OBJECTIVE: Pelvic lymph node metastases are the main prognostic factor for survival in early stage cervical cancer, yet accurate detection methods before surgery are lacking. In this study, we examined whether gene expression profiling can predict the presence of lymph node metastasis in early stage squamous cell cervical cancer before treatment. In addition, we examined gene expression in cervical cancer compared to normal cervical tissue. METHODS: Tumour samples of 35 patients with early stage cervical cancer who underwent radical hysterectomy and pelvic lymph node dissection, 16 with and 19 without lymph node metastasis, were analysed. Also five normal cervical tissues samples were analysed. We investigated differential expression and prediction of patient status for lymph node positive versus lymph node negative tumours and for healthy versus cancer tissue. Classifiers were built by using a multiple validation strategy, enabling the assessment of both classifier accuracy and variability. RESULTS: Five genes (BANF1, LARP7, SCAMP1, CUEDC1 and PEBP1) showed differential expression between tumour samples from patients with and without lymph node metastasis. Mean accuracy of class prediction is 64.5% with a 95% confidence interval (CI) of 40-90%. For healthy cervical tissue versus early stage cervical cancer, the mean accuracy of class prediction is 99.5% (95% CI of 90-100%). A subset of genes involved in cervical cancer was identified. CONCLUSION: No accurate class prediction for lymph node status in early stage cervical cancer was obtained. Replication studies are needed to determine the relevance of the differentially expressed genes according to lymph node status. Early stage cervical cancer can be perfectly differentiated from healthy cervical tissue by means of gene expression profiling.
OBJECTIVE: Pelvic lymph node metastases are the main prognostic factor for survival in early stage cervical cancer, yet accurate detection methods before surgery are lacking. In this study, we examined whether gene expression profiling can predict the presence of lymph node metastasis in early stage squamous cell cervical cancer before treatment. In addition, we examined gene expression in cervical cancer compared to normal cervical tissue. METHODS:Tumour samples of 35 patients with early stage cervical cancer who underwent radical hysterectomy and pelvic lymph node dissection, 16 with and 19 without lymph node metastasis, were analysed. Also five normal cervical tissues samples were analysed. We investigated differential expression and prediction of patient status for lymph node positive versus lymph node negative tumours and for healthy versus cancer tissue. Classifiers were built by using a multiple validation strategy, enabling the assessment of both classifier accuracy and variability. RESULTS: Five genes (BANF1, LARP7, SCAMP1, CUEDC1 and PEBP1) showed differential expression between tumour samples from patients with and without lymph node metastasis. Mean accuracy of class prediction is 64.5% with a 95% confidence interval (CI) of 40-90%. For healthy cervical tissue versus early stage cervical cancer, the mean accuracy of class prediction is 99.5% (95% CI of 90-100%). A subset of genes involved in cervical cancer was identified. CONCLUSION: No accurate class prediction for lymph node status in early stage cervical cancer was obtained. Replication studies are needed to determine the relevance of the differentially expressed genes according to lymph node status. Early stage cervical cancer can be perfectly differentiated from healthy cervical tissue by means of gene expression profiling.
Authors: Christopher J Brown; Stefan Gaunitz; Ziyu Wang; Lena Strindelius; Stephen C Jacobson; David E Clemmer; Jonathan C Trinidad; Milos V Novotny Journal: Anal Chem Date: 2020-10-13 Impact factor: 6.986