Identification of genes and long non-coding RNAs associated with the pathogenesis of gastric cancer
- Authors:
- Published online on: July 14, 2015 https://doi.org/10.3892/or.2015.4129
- Pages: 1301-1310
Abstract
Introduction
As a type of malignant epithelial tumor, gastric cancer is derived from the glandular epithelium of the gastric mucosa (1). Gastric cancer has high diffusivity, such as spread to lungs, liver and bones (2). This cancer type is prevalent in men of developing countries (3,4), and ~8.5% of cancer cases in men is gastric cancer (5). In 2012, gastric cancer ranked third after lung and liver cancer, with 700,000 mortalities (6,7). Thus, it is necessary to examine the molecular mechanisms of gastric cancer and develop therapeutic schedules.
The molecular mechanisms of gastric cancer have been previously investigated. For example, as a member of the BRICHO family, the full-length gene gastrokine 2 (GKN2) is downregulated and associated with gastric cancer (8). The expression of GKN1 and GKN2 decreased in gastric adenocarcinomas, and their loss is associated with shorter survival in the intestinal subtype of gastric adenocarcinomas (9). Expression of soluble urokinase plasminogen activator receptor (suPAR) and carbonic anhydrase IX (CA IX) is associated with the stage and presence of gastric cancer, and the overexpression of suPAR indicates a poorer prognosis in patients with gastric cancer (10). Differentially expressed microseminoprotein (MSMB), Annexin A10 (ANXA10), Annexin A1 (ANXA1) and prostate stem cell antigen (PSCA) have been identified in normal and cancer gastric tissues, and may be used as biomarkers for the diagnosis and treatment of gastric cancer (11).
Long non-coding RNAs (lncRNAs), having a length >200 nucleotides, can regulate gene expression during transport, RNA maturation and protein synthesis (12). Dysregulation of lncRNAs is associated with many types of human cancer (13). Many differentially expressed lncRNAs, such as LINC00152 and LINC00261, have been identified and may act as therapeutic targets and biomarkers in gastric cancer (12). The downregulation of lncRNA maternally expressed 3 (MEG3) is involved with cell proliferation and can be regarded as a poor prognostic biomarker in gastric cancer (14). Knockdown of lncRNA HOX transcript antisense RNA (HOTAIR) results in suppression of tumor invasion and reversal of epithelial-mesenchymal transition process in gastric cancer cells, suggesting that HOTAIR affects diagnostics and therapeutics of gastric cancer (15).
In the present study, to examine the molecular mechanisms of gastric cancer, the expression profile of GSE41476 was downloaded, which involved 3 primary cell culture samples from gastric cancer tissues, 3 gastric cancer cell lines and 2 normal tissue samples. The lncRNAs were predicted and differentially expressed genes (DEGs) were screened. The functions of the DEGs were analysed using Gene Ontology (GO) and pathway enrichment analyses. In addition, a search was conducted to determine the interaction relationships between the DEGs using protein-protein interaction (PPI) network and modules of the PPI network. Additionally, lncRNA-DEG pairs were screened, and pathway enrichment analysis was performed for DEGs co-expressed with each lncRNA.
Materials and methods
Microarray data
The expression profile of GSE41476 was downloaded from the Gene Expression Omnibus (GEO, http:/www.ncbi.nlm.nih.gov/geo/), which was based on the platform of the GPL9115 Illumina Genome Analyzer II (Homo sapiens). GSE41476 included a collective of 3 primary cell culture samples from gastric cancer tissues, 3 gastric cancer cell lines and 2 normal tissue samples.
