A Combined Prediction Model for Lymph Node Metastasis Based on a Molecular Panel and Clinicopathological Factors in Oral Squamous Cell Carcinoma

Abstract

ObjectiveLymph node metastasis is the most important factor influencing the prognosis of oral squamous cell carcinoma (OSCC) patients. However, there is no proper method for predicting lymph node metastasis. This study aimed to construct and validate a preoperative prediction model for lymph node metastasis and guide personalized neck management based on the gene expression profile and clinicopathological parameters of OSCC.MethodsBased on a previous study of related genes in OSCC, the mRNA expression of candidate genes was evaluated by real-time PCR in OSCC specimens. In this retrospective study, the gene expression profile and clinicopathological parameters of 112 OSCC patients were combined to construct the best prediction model for lymph node metastasis of OSCC. The model was validated with 95 OSCC samples in this study. Logistic regression analysis was used. The area under the curve (AUC) ultimately determined the diagnostic value of the prediction model.ResultsThe two genes CDKN2A + PLAU were closely related to lymph node metastasis of oral squamous cell carcinoma. The model with the combination of CDKN2A, PLAU, T stage and pathological grade was the best in predicting lymph node metastasis (AUC = 0.807, 95% CI: 0.713-0.881, P=0.0001). The prediction model had a specificity of 96% and sensitivity of 72.73% for stage T1 and T2 OSCC (AUC = 0.855, 95% CI: 0.697-0.949, P=0.0001).ConclusionsHigh expression of CDKN2A and PLAU was associated with lymph node metastasis in OSCC. The prediction model including CDKN2A, PLAU, T stage and pathological grade can be used as the best diagnostic model for lymph node metastasis in OSCC.

A prediction model of nodal metastasis in cN0 oral squamous cell carcinoma using metabolic and pathological variables

“…Bur et al [ 21 ] developed machine learning algorithms by using clinicopathologic data from 782 cT1-2N0 OSCC patients to predict pathologic nodal metastasis, they found the best classification performance was achieved with a decision forest algorithm (AUC = 0.840). Wang et al [ 22 ]. constructed a prediction model for nodal metastasis based on molecular panel and clinicopathological factors in 112 cN0 OSCC patients, the model with the combination of CDKN2A, PLAU, T stage and pathological grade was the best in predicting lymph node metastasis (AUC = 0.807).…”

Risk Stratification in Oral Cancer: A Novel Approach

“…Nomograms have been widely used to predict survival outcomes in a variety of cancers, including OSCC, by reflecting the prognostic strength of relevant variables ( 13 ). Previous nomograms designed for OSCC mostly focused on the prognostic effect of genes, biomarkers, or specific subsite and tumor grade of OSCC ( 3 , 11 , 13 , 16 ). Recently, Zhou et al.…”

“…Nomograms have been widely used to predict survival outcomes in a variety of cancers, including OSCC, by reflecting the prognostic strength of relevant variables (13). Previous nomograms designed for OSCC mostly focused on the prognostic effect of genes, biomarkers, or specific subsite and tumor grade of OSCC (3,11,13,16). The current practice for OSCC management is largely directed by multidisciplinary discussions in tumor boards, which take into consideration the cancer stage, adverse pathological factors, individual patient factors, and the likely functional consequences and morbidity of each treatment approach (19).…”

Expression Profiles of CDKN2A, MDM2, E2F2 and LTF Genes in Oral Squamous Cell Carcinoma

“…There are also studies demonstrating the overexpression of CDKN2A in OSCC [ 28 , 29 ]. The Wang and others’ [ 30 ] analysis demonstrated that the expression of CDKN2A was upregulated in OSCC tissues compared with normal tissues in Oncomine database ( p -Value < 0.01). Furthermore, mRNA expression of the CDKN2A in HNSCC (head and neck squamous cell carcinoma) was upregulated compared with normal tissues in the GEPIA (Gene Expression Profiling Interactive Analysis) database ( p -Value < 0.01).…”

“…Furthermore, mRNA expression of the CDKN2A in HNSCC (head and neck squamous cell carcinoma) was upregulated compared with normal tissues in the GEPIA (Gene Expression Profiling Interactive Analysis) database ( p -Value < 0.01). On the other hand, in the GEPIA database, a high expression of CDKN2A was associated with better survival in HNSCC patients ( p -Value < 0.05) [ 30 ]. The different results of the studies can be explained by differences in the pathogenesis of a subset of OSCCs [ 14 ].…”