Identification of genes and long non-coding RNAs associated with the pathogenesis of gastric cancer

  • Authors:
    • Zhiwei Zhao
    • Yan Song
    • Daxun Piao
    • Tianyou Liu
    • Liangliang Zhao
  • View Affiliations

  • Published online on: July 14, 2015     https://doi.org/10.3892/or.2015.4129
  • Pages: 1301-1310
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Abstract

Gastric cancer is a lethal disease characterized by high diffusivity and mortality. To examine the mechanisms involved in gastric cancer, we analyzed the microarray of GSE41476. GSE41476 was downloaded from the Gene Expression Omnibus and included 3 primary cell culture samples from gastric cancer tissues, 3 gastric cancer cell lines and 2 normal tissue samples. Long non-coding RNAs (lncRNAs) and differentially expressed genes (DEGs) were screened by Cuffdiff software. Functions of the DEGs were predicted by functional and pathway enrichment analyses. The interaction relationships of the proteins encoded by DEGs that were obtained from the STRING database and protein‑protein interaction (PPI) network were visualized using Cytoscape. Modules analysis of PPI network was performed using CFinder. Moreover, lncRNA analysis was performed. A total of 86 lncRNAs, and 1,088 up- and 1,537 downregulated transcriptions were screened. For DEGs in module A of the PPI network for upregulated genes, the enriched pathways included ECM-receptor interaction and focal adhesion, both of which involved COL and ITG genes. The COL genes interacted with the ITG genes (e.g., COL1A1‑ITGA5 and COL1A2‑ITGB1). For DEGs in module B of the PPI network for downregulated genes, the enriched pathways for DEGs included the T‑cell receptor signaling pathway, which involved PIK3CG and PIK3R5. PIK3CG had an interaction relationship with PIK3R5. In addition, IL7 was co-expressed with TCONS-00068220. In summary, the results showed that COL and ITG genes, PIK3CG, PIK3R5, IL7 and lncRNA TCONS‑00068220 may play a role in gastric cancer.

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).

Table I

The top 10 enriched GO functions and KEGG pathways for the DEGs.

Table I

The top 10 enriched GO functions and KEGG pathways for the DEGs.

GenesIDNameGene no.Gene symbolP-value
GO functions
 UpregulatedGO:0030198Extracellular matrix organization71ACTN1, ADAM170
GO:0043062Extracellular structure organization71ADAM9, ADAMTS20
GO:0016477Cell migration110AJUBA, AMOTL18.97E-14
GO:0031012Extracellular matrix422ADAMTS1, CD2483.06E-14
GO:0005515Protein binding521ABL2, ABLIM33.37E-11
 DownregulatedGO:0048584Positive regulation of response to stimulus189A2M, ADA6.66E-16
GO:0044699Single-organism process1017EPHX2, EPN11.33E-15
GO:0002694Regulation of leukocyte activation76AIF1, BCR1.89E-15
GO:0031224Intrinsic to membrane565A4GNT, AADAC1.11E-15
GO:0005102Receptor binding146APLP1, APOE4.68E-08
KEGG pathways
 UpregulatedKEGG:4512ECM-receptor interaction20CD44, COL1A11.18E-06
KEGG:4510Focal adhesion29ACTN1, COL1A10.000143
KEGG:5146Amoebiasis18RKCA, SHC30.000392
KEGG:5144Malaria11CCL2, CSF30.000695
KEGG:1040Biosynthesis of unsaturated fatty acids6PTPLA, SCD0.00262
 DownregulatedKEGG:4514Cell adhesion molecules (CAMs)46CD2, CD221.33E-15
KEGG:5150Staphylococcus aureus infection23DQA1, HLA-DQA22.53E-10
KEGG:4672Intestinal immune network for IgA production19ICAM2, ICAM32.80E-08
KEGG:5340Primary immunodeficiency15ADA, BLNK2.40E-07
KEGG:5416Viral myocarditis22BID, CD282.86E-07

[i] GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; DEGs, differentially expressed genes.

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 II

The top 10 enriched GO functions and KEGG pathways for DEGs in module A and B of the PPI network for the upregulated DEGs.

Table II

The top 10 enriched GO functions and KEGG pathways for DEGs in module A and B of the PPI network for the upregulated DEGs.

ModulesIDNameGene no.Gene symbolP-value
GO functions
 AGO:0030574Collagen catabolic process8COL5A2, COL4A42.22E-16
GO:0044243Multicellular organismal catabolic process8COL4A4, COL1A26.66E-16
GO:0022617Extracellular matrix disassembly8COL1A2, COL1A11.33E-15
GO:0005581Collagen8COL5A2, COL4A44.44E-16
GO:0005201Extracellular matrix structural constituent5COL1A1, COL12A14.23E-09
 BGO:0022613Ribonucleoprotein complex biogenesis11BRIX1, TSR12.22E-16
GO:0006364rRNA processing7WDR12, NOP581.17E-11
GO:0044085Cellular component biogenesis11BRIX1, TSR12.36E-07
GO:0031981Nuclear lumen21BRIX1, TSR11.33E-15
GO:0003723RNA binding7PNO1, DKC14.48E-06
KEGG pathways
 AKEGG:4512ECM-receptor interaction11COL5A2, COL4A4, COL1A20
KEGG:4510Focal adhesion10COL5A2, COL4A4, COL1A21.05E-13
KEGG:4974Protein digestion and absorption7COL5A2, COL4A4, COL1A25.49E-11
KEGG:5412Arrhythmogenic right ventricular cardiomyopathy (ARVC)4ITGB1, ITGA1, ITGA5, ITGB51.07E-05
KEGG:5410Hypertrophic cardiomyopathy (HCM)4ITGB1, ITGA1, ITGA5, ITGB51.69E-05
KEGG:5414Dilated cardiomyopathy4ITGB1, ITGA1, ITGA5, ITGB52.33E-05
KEGG:5146Amoebiasis4COL5A2, COL4A4, COL1A2, COL1A14.45E-05
KEGG:5131Shigellosis3ITGB1, ITGA5, CD440.000219795
KEGG:4640Hematopoietic cell lineage3ITGB1, ITGA5, CD440.000649577
 BKEGG:3008Ribosome biogenesis in eukaryotes9NOP58, GTPBP4, WDR3, WDR75, UTP15, WDR36, DKC1, CIRH1A, WDR435.55E-16

