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Protein-coding genes combined with long noncoding RNA as a novel transcriptome molecular staging model to predict the survival of patients with esophageal squamous cell carcinoma.

ABSTRACT: BACKGROUND:Esophageal squamous cell carcinoma (ESCC) is the predominant subtype of esophageal carcinoma in China. This study was to develop a staging model to predict outcomes of patients with ESCC. METHODS:Using Cox regression analysis, principal component analysis (PCA), partitioning clustering, Kaplan-Meier analysis, receiver operating characteristic (ROC) curve analysis, and classification and regression tree (CART) analysis, we mined the Gene Expression Omnibus database to determine the expression profiles of genes in 179 patients with ESCC from GSE63624 and GSE63622 dataset. RESULTS:Univariate cox regression analysis of the GSE63624 dataset revealed that 2404 protein-coding genes (PCGs) and 635 long non-coding RNAs (lncRNAs) were associated with the survival of patients with ESCC. PCA categorized these PCGs and lncRNAs into three principal components (PCs), which were used to cluster the patients into three groups. ROC analysis demonstrated that the predictive ability of PCG-lncRNA PCs when applied to new patients was better than that of the tumor-node-metastasis staging (area under ROC curve [AUC]: 0.69 vs. 0.65, P?

SUBMITTER: Guo JC

PROVIDER: S-EPMC5993132 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature

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Protein-coding genes combined with long noncoding RNA as a novel transcriptome molecular staging model to predict the survival of patients with esophageal squamous cell carcinoma.

Guo Jin-Cheng JC Wu Yang Y Chen Yang Y Pan Feng F Wu Zhi-Yong ZY Zhang Jia-Sheng JS Wu Jian-Yi JY Xu Xiu-E XE Zhao Jian-Mei JM Li En-Min EM Zhao Yi Y Xu Li-Yan LY

Cancer communications (London, England) 20180409 1

<h4>Background</h4>Esophageal squamous cell carcinoma (ESCC) is the predominant subtype of esophageal carcinoma in China. This study was to develop a staging model to predict outcomes of patients with ESCC.<h4>Methods</h4>Using Cox regression analysis, principal component analysis (PCA), partitioning clustering, Kaplan-Meier analysis, receiver operating characteristic (ROC) curve analysis, and classification and regression tree (CART) analysis, we mined the Gene Expression Omnibus database to de ...[more]

PMID: 29784063

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Project description:BackgroundLong noncoding RNAs (lncRNAs) are implicated in the oncogenesis and metastasis of multiple human cancers. Nonetheless, the precise molecular mechanisms underlying the oncogenic role of lncRNA in esophageal squamous cell carcinoma (ESCC) remains to be clarified.MethodsThe expression of GK intronic transcript 1 (GK-IT1) was analyzed using ESCC RNA-seq data from The Cancer Genome Atlas database. Quantitative real-time PCR was used to measure the expression of GK-IT1 in ESCC clinical samples and cells. The correlation between GK-IT1 expression and clinicopathological variables was examined using chi-squared tests. Kaplan-Meier survival and Cox regression analyses were employed to generate the survival curve and assess the prognostic value of GK-IT1. Functional experiments were utilized to explore the role of GK-IT1 in promoting cell migration, invasion, proliferation, and suppressing apoptosis and autophagy in ESCC. To understand the mechanism, an RNA pulldown assay, RNA immunoprecipitation, agarose gel electrophoresis, immunofluorescence, and co-immunoprecipitation assays were used.ResultsIn this study we identified an unreported lncRNA, termed GK-IT1 that was aberrantly overexpressed in ESCC tissues and cells. GK-IT1 was closely associated with advanced clinical stage, and it was an independent prognostic indicator of ESCC. Functional assays verified that GK-IT1 significantly promoted ESCC proliferation, invasion, and migration, and suppressed ESCC apoptosis and autophagy. Furthermore, tumorigenesis experiments in nude mice indicated that GK-IT1 promoted ESCC tumor growth and metastasis. Mechanistically, GK-IT1 competitively bound to mitogen-activated protein kinase 1 (MAPK1) to prevent the interaction between dual specificity phosphatase 6 (DUSP6) and MAPK1, thereby controlling the phosphorylation of MAPK1 and promoting ESCC progression.ConclusionOur study revealed that GK-IT1 competed with DUSP6 to attenuate the interaction between DUSP6 and MAPK1, leading to activation of the ERK/MAPK pathway, thereby promoting progression of ESCC. Our research indicated that GK-IT1 served as a novel potential target for the diagnosis and treatment of ESCC.

Dataset Information

Protein-coding genes combined with long noncoding RNA as a novel transcriptome molecular staging model to predict the survival of patients with esophageal squamous cell carcinoma.

Publications

Protein-coding genes combined with long noncoding RNA as a novel transcriptome molecular staging model to predict the survival of patients with esophageal squamous cell carcinoma.

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