Revealing the molecular mechanism of colorectal cancer by establishing LGALS3‑related protein-protein interaction network and identifying signaling pathways

  • Authors:
    • Lu Han
    • Zhixiong Wu
    • Qicheng Zhao
  • View Affiliations

  • Published online on: January 8, 2014     https://doi.org/10.3892/ijmm.2014.1620
  • Pages: 581-588
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Abstract

LGALS3 plays a role in colorectal cancer, however, the detailed molecular mechanism remains to be determined, while signaling pathways provide valuable information for understanding the underlying mechanism of the cancer. The purpose of this study was to explore the roles of LGALS3 and signaling pathways in the pathogenesis of colorectal cancer. In this study, microarray data GSE8671 were downloaded from the Gene Expression Omnibus database and differentially expressed genes (DEGs) in colorectal cancer were identified by Significant Analysis of Microarray. Gene ontology (GO) analysis was performed on the top 500 upregulated and 500 downregulated genes using DAVID. The signaling pathways were predicted by the signaling pathway impact analysis (SPIA) with pGFdr<0.05 and transcription factors were identified by TFats. The LGALS3‑related protein-protein interaction network (PPI) was established by STRING and Cytoscape. In total, 6,593 upregulated and 5,897 downregulated DEGs were identified and 41 downregulated genes, including CLND8 and CLND23 were enriched in cell adhesion. In addition, 21 pathways, such as the cell cycle, p53 signaling pathway and NF-κB signaling pathway, were selected. MYC and TCF7L2 were found to be activated while FOXO3 was suppressed in colorectal cancer. Eight downregulated and 10 upregulated genes were identified in the LGALS3 PPI network. Results of the present study shed new light on the molecular mechanism of colorectal cancer and these findings have the potential to be used in colorectal cancer treatment.

Introduction

As the leading cause of death in economically developed countries and the second leading cause of death in developing countries, cancer is a major public health concern worldwide (1). Atkin et al have reported that colorectal cancer is the third most common cancer worldwide and has a high mortality rate (2). Grady and Carethers have confirmed that colorectal cancer developed as a consequence of the accumulation of genetic alterations, such as gene mutation and gene amplification, and epigenetic alterations, including aberrant DNA methylation and chromatin modification that is able to transform colonic epithelial cells into colonic adenocarcinoma cells (3). Due to the high mortality, there is a need to investigate the pathogenesis and molecular mechanism of colorectal cancer.

During the last 15 years, the focus has been on recognition of the ‘serrated neoplastic pathway’ and has led to a paradigm shift in our understanding of the molecular basis of colorectal cancer and significant changes in clinical practice (4). The changes that have occurred in the DNA sequence of the genomes of cancer cells result in the development of various types of cancer (5) and multiple gene expression patterns are altered during the evolution of normal cells to cancer cells. Furthermore, genome-wide analysis of the gene expression has been largely used to identify important genes of human cancers (6). The gene expression profile has been previously characterized in various types of human cancer, including prostate, colorectal and epithelial ovarian cancer (68).

In addition, it has been reported that genetically altered core pathways and regulatory processes become evident once the coding regions of the genome are analyzed in depth, while dysregulation of these core pathways and processes through mutation can explain the major features of tumorigenesis (7). The development of cancer depends on the abnormal activation of signal transduction pathways that control the growth and survival of cells (8). Therefore, various signaling pathways are altered in the pathogenesis of cancer. Activation of the signaling pathway of hypoxia inducible factor (HIF) is crucial in the progression of physiological development and tumor growth (9). Activation of the Wnt signaling pathway promotes neoplastic transformation in humans (8). Other signaling pathways such as gefitinib-sensitizing EGFR, β-catenin-Tcf, and p53 have also been reported to be dysregulated in cancer (1214).

By binding to specific DNA sequences within the promoter regions of target genes, transcription factors (TFs) are able to regulate DNA expression (10). Findings of previous studies identified several cancer-related TFs, such as TMPRSS2 and ETS in prostate cancer (11). KLF4 and KLF5 affect proliferation, apoptosis and invasion in esophageal cancer cells by regulating a number of genes (12). NF-κB has an impact on the development and progression of cancer by affecting cell proliferation, migration, and apoptosis (13).

