LncRNAs classifier to accurately predict the recurrence of thymic epithelial tumors.

ABSTRACT:

Background

Long non-coding RNAs (lncRNAs), which have little or no ability to encode proteins, have attracted special attention due to their potential role in cancer disease. In this study we aimed to establish a lncRNAs classifier to improve the accuracy of recurrence prediction for thymic epithelial tumors (TETs).

Methods

TETs RNA sequencing (RNA-seq) data set and the matched clinicopathologic information were downloaded from the Cancer Genome Atlas. Using univariate Cox regression and least absolute shrinkage and selection operator (LASSO) analysis, we developed a lncRNAs classifier related to recurrence. Functional analysis was conducted to investigate the potential biological processes of the lncRNAs target genes. The independent prognostic factors were identified by Co

SUBMITTER: Su Y 

PROVIDER: S-EPMC7327696 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature