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Identification of novel prognostic biomarkers by integrating multi-omics data in gastric cancer

BACKGROUND: Gastric cancer is a fatal gastrointestinal cancer with high morbidity and poor prognosis. The dismal 5-year survival rate warrants reliable biomarkers to assess and improve the prognosis of gastric cancer. Distinguishing driver mutations that are required for the cancer phenotype from pa...

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Autores principales: Liu, Nannan, Wu, Yun, Cheng, Weipeng, Wu, Yuxuan, Wang, Liguo, Zhuang, Liwei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8073914/
https://www.ncbi.nlm.nih.gov/pubmed/33902514
http://dx.doi.org/10.1186/s12885-021-08210-y
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author Liu, Nannan
Wu, Yun
Cheng, Weipeng
Wu, Yuxuan
Wang, Liguo
Zhuang, Liwei
author_facet Liu, Nannan
Wu, Yun
Cheng, Weipeng
Wu, Yuxuan
Wang, Liguo
Zhuang, Liwei
author_sort Liu, Nannan
collection PubMed
description BACKGROUND: Gastric cancer is a fatal gastrointestinal cancer with high morbidity and poor prognosis. The dismal 5-year survival rate warrants reliable biomarkers to assess and improve the prognosis of gastric cancer. Distinguishing driver mutations that are required for the cancer phenotype from passenger mutations poses a formidable challenge for cancer genomics. METHODS: We integrated the multi-omics data of 293 primary gastric cancer patients from The Cancer Genome Atlas (TCGA) to identify key driver genes by establishing a prognostic model of the patients. Analyzing both copy number alteration and somatic mutation data helped us to comprehensively reveal molecular markers of genomic variation. Integrating the transcription level of genes provided a unique perspective for us to discover dysregulated factors in transcriptional regulation. RESULTS: We comprehensively identified 31 molecular markers of genomic variation. For instance, the copy number alteration of WASHC5 (also known as KIAA0196) frequently occurred in gastric cancer patients, which cannot be discovered using traditional methods based on significant mutations. Furthermore, we revealed that several dysregulation factors played a hub regulatory role in the process of biological metabolism based on dysregulation networks. Cancer hallmark and functional enrichment analysis showed that these key driver (KD) genes played a vital role in regulating programmed cell death. The drug response patterns and transcriptional signatures of KD genes reflected their clinical application value. CONCLUSIONS: These findings indicated that KD genes could serve as novel prognostic biomarkers for further research on the pathogenesis of gastric cancers. Our study elucidated a multidimensional and comprehensive genomic landscape and highlighted the molecular complexity of GC. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12885-021-08210-y.
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spelling pubmed-80739142021-04-26 Identification of novel prognostic biomarkers by integrating multi-omics data in gastric cancer Liu, Nannan Wu, Yun Cheng, Weipeng Wu, Yuxuan Wang, Liguo Zhuang, Liwei BMC Cancer Research BACKGROUND: Gastric cancer is a fatal gastrointestinal cancer with high morbidity and poor prognosis. The dismal 5-year survival rate warrants reliable biomarkers to assess and improve the prognosis of gastric cancer. Distinguishing driver mutations that are required for the cancer phenotype from passenger mutations poses a formidable challenge for cancer genomics. METHODS: We integrated the multi-omics data of 293 primary gastric cancer patients from The Cancer Genome Atlas (TCGA) to identify key driver genes by establishing a prognostic model of the patients. Analyzing both copy number alteration and somatic mutation data helped us to comprehensively reveal molecular markers of genomic variation. Integrating the transcription level of genes provided a unique perspective for us to discover dysregulated factors in transcriptional regulation. RESULTS: We comprehensively identified 31 molecular markers of genomic variation. For instance, the copy number alteration of WASHC5 (also known as KIAA0196) frequently occurred in gastric cancer patients, which cannot be discovered using traditional methods based on significant mutations. Furthermore, we revealed that several dysregulation factors played a hub regulatory role in the process of biological metabolism based on dysregulation networks. Cancer hallmark and functional enrichment analysis showed that these key driver (KD) genes played a vital role in regulating programmed cell death. The drug response patterns and transcriptional signatures of KD genes reflected their clinical application value. CONCLUSIONS: These findings indicated that KD genes could serve as novel prognostic biomarkers for further research on the pathogenesis of gastric cancers. Our study elucidated a multidimensional and comprehensive genomic landscape and highlighted the molecular complexity of GC. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12885-021-08210-y. BioMed Central 2021-04-26 /pmc/articles/PMC8073914/ /pubmed/33902514 http://dx.doi.org/10.1186/s12885-021-08210-y Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Liu, Nannan
Wu, Yun
Cheng, Weipeng
Wu, Yuxuan
Wang, Liguo
Zhuang, Liwei
Identification of novel prognostic biomarkers by integrating multi-omics data in gastric cancer
title Identification of novel prognostic biomarkers by integrating multi-omics data in gastric cancer
title_full Identification of novel prognostic biomarkers by integrating multi-omics data in gastric cancer
title_fullStr Identification of novel prognostic biomarkers by integrating multi-omics data in gastric cancer
title_full_unstemmed Identification of novel prognostic biomarkers by integrating multi-omics data in gastric cancer
title_short Identification of novel prognostic biomarkers by integrating multi-omics data in gastric cancer
title_sort identification of novel prognostic biomarkers by integrating multi-omics data in gastric cancer
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8073914/
https://www.ncbi.nlm.nih.gov/pubmed/33902514
http://dx.doi.org/10.1186/s12885-021-08210-y
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