Uncovering EMT-Associated Molecular Mechanisms Through Integrative Transcriptomic and Machine Learning Analyses

Epithelial-mesenchymal transition (EMT) is a fundamental biological process. During EMT, epithelial cells transition to a mesenchymal phenotype, thereby contributing to embryonic development, tissue renewal, and cancer progression. EMT is a well-recognized key driver of tumor invasion and metastasis. However, the transcriptional differences between the physiological and cancer-associated EMT remain incompletely understood.

In the present study, we applied an integrative framework that combined transcriptomic profiling, functional enrichment analysis, and machine learning. The analysis was performed on 89 RNA-sequencing datasets derived from mouse cell lines and tissues, encompassing both normal and malignant contexts. This approach aimed to identify and prioritize genes systematically and signaling pathways associated with EMT.

Differential gene expression and pathway enrichment analyses revealed an over-representation of shared core biological processes related to cell adhesion, cytoskeletal remodeling, and morphogenesis, in both normal and cancer-associated EMT. Nonetheless, cancer-associated EMT exhibited additional enrichment for developmental and neural-related programs, including neurogenesis and gliogenesis. Machine learning models consistently prioritized candidate EMT biomarkers, with greater transcriptional heterogeneity observed in cancer samples.

Collectively, this integrative analysis delineates distinct transcriptional profiles between malignant and physiological EMT. The enrichment of neural-related programs in cancer-associated EMT highlights potential mechanisms that contribute to malignant cellular plasticity. In addition, the analysis identifies candidate biomarkers for future investigation of EMT heterogeneity.