SciELO - Scientific Electronic Library Online

 
vol.35 issue3The gene expression profiles of induced pluripotent stem cells (iPSCs) generated by a non-integrating method are more similar to embryonic stem cells than those of iPSCs generated by an integrating method author indexsubject indexarticles search
Home Pagealphabetic serial listing  

Services on Demand

Journal

Article

Indicators

Related links

Share


Genetics and Molecular Biology

Print version ISSN 1415-4757

Genet. Mol. Biol. vol.35 no.3 São Paulo  2012  Epub July 13, 2012

https://doi.org/10.1590/S1415-47572012005000045 

Identification of significant pathways in gastric cancer based on protein-protein interaction networks and cluster analysis

 

 

Kongwang HuI; Feihu ChenII

IDepartment of General Surgery, The First Affiliated Hospital of Anhui Medical University, Anhui, P.R. China
IISchool of Pharmacology, Anhui Medical University, Anhui, P.R. China

Send correspondence to

 

 


ABSTRACT

Gastric cancer is one of the most common and lethal cancers worldwide. However, despite its clinical importance, the regulatory mechanisms involved in the aggressiveness of this cancer are still poorly understood. A better understanding of the biology, genetics and molecular mechanisms of gastric cancer would be useful in developing novel targeted approaches for treating this disease. In this study we used protein-protein interaction networks and cluster analysis to comprehensively investigate the cellular pathways involved in gastric cancer. A primary immunodeficiency pathway, focal adhesion, ECM-receptor interactions and the metabolism of xenobiotics by cytochrome P450 were identified as four important pathways associated with the progression of gastric cancer. The genes in these pathways, e.g., ZAP70, IGLL1, CD79A, COL6A3, COL3A1, COL1A1, CYP2C18 and CYP2C9, may be considered as potential therapeutic targets for gastric cancer.

Key words: graph clustering, pathway crosstalk, protein-protein interaction network.


 

 

Introduction

Gastric cancer is one of the most common malignancies worldwide (Lin et al., 2007b). Surgical resection is the only effective treatment for this cancer, although current surgical therapeutic strategies are far from optimal and most patients are diagnosed with late-stage disease when surgical intervention is of limited use (D'Ugo et al., 2009). Chemotherapy has been applied as a neoadjuvant treatment to improve the curative resection rate or to achieve long-term survival in patients with unresectable gastric cancer. The prognosis, however, is still unsatisfactory, with an overall five-year survival rate of 24% (Kanai et al., 2003). Hence, there is an urgent need for new therapeutic strategies.

Recently, several molecular alterations involving various pathways have been implicated in the development and late-stage progression/metastasis of gastric cancer. For example, there is emerging evidence that the Wnt signaling pathway may contribute to gastric carcinogenesis by stimulating the migration and invasion of gastric cancer cells (Kurayoshi et al., 2006). Persons with germ-line mutations in the APC tumor suppressor gene have a 10-fold increased risk of developing gastric cancer when compared with normal persons (Offerhaus et al., 1992). β-catenin is frequently mutated in gastric cancer (Clements et al., 2002). In addition, frizzled receptor E3 (FzE3) is over-expressed in 75% of gastric carcinoma tissues and secreted frizzled related protein (hsFRP) is down-regulated in 16%, suggesting that alterations in FzE3 and hsFRP expression are frequent in this pathology (To et al., 2001). Activation of the hedgehog pathway is another important mechanism associated with aggressive gastric cancer. The sonic hedgehog (Shh) transcript is restricted to cancer tissue whereas Gli1 and human patched gene 1 (PTCH1) are expressed in cancer cells and the surrounding stroma. The treatment of gastric cancer cells with 3-keto-N-aminoethylaminocaproyldihydrocinnamoyl-cyclopamine, a hedgehog signaling inhibitor, decreases the expression of Gli1 and PTCH1 and results in cell growth inhibition and apoptosis (Ma et al., 2005). The high recombinant Shh-induced migration and invasiveness of gastric cancer cells is mediated by tissue growth factor-beta (TGF-β) acting through the ALK5-Smad3 pathway (Yoo et al., 2008). The expression of lysyl oxidase-like 2 (LOXL2), which can promote tumor cell invasion via the Src kinase/focal adhesion kinase (Src/FAK) pathway, is markedly increased in gastric cancer (Peng et al., 2009). The loss of embryonic liver fodrin (ELF) can disrupt TGF-β-mediated signaling by interfering with the localization of Smad3 and Smad4 and leads to the development of gastric cancer (Kim et al., 2006).

