Prediction of Cancer Metastasis Using Correlations Between miRNAs and Competing Endogenous RNAs

Cancer metastasis is a complex process which involves the spread of tumor cells from the primary site to other parts of the body. Metastasis is the major cause of cancer mortality, accounting for about 90% of cancer deaths. Metastasis is primarily diagnosed by clinical examinations and imaging techniques, but such a diagnosis is made after metastasis has occurred. Prediction or early detection of metastasis is important for treatment planning since it has an impact on the survival of patients. Recently a few methods have been developed to predict lymph node metastasis, but few methods are available for predicting distant metastasis. Motivated by a gene regulation mechanism involving miRNAs, we have developed a new method for predicting both lymph node metastasis and distant metastasis. We have derived differential correlations of miRNAs and their target RNAs in cancer, and built prediction models using the differential correlations. Testing the method on several types of cancer showed that differential correlations of miRNAs and target RNAs are much more powerful and stable than expressions of known metastasis predictive genes in predicting distant metastasis as well as lymph node metastasis. The method developed in this study will be useful in predicting metastasis and thereby in determining treatment options for cancer patients.