The transcriptome profile of human colorectal adenomas has been previously characterized (14), however, the molecular mechanism involved remains to be determined. Galectin-3 is a human galectin (galactose-binding lectin) family member and is expressed by many types of cells. The concentration of galectin-3 is increased to almost 31-fold in the blood circulation of colorectal cancer patients and the increased concentration of circulating galectin-3 correlates closely with cancer progression and metastasis (15). Recently, we revealed that galectin-3, at concentrations similar to those found in the circulation of cancer patients, interacts with mucin protein MUC1, promoting cancer metastasis (16,17). As the Galectin-3 protein is encoded by the LGALS3 gene, the possibility that the LGALS3-related network likely represents a fundamental mechanism in promoting colon cancer metastasis was examined. In the present study, differentially expressed genes (DEGs) between colorectal cancer and normal cells were identified and functional analyses were subsequently performed. The TFs were then predicted and a LGALS3-related protein-protein interaction (PPI) network was constructed. Based on this bioinformatics information, the roles of LGALS3 and signaling pathways were analyzed in the pathogenesis of colorectal cancer.

Materials and methods

Affymetrix microarray data

The Affymetrix microarray data were accessible at the National Center for Biotechnology Information Gene Expression Omnibus data repository (http://www.ncbi.nlm.nih.gov/geo/) using the series accession number GSE8671 (14). In total, 32 adenomas and 32 normal colonic epitheliums were collected based on the GPL570 (HG-U133-Plus-2) Affymetrix Human Genome U133 plus 2.0 Array. The original data were converted into expression measures and normalized by the robust multiarray average (RMA) algorithm (18).

Identification and gene ontology analysis of DEGs

The DEGs were identified by using Significant Analysis of Microarray (SAM) with |logFC| >1.5 and a false discovery rate (FDR)<0.05 (δ=1) (19). GO analysis (20) was performed on the top 500 upregulated and 500 downregulated DEGs using DAVID (Database for Annotation, Visualization, and Integrated Discovery) (21). The biological process with P<0.05 considered statistically significant were screened in the present study.

Signaling pathway impact analysis

The signaling pathway impact analysis (SPIA) was performed to predict the signaling pathways that the DEGs would likely impact. SPIA combines the evidence obtained from the classical enrichment analysis with a novel type of evidence, which measures the actual perturbation on a given pathway under a given condition (22). In SPIA, pG combines enrichment pNDE and perturbation pPERT, and is then adjusted to pGFdr. In the present study, pGFdr<0.05 was set as a threshold.

Predication of transcription factors

TFatS (www.tfacts.org) was used as a bioinformatics tool to evaluate the transcription factor target genes among the list of regulated genes (23). The top 500 upregulated and 500 downregulated genes were mapped to TfactS to identify target genes with p<0.05, q<0.05, E<0.05 and FDR<0.05. In addition, the Fisher’s exact test was used to examine whether the transcription factor was activated or suppressed.

Protein-protein interaction (PPI) network for LGALS3

LGALS3 was submitted to STRING database to predict the potential interacted proteins. STRING (www.//string.embl.de) is a database of predicted functional associations between proteins (24). STRING database produces a score to estimate the accuracy of each pairwise association from 0 to 1. In the present study, the PPIs were screened with score >0.7. The PPI network was subsequently visualized using Cyoscape software (25).

Results

Identification and GO analysis of DEGs

Based on SAM analysis, a total of 6,593 upregulated and 5,897 downregulated DEGs were identified. Subsequently, the GO analysis was performed to the top 500 upregulated and 500 downregulated genes, respectively (Table IA and B). The results showed that 41 downregulated DEGs, including CLDN8 and CLDN23, were enriched in cell adhesion (P=2.23E-06) (Table IA). The upregulated DEGs which included KIF23, PRC1, TTK, AURKA, AURKB, PTTG1, and RUVBL1 were mainly enriched in the terms associated with cell cycle, such as the mitotic cell cycle (P=3.74E-34) and cell cycle process (P=3.49E-29) (Table IB).

