This article presents a network-based approach for classifying breast cancer metastasis using protein interaction networks. The method identifies subnetworks of interacting proteins that are more reliable and accurate in predicting metastatic versus non-metastatic tumors compared to individual gene markers. The study analyzed two breast cancer datasets, van de Vijver et al (2002) and Wang et al (2005), and identified 149 and 243 discriminative subnetworks, respectively. These subnetworks were enriched for proteins involved in key cancer hallmarks such as cell growth, survival, proliferation, apoptosis, and metabolism. The subnetwork markers showed higher reproducibility across different datasets and achieved better classification accuracy than individual gene markers. The study also demonstrated that subnetworks can include proteins not differentially expressed but essential for maintaining the network's activity, which is crucial for understanding the biological basis of metastasis. Additionally, the subnetwork markers were found to include known cancer susceptibility genes, such as TP53, KRAS, HRAS, ERBB2, and PIK3CA, which are not detected by conventional expression analysis. The network-based approach provides a more comprehensive understanding of the molecular mechanisms underlying breast cancer metastasis and offers a more accurate prediction of metastatic risk in individual patients. The results highlight the importance of integrating protein interaction networks with gene expression data to identify novel prognostic markers for breast cancer.This article presents a network-based approach for classifying breast cancer metastasis using protein interaction networks. The method identifies subnetworks of interacting proteins that are more reliable and accurate in predicting metastatic versus non-metastatic tumors compared to individual gene markers. The study analyzed two breast cancer datasets, van de Vijver et al (2002) and Wang et al (2005), and identified 149 and 243 discriminative subnetworks, respectively. These subnetworks were enriched for proteins involved in key cancer hallmarks such as cell growth, survival, proliferation, apoptosis, and metabolism. The subnetwork markers showed higher reproducibility across different datasets and achieved better classification accuracy than individual gene markers. The study also demonstrated that subnetworks can include proteins not differentially expressed but essential for maintaining the network's activity, which is crucial for understanding the biological basis of metastasis. Additionally, the subnetwork markers were found to include known cancer susceptibility genes, such as TP53, KRAS, HRAS, ERBB2, and PIK3CA, which are not detected by conventional expression analysis. The network-based approach provides a more comprehensive understanding of the molecular mechanisms underlying breast cancer metastasis and offers a more accurate prediction of metastatic risk in individual patients. The results highlight the importance of integrating protein interaction networks with gene expression data to identify novel prognostic markers for breast cancer.