A novel 4-gene signature model simultaneously predicting malignant risk of oral potentially malignant disorders and oral squamous cell carcinoma prognosis.

OBJECTIVE: Oral squamous cell carcinoma (OSCC) is often diagnosed at late stage with a poor prognosis. The study hereunder aimed to construct a multi-gene model to simultaneously promote early diagnosis of OSCC by evaluating malignant risk of oral potentially malignant disorders (OPMDs) and predict prognosis. MATERIALS AND METHODS: 3 GEO datasets including OPMDs and OSCC samples were obtained for overlapping differentially expressed genes (DEGs) being screened. The predictive model was built with optimal DEGs by SVM algorithm, estimated by receiver operator characteristic curves and validated for double prediction via oral cancer-free survival (for malignant risk of OPMDs) and overall survival time (for OSCC) analysis respectively compared to other models. The protein expression of biomarkers in the model was validated in human samples by immunohistochemistry. RESULTS: A novel predictive model of 4-gene signature was built based on 12 common DEGs revealed from 3 GEO datasets. It could well distinguish OSCC from OPMDs and normal tissues. Both oral cancer-free survival and overall survival time analysis were significantly poorer in high-risk patients than in low-risk ones in Kaplan Meier survival curve respectively. The protein expression of biomarkers in OSCC was with significant difference compared to normal and OPMDs. CONCLUSIONS: The novel 4-gene signature model presents strong ability in simultaneous prediction of the malignant risk of OPMDs and OSCC progression, potentially benefiting both the early diagnosis and therapeutic outcomes of OSCC.