| Literature DB >> 28372569 |
Chao-Nan Qian1, Yan Mei2, Jian Zhang3,4,5.
Abstract
Metastasis is the major cause of treatment failure in cancer patients and of cancer-related deaths. This editorial discusses how cancer metastasis may be better perceived and controlled. Based on big-data analyses, a collection of 150 important pro-metastatic genes was studied. Using The Cancer Genome Atlas datasets to re-analyze the effect of some previously reported metastatic genes-e.g., JAM2, PPARGC1A, SIK2, and TRAF6-on overall survival of patients with renal and liver cancers, we found that these genes are actually protective factors for patients with cancer. The role of epithelial-mesenchymal transition (EMT) in single-cell metastasis has been well-documented. However, in metastasis caused by cancer cell clusters, EMT may not be necessary. A novel role of epithelial marker E-cadherin, as a sensitizer for chemoresistant prostate cancer cells by inhibiting Notch signaling, has been found. This editorial also discusses the obstacles for developing anti-metastatic drugs, including the lack of high-throughput technologies for identifying metastasis inhibitors, less application of animal models in the pre-clinical evaluation of the leading compounds, and the need for adjustments in clinical trial design to better reflect the anti-metastatic efficacy of new drugs. We are confident that by developing more effective high-throughput technologies to identify metastasis inhibitors, we can better predict, prevent, and treat cancer metastasis.Entities:
Keywords: E-cadherin; EMT; JAM2; Metastasis; PPARGC1A; SIK2; TRAF6
Mesh:
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Year: 2017 PMID: 28372569 PMCID: PMC5379757 DOI: 10.1186/s40880-017-0206-7
Source DB: PubMed Journal: Chin J Cancer ISSN: 1944-446X
Fig. 1Survival curves of two cohorts of cancer patients separated by the mRNA levels of four genes. The data were retrieved from The Cancer Genome Atlas database. The survival curves were plotted using the Kaplan–Meier method and compared using the log-rank test. The median values were used as cutoff values to separate patients into high- or low-expression groups. ccRCC clear cell renal cell carcinoma, HCC hepatocellular carcinoma