Further, we develop a renal cancer signature of SETD2 loss using a unique machine-learning approach comprising multiple random sampling and repeated cross-validation, and successfully validated our biomarker in an independent Japanese renal cancer dataset. The gene discussed is SETD2; the disease is renal carcinoma.