Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress 2023 EHA Congress 2023 ASCO Annual Meeting
Clinical Collections
Advances in Alzheimer’s

At a glance: The ONWARDS insulin icodec trials

A round-up of the ONWARDS phase 3 clinical trials evaluating the novel weekly insulin icodec in people with diabetes.
ADVANCED SEARCH
Log in
Top
BMC Cancer
Published in: BMC Cancer 1/2010

Open Access 01-12-2010 | Research article

Meta-analysis of archived DNA microarrays identifies genes regulated by hypoxia and involved in a metastatic phenotype in cancer cells

Authors: Michael Pierre, Benoît DeHertogh, Anthoula Gaigneaux, Bertrand DeMeulder, Fabrice Berger, Eric Bareke, Carine Michiels, Eric Depiereux

Published in: BMC Cancer | Issue 1/2010

Login to get access

Abstract

Background

Metastasis is a major cancer-related cause of death. Recent studies have described metastasis pathways. However, the exact contribution of each pathway remains unclear. Another key feature of a tumor is the presence of hypoxic areas caused by a lack of oxygen at the center of the tumor. Hypoxia leads to the expression of pro-metastatic genes as well as the repression of anti-metastatic genes. As many Affymetrix datasets about metastasis and hypoxia are publicly available and not fully exploited, this study proposes to re-analyze these datasets to extract new information about the metastatic phenotype induced by hypoxia in different cancer cell lines.

Methods

Affymetrix datasets about metastasis and/or hypoxia were downloaded from GEO and ArrayExpress. AffyProbeMiner and GCRMA packages were used for pre-processing and the Window Welch t test was used for processing. Three approaches of meta-analysis were eventually used for the selection of genes of interest.

Results

Three complementary approaches were used, that eventually selected 183 genes of interest. Out of these 183 genes, 99, among which the well known JUNB, FOS and TP63, have already been described in the literature to be involved in cancer. Moreover, 39 genes of those, such as SERPINE1 and MMP7, are known to regulate metastasis. Twenty-one genes including VEGFA and ID2 have also been described to be involved in the response to hypoxia. Lastly, DAVID classified those 183 genes in 24 different pathways, among which 8 are directly related to cancer while 5 others are related to proliferation and cell motility. A negative control composed of 183 random genes failed to provide such results. Interestingly, 6 pathways retrieved by DAVID with the 183 genes of interest concern pathogen recognition and phagocytosis.

Conclusion

The proposed methodology was able to find genes actually known to be involved in cancer, metastasis and hypoxia and, thus, we propose that the other genes selected based on the same methodology are of prime interest in the metastatic phenotype induced by hypoxia.
Appendix
Available only for authorised users
Literature
1.
Friedl P, Wolf K: Tumour-cell invasion and migration: diversity and escape mechanisms. Nat Rev Cancer. 2003, 3 (5): 362-374. 10.1038/nrc1075.CrossRefPubMed
2.
Pantel K, Brakenhoff RH: Dissecting the metastatic cascade. Nat Rev Cancer. 2004, 4 (6): 448-456. 10.1038/nrc1370.CrossRefPubMed
3.
Gordan JD, Simon MC: Hypoxia-inducible factors: central regulators of the tumor phenotype. Curr Opin Genet Dev. 2007, 17 (1): 71-77. 10.1016/j.gde.2006.12.006.CrossRefPubMedPubMedCentral
4.
Vaupel P: The role of hypoxia-induced factors in tumor progression. Oncologist. 2004, 9 (Suppl 5): 10-17. 10.1634/theoncologist.9-90005-10.CrossRefPubMed
5.
Sullivan R, Graham CH: Hypoxia-driven selection of the metastatic phenotype. Cancer Metastasis Rev. 2007, 26 (2): 319-331. 10.1007/s10555-007-9062-2.CrossRefPubMed
6.
Chan DA, Giaccia AJ: Hypoxia, gene expression, and metastasis. Cancer Metastasis Rev. 2007, 26 (2): 333-339. 10.1007/s10555-007-9063-1.CrossRefPubMed
7.
DeRisi JL, Iyer VR, Brown PO: Exploring the metabolic and genetic control of gene expression on a genomic scale. Science. 1997, 278 (5338): 680-686. 10.1126/science.278.5338.680.CrossRefPubMed
8.
Kronick MN: Creation of the whole human genome microarray. Expert Rev Proteomics. 2004, 1 (1): 19-28. 10.1586/14789450.1.1.19.CrossRefPubMed
9.
Fodor SP, Read JL, Pirrung MC, Stryer L, Lu AT, Solas D: Light-directed, spatially addressable parallel chemical synthesis. Science. 1991, 251 (4995): 767-773. 10.1126/science.1990438.CrossRefPubMed
10.
Affymetrix: Affymetrix Microarray Suite User Guide version 5.0. 2001, Santa Clara: Affymetrix Manual
11.
Schena M, Shalon D, Davis RW, Brown PO: Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science. 1995, 270 (5235): 467-470. 10.1126/science.270.5235.467.CrossRefPubMed
12.
Callow MJ, Dudoit S, Gong EL, Speed TP, Rubin EM: Microarray expression profiling identifies genes with altered expression in HDL-deficient mice. Genome Res. 2000, 10 (12): 2022-2029. 10.1101/gr.10.12.2022.CrossRefPubMedPubMedCentral
13.
Benjamini Y, Hochberg Y: Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society. 1995, 57: 289-300.
14.
Student: The Probable Error of a Mean. Biometrika. 1908, 6: 1-25. 10.2307/2331554.CrossRef
15.
Baldi P, Long AD: A Bayesian framework for the analysis of microarray expression data: regularized t-test and statistical inferences of gene changes. Bioinformatics. 2001, 17 (6): 509-519. 10.1093/bioinformatics/17.6.509.CrossRefPubMed
16.
Cui X, Hwang JT, Qiu J, Blades NJ, Churchill GA: Improved statistical tests for differential gene expression by shrinking variance components estimates. Biostatistics. 2005, 6 (1): 59-75. 10.1093/biostatistics/kxh018.CrossRefPubMed
17.
Jain N, Thatte J, Braciale T, Ley K, O'Connell M, Lee JK: Local-pooled-error test for identifying differentially expressed genes with a small number of replicated microarrays. Bioinformatics. 2003, 19 (15): 1945-1951. 10.1093/bioinformatics/btg264.CrossRefPubMed
18.
Opgen-Rhein R, Strimmer K: Accurate ranking of differentially expressed genes by a distribution-free shrinkage approach. Stat Appl Genet Mol Biol. 2007, 6: Article9-PubMed
19.
Smyth GK: Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol. 2004, 3: Article3-PubMed
20.
Tusher VG, Tibshirani R, Chu G: Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci USA. 2001, 98 (9): 5116-5121. 10.1073/pnas.091062498.CrossRefPubMedPubMedCentral
21.
Dai M, Wang P, Boyd AD, Kostov G, Athey B, Jones EG, Bunney WE, Myers RM, Speed TP, Akil H, et al: Evolving gene/transcript definitions significantly alter the interpretation of GeneChip data. Nucleic Acids Res. 2005, 33 (20): e175-10.1093/nar/gni179.CrossRefPubMedPubMedCentral
22.
Gautier L, Moller M, Friis-Hansen L, Knudsen S: Alternative mapping of probes to genes for Affymetrix chips. BMC Bioinformatics. 2004, 5: 111-10.1186/1471-2105-5-111.CrossRefPubMedPubMedCentral
23.
Liu H, Zeeberg BR, Qu G, Koru AG, Ferrucci A, Kahn A, Ryan MC, Nuhanovic A, Munson PJ, Reinhold WC, et al: AffyProbeMiner: a web resource for computing or retrieving accurately redefined Affymetrix probe sets. Bioinformatics. 2007, 23 (18): 2385-2390. 10.1093/bioinformatics/btm360.CrossRefPubMed
24.
Bolstad BM, Irizarry RA, Astrand M, Speed TP: A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics. 2003, 19 (2): 185-193. 10.1093/bioinformatics/19.2.185.CrossRefPubMed
25.
