Prediction of Recurrence in Cervical Cancer Using a Nine-lncRNA Signature - Mao, Yu; Dong, Lixin; Zheng, Yue; Dong, Jing; Li, Xin (Frontiers in Genetics, 2019)
Background and Objective: As a common cancer type in women, cervical cancer remains one of the leading causes of cancer-associated mortalities word wide. Recent evidence has demonstrated the regulatory role of a large number of lncRNAs in cervical cancer. Here, we aimed to identify new biomarkers that related with the recurrence through comprehensive bioinformatics analysis. Methods: Firstly, we collected online lncRNA expression data of cervical cancer patients which were divided into training, validation and test set. Then we developed a 9-lncRNA signature from training set by conducting LASSO Cox regression model along with 10-fold cross validation. The prognostic value of this risk score was validated in all the three sets using Kaplan-Meier analysis, C-index, time-dependent ROC curves and dynamic AUC. Biological function of these lncRNAs in cervical cancer cells were evaluated by performing gene ontology biological process enrichment and Kyoto Encyclopedia of Genes and Genomes signaling pathways analysis. Results: According to the results, a higher predict accuracy was observed in the 9-lncRNA signature than that of TNM stage in all the three sets. Stratified analysis also demonstrated that the 9-lncRNA signature can predict the recurrence of cervical cancer within TNM stage. The potential mechanisms underlying the 9 lncRNAs from the signature were also identified according to the gene enrichment analysis. Conclusions: In the present article, we provided a reliable prognostic tool to facilitate the individual management of patients with cervical cancer after treatment.