Identifying conserved metastatic pathways across cancers through integrated transcriptomic and network analysis

Metastasis remains the leading cause of cancer-related mortality, yet shared molecular mechanisms across cancer types are poorly understood. Identifying conserved metastatic pathways could offer new therapeutic targets. We performed an integrative cancer analysis across five cancer types (breast, lung, endometrial, prostate, colorectal) using RNA-Seq datasets from the GEO database. Differentially expressed genes (DEGs) between primary and metastatic tumors were identified using limma. Overlapping DEGs across cancers underwent pathway enrichment and protein-protein interaction (PPI) network analysis to identify hub genes. Key pathways and hub genes were validated for prognostic relevance across 15 cancer types using independent TCGA datasets and Cox regression models. We analyzed six publicly available datasets comprising primary and metastatic tumors across five cancer types: breast, lung, endometrial, prostate, and colorectal. We identified 10 overlapping DEGs, with ITGAX and CXCL12 emerging as hub genes connected to critical pathways, such as ERBB4 signaling, integrin-mediated adhesion, glycosphingolipid metabolism, and platelet function. Validation in TCGA confirmed the prognostic significance of hub genes across multiple cancers, with ITGAX significant in 6, while CXCL12 was significant in 2 out of 15 cancers tested. Our approach identified conserved genes and pathways involved in metastasis across cancers, with ERBB4 signaling and hub genes ITGAX and CXCL12 emerging as key regulators. These findings provide a framework for understanding metastatic processes. Future studies should explore targeting the common metastatic process across cancer types.