Gene expression profiling for the prediction of lymph node metastasis in patients with cervical cancer

We investigated whether gene expression profiling of primary cervical tumor tissue could be used to predict lymph node (LN) metastasis and compared this with conventional magnetic resonance imaging. We obtained 43 primary cervical cancer samples (16 with LN metastasis and 27 without LN metastasis) for microarray analysis. A prediction model for LN metastasis from the training set was developed by support vector machine methods using a 10-fold cross-validation. The 'LN prediction model' derived from the signature of 156 distinctive genes (P < 0.01) had a prediction accuracy of 77%. Correlation between mRNA expressions measured by microarray and semiquantitative reverse transcription-polymerase chain reaction was ascertained in four (RBM8A, SDHB, SERPINB13, and gamma-interferon) out of 10 genes. Magnetic resonance imaging showed accuracy (69%) for the prediction of LN metastasis. These results suggest that gene expression profiling allows reliable prediction of LN metastasis in cervical cancer.