Predicting Cancer Metastasis From DNA Methylation and Gene Expression Profiles

Metastasis is the major cause of cancer-related mortality, accounting for about 90% of cancer deaths. So far, most computational methods for predicting metastasis relied on gene expression data or relation between genes. Motivated by an increasing evidence of inter-person variations in gene expression and DNA methylation, we developed a new method for predicting metastasis based on gene expression and DNA methylation profiles. We derived differential correlations between gene expression and DNA methylation in every tumor sample with or without metastasis. Using the differential correlations, we constructed a logistic regression model for predicting metastasis. The prediction model showed a very high performance both in lymph node metastasis and in distant metastasis. In comparison of our method with other recent methods for predicting metastasis, our method showed a much better performance. Interestingly, using DNA methylation beta values alone showed a reasonably high performance as well. When combining differential correlations between gene expression and DNA methylation with DNA methylation, the performance was improved in most performance measures. Our method can be used as useful aids in predicting metastasis, which in turn will help determine treatment options for cancer patients.