Using machine learning algorithms, including RF, SVM, GBM (XGBoost), and DT, the genes CDH3, DKC1, ESM1, MUSTN1, RCC2, TMT1A, and VEGFD were selected as key features from the tissue dataset to best discriminate between cancer and control samples and were used to design the predictive model. The gene discussed is MUSTN1; the disease is cancer.