Tumor metastasis is the leading cause of death in cancer patients, with epithelial–mesenchymal transition (EMT) playing a key role. To systematically elucidate the cellular mechanisms and molecular networks through which EMT drives metastasis across cancers, this study integrated transcriptomic data from over 1.2 million single cells across 265 samples representing 12 primary epithelial cancers, constructing a comprehensive pan-cancer single-cell atlas covering diverse stages and metastatic states. By analyzing the metastatic features and interaction networks of malignant epithelial cells and cancer-associated fibroblasts (CAFs), we identified cancer-specific metastasis-related gene sets. Based on these genes, multiple machine learning algorithms were applied to build cancer-specific and cross-cancer metastasis prediction models, leading to the development of the metastasis prediction score (MPS) and global metastasis prediction score (GMPS). Both scores showed excellent predictive performance in independent test and external validation cohorts. MPS exhibited higher cancer specificity, whereas GMPS showed stronger cross-cancer generalization. Moreover, elevated MPS and GMPS reflected immunosuppressive tumor microenvironment features and were significantly associated with poor prognosis across multiple cancer types. Finally, through a drug repositioning framework, we identified several potential anti-metastatic compounds targeting the metastasis network, among which Fostamatinib demonstrated broad-spectrum therapeutic potential against metastasis in multiple cancers.

Although single-cell analyses have advanced our understanding of pancreatic ductal adenocarcinoma (PDAC), most studies to date have focused on primary and metastatic tumors. Here, we map cell composition, phenotypic plasticity, and microenvironmental remodeling from human normal pancreas through preneoplastic lesions to PDAC, with the preneoplastic phase recognized as a critical window for carcinogenesis. We pinpoint genes that are persistently dysregulated throughout malignant transformation and are associated with a poor prognosis. Focusing on ductal and acinar cells as the principal origins of PDAC, we delineate malignant preneoplastic cell clusters that exhibit strong carcinogenic potential. Immune profiling reveals marked expansion and functional reprogramming of macrophages during disease progression. Integrative analysis with human PDAC bulk transcriptomic cohorts identifies candidate compounds, such as Brefeldin A, with potential for intervention in preneoplastic disease. Together, our study elucidates dynamic molecular and cellular mechanisms underlying PDAC carcinogenesis and provides actionable insights for early intervention and targeted therapy.