| European Journal of Medical Research | |
| Screening of key genes in gastric cancer with DNA microarray analysis | |
| Wei Da1  Yong Jin1  | |
| [1] Department of gastroenterology, The 6th People’s Hospital affiliated to Shanghai Jiaotong University, No. 600 Yishan Road, Shanghai 200233, China | |
| 关键词: Pathway analysis; Interaction network; Gastric cancer; Functional enrichment analysis; Differentially expressed gene; | |
| Others : 817839 DOI : 10.1186/2047-783X-18-37 |
|
| received in 2013-07-16, accepted in 2013-09-05, 发布年份 2013 | |
PDF
|
|
【 摘 要 】
Background
The aim of this study was to identify key genes and novel potential therapeutic targets related to gastric cancer (GC) by comparing cancer tissue samples and healthy control samples using DNA microarray analysis.
Methods
Microarray data set GSE19804 was downloaded from Gene Expression Omnibus. Preprocessing and differential analysis were conducted with of R statistical software packages, and a number of differentially expressed genes (DEGs) were obtained. Cluster analysis was also done with gene expression values. Functional enrichment analysis was performed for all the DEGs with DAVID tools. The significantly up- and downregulated genes were selected out and their interactors were retrieved with STRING and HitPredict, followed by construction of networks. For all the genes in the two networks, GeneCodis was chosen for gene function annotation.
Results
A total of 638 DEGs were identified, and we found that SPP1 and FABP4 were the markedly up- and downregulated genes, respectively. Cell cycle and regulation of proliferation were the most significantly overrepresented functional terms in up- and downregulated genes. In addition, extracellular matrix–receptor interaction was found to be significant in the SPP1-included interaction network.
Conclusions
A range of DEGs were obtained for GC. These genes not only provided insights into the pathogenesis of GC but also could develop into biomarkers for diagnosis or treatment.
【 授权许可】
2013 Jin and Da; licensee BioMed Central Ltd.
【 预 览 】
| Files | Size | Format | View |
|---|---|---|---|
| 20140711023144406.pdf | 2436KB | ||
| Figure 3. | 176KB | Image | |
| Figure 2. | 98KB | Image | |
| Figure 1. | 158KB | Image |
【 图 表 】
Figure 1.
Figure 2.
Figure 3.
【 参考文献 】
- [1]Krejs GJ: Gastric cancer: epidemiology and risk factors. Dig Dis 2010, 28:600-603.
- [2]Dong Y, Mei ZZ, Qian JJ, Song Y, Tian BL, Liu B, Sun ZX: [The molecular mechanism of survivin expression in activated human peripheral lymphocytes] [in Chinese]. Xi Bao Yu Fen Zi Mian Yi Xue Za Zhi 2008, 24:16-19.
- [3]Allgayer H, Fulda S: Molecular targeted therapy. In Hereditary Tumors: From Genes to Clinical Consequences. Edited by Allgayer H, Rehder H, Fulda S. Weinheim, Germany: Wiley-VCH Verlag GmbH & Co. KGaA; 2009:501-514. doi:10.1002/9783527627523.ch30
- [4]Ludwig JA, Weinstein JN: Biomarkers in cancer staging, prognosis and treatment selection. Nat Rev Canc 2005, 5:845-856.
- [5]Ryu JW, Kim HJ, Lee YS, Myong NH, Hwang CH, Lee GS, Yom HC: The proteomics approach to find biomarkers in gastric cancer. J Korean Med Sci 2003, 18:505-509.
- [6]Jang JS, Cho HY, Lee YJ, Ha WS, Kim HW: The differential proteome profile of stomach cancer: identification of the biomarker candidates. Oncol Res 2004, 14:491-499.
- [7]Yasui W, Oue N, Ito R, Kuraoka K, Nakayama H: Search for new biomarkers of gastric cancer through serial analysis of gene expression and its clinical implications. Cancer Sci 2004, 95:385-392.
- [8]Mitani Y, Oue N, Matsumura S, Yoshida K, Noguchi T, Ito M, Tanaka S, Kuniyasu H, Kamata N, Yasui W: Reg IV is a serum biomarker for gastric cancer patients and predicts response to 5-fluorouracil-based chemotherapy. Oncogene 2007, 26:4383-4393.
- [9]Oue N, Sentani K, Noguchi T, Ohara S, Sakamoto N, Hayashi T, Anami K, Motoshita J, Ito M, Tanaka S, Yoshida K, Yasui W: Serum olfactomedin 4 (GW112, hGC‒1) in combination with Reg IV is a highly sensitive biomarker for gastric cancer patients. Int J Cancer 2009, 125:2383-2392.
- [10]Nakajima T, Yamada Y, Hamano T, Furuta K, Gotoda T, Katai H, Kato K, Hamaguchi T, Shimada Y: Adipocytokine levels in gastric cancer patients: resistin and visfatin as biomarkers of gastric cancer. J Gastroenterol 2009, 44:685-690.
