Tumor classification using microRNA expression features and machine learning

Tumor classification and diagnosis are not an easy task as novel research keeps emerging about what makes a tumor type unique. There are several ways to classify a tumor, such as molecular analysis as well as the usual ways where biopsies or physical examinations are used to diagnose cancer. Previous studies have been using DNA or messenger RNA features in tumor classification, but there are not a lot of specific studies in the area of tumor classification using microRNA (miRNA). As new investigations suggest, miRNA serves a crucial role in tumor growth and metastasis, so my investigation dives into the relationship between miRNA and tumor classification, which is still mostly unexplored. Here, using miRNA expression data from large cancer sequencing projects for over 10,000 patients, I build machine learning models to classify more than 30 different types of cancer. Two different algorithms, k-nearest neighbors and random forest are tested and benchmarked. For certain cancer types, the random forest model can be over 95% accurate. I further demonstrate that errors of the classifier come largely from tumors with similar cell-of-origin, such as digestive organ tumor stomach cancer are sometimes mislabeled as other digestive organ tumors like esophageal cancer or colon cancer. To explore how we can use the miRNA-based tumor classifier in a clinical setting, I further demonstrate that it is possible to reach near 95% accuracy with a minimal model of only 10 miRNA genes, which suggests that a small test kit for 10 miRNA genes can reach a comparable accuracy as the more expensive option of whole miRNA sequencing. Overall, this study suggests that miRNA expression can accurately predict tumor types and may be developed into clinical diagnostic kits.