Open Access

Comparative analysis of gene expression profiles of gastric cardia adenocarcinoma and gastric non-cardia adenocarcinoma

  • Authors:
    • Bin Song
    • Juan Du
    • Neng Deng
    • Ji‑Chen Ren
    • Zhen‑Bo Shu
  • View Affiliations

  • Published online on: September 21, 2016     https://doi.org/10.3892/ol.2016.5161
  • Pages: 3866-3874
  • Copyright: © Song et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

In the present study, gene expression profiles were analyzed to identify the molecular mechanisms underlying gastric cardia adenocarcinoma (GCA) and gastric non-cardia adenocarcinoma (GNCA). A gene expression dataset (accession number GSE29272) was downloaded from Gene Expression Omnibus, and consisted of 62 GCA samples and 62 normal controls, as well as 72 GNCA samples and 72 normal controls. The two groups of differentially‑expressed genes (DEGs) were compared to obtain common and unique DEGs. A differential analysis was performed using the Linear Models for Microarray Data package in R. Functional enrichment analysis was conducted for the DEGs using the Database for Annotation, Visualization and Integrated Discovery. Protein‑protein interaction (PPI) networks were constructed for the DEGs with information from the Search Tool for the Retrieval of Interacting Genes. Subnetworks were extracted from the whole network with Cytoscape. Compared with the control, 284 and 268 genes were differentially‑expressed in GCA and GNCA, respectively, of which 194 DEGs were common between GCA and GNCA. Common DEGs [e.g., claudin (CLDN)7, CLDN4 and CLDN3] were associated with cell adhesion and digestion. GCA‑unique DEGs [e.g., MAD1 mitotic arrest deficient like 1, cyclin (CCN)B1, CCNB2 and CCNE1] were associated with the cell cycle and the regulation of cell proliferation, while GNCA‑unique DEGs (e.g., GATA binding protein 6 and hyaluronoglucosaminidase 1) were implicated in cell death. A PPI network with 141 nodes and 446 edges were obtained, from which two subnetworks were extracted. Genes [e.g., fibronectin 1, collagen type I α2 chain (COL1A2) and COL1A1] from the two subnetworks were implicated in extracellular matrix organization. These common DEGs could advance our understanding of the etiology of gastric cancer, while the unique DEGs in GCA and GNCA could better define the properties of specific cancers and provide potential biomarkers for diagnosis, prognosis or therapy.

Introduction

Gastric cancer is the third leading cause of cancer-associated mortality (1). The most common cause of gastric cancer is infection by the bacteria Helicobacter pylori, which accounts for ~60% of cases (1,2). Smoking also increases the risk significantly. The prognosis of stomach cancer is generally poor due to the fact that the tumor has often metastasized by the time of diagnosis (3), which makes it necessary to identify biomarkers for an early diagnosis.

Stomach cancers are overwhelmingly adenocarcinomas. Gastric adenocarcinomas are a heterogeneous group of tumors. The cardia lies between the end of the esophagus and the body of the stomach, and is a small macroscopically indistinct zone that lies immediately distal to the gastroesophageal junction. Gastric cardia adenocarcinoma (GCA) and esophageal squamous cell carcinoma (ESCC) share certain etiological risk factors. Abnet et al reported a shared susceptibility locus in PLCE1 at 10q23 for GCA and ESCC (4). GCA may have a distinct etiology. Substantially higher TP53 mutation rates have been detected in cases with GCA than gastric non-cardia adenocarcinoma (GNCA) (5). Kamangar et al indicated that H. pylori is a strong risk factor for non-cardia gastric cancer, but that it is inversely associated with the risk of gastric cardia cancer (6). Kim et al found differences in the clinicopathology and protein expression in cardia carcinoma and non-cardia carcinoma (7).

Although a number of genetic alterations have been identified in gastric cancer, including those in cadherin 1 (CDH1) (8,9), β-catenin (10), CDH17 (11) and Met (12), no study has distinguished these alterations by anatomical subsite. A number of previous gene expression profiling studies have also ignored the differences in diverse anatomical subsite (11,13,14), such as in GCA and GNCA. Therefore, a comparative analysis of the gene expression profiles of GCA and GNCA would provide more accurate and valuable information on GCA.

Based upon the gene expression data from Wang et al (15), the present study adopted functional enrichment analysis and protein-protein interaction (PPI) network analysis to obtain a greater understanding of the common pathogenesis of GCA and GNCA, as well as the unique molecular mechanisms underlying GCA and GNCA, which could facilitate the development of targeted strategies for early detection, prognosis, and therapy.

