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Chinese Journal of Cancer

, 36:16

First Online: 21 January 2017Received: 19 October 2016Accepted: 03 November 2016DOI: 10.1186-s40880-016-0178-z

Cite this article as: Mei, Y., Yang, JP. & Qian, CN. Chin J Cancer 2017 36: 16. doi:10.1186-s40880-016-0178-z

Abstract

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.

KeywordsPro-metastatic gene Big data analysis Renal cancer Liver cancer  Download fulltext PDF



Autor: Yan Mei - Jun-Ping Yang - Chao-Nan Qian

Fuente: https://link.springer.com/







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