Prognostic Implications of Exosomal miRNA Networks Regulating TGFβ1-induced Epithelial-Mesenchymal Transition Signature in Oral Squamous Cell Carcinoma

초록

Background/Aim: Oral squamous cell carcinoma (OSCC) prognosis is often poor due to metastasis driven by the epithelial-mesenchymal transition (EMT), creating a need for biomarkers that reflect this process. Exosomal microRNAs (miRNAs) secreted by cancer cells regulate EMT, but their complex, network-level interactions are poorly understood. This study aimed to identify a prognostic gene signature by analyzing the exosomal miRNA network from a TGF(31-induced EMT cell model and validating its clinical relevance in a patient cohort. Materials and Methods: A TGF(31-induced EMT model was established in OSCC cells and the exosomal miRNAs from their conditioned media was profiled using microarrays. A 43-gene signature was developed based on the collective targets of the EMT-regulated miRNA network. The prognostic significance of this signature was then validated in The Cancer Genome Atlas (TCGA) OSCC patient cohort (n=315) using hierarchical clustering and Kaplan-Meier survival analysis. Results: The analysis identified an Exo-miR EMT signature comprising miRNAs that collectively regulate EMTassociated gene networks. Using this signature, hierarchical clustering successfully stratified patients into two distinct subgroups with different prognoses. Kaplan-Meier analysis revealed that the high-risk group had a significantly poorer overall survival than the low-risk group (p=0.02). Multivariate analysis confirmed that the Exo-miR EMT signature was an independent prognostic factor for OSCC. Conclusion: EMT-related gene networks regulated by exosomal miRNAs carry clinically meaningful information about tumor aggressiveness in OSCC. The derived Exo-miR EMT signature offers biologically grounded risk stratification that complements conventional clinicopathological factors and supports further development of exosome-based liquid biopsies for prognostic assessment in OSCC.

키워드