This paper presents an innovative graph-based method for identifying essential proteins within PPI networks, explicitly targeting cancer-related genes in breast, lung, col- orectal, and ovarian cancers. The proposed approach explores centrality measures, utilizing betweenness centrality to distin- guish essential proteins from non-essential ones. The methodology begins by identifying common genes through a sequential pattern algorithm, a crucial step that helps select genes for further analysis. This is followed by constructing a PPI network for each cancer type. Network analysis is conducted to extract essential cancer proteins. A total of 15 top essential proteins are identified, with validation performed through permutation analyses to ensure the reliability of the findings. These proteins were mapped to significant cellular pathways, playing vital roles in cancer progression. Grouping these proteins into specific path- ways highlights their functional importance and the underlying molecular mechanisms driving cancer. The results of this study have significant implications for cancer research, particularly in the realm of precision medicine. The identification of essential proteins not only enhances our understanding of cancer but also opens avenues for developing targeted, personalized treatment strategies.