Table I

The enriched GO terms.

Table I

The enriched GO terms.

A, The top 10 GO terms of the top 500 upregulated DEGs

CategoryTermCountGenesP-value
GO:0000278Mitotic cell cycle67KIF23, PRC1, TTK3.74E-34
GO:0022402Cell cycle process75AURKA, AURKB, PTTG13.49E-29
GO:0000280Nuclear division49KIF23, AURKA, PTTG14.99E-29
GO:0007067Mitosis49KIF23, AURKA, PTTG14.99E-29
GO:0000087M phase of mitotic cell cycle49KIF23, AURKA, PTTG11.19E-28
GO:0022403Cell cycle phase64KIF23, PRC1, TTK1.65E-28
GO:0048285Organelle fission49KIF23, PTTG1, AURKA3.43E-28
GO:0007049Cell cycle85KIF23, PCR1, CDK23.01E-27
GO:0000279M phase65PCR1, KIF23, AURKA5.30E-27
GO:0051301Cell division46PRC1, KIF23, CDK11.49E-20

B, The enriched terms of the top 500 downregulated DEGs

CategoryTermCountGenesP-value

GO:0007155Cell adhesion41CLDN8, CLDN232.23E-06
GO:0022610Biological adhesion41CLDN8, CLDN232.27E-06
GO:0007584Response to nutrient14BMP2, A2M7.14E-05

[i] GO, gene ontology; DEGs, differentially expressed genes.

KEGG pathways analysis

Based on SPIA analysis, a total of 21 KEGG signaling pathways were screened to determine whether they were dysregulated in colorectal cancer (Table II). Then the cell cycle (pGFdr=3.00E-04), p53 signaling pathway (pGFdr=8.82E-03), and NF-κB signaling pathway (pGFdr=3.77E-02), which significantly correlated with cancer were selected for subsequent investigation. In detail, cyclin-dependent kinase genes, such as CDK1, CDK2, CDK4, CDK6 and CDK7 were upregulated in the cell cycle pathway (Fig. 1). In the p53 signaling pathway, ATR and p53 were upregulated (Fig. 2), while in the NF-κB pathway, TRAFs were significantly differentially expressed (Fig. 3).

Table II

The 21 pathways identified based on signaling pathway impact analysis (pGFdr<0.05).

Table II

The 21 pathways identified based on signaling pathway impact analysis (pGFdr<0.05).

PathwayCountGenespGFdr
RNA transport125XPOT, NCBP1, DDX209.08E-09
HTLV-1 infection195NRP1, SLC2A1, TGFB33.51E-05
Natural killer cell-mediated cytotoxicity87NFNT5, PPP3CB, TNFSF103.00E-04
Cell cycle97CDK1, CDK2, MCM23.00E-04
Epstein-Barr virus infection150CR2, HLA-DRA, CD381.44E-03
Fanconi anemia pathway43FANCM, FANCI, FANCF1.83E-03
Antigen processing and presentation54CD74, HLA-DMA, NFYA2.35E-03
Chemokine signaling pathway132CXCR6, CCR1, CXCR38.80E-03
Staphylococcus aureus infection37CFD, FCGR2B, HLA-DMA8.80E-03
P53 signaling pathway56P53, ATR, CDK28.82E-03
Fc γ R-mediated phagocytosis66FCGR2B, HCK, LYN1.11E-02
Pathways in cancer236CASP3, CTNNB1, WNT22.29E-02
Protein processing in endoplasmic reticulum124MAPK9, SEC61B, VCP2.29E-02
RNA degradation57EN01, TTC37, EXOSC92.29E-02
Oocyte meiosis86CDK1, MAD2L1, CCNB22.35E-02
Focal adhesion139ITGB3, ITGA8, FLNA2.39E-02
Systemic lupus erythematosus66FCGR2B, C5, TNF2.39E-02
Gap junction64CSNK1D, PRKCB, GNAI32.39E-02
NF-κB signaling pathway70TRAF5, BCL2L1, BCL23.77E-02
Lysosome94TCTRG1, ATP6VOA2, CTCS3.85E-02
T-cell receptor signaling pathway83CDK4, TNF, CSF24.43E-02
Regulation of DEGs by transcription factors