An increased concentration of BMP-2 strongly enhances the motility and invasiveness in gastric cancer cells. The stimulation of BMP-2 in gastric cancer cells induces a full epithelial-mesenchymal transition (EMT) characterized by Snail induction, E-cadherin reorganization and the down-regulation and up-regulation of mesenchymal and invasiveness markers through the activation of phosphatidylinositol 3 (PI-3) kinase/Akt (Kang et al., 2010). Cysteine-rich 61 (Cyr61) may contribute to the progression of gastric cancer by promoting tumor cell motility/invasion through the up-regulation of cyclooxygenase-2 (COX-2) in an integrin avh3/NF-kB-dependent manner. Interleukin-6 induces gastric cancer cell line AGS cell invasion through activation of the c-Src/RhoA/ROCK signaling pathway (Lin et al., 2007a).

The use of high-throughput approaches to dissect the molecular mechanisms and pathways that regulate the progress of gastric cancer is still comparatively rare. In this study, we used microarray data, protein-protein interaction (PPI) networks and cluster graph analysis to identify significant pathways involved in the development of gastric cancer. The characterization of genes and pathways involved in gastric cancer should be useful in identifying potential targets for the development of novel strategies for treating gastric carcinoma.

 

Data and Methods

Data sources

The KEGG (Kyoto Encyclopedia of Genes and Genomes) (Kanehisa, 2002) datasets were downloaded on February 19, 2011, at which time they contained 211 pathways and 5,385 genes. The PPI data were collected from the HPRD (Human Protein Reference Database) (Keshava Prasad et al., 2009), MINT (Molecular INTeraction Database) (Chatr-aryamontri et al., 2007) and BIOGRID (Biological General Repository for Interaction Datasets) (Stark et al., 2011). A total of 21,978 unique PPI pairs were obtained, of which 21,353 were from HPRD, 8,830 were from MINT and 19,243 were from BIOGRID. An ensemble PPI network was constructed by integrating three of the above PPI databases for humans, with at least two PPI databases being used to form an intersection (the PPI data are provided as Table S1 in Supplementary Material).

The gene expression profile data were accessed at the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) data repository using the accession number GSE2685. Samples of gastric cancer tissue and corresponding adjacent noncancerous tissue were obtained with the informed consent of patients who underwent gastrectomy at Jichi Medical College Hospital (Tochigi, Japan) (Hippo et al., 2002). Twenty-two gastric cancer tissue samples and eight noncancerous gastric tissue samples were analyzed with oligonucleotide microarrays (GeneChip Hu-GeneFL array; Affymetrix, Santa Clara, CA).

Analysis of significant pathways based on cluster graph analysis

The Limma eBayes analysis (Smyth, 2004) was used to assess the differential expression status of each gene. Background intensities were adjusted and the original expression datasets from all conditions were processed into expression estimates using the robust multiarray average (RMA) method (D'Souza et al., 2008) with the default settings implemented in R (version 2.12.1) (Gentleman et al., 2004); this was followed by construction of the linear model. The empirical Bayes approach was used to further justify these estimators; this process is equivalent to shrinking the estimated sample variances towards a pooled estimate and yields a far more stable inference when the number of arrays is small (Smyth, 2004). At least a two-fold change in expression and a p value of < 0.05 were considered as the threshold for defining differentially expressed genes (DEGs). Spearman's rank correlation (r) was used to assess the association between different DEGs. The level of significance was set at r > 0.75 and the false discovery rate (FDR) at < 0.05 (Strimmer, 2008). All statistical tests were done using the R program.

To identify co-expressed groups we used DPClus (Altaf-Ul-Amin et al., 2006). DPClus is a cluster graph algorithm that can extract densely connected nodes as a cluster and is based on density tracking and peripheral tracking of clusters. In this study, we used the overlapping-mode of the DPClus settings. For this analysis, we used the parameters "cluster property" (cp), a density value of 0.5 and a minimum cluster size of 5 (Fukushima et al., 2011). DAVID software (Huang da et al., 2008) was used for pathway enrichment analysis with p < 0.05 selected as the threshold for gene clusters based on their hypergeometric distribution.