- [11]DeRisi J, Penland L, Brown PO, Bittner ML, Meltzer PS, Ray M, Chen Y, Su YA, Trent JM: Use of a cDNA microarray to analyse gene expression patterns in human cancer. Nat Genet 1996, 14:457-460.
- [12]Hippo Y, Taniguchi H, Tsutsumi S, Machida N, Chong JM, Fukayama M, Kodama T, Aburatani H: Global gene expression analysis of gastric cancer by oligonucleotide microarrays. Cancer Res 2002, 62:233-240.
- [13]Troyanskaya O, Cantor M, Sherlock G, Brown P, Hastie T, Tibshirani R, Botstein D, Altman RB: Missing value estimation methods for DNA microarrays. Bioinformatics 2001, 17:520-525.
- [14]Fujita A, Sato JR, de Oliveira Rodrigues L, Ferreira CE, Sogayar MC: Evaluating different methods of microarray data normalization. BMC Bioinformatics 2006, 7:469. BioMed Central Full Text
- [15]Pollard KS, Dudoit S, van der Laan MJ: Multiple testing procedures: the multtest package and applications to genomics. In Bioinformatics and Computational Biology Solutions Using R and Bioconductor Statistics for Biology and Health. Edited by Gentleman R, Carey VJ, Huber W, Irizarry RA, Dudoit S. New York: Springer; 2005:249-271. doi:10.1007/0-387-29362-0_15
- [16]Benjamini Y, Hochberg Y: Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Series B Stat Methodol 1995, 57:289-300.
- [17]Eisen MB, Spellman PT, Brown PO, Botstein D: Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci U S A 1998, 95:14863-14868.
- [18]Nam D, Kim SY: Gene-set approach for expression pattern analysis. Brief Bioinform 2008, 9:189-197.
- [19]da Huang W, Sherman BT, Lempicki RA: Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 2009, 4:44-57.
- [20]Li S, Armstrong CM, Bertin N, Ge H, Milstein S, Boxem M, Vidalain PO, Han JD, Chesneau A, Hao T, Goldberg DS, Li N, Martinez M, Rual JF, Lamesch P, Xu L, Tewari M, Wong SL, Zhang LV, Berriz GF, Jacotot L, Vaglio P, Reboul J, Hirozane-Kishikawa T, Li Q, Gabel HW, Elewa A, Baumgartner B, Rose DJ, Yu H, Bosak S, Sequerra R, Fraser A, Mango SE, Saxton WM, Strome S, Van Den Heuvel S, Piano F, Vandenhaute J, Sardet C, Gerstein M, Doucette-Stamm L, Gunsalus KC, Harper JW, Cusick ME, Roth FP, Hill DE, Vidal M: A map of the interactome network of the metazoan C. elegans. Science 2004, 303:540-543.
- [21]Szklarczyk D, Franceschini A, Kuhn M, Simonovic M, Roth A, Minguez P, Doerks T, Stark M, Muller J, Bork P, Jensen LJ, von Mering C: The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored. Nucleic Acids Res 2011, 39(Database issue):D561-D568.
- [22]Patil A, Nakai K, Nakamura H: HitPredict: a database of quality assessed protein-protein interactions in nine species. Nucleic Acids Res 2011, 39(Database issue):D744-D749.
- [23]Kerrien S, Aranda B, Breuza L, Bridge A, Broackes-Carter F, Chen C, Duesbury M, Dumousseau M, Feuermann M, Hinz U, Jandrasits C, Jimenez RC, Khadake J, Mahadevan U, Masson P, Pedruzzi I, Pfeiffenberger E, Porras P, Raghunath A, Roechert B, Orchard S, Hermjakob H: The IntAct molecular interaction database in 2012. Nucleic Acids Res 2011, 40(Database issue):D841-D846.
- [24]Keshava Prasad TS, Goel R, Kandasamy K, Keerthikumar S, Kumar S, Mathivanan S, Telikicherla D, Raju R, Shafreen B, Venugopal A, Balakrishnan L, Marimuthu A, Banerjee S, Somanathan DS, Sebastian A, Rani S, Ray S, Harrys Kishore CJ, Kanth S, Ahmed M, Kashyap MK, Mohmood R, Ramachandra YL, Krishna V, Rahiman BA, Mohan S, Ranganathan P, Ramabadran S, Chaerkady R, Pandey A: Human Protein Reference Database–2009 update. Nucleic Acids Res 2009, 37(Database issue):D767-D772.
- [25]Patil A, Nakamura H: Filtering high-throughput protein-protein interaction data using a combination of genomic features. BMC Bioinformatics 2005, 6:100. BioMed Central Full Text
- [26]Lodish H, Berk A, Matsudaira P, Kaiser CA, Krieger M, Scott MP, Zipurksy SL, Darnell J: Molecular Cell Biology. 5th edition. New York: WH Freeman; 2004.