TFactS analysis was performed to determine changes in transcription factor activity based on upregulated and downregulated genes in colorectal cancer (Table III). The results showed that MYC and TCF7L2 were activated in colorectal cancer. A total of 26 target genes of MYC were identified, including 24 upregulated and 2 downregulated genes, while for TCF7L2, 8 target genes were upregulated and 2 genes were downregulated. Of note, TCF7L2 was activated by MYC. Additionally, 9 target genes of FOXO3 were downregulated and 1 gene was upregulated.

Table III

Results of the TfactS analysis.

Table III

Results of the TfactS analysis.

Gene nameTFRegulation typeDifferential expression type
ID1FOXO3DownUp
TNFSF10FOXO3UpDown
KLF4FOXO3UpDown
BTG1FOXO3UpDown
PINK1FOXO3UpDown
SFRP1FOXO3UpDown
BCL2L11FOXO3UpDown
HPGDFOXO3UpDown
CDKN2BFOXO3UpDown
CITED2FOXO3UpDown
MYCMYCDownUp
DUSP1MYCDownDown
CDKN2BMYCDownDown
PCNAMYCUpUp
RFC2MYCUpUp
RCC1MYCUpUp
NOP56MYCUpUp
NME1MYCUpUp
CCT6AMYCUpUp
C1QBPMYCUpUp
NPM1MYCUpUp
CCNB1MYCUpUp
CDK4MYCUpUp
ODC1MYCUpUp
CKS2MYCUpUp
CCNA2MYCUpUp
SNRPBMYCUpUp
PPATMYCUpUp
APEX1MYCUpUp
MIFMYCUpUp
H2AFZMYCUpUp
TRAP1MYCUpUp
MTHFD1MYCUpUp
TP53MYCUpUp
TYMSMYCUpUp
UBE2CMYCUpUp
CCT3MYCUpUp
CASP7TCF7L2DownDown
MXD1TCF7L2DownDown
MYCTCF7L2UpUp
ENC1TCF7L2UpUp
MMP7TCF7L2UpUp
MMP1TCF7L2UpUp
AXIN2TCF7L2UpUp
PTTG1TCF7L2UpUp
CD44TCF7L2UpUp
SP5TCF7L2UpUp
SGK1TCF7L2UpDown
CAPN2TCF7L2UpDown
TAGLNTCF7L2UpDown

[i] Regulation means the regulation pattern of the transcription factor (TF) in TfactS to target gene. Differential expression type is the differential expression of DEGs in our study, ‘Up’ means upregulated and ‘Down’ is downregulated in colorectal cancer.

The LGALS3 PPI network

Tumor metastasis is the primary cause of mortality in patients with cancer (26). LGALS3, a member of a family of β-galactoside-binding lectins, has been found to promote tumor metastasis (22,23). To investigate the function of LGALS3 in colorectal cancer, the LGALS3-related PPI network was constructed (Fig. 4). The results predicated that 8 proteins (SUFU, RUNX2, ELN, MUC2, EGFR, TLR2, KRAS, and MMP2) which were encoded by downregulated genes interacted with LGALS3, while 10 proteins (HRAS, GEMIN4, GSK3B, CCND1, ANXA7, DDOST, LGALS3BP, DMBT1, IL1B, and AXIN1) encoded by upregulated genes interacted with LGALS3. In addition, no significant changes in the expression levels of MMP9, KDR, DIF, PRKCSH, NRAS, and CDH5 were observed, however, the proteins encoded by these genes interacted with LGALS3.

Discussion

Colorectal cancer is the third most common type of cancer worldwide and has a high mortality rate (2). Although a number of studies have been conducted, the underlying mechanism of colorectal cancer remains to be clarified. In this study, the DEGs were identified between colorectal cancer and normal samples and their functions were predicted by GO analysis. The pathways which these DEGs dysregulated and the TFs were identified. A LGALS3-related PPI network was also established. Our findings provide a new angle for the prediction of the pathogenesis of colorectal cancer.

