Uncovering EMT-Associated Molecular Mechanisms Through Integrative Transcriptomic and Machine Learning Analyses
FRONTIERS IN BIOSCIENCE-LANDMARK, cilt.31, sa.1, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 31 Sayı: 1
- Basım Tarihi: 2026
- Doi Numarası: 10.31083/fbl48085
- Dergi Adı: FRONTIERS IN BIOSCIENCE-LANDMARK
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, BIOSIS, EMBASE, MEDLINE, Directory of Open Access Journals, Zoological Record
- Anahtar Kelimeler: axonogenesis, biomarker discovery, cancer, cell plasticity, epithelial–mesenchymal transition, gene expression profiling, gliogenesis, machine learning, neurogenesis
- Dokuz Eylül Üniversitesi Adresli: Evet
Özet
Introduction: 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.Methods: 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.Results: 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.Conclusion: 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.