Materials and methods

Gene expression data

A gene expression dataset (access number GSE29272) (15) was downloaded from Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo/), and consisted of 62 GCA samples and 62 normal controls, as well as 72 GNCA samples and 72 normal controls. Gene expression levels were measured using the Affymetrix Human Genome U133A Array (Affymetrix Inc., Santa Clara, CA, USA).

Pre-treatment and differential analysis

Raw data were treated with the Robust Multi-array Analysis method from the Affy package (16). Values of probes mapping to a same Entrez gene ID were averaged as a final expression level for the specific gene. A total of 22,283 probes and 12,495 genes were obtained.

Differential analysis was performed with the Linear Models for Microarray Data (17) in R to identify DEGs in GCA and GNCA. The cut-offs were set as |log (fold change)|>1 and a P-value of <0.01. Overlapping DEGs of GCA and GNCA, as well as unique DEGs, were further selected out.

Functional enrichment analysis

Gene Ontology (GO; http://www.geneontology.org/) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (http://www.genome.jp/kegg) pathway enrichment analysis were applied on the overlapping DEGs and unique DEGs using the Database for Annotation, Visualization and Integration Discovery (http://david.abcc.ncifcrf.gov/) online tool (18). P<0.05 was set as the threshold.

Construction of the PPI network

A PPI network was constructed for the overlapping DEGs using information from the Search Tool for the Retrieval of Interacting Genes (19). Interactions with a score of >0.4 were retained and then visualized by Cytoscape (20). The proteins in the network serve as the ‘nodes’, and each pairwise protein interaction is represented by an undirected link and the degree of a node corresponds to the number of interactions of a protein. Degree was calculated for each node. Hub genes were then selected out according to the degree.

Subnetworks were also identified by Cytoscape (20) and its plugin MCODE (21), on which functional enrichment analysis was then applied.

Results

Differentially-expressed genes (DEGs)

Gene expression data of GCA and GNCA prior to and after normalization are shown in Figs. 1 and 2. A good performance of normalization was achieved.

A total of 284 DEGs, 151 upregulated and 133 downregulated, were identified in GCA, while 268 DEGs, 126 upregulated and 142 downregulated, were revealed in GNCA. A total of 194 DEGs, 90 upregulated and 104 downregulated, were common between GCA and GNCA (Fig. 3).

Functional enrichment analysis result

Functional enrichment analysis was performed for the overlapping DEGs and unique DEGs. As shown in Table I, upregulated overlapping DEGs were involved in cell adhesion, the response to wounding and the regulation of cell proliferation. Extracellular matrix (ECM)-receptor interaction and focal adhesion were significantly over-represented. As for downregulated overlapping DEGs, digestion, oxidation reduction and the homeostatic process were enriched. The DEGs were associated with the metabolism of xenobiotics by cytochrome P450 and nitrogen metabolism.

Table I.

Functional enrichment analysis for common differentially-expressed genes.

Table I.

Functional enrichment analysis for common differentially-expressed genes.

