Predicting Cancer Stage from Circulating microRNA: A Comparative Analysis of Machine Learning Algorithms

Abstract

In recent years, serum-based tests for early detection and detection of tissue of origin are being developed. Circulating microRNA has been shown to be a potential source of diagnostic information that can be collected non-invasively. In this study, we investigate circulating microRNAs as predictors of cancer stage. Specifically, we predict whether a sample stems from a patient with early stage (0-II) or late stage cancer (III-IV). We trained five machine learning algorithms on a data set of cancers from twelve different primary sites. The results showed that cancer stage can be predicted from circulating microRNA with a sensitivity of 71.73%, specificity of 79.97%, as well as positive and negative predictive value of 54.81% and 89.29%, respectively. Furthermore, we compared the best pan-cancer model with models specialized on individual cancers and found no statistically significant difference. Finally, in the best performing pan-cancer model 185 microRNAs were significant. Comparing the five most relevant circulating microRNAs in the best performing model with the current literature showed some known associations to various cancers. In conclusion, the study showed the potential of circulating microRNA and machine learning algorithms to predict cancer stage and thus suggests that further research into its potential as a non-invasive clinical test is warranted.