Similarly, at the gene level our modeling is able to dive deeper into the complex relationships shared between genes and outcome prediction and is able to identify TCEB1 (Hakimi et al. 2015), PCK2 (Chen et al. 2020), ATP1A1 (Zhang et al. 2017a) as the most important genes to kidney cancer stage prediction as shown in Fig. 5 and whose hotspot mutations are also validated by existing research (Linehan and Ricketts 2019) to have been associated to kidney cancer prognosis. The gene discussed is ATP1A1; the disease is kidney cancer.