The GO enrichment analysis revealed that the upregulated genes were mainly enriched in cell proliferation processes, including mitotic cell cycle, cell cycle progression, nuclear division and cell division of tumor. The oncogene AURKA, enriched in the cell cycle, is an important protein that regulates G2 transit into M during mitosis (27). In addition, AURKA is associated with abnormal chromosome segregation, aneuploidy and predisposition (28). Previously, it was suggested that pituitary tumor transforming gene 1 (PTTG1) is an oncogene (29). The expression levels of RUVBL1 and RUVBL2 were increased in different types of cancer and interacted with oncogenic factors, including β-catenin and c-Myc to regulate their function (30). These upregulated genes led to abnormal cell accumulation in order to accelerate the process of colorectal cancer.

The downregulated genes, including CLDN8 and CLDN23, in colorectal cancer were significantly enriched in the cell adhesion biological process. Claudins, major components of the strands, promote cell-cell adhesion (31). CLDN8 codes for tight junction proteins expressed in distal nephron epithelium, and it is considered a candidate marker for distinguishing chromophobe renal cell carcinoma from other types of renal cancer (32). In addition, CLDN23 gene, frequently downregulated in intestinal-type gastric cancer, is a novel member of CLAUDIN gene family (33). Findings of the present study are consistent with those of previous studies.

The role of the signaling pathway in cancer pathogenesis has been previously investigated (34). Alterations in cyclin-dependent kinase (CDK) activity often leads to cell cycle defects in tumor growth (35). In the present study, CDK2, CDK4 and CDK6 were enriched in the cell cycle pathway. This result indicates that these DEGs are important in the development of colorectal cancer by dyregulating the cell cycle pathway. Previously, it has been shown that one of the most prominent regulators disrupted in cancer is the tumor suppressor, p53 (36). TRAF (TNF receptor-associated factor) family member-associated NF-κB activator is a negative regulator of osteoclastogenesis and bone formation (37). NF-κB is one of the best-characterized transcription factors involved in the regulation of immune responses and inflammation (38,39). It has been previously suggested that inhibition of the NF-κB signaling pathway presents a notable therapeutic potential for the diagnosis of cancer (40). Results of this study have shown that genes enriched in the cell cycle, p53 signaling pathway and NF-κB signaling pathway were differentially expressed in colorectal cancer.

The list of transcription factors in most human cancer cells is limited and these factors usually serve as targets for anticancer drugs development (41). NF-κB has been used as a target for cancer drug development which induces drug resistance by changing MDR1 expression in cancer cells (18,27). Transcription activation mediated by HIF-1α and STAT serve as targets for cancer drug development (29,30). In this study, we have shown that the transcription factors of MYC, TCF7L2, and FOXO3 were regulators of some DEGs. MYC was activated in colorectal cancer and the overexpression pattern was identified as a downstream step at the end of the Wnt/APC/β-catenin signaling pathways is crucial in human cancer (42,43). The TCF7L2 gene has been shown to be involved in renal cell carcinoma metastasis (44). Members of the FOXO transcription family were involved in several cell processes, including apoptosis, stress resistance, metabolism, cell cycle, and DNA repair (45,46). These findings are contributory to the development of cancer treatment.

Current investigations have focused on the molecular mechanism of tumor formation and metastasis (47). The expression of LGALS3 is associated with neoplastic transformation and the differentiation of monocytes into macrophages. The present study result suggest that LGALS3 may be involved in colorectal cancer progression by interacting with upregulated and downregulated genes. Due to the LGALS3-related genes being mainly differentially expressed, LGALS3 is important in the development of colorectal cancer. The predicated network of the metastatic factor LGALS3 may facilitate understanding of the mechanism of tumor cell metastasis to provide a therapeutic target in cancer treatment.

