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The Correlation Between the Immune and Epithelial-Mesenchymal Transition Signatures Suggests Potential Therapeutic Targets and Prognosis Prediction Approaches in Kidney Cancer
The Correlation Between the Immune and Epithelial-Mesenchymal Transition Signatures Suggests Potential Therapeutic Targets and Prognosis Prediction Approaches in Kidney Cancer
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The Correlation Between the Immune and Epithelial-Mesenchymal Transition Signatures Suggests Potential Therapeutic Targets and Prognosis Prediction Approaches in Kidney Cancer
The Correlation Between the Immune and Epithelial-Mesenchymal Transition Signatures Suggests Potential Therapeutic Targets and Prognosis Prediction Approaches in Kidney Cancer

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The Correlation Between the Immune and Epithelial-Mesenchymal Transition Signatures Suggests Potential Therapeutic Targets and Prognosis Prediction Approaches in Kidney Cancer
The Correlation Between the Immune and Epithelial-Mesenchymal Transition Signatures Suggests Potential Therapeutic Targets and Prognosis Prediction Approaches in Kidney Cancer
Journal Article

The Correlation Between the Immune and Epithelial-Mesenchymal Transition Signatures Suggests Potential Therapeutic Targets and Prognosis Prediction Approaches in Kidney Cancer

2018
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Overview
Both epithelial-mesenchymal transition (EMT) and immune regulation are important biological process in malignant tumours. The current research aims to comprehensively explore the potential association between the epithelial-mesenchymal transition (EMT) signature and immune checkpoint signature and its role in predicting the prognosis of clear-cell renal cell carcinoma (ccRCC) patients. EMT-related genes were collected from an experiment-based study and then were investigated using data from the Cancer Genome Atlas. A total of 357 genes were included, and 23 of them that were upregulated and correlated with prognosis were analysed further as core EMT genes in ccRCC. Interestingly, the emerging immune checkpoints CD276, OX40 and TGFB1 were found to be significantly co-expressed with core EMT genes, and TGFB1, CXCR4, IL10, and IL6 were the most important molecules potentially interacting with EMT molecules in our model, as determined from mRNA co-expression and protein-protein interaction network analysis. Additionally, an integrated scoring model based on FOXM1, TIMP1 and IL6 was successfully established to distinguish ccRCC patients with different clinical risks. Our results identified core genes in the EMT-immunophenotyping correlation and evaluated their risk assessment capabilities, providing more potential therapeutic targets and prediction approaches regarding the translational research of treatment and prognosis in ccRCC.

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