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Validated limited gene predictor for cervical cancer lymph node metastases

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 Method...

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Detalles Bibliográficos
Autores principales: Bloomstein, Joshua D., von Eyben, Rie, Chan, Andy, Rankin, Erinn B., Fregoso, Daniel R., Wang-Chiang, Jing, Lee, Lisa, Xie, Liang-Xi, David, Shannon MacLaughlan, Stehr, Henning, Esfahani, Mohammad S., Giaccia, Amato J., Kidd, Elizabeth A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Impact Journals LLC 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7299532/
https://www.ncbi.nlm.nih.gov/pubmed/32595829
http://dx.doi.org/10.18632/oncotarget.27632
Descripción
Sumario: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.