BACKGROUND: Lung adenocarcinoma (LUAD), mainly originated in lung glandular cells, is the most frequent pathological type of lung cancer and the 5-year survival rate of LUAD patients is still very low. Therefore, we aim to identify a long noncoding RNA (lncRNA)-related signature as the sensitive and novel prognostic biomarkers. METHODS: The associations between survival outcome and the intersection of lncRNAs were obtained from The Cancer Genome Atlas (TCGA) database. By the univariate and multivariate Cox analyses, key lncRNAs were identified to construct the prognostic model. The model was estimated by survival analysis and receiver operating characteristic curve, and verified by the Kaplan-Meier (K-M) plotter and quantitative reverse-transcription polymerase chain reaction (qRT-PCR). Functional enrichment analysis was also performed. RESULTS: A four-lncRNA signature (CEBPA-AS1, GVINP1, MIR31HG, and RAET1K) was developed after Cox analysis. The power of the four-lncRNA prognostic signature was effective in the TCGA database. The results from by the K-M plotter and qRT-PCR validation were consistent with our TCGA bioinformatics results. Furthermore, Gene Ontology and pathway analysis revealed the tumorigenic and prognostic function of the four lncRNAs. CONCLUSIONS: By mining the TCGA data, we built a four-lncRNA signature, which could effectively predict prognosis of LUAD. In the future, an independent cohort is needed to validate our findings. IMPACT: The four-lncRNA signature could become potential prognostic indicator of LUAD in the future.
BACKGROUND:Lung adenocarcinoma (LUAD), mainly originated in lung glandular cells, is the most frequent pathological type of lung cancer and the 5-year survival rate of LUAD patients is still very low. Therefore, we aim to identify a long noncoding RNA (lncRNA)-related signature as the sensitive and novel prognostic biomarkers. METHODS: The associations between survival outcome and the intersection of lncRNAs were obtained from The Cancer Genome Atlas (TCGA) database. By the univariate and multivariate Cox analyses, key lncRNAs were identified to construct the prognostic model. The model was estimated by survival analysis and receiver operating characteristic curve, and verified by the Kaplan-Meier (K-M) plotter and quantitative reverse-transcription polymerase chain reaction (qRT-PCR). Functional enrichment analysis was also performed. RESULTS: A four-lncRNA signature (CEBPA-AS1, GVINP1, MIR31HG, and RAET1K) was developed after Cox analysis. The power of the four-lncRNA prognostic signature was effective in the TCGA database. The results from by the K-M plotter and qRT-PCR validation were consistent with our TCGA bioinformatics results. Furthermore, Gene Ontology and pathway analysis revealed the tumorigenic and prognostic function of the four lncRNAs. CONCLUSIONS: By mining the TCGA data, we built a four-lncRNA signature, which could effectively predict prognosis of LUAD. In the future, an independent cohort is needed to validate our findings. IMPACT: The four-lncRNA signature could become potential prognostic indicator of LUAD in the future.