Li C, Wong WH: Model-based analysis of oligonucleotide arrays: expression index computation and outlier detection. Proc Natl Acad Sci USA. 2001, 98 (1): 31-36. 10.1073/pnas.011404098.CrossRefPubMed
26.
Schadt EE, Li C, Ellis B, Wong WH: Feature extraction and normalization algorithms for high-density oligonucleotide gene expression array data. J Cell Biochem Suppl. 2001, 120-125. 10.1002/jcb.10073. Suppl 37
27.
Hochreiter S, Clevert DA, Obermayer K: A new summarization method for Affymetrix probe level data. Bioinformatics. 2006, 22 (8): 943-949. 10.1093/bioinformatics/btl033.CrossRefPubMed
28.
Irizarry RA, Hobbs B, Collin F, Beazer-Barclay YD, Antonellis KJ, Scherf U, Speed TP: Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics. 2003, 4 (2): 249-264. 10.1093/biostatistics/4.2.249.CrossRefPubMed
29.
Wu Z, Irizarry R, Gentleman R, Murillo F, Spencer F: A model-based background adjustment for oligonucleotide expression arrays. Journal of the American Statistical Association. 2004, 99: 909-917. 10.1198/016214504000000683.CrossRef
30.
Yang IV, Chen E, Hasseman JP, Liang W, Frank BC, Wang S, Sharov V, Saeed AI, White J, Li J, et al: Within the fold: assessing differential expression measures and reproducibility in microarray assays. Genome Biol. 2002, 3 (11): research0062-PubMedPubMedCentral
31.
32.
Rocca-Serra P, Brazma A, Parkinson H, Sarkans U, Shojatalab M, Contrino S, Vilo J, Abeygunawardena N, Mukherjee G, Holloway E, et al: ArrayExpress: a public database of gene expression data at EBI. C R Biol. 2003, 326 (10-11): 1075-1078. 10.1016/j.crvi.2003.09.026.CrossRefPubMed
33.
34.
35.
Berger F, De Hertogh B, Pierre M, Gaigneaux A, Depiereux E: The "Window t test": a simple and powerful approach to detect differentially expressed genes in microarray datasets. Central European Journal of Biology. 2008, 3 (3): 327-344. 10.2478/s11535-008-0030-9.
36.
Dennis G, Sherman BT, Hosack DA, Yang J, Gao W, Lane HC, Lempicki RA: DAVID: Database for Annotation, Visualization, and Integrated Discovery. Genome Biol. 2003, 4 (5): P3-10.1186/gb-2003-4-5-p3.CrossRefPubMed
37.
Huang da W, Sherman BT, Lempicki RA: Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009, 4 (1): 44-57. 10.1038/nprot.2008.211.CrossRefPubMed
38.
Ogata H, Goto S, Sato K, Fujibuchi W, Bono H, Kanehisa M: KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 1999, 27 (1): 29-34. 10.1093/nar/27.1.29.CrossRefPubMedPubMedCentral
39.
40.
Ihaka R, Gentleman R: R: a language for data analysis and graphics. Journal of Computational and Graphical Statistics. 1996, 5: 299-314. 10.2307/1390807.
41.
Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S, Ellis B, Gautier L, Ge Y, Gentry J, et al: Bioconductor: open software development for computational biology and bioinformatics. Genome Biol. 2004, 5 (10): R80-10.1186/gb-2004-5-10-r80.CrossRefPubMedPubMedCentral
42.
43.
Gur-Dedeoglu B, Konu O, Kir S, Ozturk AR, Bozkurt B, Ergul G, Yulug IG: A resampling-based meta-analysis for detection of differential gene expression in breast cancer. BMC Cancer. 2008, 8: 396-10.1186/1471-2407-8-396.CrossRefPubMedPubMedCentral
44.
45.
Ochsner SA, Steffen DL, Hilsenbeck SG, Chen ES, Watkins C, McKenna NJ: GEMS (Gene Expression MetaSignatures), a Web resource for querying meta-analysis of expression microarray datasets: 17beta-estradiol in MCF-7 cells. Cancer Res. 2009, 69 (1): 23-26. 10.1158/0008-5472.CAN-08-3492.CrossRefPubMedPubMedCentral
46.
Eferl R, Wagner EF: AP-1: a double-edged sword in tumorigenesis. Nat Rev Cancer. 2003, 3 (11): 859-868. 10.1038/nrc1209.CrossRefPubMed
47.
Jochum W, Passegue E, Wagner EF: AP-1 in mouse development and tumorigenesis. Oncogene. 2001, 20 (19): 2401-2412. 10.1038/sj.onc.1204389.CrossRefPubMed
48.
Shaulian E, Karin M: AP-1 in cell proliferation and survival. Oncogene. 2001, 20 (19): 2390-2400. 10.1038/sj.onc.1204383.CrossRefPubMed
49.
van Dam H, Castellazzi M: Distinct roles of Jun: Fos and Jun: ATF dimers in oncogenesis. Oncogene. 2001, 20 (19): 2453-2464. 10.1038/sj.onc.1204239.CrossRefPubMed
50.
Tomkova K, Tomka M, Zajac V: Contribution of p53, p63, and p73 to the developmental diseases and cancer. Neoplasma. 2008, 55 (3): 177-181.PubMed
51.
Malaguarnera R, Vella V, Vigneri R, Frasca F: p53 family proteins in thyroid cancer. Endocr Relat Cancer. 2007, 14 (1): 43-60. 10.1677/erc.1.01223.CrossRefPubMed
52.
Fabre-Guillevin E, Malo M, Cartier-Michaud A, Peinado H, Moreno-Bueno G, Vallee B, Lawrence DA, Palacios J, Cano A, Barlovatz-Meimon G, et al: PAI-1 and functional blockade of SNAI1 in breast cancer cell migration. Breast Cancer Res. 2008, 10 (6): R100-10.1186/bcr2203.CrossRefPubMedPubMedCentral
53.
Beeghly-Fadiel A, Shu XO, Long J, Li C, Cai Q, Cai H, Gao YT, Zheng W: Genetic polymorphisms in the MMP-7 gene and breast cancer survival. Int J Cancer. 2009, 124 (1): 208-214. 10.1002/ijc.23859.CrossRefPubMedPubMedCentral