In conclusion, findings of the present study have demonstrated that, LGALS3, cell cycle, p53 signaling pathway and NF-κB signaling pathway are crucial in the development of colorectal cancer. Additionally, several genes that are potential candidate targets for colorectal cancer therapy have been identified. However, more studies with regard to other signaling pathway and key cancer-related proteins should be conducted in order to reveal the underlying molecular mechanism of colorectal cancer.

Acknowledgements

This study was funded by the Chongqing Natural Science Foundation (CSTC, 2011BB5120).

References

1 

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

2 

Atkin WS, Edwards R, Kralj-Hans I, et al: Once-only flexible sigmoidoscopy screening in prevention of colorectal cancer: a multicentre randomised controlled trial. Lancet. 375:1624–1633. 2010. View Article : Google Scholar : PubMed/NCBI

3 

Grady WM and Carethers JM: Genomic and epigenetic instability in colorectal cancer pathogenesis. Gastroenterology. 135:1079–1099. 2008. View Article : Google Scholar : PubMed/NCBI

4 

Leggett B and Whitehall V: Role of the serrated pathway in colorectal cancer pathogenesis. Gastroenterology. 138:2088–2100. 2010. View Article : Google Scholar : PubMed/NCBI

5 

Stratton MR, Campbell PJ and Futreal PA: The cancer genome. Nature. 458:719–724. 2009. View Article : Google Scholar

6 

Lanza G, Ferracin M, Gafa R, et al: mRNA/microRNA gene expression profile in microsatellite unstable colorectal cancer. Mol Cancer. 6:542007. View Article : Google Scholar : PubMed/NCBI

7 

Jones S, Zhang X, Parsons DW, et al: Core signaling pathways in human pancreatic cancers revealed by global genomic analyses. Science. 321:1801–1806. 2008. View Article : Google Scholar : PubMed/NCBI

8 

Lustig B and Behrens J: The Wnt signaling pathway and its role in tumor development. J Cancer Res Clin Oncol. 129:199–221. 2003.PubMed/NCBI

9 

Maxwell PH, Pugh CW and Ratcliffe PJ: Activation of the HIF pathway in cancer. Curr Opin Genet Dev. 11:293–299. 2001. View Article : Google Scholar : PubMed/NCBI

10 

Sankpal UT, Goodison S, Abdelrahim M and Basha R: Targeting SP1 transcription factor in prostate cancer therapy. Med Chem. 7:518–525. 2011. View Article : Google Scholar : PubMed/NCBI

11 

Tomlins SA, Rhodes DR, Perner S, et al: Recurrent fusion of TMPRSS2 and ETS transcription factor genes in prostate cancer. Science. 310:644–648. 2005. View Article : Google Scholar : PubMed/NCBI

12 

Yang Y, Goldstein BG, Chao HH and Katz JP: KLF4 and KLF5 regulate proliferation, apoptosis and invasion in esophageal cancer cells. Cancer Biol Ther. 4:1216–1221. 2005. View Article : Google Scholar : PubMed/NCBI

13 

Dolcet X, Llobet D, Pallares J and Matias-Guiu X: NF-κB in development and progression of human cancer. Virchows Arch. 446:475–482. 2005.

14 

Sabates-Bellver J, Van Der Flier LG, De Palo M, et al: Transcriptome profile of human colorectal adenomas. Mol Cancer Res. 5:1263–1275. 2007. View Article : Google Scholar

15 

Bresalier RS, Mazurek N, Sternberg LR, et al: Metastasis of human colon cancer is altered by modifying expression of the β-galactoside-binding protein galectin 3. Gastroenterology. 115:287–296. 1998.PubMed/NCBI

16 

Zhao Q, Guo X, Nash GB, et al: Circulating galectin-3 promotes metastasis by modifying MUC1 localization on cancer cell surface. Cancer Res. 69:6799–6806. 2009. View Article : Google Scholar : PubMed/NCBI

17 

Zhao Q, Barclay M, Hilkens J, et al: Research interaction between circulating galectin-3 and cancer-associated MUC1 enhances tumour cell homotypic aggregation and prevents anoikis. Mol Cancer. 9:1542010. View Article : Google Scholar : PubMed/NCBI

