A novel signature to predict thyroid cancer prognosis and immune landscape using immune-related LncRNA pairs.

ABSTRACT:

Background

Thyroid cancer (TC) is the most common endocrine malignancy worldwide. The incidence of TC is high and increasing worldwide due to continuous improvements in diagnostic technology. Therefore, identifying accurate prognostic predictions to stratify TC patients is important.

Methods

Raw data were downloaded from the TCGA database, and pairwise comparisons were applied to identify differentially expressed immune-related lncRNA (DEirlncRNA) pairs. Then, we used univariate Cox regression analysis and a modified Lasso algorithm on these pairs to construct a risk assessment model for TC. We further used qRT‒PCR analysis to validate the expression levels of irlncRNAs in the model. Next, TC patients were assigned to high- and low-risk groups based on the optimal cutoff sco

SUBMITTER: Song B 

PROVIDER: S-EPMC9394074 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature