Integrated Profiles Analysis Identified a Coding-Non-Coding Signature for Predicting Lymph Node Metastasis and Prognosis in Cervical Cancer.

ABSTRACT: Accumulating evidence has shown that lymph node metastasis (LNM) is not only an important prognostic factor but also an indicator of the need for postoperative chemoradiotherapy. Therefore, identifying risk factors or molecular markers related to LNM is critical for predicting the prognosis and guiding individualized treatment of patients with cervical cancer. In this study, we used the machine learning-based feature selection approach to identify eight optimal biomarkers from the list of 250 differentially expressed protein-coding genes and long non-coding RNAs (lncRNAs) in the TCGA cohort. Then a coding-non-coding signature (named CNC8SIG) was developed using the elastic-net logistic regression approach based on the expression levels of eight optimal biomarkers, which is useful in discri

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC7859285 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature