This study presents a network-based approach for classifying breast cancer metastasis using protein interaction networks. The method identifies subnetworks of interacting proteins rather than individual genes, which are more reproducible and accurate in predicting metastatic versus non-metastatic tumors. The subnetworks provide insights into the molecular mechanisms underlying metastasis and include genes that are not typically detected through differential expression analysis but play a central role in the protein network. The study shows that subnetwork markers outperform individual gene markers in classification accuracy and reproducibility across different breast cancer cohorts. The subnetworks correspond to key cancer hallmarks, such as cell growth, proliferation, apoptosis, and metabolism. They also include genes associated with breast cancer susceptibility, such as TP53, KRAS, and PIK3CA. The study further demonstrates that subnetwork markers can detect disease-related genes that are not differentially expressed but are essential for maintaining the integrity of the subnetwork. The results suggest that integrating protein networks with gene expression data can improve the identification of prognostic markers for breast cancer metastasis. The study highlights the importance of network-based approaches in understanding the complex molecular mechanisms underlying cancer progression and in developing more accurate predictive models for metastasis.This study presents a network-based approach for classifying breast cancer metastasis using protein interaction networks. The method identifies subnetworks of interacting proteins rather than individual genes, which are more reproducible and accurate in predicting metastatic versus non-metastatic tumors. The subnetworks provide insights into the molecular mechanisms underlying metastasis and include genes that are not typically detected through differential expression analysis but play a central role in the protein network. The study shows that subnetwork markers outperform individual gene markers in classification accuracy and reproducibility across different breast cancer cohorts. The subnetworks correspond to key cancer hallmarks, such as cell growth, proliferation, apoptosis, and metabolism. They also include genes associated with breast cancer susceptibility, such as TP53, KRAS, and PIK3CA. The study further demonstrates that subnetwork markers can detect disease-related genes that are not differentially expressed but are essential for maintaining the integrity of the subnetwork. The results suggest that integrating protein networks with gene expression data can improve the identification of prognostic markers for breast cancer metastasis. The study highlights the importance of network-based approaches in understanding the complex molecular mechanisms underlying cancer progression and in developing more accurate predictive models for metastasis.