18 

Irizarry RA, Hobbs B, Collin F, et al: Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics. 4:249–264. 2003. View Article : Google Scholar

19 

Tusher VG, Tibshirani R and Chu G: Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci USA. 98:5116–5121. 2001. View Article : Google Scholar : PubMed/NCBI

20 

Harris M, Clark J, Ireland A, et al: The gene ontology (GO) database and informatics resource. Nucleic Acids Res. 32:D258–D261. 2004.PubMed/NCBI

21 

Huang da W, Sherman BT and Lempicki RA: Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 4:44–57. 2008.PubMed/NCBI

22 

Tarca AL, Draghici S, Khatri P, et al: A novel signaling pathway impact analysis. Bioinformatics. 25:75–82. 2009. View Article : Google Scholar : PubMed/NCBI

23 

Essaghir A, Toffalini F, Knoops L, Kallin A, van Helden J and Demoulin JB: Transcription factor regulation can be accurately predicted from the presence of target gene signatures in microarray gene expression data. Nucleic Acids Res. 38:e1202010. View Article : Google Scholar : PubMed/NCBI

24 

von Mering C, Huynen M, Jaeggi D, Schmidt S, Bork P and Snel B: STRING: a database of predicted functional associations between proteins. Nucleic Acids Res. 31:258–261. 2003.PubMed/NCBI

25 

Kohl M, Wiese S and Warscheid B: Cytoscape: Software for visualization and analysis of biological networks. Data Mining in Proteomics. Hamacher M, Eisenacher M and Stephan C: Humana Press; pp. 291–303. 2011, PubMed/NCBI

26 

Steeg PS: Tumor metastasis: mechanistic insights and clinical challenges. Nat Med. 12:895–904. 2006. View Article : Google Scholar : PubMed/NCBI

27 

Cox DG, Hankinson SE and Hunter DJ: Polymorphisms of the AURKA (STK15/Aurora Kinase) gene and breast cancer risk (United States). Cancer Causes Control. 17:81–83. 2006. View Article : Google Scholar : PubMed/NCBI

28 

Couch FJ, Sinilnikova O, Vierkant RA, et al: AURKA F31I polymorphism and breast cancer risk in BRCA1 and BRCA2 mutation carriers: a consortium of investigators of modifiers of BRCA1/2 study. Cancer Epidemiol Biomarkers Prev. 16:1416–1421. 2007. View Article : Google Scholar : PubMed/NCBI

29 

Zhu X, Mao Z, Na Y, Guo Y, Wang X and Xin D: Significance of pituitary tumor transforming gene 1 (PTTG1) in prostate cancer. Anticancer Res. 26:1253–1259. 2006.PubMed/NCBI

30 

Gorynia S, Bandeiras TM, Pinho FG, et al: Structural and functional insights into a dodecameric molecular machine - the RuvBL1/RuvBL2 complex. J Struct Biol. 176:279–291. 2011. View Article : Google Scholar : PubMed/NCBI

31 

Carattino MD, Prakasam HS, Ruiz WG, et al: Bladder filling and voiding affect umbrella cell tight junction organization and function. Am J Physiol Renal Physiol. Jul 24–2013.(Epub ahead of print).

32 

Osunkoya AO, Cohen C, Lawson D, Picken MM, Amin MB and Young AN: Claudin-7 and claudin-8: immunohistochemical markers for the differential diagnosis of chromophobe renal cell carcinoma and renal oncocytoma. Hum Pathol. 40:206–210. 2009. View Article : Google Scholar : PubMed/NCBI

33 

Katoh M and Katoh M: CLDN23 gene, frequently down-regulated in intestinal-type gastric cancer, is a novel member of CLAUDIN gene family. Int J Mol Med. 11:683–689. 2003.PubMed/NCBI

34 

Vogelstein B and Kinzler KW: Cancer genes and the pathways they control. Nat Med. 10:789–799. 2004. View Article : Google Scholar : PubMed/NCBI

35 

Malumbres M and Barbacid M: Cell cycle, CDKs and cancer: a changing paradigm. Nat Rev Cancer. 9:153–166. 2009. View Article : Google Scholar : PubMed/NCBI

36 

Sherr CJ: Cancer cell cycles. Science. 274:1672–1677. 1996. View Article : Google Scholar : PubMed/NCBI

37 

Maruyama K, Kawagoe T, Kondo T, Akira S and Takeuchi O: TRAF family member-associated NF-κB activator (TANK) is a negative regulator of osteoclastogenesis and bone formation. J Biol Chem. 287:29114–29124. 2012.

