Transcriptional Heterogeneity Underlying Cancer Cell Migration Converges on Shared Regulatory Programs

Vollständiger Abstract

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Background/Objectives: Cancer cell migration is a hallmark of cancer and is associated with metastasis. While large-scale functional screens have identified regulators of migration, less is known about how intrinsic transcriptional heterogeneity drives highly migratory phenotypes within individual cancer models or whether these transcriptional changes are conserved across different models of varying tissues of origin. This study aims to define shared and cell line-specific transcriptional programs associated with cancer cell migration and analyze their relevance to patient datasets. Methods: Five cancer cell lines across three cancer types (breast, colorectal, and melanoma) were subjected to transwell-based migratory sorting to isolate highly and weakly migratory subpopulations. Bulk RNA sequencing, differential gene expression analysis, Gene Ontology (GO) enrichment, and upstream regulator prediction were performed. Public tumor datasets were analyzed to evaluate gene expression and its association with patient survival. Results: EVA1A was consistently upregulated in all highly migratory (HM) subpopulations. Multiple GO terms were enriched across all cell lines, often driven by distinct gene signatures, indicating convergence at the level of biological processes despite transcriptional divergence. TEAD4 was predicted as an upstream regulator, and increased TEAD4 nuclear localization was observed in four of the five HM subpopulations. EVA1A and TEAD4 expression were elevated in tumors relative to normal tissues, with cancer type-dependent survival outcomes. Conclusions: Migratory selection was accompanied by extensive transcriptional change within each model, yet across five models spanning three tissue types these changes converged on shared biological processes rather than shared genes. Migration-associated phenotypes may therefore be better defined by pathway-level than single-gene analyses, and the clinical relevance of regulatory nodes such as TEAD4 appears conditional on cancer type.