarks in Lower-Grade Glioma |
| title_sort | identifying key somatic copy number alterations driving dysregulation of cancer hallmarks in lower grade glioma |
| topic | somatic copy number alteration driver genes random walk with restart cancer hallmark LGG regression analysis |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2021.654736/full |
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