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. Researchers from the Fudan University Shanghai Cancer Center set out to develop a prognostic lncRNA signature with RNA-seq data in lung adenocarcinomas.
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.
The researchers 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.
Expression of eight lncRNAs, risk score distribution and survival in TCGA cohort
The risk scores (a) for all patients in TCGA cohort are plotted in ascending order and marked as low risk (blue) or high risk (red), as divided by median score (vertical black line). Following up and survival of each patient are shown in b, and alive or dead patient is marked as blue or red, respectively. Expression distribution of eight lncRNAs in TCGA cohort by z score are shown in c, with red indicating higher expression and green indicating lower expression