Dataset Information

Development of a RNA-Seq Based Prognostic Signature in Lung Adenocarcinoma.

ABSTRACT: Precision therapy for lung cancer will require comprehensive genomic testing to identify actionable targets as well as ascertain disease prognosis. RNA-seq is a robust platform that meets these requirements, but microarray-derived prognostic signatures are not optimal for RNA-seq data. Thus, we undertook the first prognostic analysis of lung adenocarcinoma RNA-seq data and generated a prognostic signature.Lung adenocarcinoma RNA-seq and clinical data from The Cancer Genome Atlas (TCGA) were divided chronologically into training (n?=?255) and validation (n?=?157) cohorts. In the training cohort, prognostic association was assessed by univariate Cox analysis. A prognostic signature was built with stepwise multivariable Cox analysis. Outcomes by risk group, stage, and mutation status were analyzed with Kaplan-Meier and multivariable Cox analyses. All the statistical tests were two-sided.In the training cohort, 96 genes had prognostic association with P values of less than or equal to 1.00x10-4, including five long noncoding RNAs (lncRNAs). Stepwise regression generated a four-gene signature, including one lncRNA. Signature high-risk cases had worse overall survival (OS) in the TCGA validation cohort (hazard ratio [HR] = 3.07, 95% confidence interval [CI] = 2.00 to 14.62) and a University of Michigan institutional cohort (n?=?67; HR?=?2.05, 95% CI?=?1.18 to 4.55), and worse metastasis-free survival in the TCGA validation cohort (HR?=?3.05, 95% CI?=?2.31 to 13.37). The four-gene prognostic signature also statistically significantly stratified overall survival in important clinical subsets, including stage I (HR?=?2.78, 95% CI?=?1.91 to 11.13), EGFR wild-type (HR?=?3.01, 95% CI?=?1.73 to 14.98), and EGFR mutant (HR?=?8.99, 95% CI?=?62.23 to 141.44). The four-gene prognostic signature also stood out on top when compared with other prognostic signatures.Here, we present the first RNA-seq prognostic signature for lung adenocarcinoma that can provide a powerful prognostic tool for precision oncology as part of an integrated RNA-seq clinical sequencing program.

SUBMITTER: Shukla S

PROVIDER: S-EPMC5051943 | biostudies-literature | 2017 Jan

REPOSITORIES: biostudies-literature

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Development of a RNA-Seq Based Prognostic Signature in Lung Adenocarcinoma.

Shukla Sudhanshu S Evans Joseph R JR Malik Rohit R Feng Felix Y FY Dhanasekaran Saravana M SM Cao Xuhong X Chen Guoan G Beer David G DG Jiang Hui H Chinnaiyan Arul M AM

Journal of the National Cancer Institute 20161005 1

<h4>Background</h4>Precision therapy for lung cancer will require comprehensive genomic testing to identify actionable targets as well as ascertain disease prognosis. RNA-seq is a robust platform that meets these requirements, but microarray-derived prognostic signatures are not optimal for RNA-seq data. Thus, we undertook the first prognostic analysis of lung adenocarcinoma RNA-seq data and generated a prognostic signature.<h4>Methods</h4>Lung adenocarcinoma RNA-seq and clinical data from The C ...[more]

PMID: 27707839

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Project description:Objective: The stratification of neuroblastoma (NBL) prognosis remains difficult. RNA-based signatures might be able to predict prognosis, but independent cross-platform validation is still rare. Methods: RNA-Seq-based profiles from NBL patients were acquired and then analyzed. The RNA-Seq prognostic index (RPI) and the clinically adjusted RPI (RCPI) were successively established in the training cohort (TARGET-NBL) and then verified in the validation cohort (GSE62564). Survival prediction was assessed using a time-dependent receiver operating characteristic (ROC) curve and area under the ROC curve (AUC). Functional enrichment analysis of the genes was conducted using bioinformatics methods. Results: In the training cohort, 10 gene pairs were eventually integrated into the RPI. In both cohorts, the high-risk group had poor overall survival (OS) (P < 0.001 and P < 0.001, respectively) and favorable event-free survival (EFS) (P = 0.00032 and P = 0.06, respectively). ROC curve analysis also showed that the RPI predicted OS (60 month AUC values of 0.718 and 0.593, respectively) and EFS (60 month AUC values of 0.627 and 0.852, respectively) well in both the training and validation cohorts. Clinicopathological indicators associated with prognosis in the univariate and multivariate regression analyses were identified and added to the RPI to form the RCPI. The RCPI was also used to divide populations into different risk groups, and the high-risk group had poor OS (P < 0.001 and P < 0.001, respectively) and EFS (P < 0.05 and P < 0.05, respectively). Finally, the RCPI had higher accuracy than the RPI for the prediction of OS (60 month AUC values of 0.730 and 0.852, respectively) and EFS (60 month AUC values of 0.663 and 0.763, respectively) in both the training and validation cohorts. Moreover, these differentially expressed genes may be involved in certain NBL-related events. Conclusions: The RCPI could reliably categorize NBL patients based on different risks of death.

Dataset Information

Development of a RNA-Seq Based Prognostic Signature in Lung Adenocarcinoma.

Publications

Development of a RNA-Seq Based Prognostic Signature in Lung Adenocarcinoma.

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