Abstract
The essential protein identification on the protein–protein interaction (PPI) network can have crucial applications in cancer disease diagnosis and drug target cell identification. The study uses a graph-based approach to identify essential proteins in protein–protein interaction networks. Despite significant advancements in cancer research, identifying essential cancer proteins within PPI networks remains still a major challenge. The advantages of using PPI networks are the interconnectedness of cancer proteins and prioritize with the most significant impact on cancer disease progression. The proposed approach introduces an innovative way of identifying essential cancer proteins within PPI networks associated with breast, lung, colorectal, and ovarian cancers. This study commenced with an organized sequence of analytical procedures using cancer gene datasets from the National Center for Biotechnology Information (NCBI) about breast, lung, colorectal, and ovarian cancers. A graph-based random walk with restart (EPI-GBRWR), a novel method is introduced for exploring essential proteins that integrates topological and biological properties within PPI networks. A pivotal moment ensued with the implementation of an essential protein identification using graph-based random walk with restart, shedding light on the hierarchical influence of proteins within the PPI network. The outcomes of this investigation substantiate and contextualize the functional ramifications of the identified proteins through rigorous statistical assessments, including permutation and enrichment tests. The application of pathway analysis to these findings illuminates interconnected molecular pathways in cancer. This work underscores the potency of integrative methodologies in deciphering the complexity of cancer, presenting a transformative era in cancer research and treatment. The computational results confirm EPI-GBRWR’s efficiency in predicting essential proteins. Compared to other state-of-the-art methods for identifying essential proteins, EPI-GBRWR outperforms various evaluation criteria, marking a significant advancement in precision oncology.
