A six-long noncoding RNA model predicts prognosis in lung adenocarcinoma

BACKGROUND: The incidence and mortality of lung cancer rank first among various malignant tumors. The lack of clear molecular classification and effective individualized treatment greatly limits the treatment benefits of patients. Long non-coding RNAs (lncRNAs) have been demonstrated widely involve in tumor progressing, and been proved easy to detect for occupying majority in transcriptome. However, less work focuses on studying the potency of lncRNAs as molecular typing and prognostic indicator in lung cancer. METHODS: Based on the 448 lung adenocarcinoma (LUAD) samples and the expression of 14,127 lncRNAs from the Cancer Genome Atlas (TCGA) database, we constructed a co-expression network using weighted gene co-expression network analysis. Then based on the feature module and the overall survival of patients, we constructed a risk score model through Cox proportional hazards regression and verified it with a validation cohort. Finally, according to the median of risk score, the function of this model was enriched. RESULTS: We identified a module containing 123 lncRNAs that is related with the prognosis of LUAD. Using univariate and multivariate Cox proportional hazards regression with lasso regression, six lncRNAs were identified to construct a risk score model. The calculation formula shown as follows: risk score = (−0.3057 × EXP(VIM-AS1)) + (0.9678 × EXP(AC092811.1)) + (1.0829 × EXP(NFIA-AS1)) + (−0.3505 × EXP(AL035701.1)) + (3.9378 × EXP(AC079336.4)) + (−0.2810 × EXP(AL121790.2)). Six-lncRNA model can be used as an independent prognostic indicator in LUAD (P<0.001) and the area under the 5-year receiver operating characteristic (ROC) curve is 0.715. CONCLUSIONS: We developed a six-lncRNA model, which could be used for predicting prognosis and guiding medical treatment in LUAD patients.