The clinical implication and translational research of OSCC differentiation.

ABSTRACT

BACKGROUND: The clinical value and molecular characteristics of tumor differentiation in oral squamous cell carcinoma (OSCC) remain unclear. There is a lack of a related molecular classification prediction system based on pathological images for precision medicine. METHODS: Integration of epidemiology, genomics, experiments, and deep learning to clarify the clinical value and molecular characteristics, and develop a novel OSCC molecular classification prediction system. RESULTS: Large-scale epidemiology data (n = 118,817) demonstrated OSCC differentiation was a significant prognosis indicator (p < 0.001), and well-differentiated OSCC was more chemo-resistant than poorly differentiated OSCC. These results were confirmed in the TCGA database and in vitro. Furthermore, we found chemo-resistant related pathways and cell cycle-related pathways were up-regulated in well- and poorly differentiated OSCC, respectively. Based on the characteristics of OSCC differentiation, a molecular grade of OSCC was obtained and combined with pathological images to establish a novel prediction system through deep learning, named ShuffleNetV2-based Molecular Grade of OSCC (SMGO). Importantly, our independent multi-center cohort of OSCC (n = 340) confirmed the high accuracy of SMGO. CONCLUSIONS: OSCC differentiation was a significant indicator of prognosis and chemotherapy selection. Importantly, SMGO could be an indispensable reference for OSCC differentiation and assist the decision-making of chemotherapy.