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Computational identification and characterization of glioma candidate biomarkers through multi-omics integrative profiling
BACKGROUND: Glioma is one of the most common malignant brain tumors and exhibits low resection rate and high recurrence risk. Although a large number of glioma studies powered by high-throughput sequencing technologies have led to massive multi-omics datasets, there lacks of comprehensive integratio...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7294636/ https://www.ncbi.nlm.nih.gov/pubmed/32539851 http://dx.doi.org/10.1186/s13062-020-00264-5 |
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author | Liu, Lin Wang, Guangyu Wang, Liguo Yu, Chunlei Li, Mengwei Song, Shuhui Hao, Lili Ma, Lina Zhang, Zhang |
author_facet | Liu, Lin Wang, Guangyu Wang, Liguo Yu, Chunlei Li, Mengwei Song, Shuhui Hao, Lili Ma, Lina Zhang, Zhang |
author_sort | Liu, Lin |
collection | PubMed |
description | BACKGROUND: Glioma is one of the most common malignant brain tumors and exhibits low resection rate and high recurrence risk. Although a large number of glioma studies powered by high-throughput sequencing technologies have led to massive multi-omics datasets, there lacks of comprehensive integration of glioma datasets for uncovering candidate biomarker genes. RESULTS: In this study, we collected a large-scale assemble of multi-omics multi-cohort datasets from worldwide public resources, involving a total of 16,939 samples across 19 independent studies. Through comprehensive molecular profiling across different datasets, we revealed that PRKCG (Protein Kinase C Gamma), a brain-specific gene detectable in cerebrospinal fluid, is closely associated with glioma. Specifically, it presents lower expression and higher methylation in glioma samples compared with normal samples. PRKCG expression/methylation change from high to low is indicative of glioma progression from low-grade to high-grade and high RNA expression is suggestive of good survival. Importantly, PRKCG in combination with MGMT is effective to predict survival outcomes in a more precise manner. CONCLUSIONS: PRKCG bears the great potential for glioma diagnosis, prognosis and therapy, and PRKCG-like genes may represent a set of important genes associated with different molecular mechanisms in glioma tumorigenesis. Our study indicates the importance of computational integrative multi-omics data analysis and represents a data-driven scheme toward precision tumor subtyping and accurate personalized healthcare. |
format | Online Article Text |
id | pubmed-7294636 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-72946362020-06-16 Computational identification and characterization of glioma candidate biomarkers through multi-omics integrative profiling Liu, Lin Wang, Guangyu Wang, Liguo Yu, Chunlei Li, Mengwei Song, Shuhui Hao, Lili Ma, Lina Zhang, Zhang Biol Direct Research BACKGROUND: Glioma is one of the most common malignant brain tumors and exhibits low resection rate and high recurrence risk. Although a large number of glioma studies powered by high-throughput sequencing technologies have led to massive multi-omics datasets, there lacks of comprehensive integration of glioma datasets for uncovering candidate biomarker genes. RESULTS: In this study, we collected a large-scale assemble of multi-omics multi-cohort datasets from worldwide public resources, involving a total of 16,939 samples across 19 independent studies. Through comprehensive molecular profiling across different datasets, we revealed that PRKCG (Protein Kinase C Gamma), a brain-specific gene detectable in cerebrospinal fluid, is closely associated with glioma. Specifically, it presents lower expression and higher methylation in glioma samples compared with normal samples. PRKCG expression/methylation change from high to low is indicative of glioma progression from low-grade to high-grade and high RNA expression is suggestive of good survival. Importantly, PRKCG in combination with MGMT is effective to predict survival outcomes in a more precise manner. CONCLUSIONS: PRKCG bears the great potential for glioma diagnosis, prognosis and therapy, and PRKCG-like genes may represent a set of important genes associated with different molecular mechanisms in glioma tumorigenesis. Our study indicates the importance of computational integrative multi-omics data analysis and represents a data-driven scheme toward precision tumor subtyping and accurate personalized healthcare. BioMed Central 2020-06-15 /pmc/articles/PMC7294636/ /pubmed/32539851 http://dx.doi.org/10.1186/s13062-020-00264-5 Text en © The Author(s) 2020 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/. 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 in a credit line to the data. |
spellingShingle | Research Liu, Lin Wang, Guangyu Wang, Liguo Yu, Chunlei Li, Mengwei Song, Shuhui Hao, Lili Ma, Lina Zhang, Zhang Computational identification and characterization of glioma candidate biomarkers through multi-omics integrative profiling |
title | Computational identification and characterization of glioma candidate biomarkers through multi-omics integrative profiling |
title_full | Computational identification and characterization of glioma candidate biomarkers through multi-omics integrative profiling |
title_fullStr | Computational identification and characterization of glioma candidate biomarkers through multi-omics integrative profiling |
title_full_unstemmed | Computational identification and characterization of glioma candidate biomarkers through multi-omics integrative profiling |
title_short | Computational identification and characterization of glioma candidate biomarkers through multi-omics integrative profiling |
title_sort | computational identification and characterization of glioma candidate biomarkers through multi-omics integrative profiling |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7294636/ https://www.ncbi.nlm.nih.gov/pubmed/32539851 http://dx.doi.org/10.1186/s13062-020-00264-5 |
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