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Genomic landscape and mutational impacts of recurrently mutated genes in cancers
BACKGROUND: Cancer genes tend to be highly mutated under positive selection. Better understanding the recurrently mutated genes (RMGs) in cancer is critical for explicating the mechanisms of tumorigenesis and providing vital clues for therapy. Although some studies have investigated functional impac...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6305651/ https://www.ncbi.nlm.nih.gov/pubmed/30107644 http://dx.doi.org/10.1002/mgg3.458 |
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author | Liu, Baolin Hu, Fei‐Fei Zhang, Qiong Hu, Hui Ye, Zheng Tang, Qin Guo, An‐Yuan |
author_facet | Liu, Baolin Hu, Fei‐Fei Zhang, Qiong Hu, Hui Ye, Zheng Tang, Qin Guo, An‐Yuan |
author_sort | Liu, Baolin |
collection | PubMed |
description | BACKGROUND: Cancer genes tend to be highly mutated under positive selection. Better understanding the recurrently mutated genes (RMGs) in cancer is critical for explicating the mechanisms of tumorigenesis and providing vital clues for therapy. Although some studies have investigated functional impacts of RMGs in specific cancer types, a comprehensive analysis of RMGs and their mutational impacts across cancers is still needed. METHODS: We obtained data from The Cancer Genome Atlas (TCGA) and calculated mutation rate of each gene in 31 cancer types. Functional analysis was performed to identify the important signaling pathways and enriched protein types of RMGs. In order to evaluate functional impacts of RMGs, differential expression, survival, and pairwise mutation patterns analyses were performed. RESULTS: Totally, we identified 897 RMGs and 624 of them were specifically mutant in only a single cancer type. Functional analysis demonstrated that these RMGs were enriched in hydrolases, cytoskeletal protein, and pathways like MAPK, cell cycle, PI3K‐Akt, ECM receptor interaction, and energy metabolism. The differentially expressed genes potentially affected by the same common RMG showed a relatively low overlap across different cancer types. For the 19 Mucin (MUC) family genes, nine of them were RMGs and four of them (MUC17, MUC5B, MUC4, and MUC16) were common RMGs shared in 8 to 17 cancer types. The results showed that recurrent mutations in MUC genes were significantly associated with better survival prognosis. Only a small part of RMGs was differentially expressed due to their own mutations and most of them were downregulated. In addition, pairwise mutation pattern analysis revealed the high frequency of co‐occurred mutations among RMGs in STAD. CONCLUSION: Through the functional analysis of RMGs, we found that six signaling pathways were disrupted in most cancer types and that energy metabolism was abnormal in tumors. The results also revealed a strong correlation between recurrently mutated genes from MUC family and human survival. In addition, gene expression and survival prognosis were associated with different mutation types of RMGs. |
format | Online Article Text |
id | pubmed-6305651 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-63056512019-01-02 Genomic landscape and mutational impacts of recurrently mutated genes in cancers Liu, Baolin Hu, Fei‐Fei Zhang, Qiong Hu, Hui Ye, Zheng Tang, Qin Guo, An‐Yuan Mol Genet Genomic Med Original Articles BACKGROUND: Cancer genes tend to be highly mutated under positive selection. Better understanding the recurrently mutated genes (RMGs) in cancer is critical for explicating the mechanisms of tumorigenesis and providing vital clues for therapy. Although some studies have investigated functional impacts of RMGs in specific cancer types, a comprehensive analysis of RMGs and their mutational impacts across cancers is still needed. METHODS: We obtained data from The Cancer Genome Atlas (TCGA) and calculated mutation rate of each gene in 31 cancer types. Functional analysis was performed to identify the important signaling pathways and enriched protein types of RMGs. In order to evaluate functional impacts of RMGs, differential expression, survival, and pairwise mutation patterns analyses were performed. RESULTS: Totally, we identified 897 RMGs and 624 of them were specifically mutant in only a single cancer type. Functional analysis demonstrated that these RMGs were enriched in hydrolases, cytoskeletal protein, and pathways like MAPK, cell cycle, PI3K‐Akt, ECM receptor interaction, and energy metabolism. The differentially expressed genes potentially affected by the same common RMG showed a relatively low overlap across different cancer types. For the 19 Mucin (MUC) family genes, nine of them were RMGs and four of them (MUC17, MUC5B, MUC4, and MUC16) were common RMGs shared in 8 to 17 cancer types. The results showed that recurrent mutations in MUC genes were significantly associated with better survival prognosis. Only a small part of RMGs was differentially expressed due to their own mutations and most of them were downregulated. In addition, pairwise mutation pattern analysis revealed the high frequency of co‐occurred mutations among RMGs in STAD. CONCLUSION: Through the functional analysis of RMGs, we found that six signaling pathways were disrupted in most cancer types and that energy metabolism was abnormal in tumors. The results also revealed a strong correlation between recurrently mutated genes from MUC family and human survival. In addition, gene expression and survival prognosis were associated with different mutation types of RMGs. John Wiley and Sons Inc. 2018-08-14 /pmc/articles/PMC6305651/ /pubmed/30107644 http://dx.doi.org/10.1002/mgg3.458 Text en © 2018 The Authors. Molecular Genetics & Genomic Medicine published by Wiley Periodicals, Inc. This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Articles Liu, Baolin Hu, Fei‐Fei Zhang, Qiong Hu, Hui Ye, Zheng Tang, Qin Guo, An‐Yuan Genomic landscape and mutational impacts of recurrently mutated genes in cancers |
title | Genomic landscape and mutational impacts of recurrently mutated genes in cancers |
title_full | Genomic landscape and mutational impacts of recurrently mutated genes in cancers |
title_fullStr | Genomic landscape and mutational impacts of recurrently mutated genes in cancers |
title_full_unstemmed | Genomic landscape and mutational impacts of recurrently mutated genes in cancers |
title_short | Genomic landscape and mutational impacts of recurrently mutated genes in cancers |
title_sort | genomic landscape and mutational impacts of recurrently mutated genes in cancers |
topic | Original Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6305651/ https://www.ncbi.nlm.nih.gov/pubmed/30107644 http://dx.doi.org/10.1002/mgg3.458 |
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