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Abstract Breast cancer is the second largest cause of cancer mortality among women. Breast cancer develops in a multi-step process involving various cell types, and prevention remains a global challenge. One of the most effective ways to avoid breast cancer is to diagnose it early. Breast cancer patients in certain developed nations have a 5-year relative survival rate of more than 80% thanks to early detection and treatment. Network pharmacology is a powerful approach that integrates various disciplines such as pharmacology, systems biology, and network analysis to understand the complex interactions between drugs, targets, and diseases at a systems level through various software like uniprotkb, string, cytoscape. The protein-protein interaction (PPI) analysis was conducted using STRING and Cytoscape. The resulting genes' drug-gene interaction was then used to generate candidate drugs. Our text mining searches yielded 2,658 genes associated with breast cancer. 166 genes have been selected as being important to breast cancer. Gene enrichment analysis identified ten genes encoding 10 pathways, which potentially target ten proteins in total. In conclusion, network pharmacology offers a promising approach for understanding the complex molecular mechanisms underlying breast cancer and identifying potential therapeutic targets. By integrating multi-omics data and computational techniques, we can elucidate intricate drug-target interactions, biological networks, and signaling pathways involved in breast cancer progression and treatment response. This comprehensive approach enables the discovery of novel therapeutic candidates, facilitates drug repurposing strategies, and supports the development of personalized treatment regimens.
Neha Sanjay Tikhile (Wed,) studied this question.