GroupCategoryTermCountP-value
Upregulated genesGOTERM_BP_FATGO:0007155, cell adhesion25 3.75×10−13
GOTERM_BP_FATGO:0022610, biological adhesion25 3.86×10−13
GOTERM_BP_FATGO:0001501, skeletal system development12 1.76×10−6
GOTERM_BP_FATGO:0009611, response to wounding14 8.15×10−6
GOTERM_BP_FATGO:0042127, regulation of cell proliferation13 1.54×10−3
GOTERM_CC_FATGO:0044421, extracellular region part40 1.83×10−23
GOTERM_CC_FATGO:0005576, extracellular region48 6.77×10−19
GOTERM_CC_FATGO:0005578, proteinaceous ECM23 1.45×10−17
GOTERM_CC_FATGO:0031012, ECM23 7.16×10−17
GOTERM_CC_FATGO:0005615, extracellular space24 1.16×10−11
GOTERM_MF_FATGO:0005201, ECM structural constituent12 1.03×10−12
GOTERM_MF_FATGO:0005539, glycosaminoglycan binding12 2.27×10−10
GOTERM_MF_FATGO:0001871, pattern binding12 6.34×10−10
GOTERM_MF_FATGO:0005198, structural molecule activity18 3.61×10−8
GOTERM_MF_FATGO:0005509, calcium ion binding14 1.28×10−3
KEGG_PATHWAYhsa04512: ECM-receptor interaction13 5.02×10−13
KEGG_PATHWAYhsa04510: Focal adhesion13 1.60×10−8
KEGG_PATHWAYhsa04670: Leukocyte transendothelial migration  5 1.21×10−2
KEGG_PATHWAYhsa04514: Cell adhesion molecules  5 1.77×10−2
Downregulated genesGOTERM_BP_FATGO:0007586, digestion14 1.39×10−14
GOTERM_BP_FATGO:0055114, oxidation reduction15 4.63×10−5
GOTERM_BP_FATGO:0010035, response to inorganic substance  8 3.23×10−4
GOTERM_BP_FATGO:0010033, response to organic substance11 1.59×10−2
GOTERM_BP_FATGO:0042592, homeostatic process11 2.05×10−2
GOTERM_CC_FATGO:0005576, extracellular region36 1.54×10−8
GOTERM_CC_FATGO:0005615, extracellular space16 4.01×10−5
GOTERM_CC_FATGO:0044421, extracellular region part19 4.81×10−5
GOTERM_CC_FATGO:0045177, apical part of cell  6 6.78×10−3
GOTERM_CC_FATGO:0016324, apical plasma membrane  5 1.19×10−2
GOTERM_MF_FATGO:0008289, lipid binding12 2.71×10−4
GOTERM_MF_FATGO:0048037, cofactor binding  8 1.70×10−3
GOTERM_MF_FATGO:0004175, endopeptidase activity  8 1.52×10−2
GOTERM_MF_FATGO:0070011, peptidase activity, acting on L-amino acid peptides  9 3.64×10−4
GOTERM_MF_FATGO:0008233, peptidase activity  9 4.53×10−2
KEGG_PATHWAYhsa00980: Metabolism of xenobiotics by cytochrome P450  5 6.98×10−4
KEGG_PATHWAYhsa00982: Drug metabolism  4 8.56×10−3
KEGG_PATHWAYhsa00910: Nitrogen metabolism  3 1.06×10−2

[i] GO, Gene Ontology; BP, biological process; CC, cellular component; MF, molecular function; KEGG, Kyoto Encyclopedia of Genes and Genomes; ECM, extracellular matrix.

GCA-unique DEGs were closely associated with the cell cycle and the regulation of cell proliferation (Table II). Pathways such as the cell cycle, the p53 signaling pathway and the Toll-like receptor signaling pathway were enriched (Table II).

Table II.

Functional enrichment analysis for unique differentially-expressed genes in gastric cardia adenocarcinoma.

Table II.

Functional enrichment analysis for unique differentially-expressed genes in gastric cardia adenocarcinoma.

CategoryTermCountP-value
GOTERM_BP_FATGO:0022403, cell cycle phase14 1.66×10−7
GOTERM_BP_FATGO:0000279, M phase12 8.96×10−7
GOTERM_BP_FATGO:0022402, cell cycle process14 5.45×10−6
GOTERM_BP_FATGO:0007049, cell cycle16 7.81×10−6
GOTERM_BP_FATGO:0042127, regulation of cell proliferation14 1.72×10−4
GOTERM_CC_FATGO:0044421, extracellular region part18 2.12×10−5
GOTERM_CC_FATGO:0005829, cytosol18 1.10×10−3
GOTERM_CC_FATGO:0005576, extracellular region23 1.42×10−3
GOTERM_CC_FATGO:0043228, non-membrane-bounded organelle24 1.58×10−2
GOTERM_CC_FATGO:0043232, intracellular non-membrane-bounded organelle24 1.58×10−2
GOTERM_MF_FATGO:0008009, chemokine activity  4 1.43×10−3
GOTERM_MF_FATGO:0046983, protein dimerization activity  9 4.49×10−3
GOTERM_MF_FATGO:0042802, identical protein binding  9 1.18×10−2
GOTERM_MF_FATGO:0042803, protein homodimerization activity  6 2.28×10−2
GOTERM_MF_FATGO:0008134, transcription factor binding  7 3.76×10−2
KEGG_PATHWAYhsa04110: Cell cycle11 8.65×10−8
KEGG_PATHWAYhsa04115: p53 signaling pathway  6 2.98×10−4
KEGG_PATHWAYhsa04114: Oocyte meiosis  7 3.66×10−4
KEGG_PATHWAYhsa04914: Progesterone-mediated oocyte maturation  6 8.83×10−4
KEGG_PATHWAYhsa04620: Toll-like receptor signaling pathway  5 1.17×10−2

[i] GO, Gene Ontology; BP, biological process; CC, cellular component; MF, molecular function; KEGG, Kyoto Encyclopedia of Genes and Genomes.

GNCA-unique DEGs were implicated in the regulation of angiogenesis, cell death and small GTPase-mediated signal transduction (Table III). Pathways such as focal adhesion and vascular smooth muscle contraction were enriched (Table III).

Table III.

Functional enrichment analysis for unique differentially-expressed genes in gastric non-cardia adenocarcinoma.

Table III.

Functional enrichment analysis for unique differentially-expressed genes in gastric non-cardia adenocarcinoma.

CategoryTermCountP-value
GOTERM_BP_FATGO:0045765, regulation of angiogenesis  3 2.99×10−2
GOTERM_BP_FATGO:0008219, cell death  8 3.32×10−2
GOTERM_BP_FATGO:0016265, death  8 3.43×10−2
GOTERM_BP_FATGO:0006937, regulation of muscle contraction  3 3.81×10−2
GOTERM_BP_FATGO:0007264, small GTPase mediated signal transduction  5 4.16×10−2
GOTERM_CC_FATGO:0015629, actin cytoskeleton  8 2.52×10−4
GOTERM_CC_FATGO:0005576, extracellular region21 5.64×10−4
GOTERM_CC_FATGO:0005856, cytoskeleton16 1.43×10−3
GOTERM_CC_FATGO:0044421, extracellular region part11 1.32×10−2
GOTERM_CC_FATGO:0044449, contractile fiber part  4 1.60×10−2
GOTERM_MF_FATGO:0008092, cytoskeletal protein binding  9 9.27×10−4
GOTERM_MF_FATGO:0003779, actin binding  7 2.04×10−3
GOTERM_MF_FATGO:0005198, structural molecule activity  9 3.92×10−3
GOTERM_MF_FATGO:0005509, calcium ion binding10 1.13×10−2
GOTERM_MF_FATGO:0005525, GTP binding  6 1.75×10−2
KEGG_PATHWAYhsa04510: Focal adhesion  6 3.64×10−3
KEGG_PATHWAYhsa04270: Vascular smooth muscle contraction  4 2.07×10−2

[i] GO, Gene Ontology; BP, biological process; CC, cellular component; MF, molecular function; KEGG, Kyoto Encyclopedia of Genes and Genomes; GTP, guanosine triphosphate.

PPI network

A PPI network was constructed for the overlapping DEGs (Fig. 4), including 141 nodes and 446 edges.

Subnetwork 1 consisting of nodes with a degree >10 were extracted from the whole network (Fig. 5). The subnetwork contained 14 nodes and 75 edges. Three hub genes with a degree >25 were identified: Fibronectin 1 (FN1), collagen type I α2 (COL1A2) and COL1A1. The degrees of connectivity were 38, 28 and 26, respectively. The 3 hub genes interacted with each other and their neighboring nodes were selected as subnetwork 2, which included 45 nodes and 245 edges (Fig. 5).

Functional enrichment analysis was performed for the genes in the two subnetworks (Tables IV and V). GO enrichment analysis showed that the genes in subnetwork 1 were associated with collagen fibril organization, ECM organization and cell adhesion (Table IV). ECM-receptor interaction and focal adhesion were significantly enriched (Table IV). As for genes from subnetwork 2, they were involved in ECM organization, cell adhesion and extracellular structure organization (Table V). ECM-receptor interaction, focal adhesion, and complement and coagulation cascades were enriched (Table V).

Table IV.

Functional enrichment analysis result for genes from subnetwork 1.

Table IV.

Functional enrichment analysis result for genes from subnetwork 1.

CategoryTermCountP-value
GOTERM_BP_FATGO:0030199, collagen fibril organization  8 7.46×10−17
GOTERM_BP_FATGO:0030198, ECM organization  9 4.46×10−15
GOTERM_BP_FATGO:0043062, extracellular structure organization  9 1.78×10−13
GOTERM_BP_FATGO:0001501, skeletal system development  8 2.72×10−9
GOTERM_BP_FATGO:0007155, cell adhesion  6 2.14×10−4
GOTERM_CC_FATGO:0044420, ECM part13 2.50×10−24
GOTERM_CC_FATGO:0005578, proteinaceous ECM13 6.27×10−19
GOTERM_CC_FATGO:0031012, ECM13 1.57×10−18
GOTERM_CC_FATGO:0044421, extracellular region part14 2.24×10−15
GOTERM_CC_FATGO:0005576, extracellular region14 3.48×10−11
GOTERM_MF_FATGO:0005201, ECM structural constituent10 3.45×10−18
GOTERM_MF_FATGO:0048407, platelet-derived growth factor binding  6 1.19×10−13
GOTERM_MF_FATGO:0005198, structural molecule activity10 2.88×10−10
GOTERM_MF_FATGO:0019838, growth factor binding  6 2.38×10−8
GOTERM_MF_FATGO:0046332, SMAD binding  3 7.92×10−4
KEGG_PATHWAYhsa04512: ECM-receptor interaction  9 3.52×10−14
KEGG_PATHWAYhsa04510: Focal adhesion  9 4.53×10−11

