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Identification of key genes associated with gastric cancer based on DNA microarray data

  • Authors:
    • Hui Sun
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  • Published online on: November 17, 2015     https://doi.org/10.3892/ol.2015.3929
  • Pages: 525-530
  • Copyright: © Sun . This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

The present study aimed to identify genes with a differential pattern of expression in gastric cancer (GC), and to find novel molecular biomarkers for GC diagnosis and therapeutic treatment. The gene expression profile of GSE19826, including 12 GC samples and 15 normal controls, was downloaded from the Gene Expression Omnibus database. Differentially‑expressed genes (DEGs) were screened in the GC samples compared with the normal controls. Two‑way hierarchical clustering of DEGs was performed to distinguish the normal controls from the GC samples. The co‑expression coefficient was analyzed among the DEGs using the data from COXPRESdb. The gene co‑expression network was constructed based on the DEGs using Cytoscape software, and modules in the network were analyzed by ClusterOne and Bingo. Furthermore, enrichment analysis of the DEGs in the modules was performed using the Database for Annotation, Visualization and Integrated Discovery. In total, 596 DEGs in the GC samples and 57 co‑expression gene pairs were identified. A total of 7 genes were enriched in the same module, for which the function was phosphate transport and which was annotated to participate in the extracellular matrix‑receptor interaction pathway. These genes were collagen, type VI, α3 (COL6A3), COL1A2, COL1A1, COL5A2, thrombospondin 2, COL11A1 and COL5A1. Overall, the present study identified several biomarkers for GC using the gene expression profiling of human GC samples. The COL family is a promising prognostic marker for GC. Gene expression products represent candidate biomarkers endowed with great potential for the early screening and therapy of GC patients.

Introduction

Gastric cancer (GC) is one of the leading causes of cancer-related mortality worldwide, and is particularly prevalent in East Asian countries, including China, Japan and Korea (1). Each year, ~990,000 people are diagnosed with GC worldwide; ~738,000 of whom succumb to the disease (2). The high patient mortality rate is due to the fact that the clinical manifestations of GC usually only become apparent at an advanced disease stage, when the current available therapies will have a limited effect (3,4). Therefore, it is of utmost importance to understand the associated mechanisms and to identify biomarkers for the development of strategies for the screening, early detection and treatment of GC.

GC is a complicated and multifactorial disease, and environmental and genetic factors play important roles in its etiology (5). One of the characteristics of gastric malignant cells is metastasis, whereby cancer cells penetrate vascular channels and invade parenchymal tissue to form satellite tumors in distant organs (6). In this process, the extracellular matrix (ECM) and the basement membrane provide a protective barrier to prevent cancer cell invasion and metastasis (7). Similar to other malignancies, gene expression profiling using complementary DNA microarrays has been used to identify genes involved in gastric carcinogenesis, and to identify novel diagnostic and prognostic markers for GC (811). Recent studies have reported genetic alterations in GC, involving tumor suppressor genes, cell adhesion molecules, oncogenes and growth factors, such as p53, trefoil factor 1 and E-cadherin (10,1215). However, these studies have yielded few useful biomarkers, most likely due to shortcomings concerning the experimental design, the validity of the supporting statistical analysis and the gene selection in the studies. Thus, the present study focused on the gene expression profiling of GC to identify novel biomarkers in this disease.

With the same gene expression profile, Wang et al performed gene set enrichment analysis and identified that increased INHBA expression was associated with poor survival in GC (16). A study by Liu et al demonstrated that the ECM-receptor and cell cycle pathways may play important roles in GC (17). In addition, a study using the same microarray data revealed high periostin expression in GC tissues, which was associated with gene groups that regulated the cell proliferation and cell cycle (18). The present study analyzed the differentially-expressed genes (DEGs) in GC using gene expression profiling. Comprehensive bioinformatics was used to analyze the significant pathways and functions, and to construct the gene co-expression network and sub-network to investigate the critical DEGs of GC. The study aimed to obtain a better understanding of the molecular circuitry in GC and to identify genes potentially useful as novel diagnostic or therapeutic markers for GC.

