Abstract: Network control theory can capture the state of the whole network, which makes it possible to find potential tumor-causing genes from massive complicated protein-protein interaction data. To explore the key genes of tumors, complex network control theory is applied in this paper to analyze the protein-protein interaction networks with five different kinds of cancers. We mine the minimum dominating set (MDS) of the network and select the genes that always belong to the MDS as the candidate key genes. Using the tumor related pathways and essential tumor gene sets, we find that the candidate key genes are clustered in these gene sets, which indicate the effectiveness of the methods based on MDS. In addition, a comprehensive centrality method is proposed to rank the candidate genes with this method, and then the top ranked genes are selected as the candidate biomarkers. Furthermore, we evaluate the probability of top ranked genes being biomarkers according to the network structural analysis and the enrichment of the somatic mutation. In summary, this study may shed light on the application of complex network control theory in biomedicine.