Molecular dysexpression in gastric cancer revealed by integrated analysis of transcriptome data
- Authors:
- Published online on: March 3, 2017 https://doi.org/10.3892/ol.2017.5798
- Pages: 3177-3185
Abstract
Introduction
Gastric cancer (GC) is one of the most prevalent cancers and the second most common cause of cancer-associated mortality worldwide (1). Almost one million people are diagnosed with GC each year. Despite decades of a steady decline in the incidence of GC, the GC fatality rate remains paradoxically high in most countries, particularly in those of East Asia (2). GC is a heterogeneous disease with numerous etiologies and potential pathways of carcinogenesis (3,4), resulting in a variation in the incidence rates of GC among different geographies, ethnicities and genders (5). One of the main etiological risk factors for GC is Helicobacter pylori infection, although only a small proportion of individuals infected with H. pylori develop GC (6,7).
Traditional methods for the treatment of GC include surgery, chemotherapy, radiation therapy and combination therapy, which is also known as multimodality therapy. However, GC is often asymptomatic during its early stage, which results in the advanced stage being generally refractory to those therapies (8). Even following radical gastrectomy, many patients experience disease recurrence and succumb to the disease within a few months to years; the 5-year survival rate of GC is ≤2/3 (9,10). Therefore, an early and effective detection method that improves the chance of treating GC is imperative.
Microarrays are good tools for investigating the pathogenesis of various diseases (11–14). Compared with traditional methods, next-generation sequencing-based microarrays have the advantages of being unbiased, as they are not limited to previously known or annotated transcripts, and allowing more accurate quantification of genes with very low or high expression levels (15). In addition, transcriptome data detects other types of transcriptional signals, including alternative splicing, transcriptional starts/stops, gene fusions and expressed alleles (16). Studies based on microarrays have provided significant insights into the molecular basis of GC and novel therapeutic targets. However, microarrays have predominantly been used to characterize the genomic alterations of GC patients, while the validation of potential target genes for GC has been rare (10,17,18), restricting the application of microarrays to clinical practice.
The present study employed an integrated analysis of microarray data from the Gene Expression Omnibus (GEO) to identify the differentially expressed genes (DEGs) between GC and normal control (NC) tissues, which were then used to construct a protein-protein interaction (PPI) network. Furthermore, the significantly enriched functions of these genes were screened and analyzed to discover the biological processes and signaling pathways associated with GC. Finally, reverse transcription-quantitative polymerase chain reaction (RT-qPCR) of clinical samples was performed to validate the integrated analysis approach. This study may improve the methods used to elucidate the dysexpression of various genes in GC and be of some value for the future diagnosis of GC in the clinic.
Materials and methods
Microarray data and data preprocessing
Eligible GC gene expression datasets were identified by searching the GEO database (https://www.ncbi.nlm.nih.gov/geo/). Data were included if they met the following criteria: i) The expression profile of whole genome sequencing; ii) data from the tissues of GC patients in the clinic; and iii) raw or standardized data. Raw data were normalized using the Z-score transformation method (19) to make data from different platforms comparable. Matrix Laboratory software (version 2013a; MathWorks, Natick, MA, USA) was used to identify differentially expressed probe sets between tumor and adjacent tissues. A gene-specific t-test was performed, after which P-values and the effect size of individual microarray studies were calculated. The genes with a false discovery rate of ≤0.05 were selected as the significantly DEGs. DEGs between tumor and adjacent tissues were subsequently determined. Heat map analysis was conducted using the ‘heatmap.2’ function of the R/Bioconductor package ‘gplots’ (20).
Functional enrichment analysis of DEGs
To determine the biological functions of DEGs, Gene Ontology (GO) enrichment analysis of biological processes, molecular functions and cellular components was performed. The online software GeneCodis3 (http://genecodis.cnb.csic.es/analysis) was used to perform this analysis (21). In addition, pathway enrichment was also performed based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database (http://www.genome.jp/kegg/).
PPI network construction
A PPI network of the significantly dysexpressed genes was constructed according to data from the Biological General Repository for Interaction Datasets (http://thebiogrid.org/). Among the candidate genes, the distribution characteristics of the top 20 most significantly upregulated and downregulated DEGs were visualized using Cytoscape (22).