Materials and methods

Affymetrix microarray data

The gene expression profile of GSE19826 (16) was downloaded from the Gene Expression Omnibus database (19), which freely distributes high-throughput molecular abundance data, largely gene expression data generated by microarray technology. The platform information is as follows: GPL570 [HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array (Affymetrix Inc., Santa Clara, CA, USA). In this dataset, 12 cancerous portions of gastric specimens (from Chinese patients) and 15 normal gastric tissues (controls) were included.

Data preprocessing and screening of DEGs

The preprocessed microarray data were obtained and then log2 transformation was performed on these data. The most popular method, the Linear Models for Microarray data (limma) package (20) in R language (21), was used to analyze the chip data. Upregulated and downregulated genes were identified between GC and normal controls. The false discovery rate (FDR) (22) was utilized for multiple testing correction using the Benjamini and Hochberg method (23). The threshold for the DEGs was set as |log2 fold change (FC)|>1.5 and FDR <0.05.

Hierarchical clustering

Hierarchical clustering methodology is a powerful data mining approach that has been extensively applied to identify groups of similarly expressed genes or conditions from gene expression data. In order to reveal sets of samples in which the closest groups were adjacent, two-way hierarchical clustering analysis (24) was performed on genes and conditions using Euclidean distance (25) by the ‘pheatmap’ package (http://cran.r-project.org/web/packages/pheatmap/index.html) in R language. The result was represented by a heatmap.

Co-expression network construction of DEGs

From the perspective of systems biology, functionally-related genes are frequently co-expressed across a set of samples (26). COXPRESdb (http://coxpresdb.jp) provides co-expression associations for multiple species of mammals, as comparisons of co-expressed gene lists can increase the reliability of gene co-expression determinations (27). The gene co-expression network was constructed to assess the functional associations between co-expressed genes of DEGs using COXPRESdb, in which genes were indexed by their Entrez Gene IDs. To obtain the co-expression associations, a Pearson Correlation Coefficient >0.6 was chosen as the threshold.

Selection of modules in co-expression network

Gene products in the same module often have the same or similar functions, and they work together to perform one bio-function (28). Therefore, the network was visualized using Cytoscape (29) and module division was made by using the plugin ClusterOne (30) in Cytoscape (parameters: Minimum size, 3; overlap threshold, 0.8), then module function was annotated using another plugin-Bingo (31) and the significant function of each module was achieved.

Function and pathway enrichment analysis of DEGs in modules

Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed for the DEGs in the co-expression network using the online tool, DAVID (32). P<0.05 was used to indicate statistical significance.

Results

DEG screening

Following data preprocessing, 42,450 genes were mapped to the probes; the gene expression profile after normalization is shown in Fig. 1. The black lines in each of the boxes, representing the medians of each dataset, are almost in a straight line, indicating a good degree of standardization. Compared with the normal tissues, a total of 596 genes were differentially expressed, consisting of 182 upregulated and 414 downregulated genes.

Hierarchical clustering

The hierarchical clustering algorithm was used to group the genes and samples on the basis of similarities of gene expression. In the results shown in Fig. 2, one normal sample was grouped into the region of GC samples, suggesting that 93.33% of samples were classified correctly. Thus, the DEGs screened had significant expression patterns that could distinguish the disease samples from the normal controls.

Co-expression network construction and module selection

A total of 57 co-expressed gene pairs were determined between DEGs. In Fig. 3, the upregulated and downregulated genes tended to connect up, respectively. The study identified 4 modules from the network (Fig. 4). The function of the DEGs in each module is presented in Table I. Module 1 had one upregulated gene, PDZ and LIM domain 7 (PDLIM7). Genes in modules 2 and 4, which mostly belonged to the collagen (COL) family, were significantly associated with phosphate transport.

Table I.

