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.
Highlights
Cervical cancer is the 4th most common cause of cancer death in women worldwide [1]
Metastasis is a central cause of mortality in cervical cancer, and patients with lymph node involvement are more likely to progress to have distant metastases
Using the training set of 57 samples, genes that were differentially expressed between lymph node (LN)+ patients and LN- were identified. 22 genes showed 1.5 or greater fold difference in expression between the groups with False Discovery Rate (FDR) ≤ 0.01 (Table 2)
Summary
Metastasis is a central cause of mortality in cervical cancer, and patients with lymph node involvement are more likely to progress to have distant metastases. Patients with lymph node involvement have significantly worse 5-year overall survival compared to those with localized disease only [2]. Lymph node involvement is usually determined by surgical pathology or imaging, such as FDG-PET/CT or MRI. To date there is no simple lab test that accurately predicts which patients will progress to have lymph node involvement. A pathologic tool that could help stratify patients based on lymph node status could be beneficial for determining the best utilization of treatment resources. An RNA-seqbased signature, is a sensible candidate method www.oncotarget.com for development of a model that predicts lymph node involvement
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