[i] GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; DEGs, differentially expressed genes; PPI, protein-protein interaction.

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 III

The 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.

Table III

The 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.

ModulesIDNameGene no.Gene symbolP-value
GO functions
 AGO:0006367Transcription initiation from RNA polymerase II promoter8ESRRG, ESRRB8.88E-16
GO:0006352DNA-dependent transcription, initiation8NR0B2, HNF4G2.66E-15
 BGO:0002253Activation of immune response13CD3D, CD3E2.22E-16
GO:0045321Leukocyte activation14CD3D, CD3E4.44E-16
 CGO:0019221Cytokine-mediated signaling pathway8IRF7, CIITA6.98E-14
GO:0071345Cellular response to cytokine stimulus8IRF7, CIITA5.20E-13
 DGO:0044723Single-organism carbohydrate metabolic process10GALNT12, MUC18.44E-15
GO:0018146Keratan sulfate biosynthetic process2B3GNT7, B3GNT30.000153
 EGO:0031295T-cell costimulation5CD4, CD247, CD3D, CD3E, CD3G1.20E-10
 FGO:0007165Signal transduction24CCL5, ADORA31.46E-13
KEGG pathways
 AKEGG: 04950Maturity onset diabetes of the young2HNF4G, HNF4A0.000173
KEGG:04976Bile secretion2NR0B2, RXRA0.001409
 BKEGG:4660T-cell receptor signaling pathway13CD3D, CD3E, CD3G0
KEGG:5340Primary immunodeficiency6CD3D, CD3E, CD47.35E-10
 CKEGG:4612Antigen processing and presentation2CIITA, IFI300.001613
KEGG:5160Hepatitis C2IRF7, OAS30.004944
 DKEGG:512Mucin-type O-glycan biosynthesis3COL5A2, COL4A4, COL1A2, COL1A12.39E-06
KEGG:1100Metabolic pathways4ITGB1, ITGA5, CD440.014715
 EKEGG:4660T-cell receptor signaling pathway5CD4, CD247, CD3D, CD3E, CD3G3.92E-08
 FKEGG:4080Neuroactive ligand-receptor interaction9ADORA3, C5AR1, C3AR19.26E-08

[i] GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; DEGs, differentially expressed genes; PPI, protein-protein interaction.

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).

Table IV

The top 10 enriched KEGG pathways for the DEGs co-expressed with lncRNA TCONS_00068220.

Table IV

The top 10 enriched KEGG pathways for the DEGs co-expressed with lncRNA TCONS_00068220.

IDNameGene no.Gene symbolP-value
KEGG:4514Cell adhesion molecules15CD2, CD283.10E-06
KEGG:4062Chemokine signaling pathway15CCL5, CCR10.00020233
KEGG:5219Bladder cancer5MMP9, MYC0.005609503
KEGG:4650Natural killer cell-mediated cytotoxicity8BID, ICAM20.033796553
KEGG:4672Intestinal immune network for IgA production8CD28, ICOSLG4.17E-05
KEGG:5150Staphylococcus aureus infection8C1QB, C1QC0.000114261
KEGG:5140Leishmaniasis9ITGB1, MAPK120.000142588
KEGG:4940Type I diabetes mellitus7CD28, PRF10.000149447
KEGG:5330Allograft rejection6CD28, HLA-DMB0.000460506
KEGG:5416Viral myocarditis8BID, CD280.000623762

[i] KEGG, Kyoto Encyclopedia of Genes and Genomes; DEGs, differentially expressed genes.

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 (3032). 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 (4547). 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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September-2015
Volume 34 Issue 3

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Online ISSN:1791-2431

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Spandidos Publications style
Zhao Z, Song Y, Piao D, Liu T and Zhao L: Identification of genes and long non-coding RNAs associated with the pathogenesis of gastric cancer. Oncol Rep 34: 1301-1310, 2015
APA
Zhao, Z., Song, Y., Piao, D., Liu, T., & Zhao, L. (2015). Identification of genes and long non-coding RNAs associated with the pathogenesis of gastric cancer. Oncology Reports, 34, 1301-1310. https://doi.org/10.3892/or.2015.4129
MLA
Zhao, Z., Song, Y., Piao, D., Liu, T., Zhao, L."Identification of genes and long non-coding RNAs associated with the pathogenesis of gastric cancer". Oncology Reports 34.3 (2015): 1301-1310.
Chicago
Zhao, Z., Song, Y., Piao, D., Liu, T., Zhao, L."Identification of genes and long non-coding RNAs associated with the pathogenesis of gastric cancer". Oncology Reports 34, no. 3 (2015): 1301-1310. https://doi.org/10.3892/or.2015.4129