Development of a novel prognostic signature of long non-coding RNAs in lung adenocarcinoma

Abstract

Objectives

Increasing evidence suggests that long non-coding RNAs (lncRNAs) may play a crucial role in many biological processes in a variety of cancers and serve as the basis for many clinical applications including prognostic biomarkers and potential therapeutic targets. The aim of this study is to develop a prognostic lncRNA signature with RNA-seq data in lung adenocarcinomas.

Methods

LncRNA expression profiles and clinical data of lung adenocarcinoma patients from The Cancer Genome Atlas (TCGA) were analyzed. Univariate Cox proportional regression model was used to identify prognostic lncRNAs, and then multivariate Cox proportional regression model was used to develop a prognostic signature. Survivals were compared using log-rank test, and the biological implications of prognostic lncRNAs were analyzed using the KEGG pathway functional enrichment analysis.

Results

We identified eight lncRNAs which had prognostic association with p value <0.01 in a TCGA lung adenocarcinoma cohort of 478 patients. Then a novel prognostic signature with the eight lncRNAs was developed using Cox regression model. Signature high-risk cases had worse overall survival (OS, median 85.97 vs. 38.34 months, p < 0.001) and disease-free survival (DFS, median 44.02 vs. 26.58 months, p = 0.007) than low-risk cases. Multivariate Cox regression analysis suggested that the eight-lncRNA signature was independent of clinical and pathological factors. KEGG pathway functional enrichment analysis indicated potential functional roles of the eight prognostic lncRNAs in tumorigenesis.

Conclusions

Our findings suggest that the eight-lncRNA signature might provide an effective independent prognostic model for the prediction of lung adenocarcinoma patients.

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