Construction of a 5 immune-related lncRNA-based prognostic model of NSCLC via bioinformatics

Participate in tumorigenic, oncogenic, and tumor suppressive pathways through gene expression regulation. We aimed to build an immune-related long noncoding RNA (lncRNA) prognostic model to enhance nonsmall cell lung cancer (NSCLC) prognostic prediction. The original data were collected from the cancer genome atlas database. Perl and R software were used for statistical analysis. The effects of lncRNAs expression on prognosis were analyzed by Gene Expression Profiling Interactive Analysis. Silico functional analysis were performed by DAVID Bioinformatics Resources. The median risk score as a dividing value separated patients into high- and low-risk groups. These 2 groups had different 5-year survival rates, median survival times, and immune statuses. The 5-lncRNA signature was validated as an independent prognostic factor with high accuracy (area under the receiver operating characteristic = 0.722). Silico functional analysis connected the lncRNAs with immune-related biological processes and pathways in carcinogenesis. The novel immune-related lncRNA prognostic model had significant clinical implication for enhancing lung adenocarcinoma outcome prediction and guiding the choice of treatment.