Analysis of significant pathways and pathway crosstalk based on PPI networks

Pathway crosstalk was defined as those pathways that had overlapping genes and edges. "Overlapping genes" meant that both of the pathways included these genes whereas "overlapping edges" meant that both pathways included the PPI interaction edges. Liu et al. (2010) have provided a detailed analysis of crosstalk relationships. The significance of a co-expressed gene pair in gastric cancer was assessed using Pearson's correlation coefficient and the corresponding p values, with the latter being mapped to the nodes and edges in the PPI network. The final identification of significant pathways was based on the extent of overlap of the pathways identified by the two methods (cluster graph analysis and PPI networks).

 

Results

Identification of significant pathways based on screening for differentially expressed genes and cluster graph analysis

A publicly available microarray dataset (GSE2685) was downloaded from GEO and screened for DEGs. In the microarray analysis, 723 genes with a fold change > 2 and p < 0.05 were identified as DEGs using the limma eBayes method. Based on the cutoffs established for r (> 0.75) and FDR (< 0.05) a correlation network was constructed that included 1032 relationships among 364 DEGs. At r > 0.75, DPClus identified 22 clusters that ranged in size from 5 to 24 genes, with each cluster being connected to neighboring clusters (Figure 1). The significance of the clusters was assessed by examining the over-represented pathways in these clusters (also known as pathway enrichment analysis). Table 1 shows the results of this analysis based the cluster graphs in Figure 1. Only clusters 1, 2, 3, 4, 8 and 19 contained enriched pathways.

 

 

Primary immunodeficiency (hsa05340) enriched in cluster 1 was connected with the metabolism of xenobiotics by cytochrome P450 (hsa00980), linoleic acid metabolism (hsa00591) and retinol metabolism (hsa00830), which were enriched in cluster 4 (Figure 1). Primary immunodeficiency (hsa05340) was also connected with the B cell receptor signaling pathway (hsa04662) that was enriched in cluster 19. Cluster 2, in which ECM-receptor interaction (hsa04512) and focal adhesion (hsa04510) were enriched, was indirectly connected with cluster 3 that included cell cycle (hsa04110), oocyte meiosis (hsa04114) and DNA replication (hsa03030).

Identification of significant pathways and pathway crosstalk based on PPI networks

Twenty significant pathways with p < 0.05 were detected using the KEGG pathways and PPI datasets (Table 2). Further analysis of these pathways revealed only 11 cases of crosstalk that involved nine significant pathways (Figure 2). Primary immunodeficiency (hsa05340) showed crosstalk with the ribosome (hsa03010) and chemokine signaling pathway (hsa04062). More importantly, cluster graph analysis and PPI networks identified primary immunodeficiency (hsa05340), focal adhesion (hsa04510), metabolism of xenobiotics by cytochrome P450 (hsa00980) and ECM-receptor interaction (hsa04512) as overlapping significant pathways with considerable crosstalk, e.g., between focal adhesion (hsa04510) and ECM-receptor interaction (hsa04512) based on the PPI network. Cluster graph analysis showed that both of these two pathways were enriched in cluster 2.

 

 

Discussion

Graph clustering or PPI-based pathway analysis (Hwang et al., 2008) has been successfully used to identify the underlying mechanisms associated with diseases. In this study, we used the same strategy to identify DEGs associated with gastric cancer and predict their underlying molecular mechanisms. Our cluster analysis showed that primary immunodeficiency (hsa05340) enriched in cluster 1 interacted not only with the metabolism of xenobiotics by cytochrome P450 (hsa00980), linoleic acid metabolism (hsa00591) and retinol metabolism (hsa00830) that were enriched in cluster 4, but also with the B cell receptor signaling pathway (hsa04662) enriched in cluster 19. These results indicated that the primary immunodeficiency pathway has several important roles in gastric cancer. Although additional pathways were observed in PPI-based pathway analysis, the primary immunodeficiency pathway still retained important roles in gastric cancer through crosstalk with the ribosome (hsa03010) and chemokine signaling pathway (hsa04062). In addition, focal adhesion (hsa04510), metabolism of xenobiotics by cytochrome P450 (hsa00980) and ECM-receptor interaction (hsa04512) were all enriched in the two methods used to assess pathway enrichment. Based on these findings, we suggest that the genes of the primary immunodeficiency pathway, focal adhesion, ECM-receptor interaction and the metabolism of xenobiotics by cytochrome P450 (hsa00980) are potentially important therapeutic targets for gastric cancer. These pathways are discussed below in greater detail.

Primary immunodeficiencies are a heterogeneous group of disorders that affect cellular and humoral immunity or non-specific host defense mechanisms mediated by complement proteins and by cells such as phagocytes and natural killer cells. These immune system disorders cause increased susceptibility to malignancy. For example, patients with common variable immunodeficiency, the second most prevalent primary immunodeficiency in adults, have a 10-fold increased risk of gastric cancer (Dhalla et al., 2011). Patients in advanced stages of gastric cancer frequently suffer from cell-mediated immunodeficiency, such as the inhibition of interleukin-2 production, the main cytokine that modulates the cell-mediated immune response, and a decrease in the total and T lymphocyte counts (Romano et al., 2003). In addition, the absolute number of T-regulatory lymphocytes (Tregs; CD4+CD25+Foxp3+) is significantly lower in gastric cancer patients than in normal individuals (Szczepanik et al., 2011).

Disorders in the regulation of humoral immunity also have a significant effect on the development of gastric cancer. For example, a significant increase in IgG Fc fucosylation has been observed in stages II and III of gastric cancer (Kodar et al., 2012). The widespread expression of CD40, a member of the tumor necrosis factor receptor superfamily, reflects the central role of CD40 in regulating humoral immunity and host defense. The stimulation of CD40 in gastric carcinoma makes cells less vulnerable to apoptosis induced by Fas or chemotherapy and increases cell motility (Yamaguchi et al., 2003).

ZAP70 (zeta-chain (TCR) associated protein kinase 70 kDa), IGLL1 (immunoglobulin lambda-like polypeptide 1) and CD79A (CD79a molecule, immunoglobulin-associated alpha) were enriched in the primary immunodeficiency pathway. ZAP70 may be involved in T-cell-mediated immunodeficiency. ZAP-70 ectopic expression leads to enhanced B cell receptor signaling after IgM stimulation and increased expression of CCR7 (chemokine [C-C motif] receptor 7), predominantly via ERK1/2, thereby enhancing the response to and migration towards CCL21 (chemokine [C-C motif] ligand 21). In addition, cellular subsets with high ZAP-70 expression in chronic lymphocytic leukemia show increased expression of adhesion molecules and chemokine receptors (Calpe et al., 2011). IGLL1 and CD79A are associated with B cell-mediated immunodeficiency. Mutations in IGLL1 and CD79a can result in B cell deficiency and few or no Γ-globulins or antibodies are produced (Storlazzi et al., 2002; Wang et al., 2002). These alterations may promote the metastasis of gastric cancer since an anti-Wnt5a antibody suppresses the Wnt5a-dependent internalization of receptors. This in turn prevents the metastasis of gastric cancer cells by inhibiting the activation of Rac1 (ras-related C3 botulinum toxin substrate 1 [rho family, small GTP binding protein Rac1]) and the expression of laminin Γ2 (Hanaki et al., 2012). Based on these findings, we conclude that the primary immunodeficiency pathway may affect the progress of gastric cancer by inhibiting T lymphocyte proliferation and antibody production by B lymphocytes, or by enhancing the expression of adhesion molecules and chemokine receptors.

The interaction between tumor cells and extracellular matrix (ECM) components such as laminin, fibronectin and collagen, has a crucial role in tumor invasion and metastasis. This interaction is facilitated by adhesion receptors such as integrins. Consequently, ECM-receptor interactions and the focal adhesion pathway may be involved in cancer metastasis. Collagen is the major constituent of the tumor ECM and several types of collagens have been implicated in the focal adhesion and ECM-receptor interaction pathways in gastric carcinoma (Yin et al., 2009). Watanabe et al. (1995) reported greater deposition of type III collagen at the periphery of poorly differentiated gastric cancer tissue compared with more central locations.

Microarray studies have shown the enhanced expression of several collagen genes (COL1A1, 1A2, 3A1, 4A1, 4A2, 4A6, 5A2, 6A3, 7A1, 9A3, 11A1 and 18A1) in the endothelium of gastric cancer tissue compared with normal endothelium (Hippo et al., 2002; Oue et al., 2004). The most up-regulated genes in gastric cancer, such as COL1A1, 1A2, 3A1, 4A1 and 4A2, are associated with cell adhesion or migration and the ECM. COL4A6, 6A3, 17A1 and 18A1 are also associated with cell adhesion, COL1A1 with cell growth and/or maintenance, and COL1A2 and 6A3 with the 'ECM-receptor interaction' pathway (Yasui et al., 2004). In agreement with previous studies, COL6A3, 3A1 and 1A1 were found to be involved in focal adhesion and ECM-receptor interaction pathways. These findings suggest that targeting these genes with RNA interference could decrease the collagen content of the ECM in gastric carcinoma and reduce cell proliferation and migration. This diversity of collagens suggests that each type is associated with some aspect of gastric cancer. The identification of collagens as potential therapeutic targets will require a more complete understanding of their expression and interactions in gastric cancer.

Cytochromes P450 (CYP) are a multi-gene family of constitutive and inducible heme-containing enzymes with a crucial role in the metabolism of xenobiotics, including many potential carcinogens and various anti-cancer drugs. CYP P450s have a central role in chemical carcinogenesis and are involved in tumor initiation and promotion because they can activate or deactivate most carcinogens. Furthermore, CYP P450s can influence the response of established tumors to anti-cancer drugs by metabolizing these drugs in tumor cells (Ding and Kaminsky, 2003). The expression of major isoforms of P450, such as CYPlA and CYP3A, is enhanced in gastric cancer, with CYP1A being enhanced in 51% of cases and CYP3A in 28% (Murray et al., 1998). CYP2C9, CYP3A7 and CYP3A5 that participate in drug metabolism are down-regulated in gastric cancer. In Helicobacter pylori-positive Japanese, poor metabolizers via CYP2C19 have an increased risk of developing gastric cancer, especially the diffuse type (Sugimoto et al., 2005). In Chinese with gastric cancer the frequency of poor metabolizers via CYP2C19 is 31.8% (Shi and Chen, 2004).

As shown here, CYP2C18 and CYP2C9 were associated with the development of gastric cancer through the metabolism of xenobiotics by cytochrome P450, arachidonic acid metabolism, retinol metabolism and the linoleic acid metabolism pathway. CYP2C9 is one of the predominant epoxygenase isoforms involved in the metabolism of arachidonic acid into 12-epoxyeicosatrienoic acid (EEF). CYP2C9 epoxygenases are upregulated in human tumors and promote tumor progression and metastasis (Xu et al., 2011). Retinol may influence gastric carcinogenesis through its essential role in controlling cell proliferation and differentiation. High intakes of retinol from foods or a combination of foods and supplements are associated with a lower risk of gastric cancer (Larsson et al., 2007). CYP2C18 and CYP2C9 are related to retinol metabolism in human through their ability to transform retinol into 4-OH-retinoic acid and 18-OH-retinoic acid (Marill et al., 2000). These all-trans-retinoic acids are associated with G0/G1 phase arrest and decreased VEGF expression in human gastric cancer cell lines (Zhang et al., 2007). However, dietary linoleic acid stimulates the invasion and peritoneal metastasis of gastric carcinoma cells through COX-catalyzed metabolism and the activation of ERK (Matsuoka et al., 2010). CYP2C9 is involved in linoleic acid epoxygenation and the major product of this reaction is leukotoxin that increases oxidative stress and subsequent pro-inflammatory events (Viswanathan et al., 2003), leading to tumor cell progression. We therefore suggest that P450 family genes are involved in gastric cancer by metabolizing exogenous anti-cancer drugs, stimulating arachidonic acid and linoleic acid metabolism and inhibiting retinol metabolism.

In conclusion, the results described here show that changes in the primary immunodeficiency pathway, focal adhesion, ECM-receptor interactions and the metabolism of xenobiotics by cytochrome P450 may be associated with gastric cancer. A number of candidate genes (ZAP70, IGLL1, CD79A, COL6A3, COL3A1, COL1A1, CYP2C18 and CYP2C9) that may be involved in gastric cancer were also identified. Overall, these findings shed new light on the biology of gastric cancer and indicate new avenues for future research.

 

Acknowledgments

This work was supported by the Anhui Provincial Natural Science Funding for Key Projects in 2010 (grant no. KJ2010A171) and the Medicine Research Projects Schedule of the Province Health Bureau in 2009 (grant no. 09C157).

 

References

Altaf-Ul-Amin M, Shinbo Y, Mihara K, Kurokawa K and Kanaya S (2006) Development and implementation of an algorithm for detection of protein complexes in large interaction networks. BMC Bioinformatics 7:e207.         [ Links ]

Calpe E, Codony C, Baptista MJ, Abrisqueta P, Carpio C, Purroy N, Bosch F and Crespo M (2011) ZAP-70 enhances migration of malignant B lymphocytes toward CCL21 by inducing CCR7 expression via IgM-ERK1/2 activation. Blood 118:4401-4410.         [ Links ]

Chatr-aryamontri A, Ceol A, Palazzi LM, Nardelli G, Schneider MV, Castagnoli L and Cesareni G (2007) MINT: The Molecular INTeraction database. Nucleic Acids Res 35(Database issue):D572-D574.         [ Links ]

Clements WM, Wang J, Sarnaik A, Kim OJ, MacDonald J, Fenoglio-Preiser C, Groden J and Lowy AM (2002) β-catenin mutation is a frequent cause of Wnt pathway activation in gastric cancer. Cancer Res 62:3503-3506.         [ Links ]

Dhalla F, da Silva SP, Lucas M, Travis S and Chapel H (2011) Review of gastric cancer risk factors in patients with common variable immunodeficiency disorders, resulting in a proposal for a surveillance programme. Clin Exp Immunol 165:1-7.         [ Links ]

Ding X and Kaminsky LS (2003) Human extrahepatic cytochromes P450: Function in xenobiotic metabolism and tissue-selective chemical toxicity in the respiratory and gastrointestinal tracts. Annu Rev Pharmacol Toxicol 43:149-173.         [ Links ]

D'Souza M, Zhu X and Frisina RD (2008) Novel approach to select genes from RMA normalized microarray data using functional hearing tests in aging mice. J Neurosci Methods 171:279-287.         [ Links ]

D'Ugo D, Rausei S, Biondi A and Persiani R (2009) Preoperative treatment and surgery in gastric cancer: Friends or foes? Lancet Oncol 10:191-195.         [ Links ]

Fukushima A, Kusano M, Redestig H, Arita M and Saito K (2011) Metabolomic correlation-network modules in Arabidopsis based on a graph-clustering approach. BMC Syst Biol 5:e1.         [ Links ]

Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S, Ellis B, Gautier L, Ge Y, Gentry J, et al. (2004) Bioconductor: Open software development for computational biology and bioinformatics. Genome Biol 5:R80.         [ Links ]

Hanaki H, Yamamoto H, Sakane H, Matsumoto S, Ohdan H, Sato A and Kikuchi A (2012) An anti-Wnt5a antibody suppresses metastasis of gastric cancer cells in vivo by inhibiting receptor-mediated endocytosis. Mol Cancer Ther 11:298-307.         [ Links ]

Hippo Y, Taniguchi H, Tsutsumi S, Machida N, Chong JM, Fukayama M, Kodama T and Aburatani H (2002) Global gene expression analysis of gastric cancer by oligonucleotide microarrays. Cancer Res 62:233-240.         [ Links ]

Huang da W, Sherman BT and Lempicki RA (2008) Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 4:44-57.         [ Links ]

Hwang S, Son SW, Kim SC, Kim YJ, Jeong H and Lee D (2008) A protein interaction network associated with asthma. J Theor Biol 252:722-731.         [ Links ]

Kanai M, Konda Y, Nakajima T, Izumi Y, Kanda N, Nanakin A, Kubohara Y and Chiba T (2003) Differentiation-inducing factor-1 (DIF-1) inhibits STAT3 activity involved in gastric cancer cell proliferation via MEK-ERK-dependent pathway. Oncogene 22:548-554.         [ Links ]

Kanehisa M (2002) The KEGG database. Novartis Found Symp 247:91-101 (and discussion 101-103, 119-128, 244-252).         [ Links ]

Kang MH, Kim JS, Seo JE, Oh SC and Yoo YA (2010) BMP2 accelerates the motility and invasiveness of gastric cancer cells via activation of the phosphatidylinositol 3-kinase (PI3K)/Akt pathway. Exp Cell Res 316:24-37.         [ Links ]

Keshava Prasad TS, Goel R, Kandasamy K, Keerthikumar S, Kumar S, Mathivanan S, Telikicherla D, Raju R, Shafreen B, Venugopal A, et al. (2009) Human protein reference database - 2009 update. Nucleic Acids Res 37(Suppl 1):D767-D772.         [ Links ]

Kim SS, Shetty K, Katuri V, Kitisin K, Baek HJ, Tang Y, Marshall B, Johnson L, Mishra B and Mishra L (2006) TGF-β signaling pathway inactivation and cell cycle deregulation in the development of gastric cancer: Role of the beta-spectrin, ELF. Biochem Biophys Res Commun 344:1216-1223.         [ Links ]

Kodar K, Stadlmann J, Klaamas K, Sergeyev B and Kurtenkov O (2012) Immunoglobulin G Fc N-glycan profiling in patients with gastric cancer by LC-ESI-MS: Relation to tumor progression and survival. Glycoconj J 29:57-66.         [ Links ]

