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
Clinical Collections
Advances in Alzheimer’s

Keynote webinar | Spotlight on medication adherence

LIVE: Thursday 27th June 2024, 18:00-19:30 (CEST)
Join our expert panel to discover why you need to understand the drivers of non-adherence in your patients, and how you can optimize medication adherence in your clinics to drastically improve patient outcomes.
ADVANCED SEARCH
Log in
Top
BMC Cancer
Published in: BMC Cancer 1/2014

Open Access 01-12-2014 | Research article

Large differences in global transcriptional regulatory programs of normal and tumor colon cells

Authors: David Cordero, Xavier Solé, Marta Crous-Bou, Rebeca Sanz-Pamplona, Laia Paré-Brunet, Elisabet Guinó, David Olivares, Antonio Berenguer, Cristina Santos, Ramón Salazar, Sebastiano Biondo, Víctor Moreno

Published in: BMC Cancer | Issue 1/2014

Login to get access

Abstract

Background

Dysregulation of transcriptional programs leads to cell malfunctioning and can have an impact in cancer development. Our study aims to characterize global differences between transcriptional regulatory programs of normal and tumor cells of the colon.

Methods

Affymetrix Human Genome U219 expression arrays were used to assess gene expression in 100 samples of colon tumor and their paired adjacent normal mucosa. Transcriptional networks were reconstructed using ARACNe algorithm using 1,000 bootstrap replicates consolidated into a consensus network. Networks were compared regarding topology parameters and identified well-connected clusters. Functional enrichment was performed with SIGORA method. ENCODE ChIP-Seq data curated in the hmChIP database was used for in silico validation of the most prominent transcription factors.

Results

The normal network contained 1,177 transcription factors, 5,466 target genes and 61,226 transcriptional interactions. A large loss of transcriptional interactions in the tumor network was observed (11,585; 81% reduction), which also contained fewer transcription factors (621; 47% reduction) and target genes (2,190; 60% reduction) than the normal network. Gene silencing was not a main determinant of this loss of regulatory activity, since the average gene expression was essentially conserved. Also, 91 transcription factors increased their connectivity in the tumor network. These genes revealed a tumor-specific emergent transcriptional regulatory program with significant functional enrichment related to colorectal cancer pathway. In addition, the analysis of clusters again identified subnetworks in the tumors enriched for cancer related pathways (immune response, Wnt signaling, DNA replication, cell adherence, apoptosis, DNA repair, among others). Also multiple metabolism pathways show differential clustering between the tumor and normal network.