Functions of the genes in the modules.

Table I.

Functions of the genes in the modules.

GO-IDcorr P-valueNDescriptionGenes in test set
Module 1
  31214 4.30×10−22Biomineral formationCASR, PDLIM7
  1503 4.30×10−22OssificationCASR, PDLIM7
Module 2
  6817 8.57×10−85Phosphate transportCOL6A3, COL1A2, COL1A1, COL5A2, COL5A1
  15698 7.47×10−75Inorganic anion transportCOL6A3, COL1A2, COL1A1, COL5A2, COL5A1
  6820 1.25×10−65Anion transportCOL6A3, COL1A2, COL1A1, COL5A2, COL5A1
  6811 8.30×10−45Ion transportCOL6A3, COL1A2, COL1A1, COL5A2, COL5A1
  6810 3.10×10−25TransportCOL6A3, COL1A2, COL1A1, COL5A2, COL5A1
  48513 1.76×10−46Organ developmentFAP, COL6A3, FBN1, COL1A2, COL1A1, COL5A2
  48731 9.03×10−46System developmentFAP, COL6A3, FBN1, COL1A2, COL1A1, COL5A2
  48856 1.64×10−36Anatomical structure developmentFAP, COL6A3, FBN1, COL1A2, COL1A1, COL5A2
  7275 4.45×10−36Multicellular organismal developmentFAP, COL6A3, FBN1, COL1A2, COL1A1, COL5A2
  51234 1.03×10−26Establishment of localizationFAP, COL6A3, COL1A2, COL1A1, COL5A2, COL5A1
  32502 1.29×10−26Developmental processFAP, COL6A3, FBN1, COL1A2, COL1A1, COL5A2
  51179 1.62×10−26LocalizationFAP, COL6A3, COL1A2, COL1A1, COL5A2, COL5A1
  32501 2.24×10−26Multicellular organismal processFAP, COL6A3, FBN1, COL1A2, COL1A1, COL5A2
Module 3
  6936 2.46×10−32Muscle contractionMYL1, KBTBD10
  3012 2.46×10−32Muscle system processMYL1, KBTBD10
Module 4
  6817 7.88×10−43Phosphate transportCOL6A3, COL12A1, COL1A1
  15698 2.15×10−33Inorganic anion transportCOL6A3, COL12A1, COL1A1
  6820 2.47×10−33Anion transportCOL6A3, COL12A1, COL1A1
  22610 2.64×10−23Biological adhesionCOL6A3, COL12A1, THBS2
  7155 2.64×10−23Cell adhesionCOL6A3, COL12A1, THBS2
  6811 3.28×10−23Ion transportCOL6A3, COL12A1, COL1A1
  48513 1.16×10−24Organ developmentFAP, COL6A3, COL12A1, COL1A1
  48731 2.64×10−24System developmentFAP, COL6A3, COL12A1, COL1A1
  48856 3.20×10−24Anatomical structure developmentFAP, COL6A3, COL12A1, COL1A1
  7275 3.46×10−24Multicellular organismal developmentFAP, COL6A3, COL12A1, COL1A1
  51234 4.04×10−24Establishment of localizationFAP, COL6A3, COL12A1, COL1A1
  32502 4.11×10−24Developmental processFAP, COL6A3, COL12A1, COL1A1
  51179 4.52×10−24LocalizationFAP, COL6A3, COL12A1, COL1A1

[i] Corr P-value, corrected P-value; N, nodes; GO-ID, gene ontology identification.

Pathway annotation of the DEGs in modules

Two pathways were found to be enriched (Table II), the ECM-receptor interaction and focal adhesion pathways. Of these, the ECM-receptor interaction pathway was most significantly enriched (P=9.44×10−5), and 7 genes [collagen, type VI, α3 (COL6A3), COL1A2, COL1A1, COL5A2, thrombospondin 2 (THBS2), COL11A1 and COL5A1] were predicted to participate in the pathway.

Table II.

Significant pathways of DEGs in the selected modules.

Table II.

Significant pathways of DEGs in the selected modules.

TermCountFDRGenes
hsa04512: ECM-receptor interaction7 9.44×10−5COL6A3, COL1A2, COL1A1, COL5A2, THBS2, COL11A1, COL5A1
hsa04510: Focal adhesion8 9.10×10−4PGF, COL6A3, COL1A2, COL1A1, COL5A2, THBS2, COL11A1, COL5A1

[i] Term represents the pathway name. Count represents the number of DEGs enriched in each pathway. DEGs, differentially-expressed genes; FDR, false discovery rate; ECM, extracellular matrix.

Discussion

GC is the fourth most frequently occurring malignant tumor worldwide, with high incidence and mortality rates. Therefore, it is of great importance to conduct research on the treatment of GC (33). Major efforts are being made to understand GC at a molecular level (34). Since microarrays can simultaneously investigate the expression levels of thousands of genes in the human genome, use of the technique has been widely applied in the identification of disease biomarkers (26,35). In the present study, a total of 596 DEGs were identified in the GC samples compared with the normal controls. Furthermore, the co-expression interaction network of DEGs was construction and 4 modules were identified. The upregulated PDLIM7 gene was enriched in module 1, while 7 other upregulated genes (COL6A3, COL1A2, COL1A1, COL5A2, THBS2, COL11A1 and COL5A1) were involved in the ECM-receptor interaction pathway. The COL family of genes were mainly enriched in module 2, for which the function was phosphate transport.

COL1A1 and COL1A2 encode the α1 and α2 chains of type I collagen, respectively (36). Collagen is the main constituent of the ECM component in tumors, and a number of collagen types have been found in GC tissues (37). The major constituents of the ECM are collagens, adhesive glycoproteins and proteoglycans (38). Specific interactions between cells and ECM-mediated cell-surface-associated components and transmembrane molecules result in the control of cellular activities, such as adhesion and migration (39). Matsui et al showed that collagen degradation, which was an essential step in the tumor cell invasion of the surrounding tissues, was increased in GC tissues (40). Su et al reported that COL1A1 and COL1A2 were commonly upregulated in GC, and were associated with invasion and metastasis (8). In line with this previous study, the present results showed that COL1A1 and COL1A2 were upregulated in GC, suggesting that they play an important role in cancer cell invasion and metastasis in this disease. On the other hand, the COL family genes were mainly enriched in modules 2 and 4, for which the function is phosphate transport. COL6A3 was clustered into module 2. COL6A3 encodes one of the three α chains of type VI collagen. Another significant DEG that was enriched in GC was COL11A1, another member of the COL family, which encodes one of the two α chains of type XI collagen. Using microarray technology, COL6A3 and COL11A1 levels have been proven to be elevated in GC endothelium when compared with normal endothelium (41,42). The present study demonstrated that COL6A3 and COL11A1 were upregulated and participated in the ECM-receptor interaction pathway, which was in line with these previous studies. Taken together, the results indicated that the COL family in the present study may be molecular biomarkers for GC.

THBS2, which has demonstrated functions as a potent inhibitor of tumor growth and angiogenesis, is a disulfide-linked homotrimetric glycoprotein that mediates cell-to-matrix and cell-cell interactions (43). Stamper et al reported that genes associated with ECM-receptor interactions, including TBHS2, underwent significant changes in expression when comparing craniosynostosis patients and controls (44). In addition, Yasui et al suggested that changes in the ECM could be induced by the degradation of collagen I, which was of great importance to the infiltration and metastasis of cancer cells in GC (45). In line with this previous study, the present results also indicated that TBHS2 was upregulated in GC compared with normal controls, suggesting that TBHS2 may play a role in ECM changes and promote GC progression.

