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Integrated genomic analysis identifies clinically relevant subtypes of renal clear cell carcinoma
BACKGROUND: Renal cell carcinoma (RCC) account for over 80% of renal malignancies. The most common type of RCC can be classified into three subtypes including clear cell, papillary and chromophobe. ccRCC (the Clear Cell Renal Cell Carcinoma) is the most frequent form and shows variations in genetics...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5851245/ https://www.ncbi.nlm.nih.gov/pubmed/29534679 http://dx.doi.org/10.1186/s12885-018-4176-1 |
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author | Wu, Peng Liu, Jia-Li Pei, Shi-Mei Wu, Chang-Peng Yang, Kai Wang, Shu-Peng Wu, Song |
author_facet | Wu, Peng Liu, Jia-Li Pei, Shi-Mei Wu, Chang-Peng Yang, Kai Wang, Shu-Peng Wu, Song |
author_sort | Wu, Peng |
collection | PubMed |
description | BACKGROUND: Renal cell carcinoma (RCC) account for over 80% of renal malignancies. The most common type of RCC can be classified into three subtypes including clear cell, papillary and chromophobe. ccRCC (the Clear Cell Renal Cell Carcinoma) is the most frequent form and shows variations in genetics and behavior. To improve accuracy and personalized care and increase the cure rate of cancer, molecular typing for individuals is necessary. METHODS: We adopted the genome, transcriptome and methylation HMK450 data of ccRCC in The Cancer Genome Atlas Network in this research. Consensus Clustering algorithm was used to cluster the expression data and three subtypes were found. To further validate our results, we analyzed an independent data set and arrived at a consistent conclusion. Next, we characterized the subtype by unifying genomic and clinical dimensions of ccRCC molecular stratification. We also implemented GSEA between the malignant subtype and the other subtypes to explore latent pathway varieties and WGCNA to discover intratumoral gene interaction network. Moreover, the epigenetic state changes between subgroups on methylation data are discovered and Kaplan-Meier survival analysis was performed to delve the relation between specific genes and prognosis. RESULTS: We found a subtype of poor prognosis in clear cell renal cell carcinoma, which is abnormally upregulated in focal adhesions and cytoskeleton related pathways, and the expression of core genes in the pathways are negatively correlated with patient outcomes. CONCLUSIONS: Our work of classification schema could provide an applicable framework of molecular typing to ccRCC patients which has implications to influence treatment decisions, judge biological mechanisms involved in ccRCC tumor progression, and potential future drug discovery. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12885-018-4176-1) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5851245 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-58512452018-03-21 Integrated genomic analysis identifies clinically relevant subtypes of renal clear cell carcinoma Wu, Peng Liu, Jia-Li Pei, Shi-Mei Wu, Chang-Peng Yang, Kai Wang, Shu-Peng Wu, Song BMC Cancer Research Article BACKGROUND: Renal cell carcinoma (RCC) account for over 80% of renal malignancies. The most common type of RCC can be classified into three subtypes including clear cell, papillary and chromophobe. ccRCC (the Clear Cell Renal Cell Carcinoma) is the most frequent form and shows variations in genetics and behavior. To improve accuracy and personalized care and increase the cure rate of cancer, molecular typing for individuals is necessary. METHODS: We adopted the genome, transcriptome and methylation HMK450 data of ccRCC in The Cancer Genome Atlas Network in this research. Consensus Clustering algorithm was used to cluster the expression data and three subtypes were found. To further validate our results, we analyzed an independent data set and arrived at a consistent conclusion. Next, we characterized the subtype by unifying genomic and clinical dimensions of ccRCC molecular stratification. We also implemented GSEA between the malignant subtype and the other subtypes to explore latent pathway varieties and WGCNA to discover intratumoral gene interaction network. Moreover, the epigenetic state changes between subgroups on methylation data are discovered and Kaplan-Meier survival analysis was performed to delve the relation between specific genes and prognosis. RESULTS: We found a subtype of poor prognosis in clear cell renal cell carcinoma, which is abnormally upregulated in focal adhesions and cytoskeleton related pathways, and the expression of core genes in the pathways are negatively correlated with patient outcomes. CONCLUSIONS: Our work of classification schema could provide an applicable framework of molecular typing to ccRCC patients which has implications to influence treatment decisions, judge biological mechanisms involved in ccRCC tumor progression, and potential future drug discovery. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12885-018-4176-1) contains supplementary material, which is available to authorized users. BioMed Central 2018-03-13 /pmc/articles/PMC5851245/ /pubmed/29534679 http://dx.doi.org/10.1186/s12885-018-4176-1 Text en © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Wu, Peng Liu, Jia-Li Pei, Shi-Mei Wu, Chang-Peng Yang, Kai Wang, Shu-Peng Wu, Song Integrated genomic analysis identifies clinically relevant subtypes of renal clear cell carcinoma |
title | Integrated genomic analysis identifies clinically relevant subtypes of renal clear cell carcinoma |
title_full | Integrated genomic analysis identifies clinically relevant subtypes of renal clear cell carcinoma |
title_fullStr | Integrated genomic analysis identifies clinically relevant subtypes of renal clear cell carcinoma |
title_full_unstemmed | Integrated genomic analysis identifies clinically relevant subtypes of renal clear cell carcinoma |
title_short | Integrated genomic analysis identifies clinically relevant subtypes of renal clear cell carcinoma |
title_sort | integrated genomic analysis identifies clinically relevant subtypes of renal clear cell carcinoma |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5851245/ https://www.ncbi.nlm.nih.gov/pubmed/29534679 http://dx.doi.org/10.1186/s12885-018-4176-1 |
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