Conclusions

These findings will allow a better understanding of the transcriptional regulatory programs altered in colon cancer and could be an invaluable methodology to identify potential hubs with a relevant role in the field of cancer diagnosis, prognosis and therapy.
Appendix
Available only for authorised users
Literature
1.
2.
Desvergne B, Michalik L, Wahli W: Transcriptional regulation of metabolism. Physiol Rev. 2006, 86 (2): 465-514. 10.1152/physrev.00025.2005.CrossRefPubMed
3.
Kadonaga JT: Regulation of RNA polymerase II transcription by sequence-specific DNA binding factors. Cell. 2004, 116 (2): 247-257. 10.1016/S0092-8674(03)01078-X.CrossRefPubMed
4.
5.
Choy MK, Movassagh M, Goh HG, Bennett MR, Down TA, Foo RS: Genome-wide conserved consensus transcription factor binding motifs are hyper-methylated. BMC Genomics. 2010, 11: 519-10.1186/1471-2164-11-519.CrossRefPubMedPubMedCentral
6.
7.
Goodarzi H, Elemento O, Tavazoie S: Revealing global regulatory perturbations across human cancers. Mol Cell. 2009, 36 (5): 900-911. 10.1016/j.molcel.2009.11.016.CrossRefPubMedPubMedCentral
8.
Ben-Tabou de-Leon S, Davidson EH: Gene regulation: gene control network in development. Annu Rev Biophys Biomol Struct. 2007, 36: 191-10.1146/annurev.biophys.35.040405.102002.CrossRefPubMed
9.
Anastas JN, Moon RT: WNT signalling pathways as therapeutic targets in cancer. Nat Rev Cancer. 2013, 13 (1): 11-26.CrossRefPubMed
10.
Forbes SA, Bindal N, Bamford S, Cole C, Kok CY, Beare D, Jia M, Shepherd R, Leung K, Menzies A, Teague JW, Campbell PJ, Stratton MR, Futreal PA: COSMIC: mining complete cancer genomes in the Catalogue of Somatic Mutations in Cancer. Nucleic Acids Res. 2011, 39 (Database issue): D945-950.CrossRefPubMed
11.
12.
Deng Y, Johnson DR, Guan X, Ang CY, Ai J, Perkins EJ: In vitro gene regulatory networks predict in vivo function of liver. BMC Syst Biol. 2010, 4: 153-10.1186/1752-0509-4-153.CrossRefPubMedPubMedCentral
13.
Marbach D, Costello JC, Kuffner R, Vega NM, Prill RJ, Camacho DM, Allison KR, Kellis M, Collins JJ, Stolovitzky G: Wisdom of crowds for robust gene network inference. Nat Methods. 2012, 9 (8): 796-804. 10.1038/nmeth.2016.CrossRefPubMedPubMedCentral
14.
Basso K, Margolin AA, Stolovitzky G, Klein U, Dalla-Favera R, Califano A: Reverse engineering of regulatory networks in human B cells. Nat Genet. 2005, 37 (4): 382-390. 10.1038/ng1532.CrossRefPubMed
15.
Margolin AA, Nemenman I, Basso K, Wiggins C, Stolovitzky G, Dalla Favera R, Califano A: ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC bioinformatics. 2006, 7 Suppl 1: S7-CrossRefPubMed
16.
Carro MS, Lim WK, Alvarez MJ, Bollo RJ, Zhao X, Snyder EY, Sulman EP, Anne SL, Doetsch F, Colman H, Lasorella A, Aldape K, Califano A, Iavarone A: The transcriptional network for mesenchymal transformation of brain tumours. Nature. 2010, 463 (7279): 318-325. 10.1038/nature08712.CrossRefPubMed
17.
Della Gatta G, Palomero T, Perez-Garcia A, Ambesi-Impiombato A, Bansal M, Carpenter ZW, De Keersmaecker K, Sole X, Xu L, Paietta E, Racevskis J, Wiernik PH, Rowe JM, Meijerink JP, Califano A, Ferrando AA: Reverse engineering of TLX oncogenic transcriptional networks identifies RUNX1 as tumor suppressor in T-ALL. Nat Med. 2012, 18 (3): 436-440. 10.1038/nm.2610.CrossRefPubMed
18.
Aytes A, Mitrofanova A, Lefebvre C, Alvarez MJ, Castillo-Martin M, Zheng T, Eastham JA, Gopalan A, Pienta KJ, Shen MM, Califano A, Abate-Shen C: Cross-species regulatory network analysis identifies a synergistic interaction between FOXM1 and CENPF that drives prostate cancer malignancy. Cancer Cell. 2014, 25 (5): 638-651. 10.1016/j.ccr.2014.03.017.CrossRefPubMedPubMedCentral
