Cancer is caused by the accumulation of genetic mutations and epigenetic modifications in genes that normally play a role in the regulation of cell proliferation, thus leading to uncontrolled cell growth. Cancer research has experienced remarkable advances provided by new systemic biological approaches following the development of high-throughput technologies. It has great advances in understanding of the cancer system. Identifying central genes in cancer support to identify drug targets and to understand minimal requirements for cancer. There was attempted to find central genes like centrality-lethality rule in PPI network by topological properties. The results were influenced the data quality. Data obtained diverse resources such as literatures, gene expression, and DNA methylation have special features corresponding to detecting methods. In this study, we suggested the system of predicting central genes related cancer using integrated cancer high-throughput data and central genes were compared. First of all, we obtained highly relevant cancer gene sets, denoted “golden standard sets” by collecting and filtering the all the datasets. Next, we analyze and elecidate central cancer genes by using the standard set through the following performances. First, Central genes were found by using diverse centralities but not only degree centrality. Second, differential expressed (DE) genes in several cancer cells than normal cell also might be central genes in cancer. Most DE genes are related to cell cycle, cytokine-cytokine interaction, and immune response. Third, differential methylated (DM) genes in several cancer cell lines involved transcription factor. In addition, they are related potassium transport, G-protein coupled secondary messenger, and so on. Because of central genes which have different roles in cancer system, we should look into the overall network viewpoint about central genes. We suggested system to pick up central genes on network. The system suggested in the study provided insight to identify functionally relevant and robust cancer central genes.