Sequence alignment
After GSE41476 was downloaded, the SRA format sequences were translated into FASTQ format, and microarray data were preprocessed by NGSQC software (16). The ratio of bases with base sequencing quality <20 was required to be <0.1. The remaining high quality sequences were compared to human genome 19 (hg19) using TopHat2 (17). The parameter was set to - no-discordant - phred64-quals, and the remaining parameters were set to the default values.
lncRNA prediction
The alignment results were obtained via transcriptome assembly using Cufflinks software (18). Subsequently, the assembly results were integrated using Cuffmerge software (19). According to the gene annotation information of the genome in the UCSC, assembly results that did not overlap with the extracted arbitrary genes were extracted. Transcriptions with a length of >200 nt and with ≥2 exons were screened. According to information obtained from the 29 mammalian genome alignment, transcriptions with scores <100 were screened using PhyloCSF software (20). Moreover, HMMER software was used to compare transcriptions to Pfam database (21). E-value <1e-5 was used as the cut-off criterion.
DEGs screening
A search through the RefSeq annotation files in UCSC website identified known lncRNA comments (LNCipedia1.0 database) and the predicted lncRNA transcription document, DEGs and lncRNAs, which were screened from the alignment results using Cuffdiff software (22). The adjusted p-value of <0.05 and |log fold-change (FC)| >1 were used as the cut-off criteria.
Functional and pathway enrichment analyses
As a functional study method, GO analysis is used to assess large-scale transcriptomic or genomic data (23). The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database shows how molecules or genes act (24). The GO and KEGG pathway enrichment analyses were conducted for DEGs. The GO functional enrichment analysis was mainly focused on the biological process (BP). P<0.05 was used and ≥2 genes were used as the cut-off criteria.
PPI network and module analysis
The STRING online software (25) was used to examine the interaction relationships of the proteins encoded by DEGs, and the combined score >0.7 was used as the cut-off criterion. The Cytoscape software (26) was used to visualize the PPI network. The CFinder software (27) was used to screen modules of the PPI network, and the parameter k was set to 8.
lncRNA analysis
According to the expression matrix of the differentially expressed lncRNA and the DEGs, the relationship between lncRNA and the DEGs was calculated and lncRNA-DEG pairs were screened. Pearson's correlation of >0.99 was used as the cut-off criterion.
Results
lncRNA prediction and DEGs analysis
A total of 86 lncRNA transcriptions were obtained, including 37 transcriptions with an overlap of >50% when compared with known lncRNAs.
Compared to normal tissue samples, 1,088 upregulated transcriptions (including 16 known lncRNAs, 1 predicted lncRNAs and 1,071 mRNAs) and 1,537 downregulated transcriptions (including 18 known lncRNAs, 4 predicted lncRNAs and 1,515 mRNAs) were identified in the gastric cancer samples.
Functional and pathway enrichment analyses
The enriched GO functions for the DEGs are provided in Table I, including cell migration (P=8.97E-14), extracellular matrix (P=3.06E-14), positive regulation of response to stimulus (P=6.66E-16) and single-organism process (P=1.33E-15).
The enriched KEGG pathways for the DEGs are shown in Table I, including ECM-receptor interaction (P=1.18E-06), focal adhesion (P=0.000143), cell adhesion molecules (CAMs, P=1.33E-15) and Staphylococcus aureus infection (P=2.53E-10).
PPI network and module analysis
The PPI network of the upregulated genes had 568 nodes and 1,522 interactions (Fig. 1). Module A (Fig. 2A) and module B (Fig. 2B) were obtained from the PPI network. Module A had 13 nodes and 65 interactions. The enriched GO functions for DEGs in module A are provided in Table II, including collagen catabolic process (P=2.22E-16), multicellular organismal catabolic process (P=6.66E-16) and extracellular matrix disassembly (P=1.33E-15). The enriched KEGG pathways for DEGs in module A are also listed in Table II, including ECM-receptor interaction [P=0, which involved proteins encoded by collagen (COL) genes such as collagen, type I, α 1 (COL1A1)], collagen, type I, α 2 (COL1A2) and collagen, type IV, α 4 (COL4A4), as well as proteins encoded by integrin (ITG) genes such as integrin, α 1 (ITGA1), integrin, α 5 (ITGA5) and integrin, β 1 (ITGB1), focal adhesion (P=1.05E-13, which also involved proteins encoded by the COL and ITG genes) and protein digestion and absorption (P=5.49E-11). Proteins encoded by COL genes interacted with those of the ITG genes, such as COL1A1-ITGA5 and COL1A2-ITGB1. Module B had 27 nodes and 261 interactions. The enriched GO functions for DEGs in module B are provided in Table II, including ribonucleoprotein complex biogenesis (P=2.22E-16), rRNA processing (P=1.17E-11) and cellular component biogenesis (P=2.36E-07). The enriched KEGG pathway for DEGs in module B was ribosome biogenesis in eukaryotes (P=5.55E-16) (Table II).
