Dataset Information

Identification of a RNA-Seq based prognostic signature with five lncRNAs for lung squamous cell carcinoma.

ABSTRACT: Long non-coding RNAs (lncRNAs) expression profile signature for survival assessment in lung squamous cell carcinoma (LUSC) are largely inconsistent due to distinct detecting approaches and small sample size. Systematic and integrative investigation of RNA-Seq based data from The Cancer Genome Atlas (TCGA) herein was performed to determine candidate lncRNAs for prognosis evaluation of LUSC. A total of 60483 genes, including 7589 lncRNAs were assessed in a cohort including 478 LUSC cases with follow-up data. Firstly, 4225 differentially expressed lncRNAs were obtained via R packages. Next, univariate and multivariate Cox proportional hazards regression revealed that 41 lncRNAs were closely related to the survival of LUSC. Finally, lncRNA based prognosis index (PI) could predict overall survival of LUSC with high accuracy (AUC = 0.652, CI: 0.598, 0.705), PI = expCYP4F26P*?CYP4F26P+expRP11-108M12.3*?RP11-108M12.3+expRP11-38M8.1*?RP11-38M8.1+expRP11-54H7.4*?RP11-54H7.4+expZNF503-AS1*?ZNF503-AS1. Furthermore, it was confirmed that the five-lncRNA signature could act as an independent prognostic indicator for LUSC (HR = 2.068, p < 0.001 with univariate analysis, HR = 1.928, p = 0.038 with multivariate). Besides, we constructed a weighted gene co-expression network analysis (WGCNA) of key lncRNA RP11-54H7.4 according to the p-value of related genes' weight. This study provides a RNA-Seq based prognostic signature with five lncRNAs for further clinical application to LUSC patients.

SUBMITTER: Tang RX

PROVIDER: S-EPMC5584202 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature

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Identification of a RNA-Seq based prognostic signature with five lncRNAs for lung squamous cell carcinoma.

Tang Rui-Xue RX Chen Wen-Jie WJ He Rong-Quan RQ Zeng Jiang-Hui JH Liang Liang L Li Shi-Kang SK Ma Jie J Luo Dian-Zhong DZ Chen Gang G

Oncotarget 20170413 31

Long non-coding RNAs (lncRNAs) expression profile signature for survival assessment in lung squamous cell carcinoma (LUSC) are largely inconsistent due to distinct detecting approaches and small sample size. Systematic and integrative investigation of RNA-Seq based data from The Cancer Genome Atlas (TCGA) herein was performed to determine candidate lncRNAs for prognosis evaluation of LUSC. A total of 60483 genes, including 7589 lncRNAs were assessed in a cohort including 478 LUSC cases with foll ...[more]

PMID: 28881601

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Project description:BackgroundEarly-stage lung squamous cell carcinoma (LUSC) progression is accompanied by changes in immune microenvironments and the expression of immune-related genes (IRGs). Identifying innate IRGs associated with prognosis may improve treatment and reveal new immunotherapeutic targets.MethodsGene expression profiles and clinical data of early-stage LUSC patients were obtained from the Gene Expression Omnibus and The Cancer Genome Atlas databases and IRGs from the InnateDB database. Univariate and multivariate Cox regression and LASSO regression analyses were performed to identify an innate IRG signature model prognostic in patients with early-stage LUSC. The predictive ability of this model was assessed by time-dependent receiver operator characteristic curve analysis, with the independence of the model-determined risk score assessed by univariate and multivariate Cox regression analyses. Overall survival (OS) in early-stage LUSC patients was assessed using a nomogram and decision curve analysis (DCA). Functional and biological pathways were determined by gene set enrichment analysis, and differences in biological functions and immune microenvironments between the high- and low-risk groups were assessed by ESTIMATE and the CIBERSORT algorithm.ResultsA signature involving six IRGs (SREBF2, GP2, BMX, NR1H4, DDX41, and GOPC) was prognostic of OS. Samples were divided into high- and low-risk groups based on median risk scores. OS was significantly shorter in the high-risk than in the low-risk group in the training (P < 0.001), GEO validation (P = 0.00021) and TCGA validation (P = 0.034) cohorts. Multivariate Cox regression analysis showed that risk score was an independent risk factor for OS, with the combination of risk score and T stage being optimally predictive of clinical benefit. GSEA, ESTIMATE, and the CIBERSORT algorithm showed that immune cell infiltration was higher and immune-related pathways were more strongly expressed in the low-risk group.ConclusionA signature that includes these six innate IRGs may predict prognosis in patients with early-stage LUSC.

Project description: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.

Project description:BackgroundLung squamous cell carcinoma (LUSC), as the second frequent subtype of lung cancer, causes lots of mortalities primarily due to a lack of precise prognostic markers and timely treatment intervention. Previous studies have constructed several risk prognostic models based on DNA methylation sites in multiple tumors, whereas, DNA methylation signature of LUSC remains to be built, and its predictive value need to be evaluated.MethodsThe genome-wide DNA methylation data of LUSC samples was obtained from The Cancer Genome Atlas dataset. Univariate Cox analysis and the least absolute shrinkage and selection operator (LASSO) were implemented to identify DNA methylation sites related to overall survival of LUSC patients. Thus, we performed multivariate Cox regression to establish a DNA methylation signature. The Kaplan-Meier (K-M) survival curves and time-dependent receiver operating characteristic (ROC) curves were plotted to estimate the prognostic power of the signature. Comparison with other known prognostic biomarkers, our DNA methylation signature showed higher predictive specificity and sensitivity. In addition, multivariate Cox regression screened out independent prognostic factors and constructed a nomogram.ResultsSeveral statistical methods were performed to construct an 11-DNA methylation signature. LUSC patients were divided into low- and high-risk group based on risk score, and high-risk group had a shorter survival time. According to the results of K-M and ROC analyses, the 11-DNA methylation signature showed significant sensitivity and specificity in predicting the LUSC patients’ overall survival. Finally, we integrated some independent prognostic factors (risk score, metastasis stage, and tobacco smoking history) to construct a nomogram, which has excellent prognostic power and may provide guidance for the therapeutic strategies.ConclusionsWe constructed the first risk prognosis model based on DNA methylation site in LUSC, which showed better predictive ability. In addition, a nomogram integrating the DNA methylation signature, metastasis stage, and tobacco smoking history was developed.

Dataset Information

Identification of a RNA-Seq based prognostic signature with five lncRNAs for lung squamous cell carcinoma.

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Identification of a RNA-Seq based prognostic signature with five lncRNAs for lung squamous cell carcinoma.

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