[i] GO, Gene Ontology; BP, biological process; CC, cellular component; MF, molecular function; KEGG, Kyoto Encyclopedia of Genes and Genomes; ECM, extracellular matrix; SMAD

Table V.

Functional enrichment analysis result for genes from subnetwork 2.

Table V.

Functional enrichment analysis result for genes from subnetwork 2.

CategoryTermCountP-value
GOTERM_BP_FATGO:0030198, ECM organization11 3.24×10−13
GOTERM_BP_FATGO:0007155, cell adhesion18 3.27×10−12
GOTERM_BP_FATGO:0022610, biological adhesion18 3.35×10−12
GOTERM_BP_FATGO:0043062, extracellular structure organization11 3.04×10−11
GOTERM_BP_FATGO:0009611, response to wounding12 2.48×10−7
GOTERM_CC_FATGO:0044421, extracellular region part36 8.96×10−32
GOTERM_CC_FATGO:0005576, extracellular region39 5.65×10−25
GOTERM_CC_FATGO:0005578, proteinaceous ECM23 3.57×10−24
GOTERM_CC_FATGO:0031012, ECM23 1.89×10−23
GOTERM_CC_FATGO:0005615, extracellular space21 1.53×10−14
GOTERM_MF_FATGO:0005198, structural molecule activity15 2.71×10−9
GOTERM_MF_FATGO:0005201, ECM structural constituent13 1.53×10−17
GOTERM_MF_FATGO:0048407, platelet-derived growth factor binding  6 9.76×10−11
GOTERM_MF_FATGO:0030246, carbohydrate binding  7 6.90×10−4
GOTERM_MF_FATGO:0005509, calcium ion binding  8 2.08×10−2
KEGG_PATHWAYhsa04512: ECM-receptor interaction13 7.92×10−16
KEGG_PATHWAYhsa04510: Focal adhesion13 3.48×10−11
KEGG_PATHWAYhsa04610: Complement and coagulation cascades  3 4.45×10−2

[i] GO, Gene Ontology; BP, biological process; CC, cellular component; MF, molecular function; KEGG, Kyoto Encyclopedia of Genes and Genomes; ECM, extracellular matrix.

Discussion

A comparative analysis of gene expression data of GCA and GNCA was performed in the present study. A total of 284 DEGs were identified in GCA, while 268 DEGs were revealed in GNCA. As many as 194 DEGs were shared by GCA and GNCA, while 90 DEGs were unique in GCA.

Upregulated common DEGs were involved in cell adhesion and the regulation of cell proliferation. Several members of the claudin family were revealed, including claudin 7 (CLDN7), CLDN4 and CLDN3. CLDN7 expression is an early event in gastric tumorigenesis (22). Zavala-Zendejas et al also reported that the overexpression of CLDN7 in the human gastric adenocarcinoma AGS cell line increased its invasiveness, migration and proliferation rate (23). CLDN4 and CLDN3 may also play roles in the pathogenesis of gastric cancer. Cadherin 11 (CDH11) and CDH17 were also found to be upregulated in GCA and GNCA in the present study. CDH17 is reported as a prognostic marker in early-stage gastric cancer (11). Zhang et al blocked the proliferation and migration of gastric cancer via targeting CDH17 with an artificial microRNA (24), suggesting that CDH17 is a potential target for the control of gastric cancer progression. In the present study, downregulated common DEGs were associated with digestion and metabolism, suggesting that the function of the stomach was impaired by the cancer. Several enzymes were included in the affected list, such as progastricsin, calpain 9 and aldo-keto reductase family 1 member C2. Mucin 6 (MUC6) and MUC5AC, playing essential roles in epithelial cytoprotection from acids and proteases, were also downregulated.

