Cancer Communications
indexed by SCI
BMC

doi: 10.1186/s40880-016-0178-z
For robust big data analyses: a collection of 150 important pro-metastatic genes
Yan Mei, Jun-Ping Yang and Chao-Nan Qian
State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center; Department of Nasopharyngeal Carcinoma, Sun Yat-sen University Cancer Center
[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.
Chinese Journal of Cancer 2017, Volume: 36, Issue 3
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Yan Mei, Jun-Ping Yang and Chao-Nan Qian. For robust big data analyses: a collection of 150 important pro-metastatic genes. Chin J Cancer. 2017, 36:16. doi:10.1186/s40880-016-0178-z


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