PDLIM7 is a family of proteins composed of PDZ and LIM domains that have been proposed to direct protein-protein interactions. Wu et al demonstrated that the LIM domains of Enigma recognized tyrosine-containing motifs with specificity residing in the target structures and the LIM domains (46). Another study showed that receptor tyrosine kinases play essential roles in the control of cancer cell growth and differentiation (47). In the present study, PDLIM7 was found to be upregulated, showing enrichment in module 1, and interacted with other DEGs identified in the study. Another hub gene in module 1 was adenosine deaminase, RNA-specific, B1 (ADARB1) (Fig. 4). ADARB1, also known as ADAR2, encodes the enzyme responsible for pre-mRNA editing of the glutamate receptor subunit B by site-specific deamination of adenosines (48). A previous study demonstrated that the dysregulation of adenosine to inosine in human cancers possibly contributed to the altered transcriptional program required to sustain carcinogenesis (49). Moreover, Camarata et al reported that PDLIM7 could regulate T-box protein 5 transcriptional activity, which is involved in the transcriptional regulation of genes required for mesoderm differentiation (50). In this context, we speculate that PDLIM7 may play a crucial role in GC development via the interaction with ADARB1.

In conclusion, the present study investigated the critical genes in GC based on microarray data. The target genes COL1A1, COL1A2, COL6A3, THBS2, COL11A1, PDLIM7 and ADARB1 were involved in the progression of GC. COL6A3, COL1A2, COL1A1, THBS2 and COL11A1were identified to be involved in the ECM-receptor interaction pathway. Furthermore, the genes of the COL family were associated with phosphate transport. COL1A1 and COL1A2 may play an important role in tumor invasion and metastasis in GC. TBHS2 may impact ECM changes and promote GC progression. Moreover, PDLIM7 may play a crucial role in GC development via the interaction with ADARB1. The genes identified in GC tissues in the present study may prove to be molecular biomarkers for this disease, although further studies must be performed to confirm these results.

Acknowledgements

The authors wish to express their warm thanks to Fenghe (Shanghai) Information Technology Co., Ltd. (Shanghai, China), whose ideas and assistance provided a valuable added dimension to this study.

References

1 

Polk DB and Peek RM Jr: Helicobacter pylori: Gastric cancer and beyond. Nat Rev Cancer. 10:403–414. 2010. View Article : Google Scholar : PubMed/NCBI

2 

Ferlay J, Shin HR, Bray F, Forman D, Mathers C and Parkin DM: Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008. Int J Cancer. 127:2893–2917. 2010. View Article : Google Scholar : PubMed/NCBI

3 

Schønnemann K, Jensen HA, Yilmaz M, Jensen BV, Larsen O and Pfeiffer P: Phase II study of short-time oxaliplatin, capecitabine and epirubicin (EXE) as first-line therapy in patients with non-resectable gastric cancer. Br J Cancer. 99:858–861. 2008. View Article : Google Scholar : PubMed/NCBI

4 

Cunningham D, Allum WH, Stenning SP, Thompson JN, Van de Velde CJ, Nicolson M, Scarffe JH, Lofts FJ, Falk SJ, Iveson TJ, et al: Perioperative chemotherapy versus surgery alone for resectable gastroesophageal cancer. N Engl J Med. 355:11–20. 2006. View Article : Google Scholar : PubMed/NCBI

5 

Guggenheim DE and Shah MA: Gastric cancer epidemiology and risk factors. J Surg Oncol. 107:230–236. 2013. View Article : Google Scholar : PubMed/NCBI

6 

Tang W, Nakamura Y, Tsujimoto M, Sato M, Wang X, Kurozumi K, Nakahara M, Nakao K, Nakamura M, Mori I and Kakudo K: Heparanase: A key enzyme in invasion and metastasis of gastric carcinoma. Mod Pathol. 15:593–598. 2002. View Article : Google Scholar : PubMed/NCBI

7 

Gupta GP and Massagué J: Cancer metastasis: Building a framework. Cell. 127:679–695. 2006. View Article : Google Scholar : PubMed/NCBI