54.
Fang YJ, Lu ZH, Wang GQ, Pan ZZ, Zhou ZW, Yun JP, Zhang MF, Wan DS: Elevated expressions of MMP7, TROP2, and survivin are associated with survival, disease recurrence, and liver metastasis of colon cancer. Int J Colorectal Dis. 2009, 24 (8): 875-884. 10.1007/s00384-009-0725-z.CrossRefPubMed
55.
Liu D, Nakano J, Ishikawa S, Yokomise H, Ueno M, Kadota K, Urushihara M, Huang CL: Overexpression of matrix metalloproteinase-7 (MMP-7) correlates with tumor proliferation, and a poor prognosis in non-small cell lung cancer. Lung Cancer. 2007, 58 (3): 384-391. 10.1016/j.lungcan.2007.07.005.CrossRefPubMed
56.
Neufeld G, Cohen T, Gengrinovitch S, Poltorak Z: Vascular endothelial growth factor (VEGF) and its receptors. Faseb J. 1999, 13 (1): 9-22.PubMed
57.
Roskoski R: Vascular endothelial growth factor (VEGF) signaling in tumor progression. Crit Rev Oncol Hematol. 2007, 62 (3): 179-213. 10.1016/j.critrevonc.2007.01.006.CrossRefPubMed
58.
Tsunedomi R, Iizuka N, Tamesa T, Sakamoto K, Hamaguchi T, Somura H, Yamada M, Oka M: Decreased ID2 promotes metastatic potentials of hepatocellular carcinoma by altering secretion of vascular endothelial growth factor. Clin Cancer Res. 2008, 14 (4): 1025-1031. 10.1158/1078-0432.CCR-07-1116.CrossRefPubMed
59.
Lofstedt T, Jogi A, Sigvardsson M, Gradin K, Poellinger L, Pahlman S, Axelson H: Induction of ID2 expression by hypoxia-inducible factor-1: a role in dedifferentiation of hypoxic neuroblastoma cells. J Biol Chem. 2004, 279 (38): 39223-39231. 10.1074/jbc.M402904200.CrossRefPubMed
60.
Chandana SR, Movva S, Arora M, Singh T: Primary brain tumors in adults. Am Fam Physician. 2008, 77 (10): 1423-1430.PubMed
61.
Kaliks RA, Del Giglio A: Management of advanced prostate cancer. Rev Assoc Med Bras. 2008, 54 (2): 178-182. 10.1590/S0104-42302008000200025.CrossRefPubMed
62.
Alberts SR: Updated options for liver-limited metastatic colorectal cancer. Clin Colorectal Cancer. 2008, 7 (Suppl 2): S58-62. 10.3816/CCC.2008.s.009.CrossRefPubMed
63.
Petit V, Thiery JP: Focal adhesions: structure and dynamics. Biol Cell. 2000, 92 (7): 477-494. 10.1016/S0248-4900(00)01101-1.CrossRefPubMed
64.
Mitra SK, Hanson DA, Schlaepfer DD: Focal adhesion kinase: in command and control of cell motility. Nat Rev Mol Cell Biol. 2005, 6 (1): 56-68. 10.1038/nrm1549.CrossRefPubMed
65.
Tanoue T, Nishida E: Docking interactions in the mitogen-activated protein kinase cascades. Pharmacol Ther. 2002, 93 (2-3): 193-202. 10.1016/S0163-7258(02)00188-2.CrossRefPubMed
66.
Biondi RM, Nebreda AR: Signalling specificity of Ser/Thr protein kinases through docking-site-mediated interactions. Biochem J. 2003, 372 (Pt 1): 1-13. 10.1042/BJ20021641.CrossRefPubMedPubMedCentral
67.
Hoeben A, Landuyt B, Highley MS, Wildiers H, Van Oosterom AT, De Bruijn EA: Vascular endothelial growth factor and angiogenesis. Pharmacol Rev. 2004, 56 (4): 549-580. 10.1124/pr.56.4.3.CrossRefPubMed
68.
Holbro T, Hynes NE: ErbB receptors: directing key signaling networks throughout life. Annu Rev Pharmacol Toxicol. 2004, 44: 195-217. 10.1146/annurev.pharmtox.44.101802.121440.CrossRefPubMed
69.
Yarden Y, Sliwkowski MX: Untangling the ErbB signalling network. Nat Rev Mol Cell Biol. 2001, 2 (2): 127-137. 10.1038/35052073.CrossRefPubMed
70.
Pollard TD: The cytoskeleton, cellular motility and the reductionist agenda. Nature. 2003, 422 (6933): 741-745. 10.1038/nature01598.CrossRefPubMed
71.
Kaper JB, Nataro JP, Mobley HL: Pathogenic Escherichia coli. Nat Rev Microbiol. 2004, 2 (2): 123-140. 10.1038/nrmicro818.CrossRefPubMed
72.
Kawai T, Akira S: Antiviral signaling through pattern recognition receptors. J Biochem. 2007, 141 (2): 137-145. 10.1093/jb/mvm032.CrossRefPubMed
73.
Dewas C, Fay M, Gougerot-Pocidalo MA, El-Benna J: The mitogen-activated protein kinase extracellular signal-regulated kinase 1/2 pathway is involved in formyl-methionyl-leucyl-phenylalanine-induced p47phox phosphorylation in human neutrophils. J Immunol. 2000, 165 (9): 5238-5244.CrossRefPubMed
74.
Kitaura J, Xiao W, Maeda-Yamamoto M, Kawakami Y, Lowell CA, Kawakami T: Early divergence of Fc epsilon receptor I signals for receptor up-regulation and internalization from degranulation, cytokine production, and survival. J Immunol. 2004, 173 (7): 4317-4323.CrossRefPubMed
Metadata
Title
Meta-analysis of archived DNA microarrays identifies genes regulated by hypoxia and involved in a metastatic phenotype in cancer cells
Authors
Michael Pierre
Benoît DeHertogh
Anthoula Gaigneaux
Bertrand DeMeulder
Fabrice Berger
Eric Bareke
Carine Michiels
Eric Depiereux
Publication date
01-12-2010
Publisher
BioMed Central
Published in
BMC Cancer / Issue 1/2010
Electronic ISSN: 1471-2407
DOI
https://doi.org/10.1186/1471-2407-10-176