Table IIThe top 10 enriched GO functions and KEGG pathways for DEGs in module A and B of the PPI network for the upregulated DEGs. |
The PPI network of the downregulated genes had 734 nodes and 2345 interactions (Fig. 3). In addition, 6 modules (module A–F) were obtained from the PPI network (Fig. 4). Module A had 8 nodes and 28 interactions. The enriched GO functions for DEGs in module A included transcription initiation from RNA polymerase II promoter (P=8.88E-16) and DNA-dependent transcription, initiation (P=2.66E-15). The enriched KEGG pathways for DEGs in module A included maturity onset diabetes of the young (P=0.000173) and bile secretion (P=0.001409). Module B had 20 nodes and 122 interactions. The enriched GO functions for DEGs in module B included activation of immune response (P=2.22E-16) and leukocyte activation (P=4.44E-16). The enriched KEGG pathways for DEGs in module B included T-cell receptor signaling pathway [P=0, which involved phosphoinositide-3-kinase, catalytic, γ polypeptide (PIK3CG) and phosphoinositide-3-kinase, regulatory subunit 5 (PIK3R5)] and primary immunodeficiency (P=7.35E-10). Specifically, PIK3CG had an interaction relationship with PIK3R5. Module C had 8 nodes and 28 interactions. The enriched GO functions for DEGs in module C included cytokine-mediated signaling pathway (P=6.98E-14) and cell response to cytokine stimulus (P=5.20E-13). The enriched KEGG pathways for DEGs in module C included antigen processing and presentation (P=0.001613) and hepatitis C (P=0.004944). Module D had 10 nodes and 44 interactions. The enriched GO functions for DEGs in module D included single-organism carbohydrate metabolic process (P=8.44E-15) and keratan sulfate biosynthetic process (P=0.000153). The enriched KEGG pathways for DEGs in module D included mucin-type O-glycan biosynthesis (P=2.39E-06) and metabolic pathways (P=0.014715). Module E had 8 nodes and 28 interactions. The enriched GO functions for DEGs in module E included T-cell costimulation (P=1.20E-10). The enriched KEGG pathways for DEGs in module E included T-cell receptor signaling pathway (P=3.92E-08). Module F had 24 nodes and 273 interactions. The enriched GO functions for DEGs in module F included signal transduction (P=1.46E-13). The enriched KEGG pathways for DEGs in module F included neuroactive ligand-receptor interaction (P=9.26E-08) (Table III).
Table IIIThe top 10 enriched GO functions and KEGG pathways for DEGs in module A, B, C, D, E and F of the PPI network for the downregulated DEGs. |
lncRNA analysis
After lncRNA-DEG pairs, such as TCONS-00068220-IL7, were screened, KEGG pathway enrichment analysis was conducted for DEGs co-expressed with each lncRNA. Proteins encoded with co-expressed DEGs of TCONS_00068220 were enriched in cancer-related pathways, such as bladder cancer, CAMs, chemokine signaling pathway and natural killer cell-mediated cytotoxicity (Table IV) (28).
Discussion
In the present study, 86 lncRNA transcriptions were obtained, including 37 transcriptions with an overlap of >50% when compared with known lncRNAs. Additionally, 1,088 upregulated and 1,537 downregulated transcriptions were screened. The functions of cell migration, positive regulation of response to stimulus and single-organism process were enriched for the DEGs.