19.
Li J, Hua X, Haubrock M, Wang J, Wingender E: The architecture of the gene regulatory networks of different tissues. Bioinformatics. 2012, 28 (18): i509-i514. 10.1093/bioinformatics/bts387.CrossRefPubMedPubMedCentral
20.
Fu J, Tang W, Du P, Wang G, Chen W, Li J, Zhu Y, Gao J, Cui L: Identifying microRNA-mRNA regulatory network in colorectal cancer by a combination of expression profile and bioinformatics analysis. BMC Syst Biol. 2012, 6: 68-10.1186/1752-0509-6-68.CrossRefPubMedPubMedCentral
21.
Vineetha S, Chandra Shekara Bhat C, Idicula SM: Gene regulatory network from microarray data of colon cancer patients using TSK-type recurrent neural fuzzy network. Gene. 2012, 506 (2): 408-416. 10.1016/j.gene.2012.06.042.CrossRefPubMed
22.
Wang X, Gotoh O: Inference of cancer-specific gene regulatory networks using soft computing rules. Gene Regul Syst Biol. 2010, 4: 19-34.CrossRef
23.
Weltmeier F, Borlak J: A high resolution genome-wide scan of HNF4alpha recognition sites infers a regulatory gene network in colon cancer. PLoS One. 2011, 6 (7): e21667-10.1371/journal.pone.0021667.CrossRefPubMedPubMedCentral
24.
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
25.
Gautier L, Cope L, Bolstad BM, Irizarry RA: affy–analysis of Affymetrix GeneChip data at the probe level. Bioinformatics. 2004, 20 (3): 307-315. 10.1093/bioinformatics/btg405.CrossRefPubMed
26.
Vaquerizas JM, Kummerfeld SK, Teichmann SA, Luscombe NM: A census of human transcription factors: function, expression and evolution. Nat Rev Genet. 2009, 10 (4): 252-263. 10.1038/nrg2538.CrossRefPubMed
27.
Carbon S, Ireland A, Mungall CJ, Shu S, Marshall B, Lewis S: AmiGO: online access to ontology and annotation data. Bioinformatics. 2009, 25 (2): 288-289. 10.1093/bioinformatics/btn615.CrossRefPubMed
28.
Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T: Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003, 13 (11): 2498-2504. 10.1101/gr.1239303.CrossRefPubMedPubMedCentral
29.
Doncheva NT, Assenov Y, Domingues FS, Albrecht M: Topological analysis and interactive visualization of biological networks and protein structures. Nat Protoc. 2012, 7 (4): 670-685. 10.1038/nprot.2012.004.CrossRefPubMed
30.
Foroushani AB, Brinkman FS, Lynn DJ: Pathway-GPS and SIGORA: identifying relevant pathways based on the over-representation of their gene-pair signatures. PeerJ. 2013, 1: e229-CrossRefPubMedPubMedCentral
31.
32.
Durinck S, Spellman PT, Birney E, Huber W: Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt. Nat Protoc. 2009, 4 (8): 1184-1191. 10.1038/nprot.2009.97.CrossRefPubMedPubMedCentral
33.
Chen L, Wu G, Ji H: hmChIP: a database and web server for exploring publicly available human and mouse ChIP-seq and ChIP-chip data. Bioinformatics. 2011, 27 (10): 1447-1448. 10.1093/bioinformatics/btr156.CrossRefPubMedPubMedCentral
34.
Kanehisa M, Goto S, Sato Y, Furumichi M, Tanabe M: KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res. 2012, 40 (Database issue): D109-114.CrossRefPubMed
35.
Bernstein BE, Birney E, Dunham I, Green ED, Gunter C, Snyder M, Encode Project Consortium: An integrated encyclopedia of DNA elements in the human genome. Nature. 2012, 489 (7414): 57-74. 10.1038/nature11247.CrossRef
36.