In the present study, a PPI network, including 141 nodes and 446 edges, was constructed for the common DEGs, from which two subnetworks were disclosed. Genes from the two subnetworks were associated with cell adhesion and ECM organization. The ECM provides a microenvironment for cell proliferation, cell adhesion and cell motion, and thus plays a critical role in cancer development (25) and metastasis (26). FN1, COL1A2 and COL1A1 were the top 3 hub genes in the whole network. FN1 is involved in cell adhesion and migration processes such as metastasis. David et al found that fibronectin expression is significantly associated with the expanding growth pattern of the gastric cancer (27). Yang et al found that Twist regulates cell motility and invasion in gastric cancer cell lines through N-cadherin and fibronectin production (28).

GCA-unique DEGs were implicated in the cell cycle and the regulation of cell proliferation in the present study. MAD1 mitotic arrest deficient-like 1 (MAD1L1) is a component of the mitotic spindle-assembly checkpoint that prevents the onset of anaphase until all chromosomes are properly aligned at the metaphase plate, and it may play a role in cell cycle control and tumor suppression. Coe et al report that MAD1L1 is the most frequent copy number gain in small cell lung cancer cell lines (29). Guo et al further suggested that genetic variants in MAD1L1 and MAD2L1 confer a susceptibility to lung cancer, which may result from reduced spindle checkpoint function due to the attenuated function of MAD1L1 and/or MAD2L1 (30). We supposed that MAD1L1 had a similar role in the pathogenesis of GCA. Three members of the cyclin family, CCNB1, CCNB2 and CCNE1, were also found to be upregulated in GCA. The B-type cyclins, CCNB1 and CCNB2, associate with p34cdc2 and are essential components of the cell cycle regulatory machinery. Begnami et al found that the expression of cyclin B1 is associated with regional lymph node metastasis and a poor prognosis in gastric cancer (31).

GNCA-unique DEGs were found to be associated with cell death in the present study. Abnormalities in cell death are closely associated with tumorigenesis. The GATA family of transcription factors participates in gastrointestinal development. GATA binding protein 6 (GATA6) is shown to be expressed in gastric cancer; it activates the expression of gastroprotective trefoil genes, trefoil factor 1 (TFF1) and TFF2 (32). Haveri et al further reported that GATA6 expression is altered in neoplastic human gastrointestinal mucosa (33), suggesting that it may play a role in tumor progression. Hyaluronoglucosaminidase 1 (HYAL1) is a lysosomal hyaluronidase. Hyaluronan is believed to be involved in cell proliferation, migration and differentiation. HYAL1 is suggested to exhibit prognostic potential in prostate cancer (34). The study by Lokeshwar et al showed that, depending on the concentration, HYAL1 functions as a tumor promoter or as a suppressor in prostate cancer (35). We speculated that it may exert a similar function in the progression of GNCA.

Overall, in the present study, a number of common DEGs were identified in GCA and GNCA, which could advance our understanding of the etiology of gastric cancer. Furthermore, DEGs unique to GCA and to GNCA were identified, which supplemented our knowledge on the pathogenetic mechanisms involved and provided potential biomarkers for the diagnosis, prognosis or treatment of the disease.

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November-2016
Volume 12 Issue 5

Print ISSN: 1792-1074
Online ISSN:1792-1082

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Copy and paste a formatted citation
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Spandidos Publications style
Song B, Du J, Deng N, Ren JC and Shu ZB: Comparative analysis of gene expression profiles of gastric cardia adenocarcinoma and gastric non-cardia adenocarcinoma. Oncol Lett 12: 3866-3874, 2016.
APA
Song, B., Du, J., Deng, N., Ren, J., & Shu, Z. (2016). Comparative analysis of gene expression profiles of gastric cardia adenocarcinoma and gastric non-cardia adenocarcinoma. Oncology Letters, 12, 3866-3874. https://doi.org/10.3892/ol.2016.5161
MLA
Song, B., Du, J., Deng, N., Ren, J., Shu, Z."Comparative analysis of gene expression profiles of gastric cardia adenocarcinoma and gastric non-cardia adenocarcinoma". Oncology Letters 12.5 (2016): 3866-3874.
Chicago
Song, B., Du, J., Deng, N., Ren, J., Shu, Z."Comparative analysis of gene expression profiles of gastric cardia adenocarcinoma and gastric non-cardia adenocarcinoma". Oncology Letters 12, no. 5 (2016): 3866-3874. https://doi.org/10.3892/ol.2016.5161