Molecular dysexpression in gastric cancer revealed by integrated analysis of transcriptome data

Abstract

Abstract. Gastric cancer (GC) is often diagnosed in the advanced stages and is associated with a poor prognosis. Obtaining an in depth understanding of the molecular mechanisms of GC has lagged behind compared with other cancers. This study aimed to identify candidate biomarkers for GC. An integrated analysis of microarray datasets was performed to identify differentially expressed genes (DEGs) between GC and normal tissues. Gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were then performed to identify the functions of the DEGs. Furthermore, a protein-protein interaction (PPI) network of the DEGs was constructed. The expression levels of the DEGs were validated in human GC tissues using reverse transcription-quantitative polymerase chain reaction (RT-qPCR). A set of 689 DEGs were identified in GC tissues, as compared with normal tissues, including 202 upregulated DEGs and 487 downregulated DEGs. The KEGG pathway analysis suggested that various pathways may play important roles in the pathology of GC, including pathways related to protein digestion and absorption, extracellular matrix-receptor interaction, and the metabolism of xenobiotics by cytochrome P450. The PPI network analysis indicated that the significant hub proteins consisted of SPP1, TOP2A and ARPC1B. RT-qPCR validation indicated that the expression levels of the top 10 most significantly dysexpressed genes were consistent with the illustration of the integrated analysis. The present study yielded a reference list of reliable DEGs, which represents a robust pool of candidates for further evaluation of GC pathogenesis and treatment.

Identification of Potential Key Genes Associated With the Pathogenesis and Prognosis of Gastric Cancer Based on Integrated Bioinformatics Analysis

“…Integrated bioinformatics analysis mainly focusing on differentially expressed molecule screen, network-based hub node discovery, and survival analysis has been extensively applied to identify potential biomarkers associated with the diagnosis, treatment, and prognosis of GC. For example, Chang et al identified hub genes related to liver metastasis of GC from four GEO datasets by developing an integrated method including DEG screen, pathway analysis, literature-based annotations, PPI networks, reverse transcription-quantitative polymerase chain reaction (RT-qPCR), and immunohistochemistry ( Chang et al, 2009 ); Sun et al identified key genes in the occurrence and development of GC from one GEO dataset using a bioinformatics approach incorporating DEG screen, functional enrichment analysis, PPI network construction, and survival analysis ( Sun C. et al, 2017 ); Li X. et al (2017) identified candidate biomarkers for GC from six GEO datasets by performing DEG, gene functional enrichment, and PPI network analyses, and validated their results with RT-qPCR; Ren et al (2017) identified key genes and pathways for GC by a network-based method that combined data on gene expression, miRNA expression, DNA methylation, and DNA copy number in TCGA; Wang et al (2017) used the gene expression profiles from one GEO dataset and TCGA, and identified a prognostic gene signature for predicting the survival of GC patients by a robust likelihood-based survival model. Compared with previous works, the current study not only integrated microarray data with relative large sample size from multiple GEO datasets and RNA sequencing data from TCGA, but also built gene networks and a Cox proportional hazards model to identify potential diagnostic and prognostic biomarkers in GC.…”

Prognostic value of gastric cancer‐associated gene signatures: Evidence based on a meta‐analysis using integrated bioinformatics methods

“…Microarray and RNA sequencing technologies, as well as gene profiling data sets such as The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO), have been used to identify various DEGs and significant biological pathways in different cancers. Several recent studies of DEGs associated with GC have employed integrated bioinformatics analyses to explore the patterns of gene expression. However, biased gene expression results may be obtained using a single data set because of data outliers, noise, and errors, as well as insufficient sample sizes.…”

“…cancers. Several recent studies of DEGs associated with GC [3][4][5] have employed integrated bioinformatics analyses to explore the patterns of gene expression. However, biased gene expression results may be obtained using a single data set because of data outliers, noise, and errors, as well as insufficient sample sizes.…”

Integrated analysis of differentially expressed profiles and construction of a competing endogenous long non-coding RNA network in renal cell carcinoma

“…Many of them were classical pathways comprising important areas of RCC research, such as immune responses, signal transduction, and metabolism ( Labrousse-Arias et al, 2017 ; Sakai, Miyake & Fujisawa, 2013 ; Wettersten et al, 2017 ). However, some of biological processes and pathways, such as protein digestion and absorption, were reported in other malignancies ( Dong et al, 2017 ; Li et al, 2017 ). From our enrichment analysis of large-scale samples focusing on RCC, we discovered multiple processes involved in neoplasm formation, which was supplementary to the results of previous studies.…”