Gene Expression Signature-Based Prediction of Lymph Node Metastasis in Patients With Endometrioid Endometrial Cancer

This study aimed to develop a prediction model for lymph node metastasis using a gene expression signature in patients with endometrioid-type endometrial cancer.

Newly diagnosed endometrioid-type endometrial cancer cases in which the patients had undergone lymphadenectomy during a surgical staging procedure were identified from a national dataset (N = 330). Clinical and pathologic data were extracted from patient medical records, and gene expression datasets of their tumors were used to create a 12-gene predictive model for lymph node metastasis. We used principal components analysis on a training set (n = 110) to develop multivariate logistic models to predict low-risk patients having a probability of lymph node metastasis of less than 4%. The model with the highest prediction performance was selected for an evaluation set (n = 112), which, in turn, was validated in an independent validation set (n = 108).

The model applied to the evaluation set showed 100% sensitivity (90% confidence interval [CI], 74%-100%) and 42% specificity (90% CI, 34%-51%), which resulted in 100% negative predictive value (90% CI, 89%-100%). In the validation set, we confirmed that the model consistently showed 100% sensitivity (90% CI, 88%-100%), 42% specificity (90% CI, 32%-50%), and 100% negative predictive value (90% CI, 88%-100%).

Our 12-gene signature model is a useful tool for the identification of patients with endometrioid-type endometrial cancer at low risk of lymph node metastasis, particularly given that it can be used to analyze histologic tissue before surgery and used to tailor surgical options.