Wang K, Saito M, Bisikirska BC, Alvarez MJ, Lim WK, Rajbhandari P, Shen Q, Nemenman I, Basso K, Margolin AA, Klein U, Dalla-Favera R, Califano A: Genome-wide identification of post-translational modulators of transcription factor activity in human B cells. Nat Biotechnol. 2009, 27 (9): 829-839. 10.1038/nbt.1563.CrossRefPubMedPubMedCentral
37.
Horn S, Figl A, Rachakonda PS, Fischer C, Sucker A, Gast A, Kadel S, Moll I, Nagore E, Hemminki K, Schadendorf D, Kumar R: TERT promoter mutations in familial and sporadic melanoma. Science. 2013, 339 (6122): 959-961. 10.1126/science.1230062.CrossRefPubMed
38.
Huang FW, Hodis E, Xu MJ, Kryukov GV, Chin L, Garraway LA: Highly recurrent TERT promoter mutations in human melanoma. Science. 2013, 339 (6122): 957-959. 10.1126/science.1229259.CrossRefPubMedPubMedCentral
39.
Suzuki A, Iida S, Kato-Uranishi M, Tajima E, Zhan F, Hanamura I, Huang Y, Ogura T, Takahashi S, Ueda R, Barlogie B, Shaughnessy J, Esumi H: ARK5 is transcriptionally regulated by the Large-MAF family and mediates IGF-1-induced cell invasion in multiple myeloma: ARK5 as a new molecular determinant of malignant multiple myeloma. Oncogene. 2005, 24 (46): 6936-6944. 10.1038/sj.onc.1208844.CrossRefPubMed
40.
Ruiz i Altaba A: Hedgehog signaling and the Gli code in stem cells, cancer, and metastases. Sci Signal. 2011, 4 (200): pt9-CrossRefPubMed
41.
Katoh M: Notch signaling in gastrointestinal tract (review). Int J Oncol. 2007, 30 (1): 247-251.PubMed
42.
Biasi F, Tessitore L, Zanetti D, Cutrin JC, Zingaro B, Chiarpotto E, Zarkovic N, Serviddio G, Poli G: Associated changes of lipid peroxidation and transforming growth factor beta1 levels in human colon cancer during tumour progression. Gut. 2002, 50 (3): 361-367. 10.1136/gut.50.3.361.CrossRefPubMedPubMedCentral
43.
Wang Y, Ngo VN, Marani M, Yang Y, Wright G, Staudt LM, Downward J: Critical role for transcriptional repressor Snail2 in transformation by oncogenic RAS in colorectal carcinoma cells. Oncogene. 2010, 29 (33): 4658-4670. 10.1038/onc.2010.218.CrossRefPubMed
44.
Zitt M, Untergasser G, Amberger A, Moser P, Stadlmann S, Muller HM, Muhlmann G, Perathoner A, Margreiter R, Gunsilius E, Ofner D: Dickkopf-3 as a new potential marker for neoangiogenesis in colorectal cancer: expression in cancer tissue and adjacent non-cancerous tissue. Dis Markers. 2008, 24 (2): 101-109. 10.1155/2008/160907.CrossRefPubMedPubMedCentral
45.
Jaeger E, Webb E, Howarth K, Carvajal-Carmona L, Rowan A, Broderick P, Walther A, Spain S, Pittman A, Kemp Z, Sullivan K, Heinimann K, Lubbe S, Domingo E, Barclay E, Martin L, Gorman M, Chandler I, Vijayakrishnan J, Wood W, Papaemmanuil E, Penegar S, Qureshi M, Farrington S, Tenesa A, Cazier JB, Kerr D, Gray R, Peto J, Dunlop M, et al: Common genetic variants at the CRAC1 (HMPS) locus on chromosome 15q13.3 influence colorectal cancer risk. Nat Genet. 2008, 40 (1): 26-28. 10.1038/ng.2007.41.CrossRefPubMed
46.
Jaeger E, Leedham S, Lewis A, Segditsas S, Becker M, Cuadrado PR, Davis H, Kaur K, Heinimann K, Howarth K, East J, Taylor J, Thomas H, Tomlinson I: Hereditary mixed polyposis syndrome is caused by a 40-kb upstream duplication that leads to increased and ectopic expression of the BMP antagonist GREM1. Nat Genet. 2012, 44 (6): 699-703. 10.1038/ng.2263.CrossRefPubMedPubMedCentral
47.
Galamb O, Wichmann B, Sipos F, Spisak S, Krenacs T, Toth K, Leiszter K, Kalmar A, Tulassay Z, Molnar B: Dysplasia-carcinoma transition specific transcripts in colonic biopsy samples. PLoS One. 2012, 7 (11): e48547-10.1371/journal.pone.0048547.CrossRefPubMedPubMedCentral
48.