- [27]Tabas-Madrid D, Nogales-Cadenas R, Pascual-Montano A: GeneCodis3: a non-redundant and modular enrichment analysis tool for functional genomics. Nucleic Acids Res 2012, 40(Web Server issue):W478-W483.
- [28]Nogales-Cadenas R, Carmona-Saez P, Vazquez M, Vicente C, Yang X, Tirado F, Carazo JM, Pascual-Montano A: GeneCodis: interpreting gene lists through enrichment analysis and integration of diverse biological information. Nucleic Acids Res 2009, 37(Web Server issue):W317-W322.
- [29]Carmona-Saez P, Chagoyen M, Tirado F, Carazo JM, Pascual-Montano A: GENECODIS: a web-based tool for finding significant concurrent annotations in gene lists. Genome Biol 2007, 8:R3. BioMed Central Full Text
- [30]Hunter T, Pines J: Cyclins and cancer II: cyclin D and CDK inhibitors come of age. Cell 1994, 79:573-582.
- [31]Bennett MW, O’Connell J, O’Sullivan GC, Roche D, Brady C, Kelly J, Collins JK, Shanahan F: Expression of Fas ligand by human gastric adenocarcinomas: a potential mechanism of immune escape in stomach cancer. Gut 1999, 44:156-162.
- [32]He W, Liu Q, Wang L, Chen W, Li N, Cao X: TLR4 signaling promotes immune escape of human lung cancer cells by inducing immunosuppressive cytokines and apoptosis resistance. Mol Immunol 2007, 44:2850-2859.
- [33]Hayward DG, Fry AM: Nek2 kinase in chromosome instability and cancer. Cancer Lett 2006, 237:155-166.
- [34]Nakayama KI, Nakayama K: Ubiquitin ligases: cell-cycle control and cancer. Nat Rev Cancer 2006, 6:369-381.
- [35]Supernat A, Łapińska-Szumczyk S, Sawicki S, Wydra D, Biernat W, Żaczek AJ: Deregulation of RAD21 and RUNX1 expression in endometrial cancer. Oncol Lett 2012, 4:727-732.
- [36]Yamamoto G, Irie T, Aida T, Nagoshi Y, Tsuchiya R, Tachikawa T: Correlation of invasion and metastasis of cancer cells, and expression of the RAD21 gene in oral squamous cell carcinoma. Virchows Arch 2006, 448:435-441.
- [37]Atienza JM, Roth RB, Rosette C, Smylie KJ, Kammerer S, Rehbock J, Ekblom J, Denissenko MF: Suppression of RAD21 gene expression decreases cell growth and enhances cytotoxicity of etoposide and bleomycin in human breast cancer cells. Mol Cancer Ther 2005, 4:361-368.
- [38]Lin XD, Chen SQ, Qi YL, Zhu JW, Tang Y, Lin JY: Polymorphism of THBS1 rs1478604 A>G in 5-untranslated region is associated with lymph node metastasis of gastric cancer in a Southeast Chinese population. DNA Cell Biol 2012, 31:511-519.
- [39]Himoudi N, Nabarro S, Yan M, Gilmour K, Thrasher AJ, Anderson J: Development of anti-PAX3 immune responses: a target for cancer immunotherapy. Cancer Immunol Immunother 2007, 56:1381-1395.
- [40]Bijian K, Takano T, Papillon J, Khadir A, Cybulsky AV: Extracellular matrix regulates glomerular epithelial cell survival and proliferation. Am J Physiol Renal Physiol 2004, 286:F255-F266.
- [41]Desgrosellier JS, Cheresh DA: Integrins in cancer: biological implications and therapeutic opportunities. Nat Rev Cancer 2010, 10:9-22.
- [42]Lei Y, Huang K, Gao C, Lau QC, Pan H, Xie K, Li J, Liu R, Zhang T, Xie N, Nai HS, Wu H, Dong Q, Zhao X, Nice EC, Huang C, Wei Y: Proteomics identification of ITGB3 as a key regulator in reactive oxygen species-induced migration and invasion of colorectal cancer cells. Mol Cell Proteomics 2011, 10:M110.005397.
- [43]Zhao ZS, Li L, Wang HJ, Wang YY: Expression and prognostic significance of CEACAM6, ITGB1, and CYR61 in peripheral blood of patients with gastric cancer. J Surg Oncol 2011, 104:525-529.
- [44]Xu Z, Wu R: Alteration in metastasis potential and gene expression in human lung cancer cell lines by ITGB8 silencing. Anat Rec (Hoboken) 2012, 295:1446-1454.
- [45]Chen J, Liu NN, Li JQ, Yang L, Zeng Y, Zhao XM, Xu LL, Luo X, Wang B, Wang XR: Association between ITGA2 C807T polymorphism and gastric cancer risk. World J Gastroenterol 2011, 17:2860-2866.
PDF