Essential proteins in cancer networks: a graph-based perspective using Dijkstra’s algorithm

Identifying essential proteins within cancer-related PPI networks is a significant challenge due to the heterogeneity and complexity of cancer diseases. Identifying these proteins is crucial for developing effective therapeutic strategies and understanding cancer biology. This study introduces a novel graph-based approach to identify essential cancer proteins within PPI networks, focusing on breast, lung, colorectal, and ovarian cancers. The proposed methodology involves a multi-step process beginning with identifying and preprocessing common genes associated with breast, colorectal, lung, and ovarian cancers. The PPI networks are constructed using these common genes. The PPI networks are analyzed to find the shortest paths using centrality measures. Centrality measures, particularly betweenness centrality, prioritize proteins with the highest impact on cancer progression. Betweenness centrality is used as a threshold to exclude nonessential proteins. The identified proteins are validated and categorized into cancer-related pathways through permutation and enrichment tests. The proposed approach successfully identified 64 essential proteins across breast, lung, colorectal, and ovarian cancers. These proteins were categorized into 14 cancer-related pathways, including cell cycle regulation, Wnt/ \(\beta \) $\beta$ -Catenin signaling, RTK/RAS/RAF/MEK/ERK signaling, and PI3K/AKT/mTOR signaling. The identified pathways highlight complex interactions of these proteins, pivotal functions in cancer progression, and therapeutic targets. The validation process, through permutation and enrichment tests, confirmed the robustness and relevance of these findings, indicating their significant impact on understanding and potentially treating cancer. Identifying essential cancer proteins using this novel graph-based approach has significant clinical relevance, particularly for precision medicine. These findings can guide personalized treatment strategies and enhance the understanding of cancer biology. Future research will extend this methodology to other types of cancers and clinical applicability.