8 

Yasui W, Oue N, Ito R, Kuraoka K and Nakayama H: Search for new biomarkers of gastric cancer through serial analysis of gene expression and its clinical implications. Cancer Sci. 95:385–392. 2004. View Article : Google Scholar : PubMed/NCBI

9 

Yasui W, Oue N, Aung PP, Matsumura S, Shutoh M and Nakayama H: Molecular-pathological prognostic factors of gastric cancer: A review. Gastric cancer. 8:86–94. 2005. View Article : Google Scholar : PubMed/NCBI

10 

Kim JM, Sohn HY, Yoon SY, Oh JH, Yang JO, Kim JH, Song KS, Rho SM, Yoo HS, Kim YS, et al: Identification of gastric cancer-related genes using a cDNA microarray containing novel expressed sequence tags expressed in gastric cancer cells. Clin Cancer Res. 11:473–482. 2005.PubMed/NCBI

11 

Li BS, Zhao YL, Guo G, Li W, Zhu ED, Luo X, Mao XH, Zou QM, Yu PW, Zuo QF, et al: Plasma microRNAs, miR-223, miR-21 and miR-218, as novel potential biomarkers for gastric cancer detection. PLoS One. 7:e416292012. View Article : Google Scholar : PubMed/NCBI

12 

Wu MS, Lin YS, Chang YT, Shun CT, Lin MT and Lin JT: Gene expression profiling of gastric cancer by microarray combined with laser capture microdissection. World J Gastroenterol. 11:7405–7412. 2005.PubMed/NCBI

13 

Yokozaki H, Yasui W and Tahara E: Genetic and epigenetic changes in stomach cancer. Int Rev Cytol. 204:49–95. 2001. View Article : Google Scholar : PubMed/NCBI

14 

Rubin MA, Mucci NR, Figurski J, Fecko A, Pienta KJ and Day ML: E-cadherin expression in prostate cancer: A broad survey using high-density tissue microarray technology. Hum Pathol. 32:690–697. 2001. View Article : Google Scholar : PubMed/NCBI

15 

Ajani JA: Evolving chemotherapy for advanced gastric cancer. Oncologist. 10(Suppl): 49–58. 2005. View Article : Google Scholar : PubMed/NCBI

16 

Wang Q, Wen YG, Li DP, Xia J, Zhou CZ, Yan DW, Tang HM and Peng ZH: Upregulated INHBA expression is associated with poor survival in gastric cancer. Med Oncol. 29:77–83. 2012. View Article : Google Scholar : PubMed/NCBI

17 

Liu P, Wang X, Hu C and Hu T: Bioinformatics analysis with graph-based clustering to detect gastric cancer-related pathways. Genet Mol Res. 11:3497–3504. 2012. View Article : Google Scholar : PubMed/NCBI

18 

Kikuchi Y, Kunita A, Iwata C, Komura D, Nishiyama T, Shimazu K, Takeshita K, Shibahara J, Kii I, Morishita Y, et al: The niche component periostin is produced by cancer-associated fibroblasts, supporting growth of gastric cancer through ERK activation. Am J Pathol. 184:859–870. 2014. View Article : Google Scholar : PubMed/NCBI

19 

Barrett T, Troup DB, Wilhite SE, Ledoux P, Rudnev D, Evangelista C, Kim IF, Soboleva A, Tomashevsky M, Edgar R, et al: NCBI GEO: mining tens of millions of expression profiles-database and tools update. Nucleic Acids Res. 35(Database Issue): D760–D765. 2007. View Article : Google Scholar : PubMed/NCBI

20 

Smyth GK: Limma: Linear models for microarray data. Bioinformatics and Computational Biology Solutions Using R and Bioconductor. Gentleman R, Carey V, Dudoit S, Irizarry R and Huber W: Springer. 397–420. 2005. View Article : Google Scholar

21 

Ihaka R and Gentleman R: R: A language for data analysis and graphics. J Comput Graph Stat. 5:299–314. 1996. View Article : Google Scholar

22 

Reiner-Benaim A: FDR control by the BH procedure for two-sided correlated tests with implications to gene expression data analysis. Biom J. 49:107–126. 2007. View Article : Google Scholar : PubMed/NCBI

23 

Benjamini Y and Hochberg Y: Controlling the false discovery rate: A practical and powerful approach to multiple testing. J R Statist Soc B. 57:289–300. 1995.

24 

Szekely GJ and Rizzo ML: Hierarchical clustering via joint between-within distances: Extending Ward's minimum variance method. J Classif. 22:151–183. 2005. View Article : Google Scholar

25 

Deza MM and Deza E: Encyclopedia of distances. Berlin: Springer-Verlag. 2009. View Article : Google Scholar

26 

Diao H, Li X, Hu S and Liu Y: Gene expression profiling combined with bioinformatics analysis identify biomarkers for Parkinson disease. PLoS One. 7:e523192012. View Article : Google Scholar : PubMed/NCBI

27 

Obayashi T, Okamura Y, Ito S, Tadaka S, Motoike IN and Kinoshita K: COXPRESdb: A database of comparative gene coexpression networks of eleven species for mammals. Nucleic Acids Res. 41(Database Issue): D1014–D1020. 2013. View Article : Google Scholar : PubMed/NCBI

28 

Barabasi AL and Oltvai ZN: Network biology: Understanding the cell's functional organization. Nat Rev Genet. 5:101–113. 2004. View Article : Google Scholar : PubMed/NCBI

29 

Smoot ME, Ono K, Ruscheinski J, Wang PL and Ideker T: Cytoscape 2.8: New features for data integration and network visualization. Bioinformatics. 27:431–432. 2011. View Article : Google Scholar : PubMed/NCBI

30 

Bader GD and Hogue CW: An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics. 4:22003. View Article : Google Scholar : PubMed/NCBI

31 

Maere S, Heymans K and Kuiper M: BiNGO: A Cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks. Bioinformatics. 21:3448–3449. 2005. View Article : Google Scholar : PubMed/NCBI

32 

da Huang W, Sherman BT and Lempicki RA: Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 4:44–57. 2009. View Article : Google Scholar : PubMed/NCBI

33 

Jemal A, Bray F, Center MM, Ferlay J, Ward E and Forman D: Global cancer statistics. CA Cancer J Clin. 61:69–90. 2011. View Article : Google Scholar : PubMed/NCBI

34 

Figueiredo C, GarciaGonzalez MA and Machado JC: Molecular pathogenesis of gastric cancer. Helicobacter. 18(Suppl 1): 28–33. 2013. View Article : Google Scholar : PubMed/NCBI

35 

Guttula SV, Allam A and Gumpeny RS: Analyzing microarray data of Alzheimer's using cluster analysis to identify the biomarker genes. Int J Alzheimers Dis. 2012:6494562012.PubMed/NCBI

36 

Chan TF, Poon A, Basu A, Addleman NR, Chen J, Phong A, Byers PH, Klein TE and Kwok PY: Natural variation in four human collagen genes across an ethnically diverse population. Genomics. 91:307–314. 2008. View Article : Google Scholar : PubMed/NCBI

37 

Yin Y, Zhao Y, Li AQ and Si JM: Collagen: A possible prediction mark for gastric cancer. Med Hypotheses. 72:163–165. 2009. View Article : Google Scholar : PubMed/NCBI

38 

Bosman FT and Stamenkovic I: Functional structure and composition of the extracellular matrix. J Pathol. 200:423–428. 2003. View Article : Google Scholar : PubMed/NCBI

39 

Uitto VJ and Larjava H: Extracellular matrix molecules and their receptors: An overview with special emphasis on periodontal tissues. Crit Rev Oral Biol Med. 2:323–354. 1991.PubMed/NCBI

