Predicting the immune landscape of invasive breast carcinoma based on the novel signature of immune-related lncRNA.

ABSTRACT:

Background

The composition of the population of immune-related long non-coding ribonucleic acid (irlncRNA) generates a signature, irrespective of expression level, with potential value in predicting the survival status of patients with invasive breast carcinoma.

Methods

The current study uses univariate analysis to identify differentially expressed irlncRNA (DEirlncRNA) pairs from RNA-Seq data from The Cancer Genome Atlas (TCGA). 36 pairs of DEirlncRNA pairs were identified. Using various algorithms to construct a model, we have compared the area under the curve and calculated the 5-year curve of Akaike information criterion (AIC) values, which allows determination of the threshold indicating the maximum value for differentiation. Through cut-off point to establish the optim

SUBMITTER: Shen S 

PROVIDER: S-EPMC8446415 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature