Prediction of Recurrence in Cervical Cancer Using a Nine-lncRNA Signature.

ABSTRACT:

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 long non-coding RNAs (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 nine-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 anal

SUBMITTER: Mao Y 

PROVIDER: S-EPMC6456668 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature