State Key Laboratory of Oral Diseases, National Center of Stomatology, National Clinical Research Center for Oral Disease, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, People's Republic of China; Department of Stomatology, Chengdu Fifth People's Hospital/The Second Clinical Medical College, Chengdu University of TCM, Chengdu, Sichuan, People's Republic of China.
State Key Laboratory of Oral Diseases, National Center of Stomatology, National Clinical Research Center for Oral Disease, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, People's Republic of China.
Arch Oral Biol. 2021 Sep;129:105203. doi: 10.1016/j.archoralbio.2021.105203. Epub 2021 Jun 30.
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.
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.
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.
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.
口腔鳞状细胞癌（OSCC）常被诊断为晚期，预后不良。本研究旨在构建一个多基因模型，通过评估口腔潜在恶性疾病（OPMDs）的恶性风险来同时促进 OSCC 的早期诊断，并预测预后。
从 3 个 GEO 数据集获得 OPMD 和 OSCC 样本的重叠差异表达基因（DEGs）进行筛选。使用 SVM 算法构建具有最佳 DEGs 的预测模型，通过接受者操作特征曲线进行评估，并通过口腔癌无复发生存（用于评估 OPMD 的恶性风险）和总生存时间（用于评估 OSCC）分析分别与其他模型进行双重预测验证。通过免疫组织化学验证模型中生物标志物在人类样本中的蛋白表达。
基于 3 个 GEO 数据集中揭示的 12 个共同 DEGs，构建了一个新的 4 基因特征预测模型。该模型能够很好地区分 OSCC 与 OPMD 和正常组织。在 Kaplan-Meier 生存曲线中，高危患者的口腔癌无复发生存和总生存时间分析均显著差于低危患者。与正常和 OPMD 相比，OSCC 中生物标志物的蛋白表达有显著差异。
新型 4 基因特征模型在同时预测 OPMD 的恶性风险和 OSCC 进展方面具有很强的能力，可能有助于 OSCC 的早期诊断和治疗效果。
