Network analysis of aging acceleration reveals systematic properties of 11 types of cancers
Cancers are known to be associated with accelerated aging, but to date, there has been a paucity of systematic and in‐depth studies of the correlation between aging and cancer. DNA methylation (DNAm) profiles can be used as aging markers and utilized to construct aging predictors. In this study, we...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2019-07-01
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| Series: | FEBS Open Bio |
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| Online Access: | https://doi.org/10.1002/2211-5463.12679 |
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| _version_ | 1846139249043177472 |
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| author | Xiaoqiong Xia Mengyu Zhou Hao Yan Sijia Li Xianzheng Sha Yin Wang |
| author_facet | Xiaoqiong Xia Mengyu Zhou Hao Yan Sijia Li Xianzheng Sha Yin Wang |
| author_sort | Xiaoqiong Xia |
| collection | DOAJ |
| description | Cancers are known to be associated with accelerated aging, but to date, there has been a paucity of systematic and in‐depth studies of the correlation between aging and cancer. DNA methylation (DNAm) profiles can be used as aging markers and utilized to construct aging predictors. In this study, we downloaded 333 paired samples of DNAm, expression and mutation profiles encompassing 11 types of tissues from The Cancer Genome Atlas public access portal. The DNAm aging scores were calculated using the Support Vector Machine regression model. The DNAm aging scores of cancers revealed significant aging acceleration compared to adjacent normal tissues. Aging acceleration‐associated mutation modules and expression modules were identified in 11 types of cancers. In addition, we constructed bipartite networks of mutations and expression, and the differential expression modules related to aging‐associated mutations were selected in 11 types of cancers using the expression quantitative trait locus method. The results of enrichment analyses also identified common functions across cancers and cancer‐specific characteristics of aging acceleration. The aging acceleration interaction network across cancers suggested a core status of thyroid carcinoma and neck squamous cell carcinoma in the aging process. In summary, we have identified correlations between aging and cancers and revealed insights into the biological functions of the modules in aging and cancers. |
| format | Article |
| id | doaj-art-3f6435d7987449b0b82d43e71b6ae2bb |
| institution | Kabale University |
| issn | 2211-5463 |
| language | English |
| publishDate | 2019-07-01 |
| publisher | Wiley |
| record_format | Article |
| series | FEBS Open Bio |
| spelling | doaj-art-3f6435d7987449b0b82d43e71b6ae2bb2024-12-06T11:26:25ZengWileyFEBS Open Bio2211-54632019-07-01971292130410.1002/2211-5463.12679Network analysis of aging acceleration reveals systematic properties of 11 types of cancersXiaoqiong Xia0Mengyu Zhou1Hao Yan2Sijia Li3Xianzheng Sha4Yin Wang5Department of Biomedical Engineering School of Fundamental Sciences China Medical University Shenyang ChinaDepartment of Biomedical Engineering School of Fundamental Sciences China Medical University Shenyang ChinaDepartment of Biomedical Engineering School of Fundamental Sciences China Medical University Shenyang ChinaDepartment of Biomedical Engineering School of Fundamental Sciences China Medical University Shenyang ChinaDepartment of Biomedical Engineering School of Fundamental Sciences China Medical University Shenyang ChinaDepartment of Biomedical Engineering School of Fundamental Sciences China Medical University Shenyang ChinaCancers are known to be associated with accelerated aging, but to date, there has been a paucity of systematic and in‐depth studies of the correlation between aging and cancer. DNA methylation (DNAm) profiles can be used as aging markers and utilized to construct aging predictors. In this study, we downloaded 333 paired samples of DNAm, expression and mutation profiles encompassing 11 types of tissues from The Cancer Genome Atlas public access portal. The DNAm aging scores were calculated using the Support Vector Machine regression model. The DNAm aging scores of cancers revealed significant aging acceleration compared to adjacent normal tissues. Aging acceleration‐associated mutation modules and expression modules were identified in 11 types of cancers. In addition, we constructed bipartite networks of mutations and expression, and the differential expression modules related to aging‐associated mutations were selected in 11 types of cancers using the expression quantitative trait locus method. The results of enrichment analyses also identified common functions across cancers and cancer‐specific characteristics of aging acceleration. The aging acceleration interaction network across cancers suggested a core status of thyroid carcinoma and neck squamous cell carcinoma in the aging process. In summary, we have identified correlations between aging and cancers and revealed insights into the biological functions of the modules in aging and cancers.https://doi.org/10.1002/2211-5463.12679accelerated agingcancerDNA methylationnetwork analysispan‐cancer |
| spellingShingle | Xiaoqiong Xia Mengyu Zhou Hao Yan Sijia Li Xianzheng Sha Yin Wang Network analysis of aging acceleration reveals systematic properties of 11 types of cancers FEBS Open Bio accelerated aging cancer DNA methylation network analysis pan‐cancer |
| title | Network analysis of aging acceleration reveals systematic properties of 11 types of cancers |
| title_full | Network analysis of aging acceleration reveals systematic properties of 11 types of cancers |
| title_fullStr | Network analysis of aging acceleration reveals systematic properties of 11 types of cancers |
| title_full_unstemmed | Network analysis of aging acceleration reveals systematic properties of 11 types of cancers |
| title_short | Network analysis of aging acceleration reveals systematic properties of 11 types of cancers |
| title_sort | network analysis of aging acceleration reveals systematic properties of 11 types of cancers |
| topic | accelerated aging cancer DNA methylation network analysis pan‐cancer |
| url | https://doi.org/10.1002/2211-5463.12679 |
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