Identification and verification of a ten-gene signature predicting overall survival for ovarian cancer.

BACKGROUND

This study was aimed to identify an accurate gene expression signature to predict overall survival (OS) in patients with ovarian cancer (OC).

METHODS

Expression data and corresponding clinical information were obtained from two independent databases: the Cancer Genome Atlas (TCGA) dataset and International Cancer Genome Consortium (ICGC) dataset. Multiple analysis methods including univariate and multivariate COX regression analysis and Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis were utilized to build the signature. Receiver operating characteristic (ROC) and Kaplan-Meier (KM) survival analyses were used to assess the predictive accuracy of this gene signature.

RESULTS

A novel 10-gene signature with high predictive accuracy for OS in OC patients was constructed and validated in the training and validation set. Based on the results of univariate and multivariate analyses, the presence of risk Score was identified as an independent prognostic factor for survival of OC patients. Moreover, we developed a nomogram model based on these 10 genes in the signature, which also displayed a favorable predictive efficacy for prognosis in OC.

CONCLUSIONS

Our results identified a robust 10-gene signature for OC prognosis prediction, which might be applied to assist clinical decision-making and individualized treatment.