Kurayoshi M, Oue N, Yamamoto H, Kishida M, Inoue A, Asahara T, Yasui W and Kikuchi A (2006) Expression of Wnt-5a is correlated with aggressiveness of gastric cancer by stimulating cell migration and invasion. Cancer Res 66:10439-10448.         [ Links ]

Larsson SC, Bergkvist L, Naslund I, Rutegard J and Wolk A (2007) Vitamin A, retinol, and carotenoids and the risk of gastric cancer: A prospective cohort study. Am J Clin Nutr 85:497-503.         [ Links ]

Lin MT, Lin BR, Chang CC, Chu CY, Su HJ, Chen ST, Jeng YM and Kuo ML (2007a) IL-6 induces AGS gastric cancer cell invasion via activation of the c-Src/RhoA/ROCK signaling pathway. Int J Cancer 120:2600-2608.         [ Links ]

Lin HL, Yang MH, Wu CW, Chen PM, Yang YP, Chu YR, Kao CL, Ku HH, Lo JF, Liou JP, et al. (2007b) 2-Methoxyestradiol attenuates phosphatidylinositol 3-kinase/Akt pathway-mediated metastasis of gastric cancer. Int J Cancer 121:2547-2555.         [ Links ]

Liu ZP, Wang Y, Zhang XS and Chen L (2010) Identifying dysfunctional crosstalk of pathways in various regions of Alzheimer's disease brains. BMC Syst Biol 4(Suppl 2):S11.         [ Links ]

Ma X, Chen K, Huang S, Zhang X, Adegboyega PA, Evers BM, Zhang H and Xie J (2005) Frequent activation of the hedgehog pathway in advanced gastric adenocarcinomas. Carcinogenesis 26:1698-1705.         [ Links ]

Marill J, Cresteil T, Lanotte M and Chabot GG (2000) Identification of human cytochrome P450s involved in the formation of all-trans-retinoic acid principal metabolites. Mol Pharmacol 58:1341-1348.         [ Links ]

Matsuoka T, Adair JE, Lih FB, Hsi LC, Rubino M, Eling TE, Tomer KB, Yashiro M, Hirakawa K, Olden K, et al. (2010) Elevated dietary linoleic acid increases gastric carcinoma cell invasion and metastasis in mice. Br J Cancer 103:1182-1191.         [ Links ]

Murray GI, Taylor MC, Burke MD and Melvin WT (1998) Enhanced expression of cytochrome P450 in stomach cancer. Br J Cancer 77:1040-1044.         [ Links ]

Offerhaus GJ, Giardiello FM, Krush AJ, Booker SV, Tersmette AC, Kelley NC and Hamilton SR (1992) The risk of upper gastrointestinal cancer in familial adenomatous polyposis. Gastroenterology 102:1980-1982.         [ Links ]

Oue N, Hamai Y, Mitani Y, Matsumura S, Oshimo Y, Aung PP, Kuraoka K, Nakayama H and Yasui W (2004) Gene expression profile of gastric carcinoma. Cancer Res 64:2397-2405.         [ Links ]

Peng L, Ran YL, Hu H, Yu L, Liu Q, Zhou Z, Sun YM, Sun LC, Pan J, Sun LX, et al. (2009) Secreted LOXL2 is a novel therapeutic target that promotes gastric cancer metastasis via the Src/FAK pathway. Carcinogenesis 30:1660-1669.         [ Links ]

Romano F, Caprotti R, Bravo AF, Conti M, Colombo G, Piacentini G, Uggeri F Jr and Uggeri F (2003) Radical surgery does not recover immunodeficiency associated with gastric cancer. J Exp Clin Cancer Res 22:179-184.         [ Links ]

Shi WX and Chen SQ (2004) Frequencies of poor metabolizers of cytochrome P450 2C19 in esophagus cancer, stomach cancer, lung cancer and bladder cancer in Chinese population. World J Gastroenterol 10:1961-1963.         [ Links ]

Smyth GK (2004) Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol 3:Article3.         [ Links ]

Stark C, Breitkreutz BJ, Chatr-Aryamontri A, Boucher L, Oughtred R, Livstone MS, Nixon J, Van Auken K, Wang X, Shi X, et al. (2011) The BioGRID Interaction Database: 2011 update. Nucleic Acids Res 39(Suppl 1):D698-D704.         [ Links ]