40 

Matsui H, Kubochi K, Okazaki I, Yoshino K, Ishibiki K and Kitajima M: Collagen biosynthesis in gastric cancer: Immunohistochemical analysis of prolyl 4hydroxylase. J Surg Oncol. 70:239–246. 1999. View Article : Google Scholar : PubMed/NCBI

41 

Hippo Y, Taniguchi H, Tsutsumi S, Machida N, Chong JM, Fukayama M, Kodama T and Aburatani H: Global gene expression analysis of gastric cancer by oligonucleotide microarrays. Cancer Res. 62:233–240. 2002.PubMed/NCBI

42 

Oue N, Hamai Y, Mitani Y, Matsumura S, Oshimo Y, Aung PP, Kuraoka K, Nakayama H and Yasui W: Gene expression profile of gastric carcinoma identification of genes and tags potentially involved in invasion, metastasis and carcinogenesis by serial analysis of gene expression. Cancer Res. 64:2397–2405. 2004. View Article : Google Scholar : PubMed/NCBI

43 

Tokunaga T, Nakamura M, Oshika Y, Abe Y, Ozeki Y, Fukushima Y, Hatanaka H, Sadahiro S, Kijima H, Tsuchida T, et al: Thrombospondin 2 expression is correlated with inhibition of angiogenesis and metastasis of colon cancer. Br J Cancer. 79:354–359. 1999. View Article : Google Scholar : PubMed/NCBI

44 

Stamper BD, Park SS, Beyer RP, Bammler TK, Farin FM, Mecham B and Cunningham ML: Differential expression of extracellular matrix-mediated pathways in single-suture craniosynostosis. PLoS One. 6:e265572011. View Article : Google Scholar : PubMed/NCBI

45 

Su CQ, Qiu H and Zhang Y: Localization of keratin mRNA and collagen I mRNA in gastric cancer by in situ hybridization and hybridization electron microscopy. World J Gastroenterol. 5:527–530. 1999.PubMed/NCBI

46 

Wu R, Durick K, Songyang Z, Cantley LC, Taylor SS and Gill GN: Specificity of LIM domain interactions with receptor tyrosine kinases. J Biol Chem. 271:15934–15941. 1996. View Article : Google Scholar : PubMed/NCBI

47 

Gschwind A, Fischer OM and Ullrich A: The discovery of receptor tyrosine kinases: Targets for cancer therapy. Nat Rev Cancer. 4:361–370. 2004. View Article : Google Scholar : PubMed/NCBI

48 

Mittaz L, Scott HS, Rossier C, Seeburg PH, Higuchi M and Antonarakis SE: Cloning of a human RNA editing deaminase (ADARB1) of glutamate receptors that maps to chromosome 21q22. 3. Genomics. 41:210–217. 1997. View Article : Google Scholar : PubMed/NCBI

49 

Miura K, Fujibuchi W and Sasaki I: Alternative premRNA splicing in digestive tract malignancy. Cancer Sci. 102:309–316. 2011. View Article : Google Scholar : PubMed/NCBI

50 

Camarata T, Krcmery J, Snyder D, Park S, Topczewski J and Simon HG: Pdlim7 (LMP4) regulation of Tbx5 specifies zebrafish heart atrio-ventricular boundary and valve formation. Dev Biol. 337:233–245. 2010. View Article : Google Scholar : PubMed/NCBI

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Sun H: Identification of key genes associated with gastric cancer based on DNA microarray data. Oncol Lett 11: 525-530, 2016
APA
Sun, H. (2016). Identification of key genes associated with gastric cancer based on DNA microarray data. Oncology Letters, 11, 525-530. https://doi.org/10.3892/ol.2015.3929
MLA
Sun, H."Identification of key genes associated with gastric cancer based on DNA microarray data". Oncology Letters 11.1 (2016): 525-530.
Chicago
Sun, H."Identification of key genes associated with gastric cancer based on DNA microarray data". Oncology Letters 11, no. 1 (2016): 525-530. https://doi.org/10.3892/ol.2015.3929