Combining multi-dimensional data to identify key genes and pathways in gastric cancer

Abstract

Gastric cancer is an aggressive cancer that is often diagnosed late. Early detection and treatment require a better understanding of the molecular pathology of the disease. The present study combined data on gene expression and regulatory levels (microRNA, methylation, copy number) with the aim of identifying key genes and pathways for gastric cancer. Data used in this study was retrieved from The Cancer Genomic Atlas. Differential analyses between gastric cancer and normal tissues were carried out using Limma. Copy number alterations were identified for tumor samples. Bimodal filtering of differentially expressed genes (DEGs) based on regulatory changes was performed to identify candidate genes. Protein–protein interaction networks for candidate genes were generated by Cytoscape software. Gene ontology and pathway analyses were performed, and disease-associated network was constructed using the Agilent literature search plugin on Cytoscape. In total, we identified 3602 DEGs, 251 differentially expressed microRNAs, 604 differential methylation-sites, and 52 copy number altered regions. Three groups of candidate genes controlled by different regulatory mechanisms were screened out. Interaction networks for candidate genes were constructed consisting of 415, 228, and 233 genes, respectively, all of which were enriched in cell cycle, P53 signaling, DNA replication, viral carcinogenesis, HTLV-1 infection, and progesterone mediated oocyte maturation pathways. Nine hub genes (SRC, KAT2B, NR3C1, CDK6, MCM2, PRKDC, BLM, CCNE1, PARK2) were identified that were presumed to be key regulators of the networks; seven of these were shown to be implicated in gastric cancer through disease-associated network construction. The genes and pathways identified in our study may play pivotal roles in gastric carcinogenesis and have clinical significance.

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

“…Particularly, many up-regulated genes were enriched in cancer-related pathways, such as ECM-receptor interaction, PI3K-Akt signaling pathway, and Toll-like receptor signaling pathway, which suggested these genes might be important in carcinogenesis and metastasis of GC. Our findings in the functional enrichment analysis agreed with previous works ( Li H. et al, 2015 ; Li X. et al, 2017 ; Ren et al, 2017 ; Sun C. et al, 2017 ).…”

“…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.…”

A predictive model for assessing prognostic risks in gastric cancer patients using gene expression and methylation data

“…The NR3C1 gene encodes a glucocorticoid receptor. NR3C1 is important in the carcinogenesis of GC and has been used as a marker to identify primary GC [ 23 , 24 ]. The high degree of methylation within the NR3C1 promoter was also implicated in the initiation of GC progression, and four SNPs at this locus have been shown to be strongly associated with increased risk for GC in a Chinese population [ 20 ].…”

Emerging functions of PRKDC in the initiation and progression of cancer

“…In the methylation regulatory network of GC, PRKDC is considered to be one of the key regulators. 42 Proteomic analysis showed that endoplasmic reticulum-Golgi intermediate compartment 1 (ERGIC1) expression decreases, whereas DNA-PKcs expression increases during GC progression, indicating that both ERGIC1 and DNA-PKcs are involved in GC initiation. 43 Both DNA-PKcs and Ku70/80 are overexpressed in GC tissues, and a negative correlation between DNA-PKcs expression and the degree of tumor differentiation has been reported.…”