Other articles of this Issue 1/2010

BMC Cancer 1/2010 Go to the issue

Research article

Metastatic lymph node in gastric cancer; Is it a real distant metastasis?

Research article

Circulating endothelial cells are an early predictor in renal cell carcinoma for tumor response to sunitinib

Research article

Enhanced antiproliferative and apoptotic response to combined treatment of γ-tocotrienol with erlotinib or gefitinib in mammary tumor cells

Research article

High number of CD45RO+ tumor infiltrating lymphocytes is an independent prognostic factor in non-metastasized (stage I-IIA) esophageal adenocarcinoma

Study protocol

Design of the Quality of Life in Motion (QLIM) study: a randomized controlled trial to evaluate the effectiveness and cost-effectiveness of a combined physical exercise and psychosocial training program to improve physical fitness in children with cancer

Research article

Phosphorylated Smad2 in Advanced Stage Gastric Carcinoma
Webinar | 19-02-2024 | 17:30 (CET)

Keynote webinar | Spotlight on antibody–drug conjugates in cancer

Watch now

Antibody–drug conjugates (ADCs) are novel agents that have shown promise across multiple tumor types. Explore the current landscape of ADCs in breast and lung cancer with our experts, and gain insights into the mechanism of action, key clinical trials data, existing challenges, and future directions.

Dr. Véronique Diéras
Prof. Fabrice Barlesi
Developed by: Springer Medicine
Image Credits