Collection of clinical specimens
A total of 10 patients, including 8 males and 2 females, were enrolled in the present study, among which 5 had been diagnosed with GC. The average age of the patients was 54 years (age range, 38–79 years). Frozen tissue sections were generated and examined independently by senior pathologists. Parts of each tumor tissues were frozen immediately following the operation and stored at −135°C for RNA extraction. This study was approved by the Ethics Committee of The First Affiliated Hospital of PLA General Hospital (Beijing, China).
RNA preparation and RT-qPCR
Total RNA of each sample was extracted using the RNAeasy Mini kit (Qiagen, Inc., Valencia, CA, USA), according to the manufacturer's protocol. According to previous studies (23–27), 10 DEGs were retrieved from the top 20 upregulated and downregulated genes. Primers for the 10 target genes were designed using PrimerPlex 2.61 (Premier Biosoft International, Palo Alto, CA, USA) and are shown in Table I. cDNA templates were synthesized from 1–5 µg RNA using Superscript Reverse Transcriptase II (Thermo Fisher Scientific, Inc., Waltham, MA, USA). qPCR was performed on the ABI 7500 Real-Time PCR System (Applied Biosystems; Thermo Fisher Scientific, Inc.) with SYBR dye (Thermo Fisher Scientific, Inc.). The final reaction mixture of 12.5 µl consisted of 6.25 µl Power SYBR Green PCR Master Mix, 50 ng diluted cDNA and 1 µM of each primer. Reactions were conducted in triplicate under the following conditions: 50°C for 2 min, 95°C for 10 min, and 40 cycles of 95°C for 15 sec and 60°C for 1 min. Melting curves (60 to 95°C) were derived for every reaction to insure a single product. Relative gene expression was evaluated using Data Assist Software, version 3.0 (Thermo Fisher Scientific, Inc.), with the human actin gene as a reference. The expression levels of each gene were determined using the 2−ΔΔCq method (28).
Statistical analysis
Data are expressed as the mean ± standard deviation. Comparisons of the expression levels of different genes were conducted using Student's t-test with a significance level of 0.05. Statistical analyses were conducted using SPSS version 16.0 (SPSS, Inc., Chicago, IL, USA).
Results
DEGs in the integrated analysis of microarray datasets
Following the electronic database search, six microarray studies were obtained according to the inclusion criteria. The characteristics of the individual studies that were included in the integrated analysis are displayed in Table II. There were 53 GC patients and 259 NC patients. The integrated analysis identified a set of 689 DEGs in the GC tissues, as compared with the normal tissues, including 202 upregulated and 487 downregulated DEGs. In addition, the hierarchical clustering analysis indicated that the DEGs in GC were distinguished from those in normal tissues (Fig. 1).
Functional enrichment analysis
GO provides a common descriptive framework and functional annotation and classification system for analyzing the gene set data. The 689 DEGs were involved in 86 signaling pathways, including the digestion and absorption of proteins, interactions between extracellular matrix (ECM) receptors, the p53 signaling pathway, the metabolism of propionate, the absorption of minerals, etc. The results of the KEGG enrichment analysis revealed that the first three most enriched pathways included protein digestion and absorption, ECM-receptor interaction and the metabolism of xenobiotics by cytochrome P450 (Tables III and IV). The detailed information on the 20 most significantly upregulated and downregulated genes is shown in Table V.
PPI network construction
A PPI network of the top 10 upregulated and downregulated DEGs is shown in Fig. 2. The network consisted of 243 edges and 251 nodes. Generally, nodes with a high degree, which measures how many neighbors a node is directly connected to, are defined as hub proteins (29,30). Three nodes, including SPP1, TOP2A and ARPC1B, showed the highest degrees.
RT-qPCR validation
Five genes were randomly retrieved from the 10 upregulated and downregulated genes, respectively. It was shown that the expression patterns of the selected genes in GC and normal tissues in the RT-qPCR analysis were similar to those in the integrated analysis (Fig. 3; Table V), and that the difference between the GC patients and control samples were significantly different (P<0.01). The expression of SULF1, SPP1, THBS2, TOP2A and HOXC6 was upregulated in GC tissues compared with normal tissues, while the expression of GIF, KCNE2, SST, GKN1 and LIPF was downregulated in GC tissues compared with normal tissues.
