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Prediction of lymph node metastasis in oral squamous cell carcinoma based on protein profile
Authors:Binbin Yu  Wei Cao  Chenping Zhang  Ronghui Xia  Jinlin Liu
Affiliation:1. Department of Oral and Maxillofacial-Head &2. Neck Oncology, Shanghai Ninth People’s Hospital &3. College of Stomatology, Shanghai Jiao Tong University School of Medicine, Shanghai, China;4. National Clinical Research Center of Stomatology, Shanghai Key Laboratory of Stomatology &5. Shanghai Research Institute of Stomatology, Shanghai, China;6. National Clinical Research Center of Stomatology, Shanghai Key Laboratory of Stomatology &7. Department of Oral Pathology, Shanghai Ninth People’s Hospital &
Abstract:Objective: Lymph node metastasis leads to high mortality rates of oral squamous cell carcinoma (OSCC). However, it is still controversial to define clinically negative neck (cN0) and positive neck (cN1-3).

Methods: We retrieved candidate biomarkers identified by proteomic analysis in OSCC from published works of literature. In training stage, immunohistochemistry (IHC) analysis was used to determine the expression of proteins and logistic regression models with stepwise variable selection were used to identify potential factors that might affect lymph node metastasis and life status. Furthermore, the prediction model was validated in validating stage.

Results: We screened eight highly expressed proteins related to lymph node metastasis in OSCC and found that the expression levels of SOD2, BST2, CAD, ITGB6, and PRDX4 were significantly elevated in patients with lymph node metastasis compared to the patients without lymph node metastasis. Furthermore, in training and validating stages, the prediction model base on the combination of CAD, SOD2 expression levels, and histopathologic grade was developed and validated in patients with OSCC.

Conclusions: Our findings showed that the developed model well predicts the lymph node metastasis and life status in patients with OSCC, independent of TNM stage.

Keywords:lymph node metastasis  oral squamous cell carcinoma  prediction model
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