Validated limited gene predictor for cervical cancer lymph node metastases. Oncotarget Bloomstein, J. D., von Eyben, R., Chan, A., Rankin, E. B., Fregoso, D. R., Wang-Chiang, J., Lee, L., Xie, L., David, S. M., Stehr, H., Esfahani, M. S., Giaccia, A. J., Kidd, E. A. 2020; 11 (24): 2302–9

Abstract

PURPOSE: Recognizing the prognostic significance of lymph node (LN) involvement for cervical cancer, we aimed to identify genes that are differentially expressed in LN+ versus LN- cervical cancer and to potentially create a validated predictive gene signature for LN involvement.MATERIALS AND METHODS: Primary tumor biopsies were collected from 74 cervical cancer patients. RNA was extracted and RNA sequencing was performed. The samples were divided by institution into a training set (n = 57) and a testing set (n = 17). Differentially expressed genes were identified among the training cohort and used to train a Random Forest classifier.RESULTS: 22 genes showed > 1.5 fold difference in expression between the LN+ and LN- groups. Using forward selection 5 genes were identified and, based on the clinical knowledge of these genes and testing of the different combinations, a 2-gene Random Forest model of BIRC3 and CD300LG was developed. The classification accuracy of lymph node (LN) status on the test set was 88.2%, with an Area under the Receiver Operating Characteristic curve (ROC-AUC) of 98.6%.CONCLUSIONS: We identified a 2 gene Random Forest model of BIRC3 and CD300LG that predicted lymph node involvement in a validation cohort. This validated model, following testing in additional cohorts, could be used to create a reverse transcription-quantitative polymerase chain reaction (RT-qPCR) tool that would be useful for helping to identify patients with LN involvement in resource-limited settings.

View details for DOI 10.18632/oncotarget.27632

View details for PubMedID 32595829