Discussion
Microarrays are powerful tools for revealing the pathogenesis of human cancer and identify potential therapeutic targets (12). Microarray-based technology has been used in several studies to detect candidate genes involved in the occurrence of GC (31–34). In the present study, an integrated analysis of six transcriptome datasets was conducted and 689 DEGs were identified based on 612 samples, including 202 upregulated genes and 487 downregulated genes. The results of GO and KEGG analyses showed that the most enriched pathways included protein digestion and absorption, ECM-receptor interaction, and the metabolism of xenobiotics by cytochrome P450. The results were consistent with previous research (35,36), which identified DEGs with biological functions that were mainly involved in cell adhesion and ECM interactions.
Generally, it is accepted that, although the number of dysfunctional genes in a cancer may be limited, a large number of genes in related pathways may be affected at the expression level, and this aberrant gene transcriptional expression network is likely essential in the initiation and maintenance of the malignant phenotype (37–39). Most of the 20 most significantly DEGs have been reported to be involved in the oncogenesis and development of GC (23–26), and the functional classification of these genes was consistent with the results of GO and KEGG analyses. Three genes, including SPP1, TOP2A and ARPC1B, showed the highest connection in the PPI analysis and may have key roles in GC. In addition, SUL1, THBS2, HOXC6, SST, KCNE2, GIF, GKN1 and LIPF were also linked with more than one edge, indicating the potential role of these genes in the pathogenesis of GC.
However, the expression levels of six genes (MEST, GIF, CHIA, DUOX1, KIF4 and AKR7A3) in clinical samples were either inconsistent or ignored in previous studies (35,40). In the current study, dysregulation of these DEGs suggests that they serve roles in the oncogenesis and the development of GC. Among the six genes, the associations between GC and MEST, GIF, CHIA, and DUOX1 have never been reported. MEST encodes a member of the α/β hydrolase fold family and has the characteristic of isoform-specific imprinting. The aberrant imprinting of this gene has been linked to certain types of cancer and may be caused by promoter switching (41). GIF encodes intrinsic factor (IF), also known as gastric IF, which is a glycoprotein secreted by the parietal cells of the stomach that is necessary for the absorption of vitamin B12 (cobalamin) in the small intestine (35). CHIA may participate in the defense against nematodes, fungi and other pathogens, and play a role in the T-helper cell type 2 immune response; it is also involved in the inflammatory response and in protecting cells against apoptosis. Furthermore, CHIA is inhibited by allosamidin, suggesting that the function of this protein is dependent on carbohydrate binding (42–45). DUOX1 is the member of gp91phox homologs family and produces reactive oxygen species in various cells in response to stimuli, including growth factors, cytokines and calcium. A key role for DUOX1 in lung cancer, but not in GC, has been revealed (46). Although further studies on these genes have not been conducted, the results of the present study suggest that these genes may be considered as novel indicators for GC in the clinic.
Except for the four genes that have not previously been reported in GC, the expression levels of two genes in the current study were different from those reported in previous studies (40,47). Chromokinesin KIF4 is a member of the KIF4 subfamily and has been reported as an essential factor involved in multiple cellular process, including cell proliferation, DNA damage responses, immune cell activation, viral protein intracellular trafficking and neuronal survival in brain development (48). The overexpression of this subfamily was reported to inhibit GC cell proliferation in vitro, as well as their ability to form tumors in vivo (40). However, the expression level of KIF4 was significantly upregulated in GC samples in the current study. In addition, the expression of AKR7A3, which was reported to be upregulated in Singaporean GC patients (47), was downregulated in the RT-qPCR validation in the present study. This discrepancy in the results may be due to the heterogeneity of the GEO database, although the results still suggest complicated functions of KIF4 and AKR7A3 in the oncogenesis and development of GC.
In conclusion, the current study demonstrated that the analysis of expression profiles and RT-qPCR validation was able to give an explicit elucidation of the dysexpression of genes in GC. However, the results of the analysis of the expression profiles varied from study to study. Based on our results, the expression levels of six genes, including MEST, GIF, CHIA, DUOX1, KIF4 and AKR7A3, were found to be inconsistent with previous studies. These genes could potentially be valuable in the clinical treatment of GC. The present study may improve the understanding of the transcriptome status of GC and lay a foundation for further investigation of the mechanisms underlying this cancer of clinical and biological significance.
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