At the tumor periphery of scirrhous gastric carcinoma, collagen biosynthesis is increased and may contribute to the invasion of tumor cells (29). Collagen IV can play a role in cell adhesion, as well as tumor metastasis and invasion (30–32). It is reported that integrin (ITG) α2β1 can be used as a candidate target molecule involved in the prevention of gastric cancer peritoneal dissemination (33). Integrin α6β4 is a suppressor and can be a biomarker for peritoneal dissemination in gastric cancer (34). In module A of the PPI network for the upregulated genes, the enriched KEGG pathways for DEGs included ECM-receptor interaction and focal adhesion, both of which involved proteins encoded by COL and ITG genes. ECM-receptor interaction and focal adhesion are associated with cancer metastasis and aggression (35,36), and can represent some molecular differences in gastric cancer (37). These observations may indicate that the COL and ITG genes were associated with gastric cancer. In module A of the PPI network for proteins encoded by the upregulated genes, proteins encoded by COL genes were able to interact with those of ITG genes, suggesting that COL genes are involved in gastric cancer through the regulation of ITG genes.
There is a high mutation probability of phosphoinositide-3-kinase, catalytic, α (PIK3CA) in human cancers and it is a potential therapy target for various tumors (38). The mutations of PIK3CA can lead to the attenuation of apoptosis and assist tumor invasion (39). The phosphatidylinositol 3-kinase (PI3K) pathway is of great alteration frequency in gastric tumors and can be used as a therapeutic target in gastric cancer (40). In module B of the PPI network for the downregulated genes, the enriched KEGG pathways for DEGs included the T-cell receptor signaling pathway, which involved proteins encoded by PIK3CG and PIK3R5. It has been reported that the T-cell receptor signaling pathway plays a role in gastric cancer (41). The abovementioned findings showed that PIK3CG and PIK3R5 may be associated with gastric cancer. In module B of the PPI network for proteins encoded by the downregulated genes, PIK3CG also had an interaction relationship with PIK3R5, indicating that PIK3CG may be involved in gastric cancer by mediating PIK3R5.
The interleukin-8 (IL8) promoter polymorphism plays a role in atrophic gastritis and gastric cancer (42). Serum IL6 is correlated with the progression of gastric cancer and may be used as a biomarker for monitoring the treatment and response of patients with gastric cancer (43). Recombinant human IL7 can retard tumor growth and induce complete regression (44). By activating p53 and inhibiting cell proliferation, lncRNA TCONS-00090092-MEG3 may act as a putative tumor-suppressor gene (45–47). In the present study, IL7 was co-expressed with TCONS-00068220. Proteins encoded with co-expressed DEGs of TCONS_00068220 were enriched in cancer-associated pathways. Thus, the expression levels of IL7 and TCONS-00068220 may be associated with gastric cancer, and IL7 may function in gastric cancer by regulating TCONS-00068220.
In conclusion, we have conducted a comprehensive bioinformatics analysis of genes and lncRNAs that may be associated with gastric cancer. A total of 86 lncRNA transcriptions were obtained, as well as 1,088 upregulated and 1,537 down-regulated transcriptions were screened. COL and ITG genes, PIK3CG, PIK3R5, IL7 and lncRNA TCONS-00068220 may be correlated with gastric cancer. However, investigations are to be conducted to determine the functional mechanisms of these genes in gastric cancer.
Abbreviations:
ANXA1 |
Annexin A1 |
BP |
biological process |
CA IX |
carbonic anhydrase IX |
DEGs |
differentially expressed genes |
GKN2 |
gastrokine 2 |
GO |
Gene Ontology |
hg19 |
human genome 19 |
HOTAIR |
HOX transcript antisense RNA |
IL8 |
interleukin-8 |
lncRNAs |
long non-coding RNAs |
KEGG |
Kyoto Encyclopedia of Genes and Genomes |
MEG3 |
maternally expressed 3 |
PI3K |
phosphatidylinositol 3-kinase |
PPI |
protein-protein interaction |
PSCA |
prostate stem cell antigen |
suPAR |
soluble urokinase plasminogen activator receptor |
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