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For robust big data analyses: a collection of 150 important pro-metastatic genes

Metastasis is the greatest contributor to cancer-related death. In the era of precision medicine, it is essential to predict and to prevent the spread of cancer cells to significantly improve patient survival. Thanks to the application of a variety of high-throughput technologies, accumulating big d...

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Autores principales: Mei, Yan, Yang, Jun-Ping, Qian, Chao-Nan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5251273/
https://www.ncbi.nlm.nih.gov/pubmed/28109319
http://dx.doi.org/10.1186/s40880-016-0178-z
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author Mei, Yan
Yang, Jun-Ping
Qian, Chao-Nan
author_facet Mei, Yan
Yang, Jun-Ping
Qian, Chao-Nan
author_sort Mei, Yan
collection PubMed
description Metastasis is the greatest contributor to cancer-related death. In the era of precision medicine, it is essential to predict and to prevent the spread of cancer cells to significantly improve patient survival. Thanks to the application of a variety of high-throughput technologies, accumulating big data enables researchers and clinicians to identify aggressive tumors as well as patients with a high risk of cancer metastasis. However, there have been few large-scale gene collection studies to enable metastasis-related analyses. In the last several years, emerging efforts have identified pro-metastatic genes in a variety of cancers, providing us the ability to generate a pro-metastatic gene cluster for big data analyses. We carefully selected 285 genes with in vivo evidence of promoting metastasis reported in the literature. These genes have been investigated in different tumor types. We used two datasets downloaded from The Cancer Genome Atlas database, specifically, datasets of clear cell renal cell carcinoma and hepatocellular carcinoma, for validation tests, and excluded any genes for which elevated expression level correlated with longer overall survival in any of the datasets. Ultimately, 150 pro-metastatic genes remained in our analyses. We believe this collection of pro-metastatic genes will be helpful for big data analyses, and eventually will accelerate anti-metastasis research and clinical intervention.
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spelling pubmed-52512732017-01-26 For robust big data analyses: a collection of 150 important pro-metastatic genes Mei, Yan Yang, Jun-Ping Qian, Chao-Nan Chin J Cancer Review Metastasis is the greatest contributor to cancer-related death. In the era of precision medicine, it is essential to predict and to prevent the spread of cancer cells to significantly improve patient survival. Thanks to the application of a variety of high-throughput technologies, accumulating big data enables researchers and clinicians to identify aggressive tumors as well as patients with a high risk of cancer metastasis. However, there have been few large-scale gene collection studies to enable metastasis-related analyses. In the last several years, emerging efforts have identified pro-metastatic genes in a variety of cancers, providing us the ability to generate a pro-metastatic gene cluster for big data analyses. We carefully selected 285 genes with in vivo evidence of promoting metastasis reported in the literature. These genes have been investigated in different tumor types. We used two datasets downloaded from The Cancer Genome Atlas database, specifically, datasets of clear cell renal cell carcinoma and hepatocellular carcinoma, for validation tests, and excluded any genes for which elevated expression level correlated with longer overall survival in any of the datasets. Ultimately, 150 pro-metastatic genes remained in our analyses. We believe this collection of pro-metastatic genes will be helpful for big data analyses, and eventually will accelerate anti-metastasis research and clinical intervention. BioMed Central 2017-01-21 /pmc/articles/PMC5251273/ /pubmed/28109319 http://dx.doi.org/10.1186/s40880-016-0178-z Text en © The Author(s) 2017 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 Review
Mei, Yan
Yang, Jun-Ping
Qian, Chao-Nan
For robust big data analyses: a collection of 150 important pro-metastatic genes
title For robust big data analyses: a collection of 150 important pro-metastatic genes
title_full For robust big data analyses: a collection of 150 important pro-metastatic genes
title_fullStr For robust big data analyses: a collection of 150 important pro-metastatic genes
title_full_unstemmed For robust big data analyses: a collection of 150 important pro-metastatic genes
title_short For robust big data analyses: a collection of 150 important pro-metastatic genes
title_sort for robust big data analyses: a collection of 150 important pro-metastatic genes
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5251273/
https://www.ncbi.nlm.nih.gov/pubmed/28109319
http://dx.doi.org/10.1186/s40880-016-0178-z
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