Shanbo Zheng1,2, Difan Zheng1,2, Chuanpeng Dong3, Jiahua Jiang4, Juntao Xie1,2, Yihua Sun5,6, Haiquan Chen7,8. 1. Department of Thoracic Surgery, Fudan University Shanghai Cancer Center, 270 Dong-An Road, Shanghai, 200032, People's Republic of China. 2. Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China. 3. Institute of Biomedical Sciences, Fudan University, Shanghai, China. 4. Department of Translational Medicine, Shanghai Hengrui Pharmaceuticals Co. Ltd., Shanghai, China. 5. Department of Thoracic Surgery, Fudan University Shanghai Cancer Center, 270 Dong-An Road, Shanghai, 200032, People's Republic of China. sun_yihua76@hotmail.com. 6. Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China. sun_yihua76@hotmail.com. 7. Department of Thoracic Surgery, Fudan University Shanghai Cancer Center, 270 Dong-An Road, Shanghai, 200032, People's Republic of China. hqchen1@yahoo.com. 8. Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China. hqchen1@yahoo.com.
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.
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 adenocarcinomapatients 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 adenocarcinomapatients.
Entities:
Keywords:
Long non-coding RNA; Lung adenocarcinoma; Prognostic signature; TCGA
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