38 

O’neill LA and Kaltschmidt C: NF-κB: a crucial transcription factor for glial and neuronal cell function. Trends Neurosci. 20:252–258. 1997.

39 

Barnes PJ: Nuclear factor-κB. Int J Biochem Cell Biol. 29:867–870. 1997.

40 

Scartozzi M, Bearzi I, Pierantoni C, et al: Nuclear factor-κB tumor expression predicts response and survival in irinotecan-refractory metastatic colorectal cancer treated with cetuximab-irinotecan therapy. J Clin Oncol. 25:3930–3935. 2007.

41 

Darnell JE Jr: Transcription factors as targets for cancer therapy. Nat Rev Cancer. 2:740–749. 2002. View Article : Google Scholar : PubMed/NCBI

42 

Bièche I, Laurendeau I, Tozlu S, et al: Quantitation of MYC gene expression in sporadic breast tumors with a real-time reverse transcription-PCR assay. Cancer Res. 59:2759–2765. 1999.

43 

Le Floch N, Rivat C, De Wever O, et al: The proinvasive activity of Wnt-2 is mediated through a noncanonical Wnt pathway coupled to GSK-3β and c-Jun/AP-1 signaling. FASEB J. 19:144–146. 2005.PubMed/NCBI

44 

Kojima T, Shimazui T, Horie R, et al: FOXO1 and TCF7L2 genes involved in metastasis and poor prognosis in clear cell renal cell carcinoma. Genes Chromosomes Cancer. 49:379–389. 2010.PubMed/NCBI

45 

Arden KC: Multiple roles of FOXO transcription factors in mammalian cells point to multiple roles in cancer. Exp Gerontol. 41:709–717. 2006. View Article : Google Scholar : PubMed/NCBI

46 

Roy S, Srivastava R and Shankar S: Inhibition of PI3K/AKT and MAPK/ERK pathways causes activation of FOXO transcription factor, leading to cell cycle arrest and apoptosis in pancreatic cancer. J Mol Signal. 5:102010. View Article : Google Scholar : PubMed/NCBI

47 

John A and Tuszynski G: The role of matrix metalloproteinases in tumor angiogenesis and tumor metastasis. Pathol Oncol Res. 7:14–23. 2001. View Article : Google Scholar : PubMed/NCBI

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2014-March
Volume 33 Issue 3

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Han L, Wu Z and Zhao Q: Revealing the molecular mechanism of colorectal cancer by establishing LGALS3‑related protein-protein interaction network and identifying signaling pathways. Int J Mol Med 33: 581-588, 2014
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Han, L., Wu, Z., & Zhao, Q. (2014). Revealing the molecular mechanism of colorectal cancer by establishing LGALS3‑related protein-protein interaction network and identifying signaling pathways. International Journal of Molecular Medicine, 33, 581-588. https://doi.org/10.3892/ijmm.2014.1620
MLA
Han, L., Wu, Z., Zhao, Q."Revealing the molecular mechanism of colorectal cancer by establishing LGALS3‑related protein-protein interaction network and identifying signaling pathways". International Journal of Molecular Medicine 33.3 (2014): 581-588.
Chicago
Han, L., Wu, Z., Zhao, Q."Revealing the molecular mechanism of colorectal cancer by establishing LGALS3‑related protein-protein interaction network and identifying signaling pathways". International Journal of Molecular Medicine 33, no. 3 (2014): 581-588. https://doi.org/10.3892/ijmm.2014.1620