Assessment of MicroRNAs Associated with Tumor Purity by Random Forest Regression

SIMPLE SUMMARY: Cancer is a disease with high mortality and recurrence rates. To understand cancer biology, it is important to accurately determine the proportion of tumor and non-tumor cells in tumor tissues. In this study, the proportion of tumor cells in tumor tissues was predicted using miRNA expression data that had not been sufficiently studied before. Using a random forest regression model, the tumor purity was predicted accurately, and subsequent investigations into the association between the informative microRNAs and tumor purity could be conducted. ABSTRACT: Tumor purity refers to the proportion of tumor cells in tumor tissue samples. This value plays an important role in understanding the mechanisms of the tumor microenvironment. Although various attempts have been made to predict tumor purity, attempts to predict tumor purity using miRNAs are still lacking. We predicted tumor purity using miRNA expression data for 16 TCGA tumor types using random forest regression. In addition, we identified miRNAs with high feature-importance scores and examined the extent of the change in predictive performance using informative miRNAs. The predictive performance obtained using only 10 miRNAs with high feature importance was close to the result obtained using all miRNAs. Furthermore, we also found genes targeted by miRNAs and confirmed that these genes were mainly related to immune and cancer pathways. Therefore, we found that the miRNA expression data could predict tumor purity well, and the results suggested the possibility that 10 miRNAs with high feature importance could be used as potential markers to predict tumor purity and to help improve our understanding of the tumor microenvironment.