Abstract
BACKGROUND: Metastasis, the spread of cancer cells from the primary tumor to distant organs, is the leading cause of mortality in cancer patients. This process often exhibits a preference for specific organs, a phenomenon known as tumor organotropism. This study focuses on the organotropism of breast cancer and analyzes its genomic alterations following metastasis to four organs (bone, brain, liver, and lung). The research aims to explore the intrinsic characteristics of primary breast cancer and the interactions between tumor cells and the tumor microenvironment (TME) within these target organs. Building upon this foundation, we developed a deep learning model to identify organ-specific metastatic genes, providing insights into the molecular mechanisms of metastasis. METHODS: To investigate the mechanisms of organ-specific metastasis in breast cancer, we employed an integrative approach combining single-cell RNA sequencing, bulk RNA sequencing, ChIP-seq data, and deep learning techniques. Single-cell analysis provided detailed insights into cellular heterogeneity and microenvironment interactions at metastatic sites. Bulk RNA sequencing enabled the identification of gene expression patterns associated with metastatic propensity. A deep neural network (DNN) model was developed to analyze these complex datasets and identify key predictors of organ-specific metastasis. RESULTS: Our integrative analysis revealed distinct gene expression profiles and cellular compositions in metastatic lesions across different organs. We have identified that, regardless of the target organ, breast cancer metastasis critically depends on specific biological signaling pathways, including the MAPK signaling pathway, metabolic pathways, the PI3K-Akt signaling pathway, and the positive regulation of cell adhesion. Single-cell sequencing highlighted unique interactions between tumor cells and the microenvironment, which varied significantly depending on the metastatic site. Fibroblasts play a critical role in facilitating the colonization of breast cancer cells in metastatic organs. The deep learning models effectively identified key molecular signatures and pathways associated with organ-specific metastasis, providing insights into the metastatic process. CONCLUSION: The study underscores the importance of the tumor microenvironment in influencing breast cancer metastasis to distant organs. We also established a comprehensive framework for understanding the mechanisms driving organotropism metastasis in breast cancer. Additionally, we identified key genes and signaling pathways associated with organ-specific metastasis, providing insights that may inform future studies on risk assessment and potential therapeutic targets for metastatic breast cancer.