Dataset Information

A novel gene expression-based prognostic scoring system to predict survival in gastric cancer.

ABSTRACT: Analysis of gene expression patterns in gastric cancer (GC) can help to identify a comprehensive panel of gene biomarkers for predicting clinical outcomes and to discover potential new therapeutic targets. Here, a multi-step bioinformatics analytic approach was developed to establish a novel prognostic scoring system for GC. We first identified 276 genes that were robustly differentially expressed between normal and GC tissues, of which, 249 were found to be significantly associated with overall survival (OS) by univariate Cox regression analysis. The biological functions of 249 genes are related to cell cycle, RNA/ncRNA process, acetylation and extracellular matrix organization. A network was generated for view of the gene expression architecture of 249 genes in 265 GCs. Finally, we applied a canonical discriminant analysis approach to identify a 53-gene signature and a prognostic scoring system was established based on a canonical discriminant function of 53 genes. The prognostic scores strongly predicted patients with GC to have either a poor or good OS. Our study raises the prospect that the practicality of GC patient prognosis can be assessed by this prognostic scoring system.

SUBMITTER: Wang P

PROVIDER: S-EPMC5342421 | biostudies-literature | 2016 Aug

REPOSITORIES: biostudies-literature

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A novel gene expression-based prognostic scoring system to predict survival in gastric cancer.

Wang Pin P Wang Yunshan Y Hang Bo B Zou Xiaoping X Mao Jian-Hua JH

Oncotarget 20160801 34

Analysis of gene expression patterns in gastric cancer (GC) can help to identify a comprehensive panel of gene biomarkers for predicting clinical outcomes and to discover potential new therapeutic targets. Here, a multi-step bioinformatics analytic approach was developed to establish a novel prognostic scoring system for GC. We first identified 276 genes that were robustly differentially expressed between normal and GC tissues, of which, 249 were found to be significantly associated with overall ...[more]

PMID: 27419373

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Project description:Background: Autophagy plays a vital role in cancer initiation, malignant progression, and resistance to treatment. However, autophagy-related genes (ARGs) have rarely been analyzed in gastric cancer (GC). The purpose of this study was to analyze ARGs in GC using bioinformatic analysis and to identify new biomarkers for predicting the overall survival (OS) of patients with GC. Methods: The gene expression profiles and clinical data of patients with GC were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets, and ARGs were obtained from two other datasets (the Human Autophagy Database and Molecular Signatures Database). Lasso, univariate, and multivariate Cox regression analyses were performed to identify the OS-related ARGs. Finally, a six-ARG model was identified as a prognostic indicator using the risk-score model, and survival and prognostic performance were analyzed based on the Kaplan-Meier test and ROC curve. Estimate calculations were used to assess the immune status of this model, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were employed for investigating the functions and terms associated with the model-related genes in GC. Results: The six ARGs, DYNLL1, PGK2, HPR, PLOD2, PHYHIP, and CXCR4, were identified using Lasso and Cox regression analyses. Survival analysis revealed that the OS of GC patients in the high-risk group was significantly lower than that of the low-risk group (p < 0.05). The ROC curves revealed that the risk score model exhibited better prognostic performance with respect to OS. Multivariate Cox regression analysis indicated that the model was an independent predictor of OS and was not affected by most of the clinical traits (p < 0.05). The model-related genes were associated with immune suppression and several biological process terms, such as extracellular structure organization and matrix organization. Moreover, the genes were associated with the P13K-Akt signaling pathway, focal adhesion, and MAPK signaling pathway. Conclusions: This study presents potential prognostic biomarkers for GC patients that would aid in determining the best patient-specific course of treatment.

Project description:Gastric cancer (GC) is the fifth most common cancer and the third leading cause of cancer-associated mortality worldwide. In the current study, comprehensive bioinformatic analyses were performed to develop a novel scoring system for GC risk assessment based on CAP-Gly domain containing linker protein family member 4 (CLIP4) DNA methylation status. Two GC datasets with methylation sequencing information and mRNA expression profiling were downloaded from the The Cancer Genome Atlas and Gene Expression Omnibus databases. Differentially expressed genes (DEGs) between the CLIP4 hypermethylation and CLIP4 hypomethylation groups were screened using the limma package in R 3.3.1, and survival analysis of these DEGs was performed using the survival package. A risk scoring system was established via regression factor-weighted gene expression based on linear combination to screen the most important genes associated with CLIP4 methylation and prognosis. Genes associated with high/low-risk value were selected using the limma package. Functional enrichment analysis of the top 500 DEGs that positively and negatively associated with risk values was performed using DAVID 6.8 online and the gene set enrichment analysis (GSEA) software. In total, 35 genes were identified to be that significantly associated with prognosis and CLIP4 DNA methylation, and three prognostic signature genes, claudin-11 (CLDN11), apolipoprotein D (APOD), and chordin like 1 (CHRDL1), were used to establish a risk assessment system. The prognostic scoring system exhibited efficiency in classifying patients with different prognoses, where the low-risk groups had significantly longer overall survival times than those in the high-risk groups. CLDN11, APOD and CHRDL1 exhibited reduced expression in the hypermethylation and low-risk groups compare with the hypomethylation and high-risk groups, respectively. Multivariate Cox analysis indicated that risk value could be used as an independent prognostic factor. In functional analysis, six functional gene ontology terms and five GSEA pathways were associated with CLDN11, APOD and CHRDL1. The results established the credibility of the scoring system in this study. Additionally, these three genes, which were significantly associated with CLIP4 DNA methylation and GC risk assessment, were identified as potential prognostic biomarkers.

Dataset Information

A novel gene expression-based prognostic scoring system to predict survival in gastric cancer.

Publications

A novel gene expression-based prognostic scoring system to predict survival in gastric cancer.

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