Cancer has been a prevalent medical concern among many scientists. Despite modern treatments, many patients still have a low recovery rate due to late diagnosis. MicroRNAs (miRNAs) are endogenous non-coding functional RNAs that regulate gene expression by inhibiting/promoting certain signaling pathways.5 They could be a potential indicator of cancer and can be detected from miRNA screening of patients’ blood samples. This indicator could allow scientists to detect potential cancer at an early stage and begin targeted therapy or early treatment. In this project, the aim to was to improve the understanding of gene expression in relation to cancer, using machine learning to identify key miRNAs with a high relatedness to cancer and find pathways connected to the relatively novel field of miRNAs. We carried out data collection (data from Toray Industries, Japan, for three cancers: lung, esophageal, and gastric cancer), model development, pathway analysis, and app development along with generating figures such as variable importance and partial dependence plots. The models averaged a very high AUC of 0.99. All of this allowed for a further understanding of miRNAs in relation to gene expression. Crucial statistics such as specific threshold levels of miRNA expression most indicative of cancer were generated. Overall, this project serves as a prototypical model which has a high diagnostic accuracy for predicting cancer outcomes from patient miRNA data, and an app displaying the gene targets and basic descriptions of said targets was produced.

Keywords : miRNA, Machine Learning, Liquid Biopsy, Lung Cancer, Esophageal Cancer, Gastric Cancer.