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Characterization of Tumor-Suppressor Gene Inactivation Events in 33 Cancer Types

We systematically investigated the landscape of tumor-suppressor gene (TSG) inactivation events in 33 cancer types by quantitatively measuring their global and local genomic features and their transcriptional and signaling footprints. Using The Cancer Genome Atlas data, we identified with high confi...

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Detalles Bibliográficos
Autores principales: Jia, Peilin, Zhao, Zhongming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7375892/
https://www.ncbi.nlm.nih.gov/pubmed/30625331
http://dx.doi.org/10.1016/j.celrep.2018.12.066
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author Jia, Peilin
Zhao, Zhongming
author_facet Jia, Peilin
Zhao, Zhongming
author_sort Jia, Peilin
collection PubMed
description We systematically investigated the landscape of tumor-suppressor gene (TSG) inactivation events in 33 cancer types by quantitatively measuring their global and local genomic features and their transcriptional and signaling footprints. Using The Cancer Genome Atlas data, we identified with high confidence 337 TSG × cancer events in 30 cancer types, of which 277 were unique events. The majority (91.0%) of these events had a significant downstream impact measured by reduced expression of the TSG itself (cis-effect), disturbance of the transcriptome (trans-effect), or combinatorial effects. Importantly, the transcriptomic changes associated with TSG inactivation events were stronger than the cancer lineage difference, and the same TSGs inactivated in different cancer types tended to cluster together. Several TSGs (e.g., RB1, TP53, and CDKN2A) involved in the regulation of the cell-cycle-formed clusters. Finally, we constructed subnetworks of the TSG × cancer inactivation events, including the local genes frequently disturbed upon the inactivation events.
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spelling pubmed-73758922020-07-23 Characterization of Tumor-Suppressor Gene Inactivation Events in 33 Cancer Types Jia, Peilin Zhao, Zhongming Cell Rep Article We systematically investigated the landscape of tumor-suppressor gene (TSG) inactivation events in 33 cancer types by quantitatively measuring their global and local genomic features and their transcriptional and signaling footprints. Using The Cancer Genome Atlas data, we identified with high confidence 337 TSG × cancer events in 30 cancer types, of which 277 were unique events. The majority (91.0%) of these events had a significant downstream impact measured by reduced expression of the TSG itself (cis-effect), disturbance of the transcriptome (trans-effect), or combinatorial effects. Importantly, the transcriptomic changes associated with TSG inactivation events were stronger than the cancer lineage difference, and the same TSGs inactivated in different cancer types tended to cluster together. Several TSGs (e.g., RB1, TP53, and CDKN2A) involved in the regulation of the cell-cycle-formed clusters. Finally, we constructed subnetworks of the TSG × cancer inactivation events, including the local genes frequently disturbed upon the inactivation events. 2019-01-08 /pmc/articles/PMC7375892/ /pubmed/30625331 http://dx.doi.org/10.1016/j.celrep.2018.12.066 Text en This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Article
Jia, Peilin
Zhao, Zhongming
Characterization of Tumor-Suppressor Gene Inactivation Events in 33 Cancer Types
title Characterization of Tumor-Suppressor Gene Inactivation Events in 33 Cancer Types
title_full Characterization of Tumor-Suppressor Gene Inactivation Events in 33 Cancer Types
title_fullStr Characterization of Tumor-Suppressor Gene Inactivation Events in 33 Cancer Types
title_full_unstemmed Characterization of Tumor-Suppressor Gene Inactivation Events in 33 Cancer Types
title_short Characterization of Tumor-Suppressor Gene Inactivation Events in 33 Cancer Types
title_sort characterization of tumor-suppressor gene inactivation events in 33 cancer types
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7375892/
https://www.ncbi.nlm.nih.gov/pubmed/30625331
http://dx.doi.org/10.1016/j.celrep.2018.12.066
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