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

Overview of attention for article published in Cancer Communications, January 2017
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Title
For robust big data analyses: a collection of 150 important pro‐metastatic genes
Published in
Cancer Communications, January 2017
DOI 10.1186/s40880-016-0178-z
Pubmed ID
Authors

Yan Mei, Jun-Ping Yang, Chao-Nan Qian

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.

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Mendeley readers

The data shown below were compiled from readership statistics for 38 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Denmark 1 3%
Unknown 37 97%

Demographic breakdown

Readers by professional status Count As %
Student > Master 8 21%
Student > Ph. D. Student 7 18%
Researcher 5 13%
Student > Bachelor 4 11%
Other 2 5%
Other 6 16%
Unknown 6 16%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 7 18%
Medicine and Dentistry 6 16%
Agricultural and Biological Sciences 4 11%
Nursing and Health Professions 3 8%
Engineering 3 8%
Other 8 21%
Unknown 7 18%