Storlazzi CT, Specchia G, Anelli L, Albano F, Pastore D, Zagaria A, Rocchi M and Liso V (2002) Breakpoint characterization of der(9) deletions in chronic myeloid leukemia patients. Genes Chromosomes Cancer 35:271-276.         [ Links ]

Strimmer K (2008) fdrtool: A versatile R package for estimating local and tail area-based false discovery rates. Bioinformatics 24:1461-1462.         [ Links ]

Sugimoto M, Furuta T, Shirai N, Nakamura A, Kajimura M, Sugimura H, Hishida A and Ishizaki T (2005) Poor metabolizer genotype status of CYP2C19 is a risk factor for developing gastric cancer in Japanese patients with Helicobacter pylori infection. Aliment Pharmacol Ther 22:1033-1040.         [ Links ]

Szczepanik AM, Siedlar M, Sierzega M, Goroszeniuk D, Bukowska-Strakova K, Czupryna A and Kulig J (2011) T-regulatory lymphocytes in peripheral blood of gastric and colorectal cancer patients. World J Gastroenterol 17:343-348.         [ Links ]

To KF, Chan MW, Leung WK, Yu J, Tong JH, Lee TL, Chan FK and Sung JJ (2001) Alterations of frizzled (FzE3) and secreted frizzled related protein (hsFRP) expression in gastric cancer. Life Sci 70:483-489.         [ Links ]

Viswanathan S, Hammock BD, Newman JW, Meerarani P, Toborek M and Hennig B (2003) Involvement of CYP 2C9 in mediating the proinflammatory effects of linoleic acid in vascular endothelial cells. J Am Coll Nutr 22:502-510.         [ Links ]

Wang Y, Kanegane H, Sanal O, Tezcan I, Ersoy F, Futatani T and Miyawaki T (2002) Novel Igα (CD79a) gene mutation in a Turkish patient with B cell-deficient agammaglobulinemia. Am J Med Genet 108:333-336.         [ Links ]

Watanabe M, Hirano T and Asano G (1995) Roles of myofibroblasts in the stroma of human gastric carcinoma. Nippon Geka Gakkai Zasshi 96:10-18.         [ Links ]

Xu X, Zhang XA and Wang DW (2011) The roles of CYP450 epoxygenases and metabolites, epoxyeicosatrienoic acids, in cardiovascular and malignant diseases. Adv Drug Deliv Rev 63:597-609.         [ Links ]

Yamaguchi H, Tanaka F, Sadanaga N, Ohta M, Inoue H and Mori M (2003) Stimulation of CD40 inhibits Fas- or chemotherapy-mediated apoptosis and increases cell motility in human gastric carcinoma cells. Int J Oncol 23:1697-1702.         [ Links ]

Yasui W, Oue N, Ito R, Kuraoka K and Nakayama H (2004) Search for new biomarkers of gastric cancer through serial analysis of gene expression and its clinical implications. Cancer Sci 95:385-392.         [ Links ]

Yin Y, Zhao Y, Li AQ and Si JM (2009) Collagen: A possible prediction mark for gastric cancer. Med Hypotheses 72:163-165.         [ Links ]

Yoo YA, Kang MH, Kim JS and Oh SC (2008) Sonic hedgehog signaling promotes motility and invasiveness of gastric cancer cells through TGF-β-mediated activation of the ALK5-Smad 3 pathway. Carcinogenesis 29:480.         [ Links ]

Zhang JP, Chen XY and Li JS (2007) Effects of all-trans-retinoic on human gastric cancer cells BGC-823. J Dig Dis 8:29-34.         [ Links ]

 

Internet Resources

Gene Expression Omnibus (GEO) data repository at NCBI, http://www.ncbi.nlm.nih.gov/geo/GSE2685 (November 8, 2011).         [ Links ]

R program: http://www.r-project.org/ (November 11, 2011).         [ Links ]

DPClus, http://kanaya.naist.jp/DPClus/.         [ Links ]

 

 

Send correspondence to:
Feihu Chen
School of Pharmacology
Anhui Medical University
Meishan Road 81
230032 Hefei, Anhui, P.R. China
E-mail: feihuchensci@gmail.com

Received: March 12, 2012
Accepted: May 4, 2012

 

 

Associate Editor: Carlos F.M. Menck
License information: This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

 

 

Supplementary Material

The following online material is available for this article:

Table S1 - PPI data.

This material is available as part of the online article from http://www.scielo.br/gmb.

Table S1 available only in pdf.

Creative Commons License All the contents of this journal, except where otherwise noted, is licensed under a Creative Commons Attribution License