Ahmad FK, Deris S, Othman NH: The inference of breast cancer metastasis through gene regulatory networks. J Biomed Inform. 2012, 45 (2): 350-362. 10.1016/j.jbi.2011.11.015.CrossRefPubMed
49.
Demicheli R, Coradini D: Gene regulatory networks: a new conceptual framework to analyse breast cancer behaviour. Ann Oncol. 2011, 22 (6): 1259-1265. 10.1093/annonc/mdq546.CrossRefPubMed
50.
Madhamshettiwar PB, Maetschke SR, Davis MJ, Reverter A, Ragan MA: Gene regulatory network inference: evaluation and application to ovarian cancer allows the prioritization of drug targets. Genome Med. 2012, 4 (5): 41-10.1186/gm340.CrossRefPubMedPubMedCentral
51.
Sandelin A, Alkema W, Engstrom P, Wasserman WW, Lenhard B: JASPAR: an open-access database for eukaryotic transcription factor binding profiles. Nucleic Acids Res. 2004, 32 (Database issue): D91-94.CrossRefPubMedPubMedCentral
52.
Matys V, Kel-Margoulis OV, Fricke E, Liebich I, Land S, Barre-Dirrie A, Reuter I, Chekmenev D, Krull M, Hornischer K, Voss N, Stegmaier P, Lewicki-Potapov B, Saxel H, Kel AE, Wingender E: TRANSFAC and its module TRANSCompel: transcriptional gene regulation in eukaryotes. Nucleic Acids Res. 2006, 34 (Database issue): D108-110.CrossRefPubMed
53.
Margolin AA, Wang K, Lim WK, Kustagi M, Nemenman I, Califano A: Reverse engineering cellular networks. Nat Protoc. 2006, 1 (2): 662-671. 10.1038/nprot.2006.106.CrossRefPubMed
54.
Cancer Genome Atlas Network: Comprehensive molecular characterization of human colon and rectal cancer. Nature. 2012, 487 (7407): 330-337. 10.1038/nature11252.CrossRef
55.
Levitsky VG, Kulakovskiy IV, Ershov NI, Oschepkov DY, Makeev VJ, Hodgman TC, Merkulova TI: Application of experimentally verified transcription factor binding sites models for computational analysis of ChIP-Seq data. BMC Genomics. 2014, 15 (1): 80-10.1186/1471-2164-15-80.CrossRefPubMedPubMedCentral
56.
Jang IS, Margolin A, Califano A: hARACNe: improving the accuracy of regulatory model reverse engineering via higher-order data processing inequality tests. Interface Focus. 2013, 3 (4): 20130011-10.1098/rsfs.2013.0011.CrossRefPubMedPubMedCentral
57.
Feizi S, Marbach D, Medard M, Kellis M: Network deconvolution as a general method to distinguish direct dependencies in networks. Nat Biotechnol. 2013, 31 (8): 726-733. 10.1038/nbt.2635.CrossRefPubMedPubMedCentral
Metadata
Title
Large differences in global transcriptional regulatory programs of normal and tumor colon cells
Authors
David Cordero
Xavier Solé
Marta Crous-Bou
Rebeca Sanz-Pamplona
Laia Paré-Brunet
Elisabet Guinó
David Olivares
Antonio Berenguer
Cristina Santos
Ramón Salazar
Sebastiano Biondo
Víctor Moreno
Publication date
01-12-2014
Publisher
BioMed Central
Published in
BMC Cancer / Issue 1/2014
Electronic ISSN: 1471-2407
DOI
https://doi.org/10.1186/1471-2407-14-708

Other articles of this Issue 1/2014

BMC Cancer 1/2014 Go to the issue

Study protocol

The CAIRO4 study: the role of surgery of the primary tumour with few or absent symptoms in patients with synchronous unresectable metastases of colorectal cancer – a randomized phase III study of the Dutch Colorectal Cancer Group (DCCG)

Research article

Study of the cytotoxicity of asiaticoside on rats and tumour cells

Research article

Comparison of the clinical courses and chemotherapy outcomes in metastatic colorectal cancer patients with and without active Mycobacterium tuberculosis or Mycobacterium kansasiiinfection: a retrospective study

Research article

Constitutive activation of the ERK pathway in melanoma and skin melanocytes in Grey horses

Research article

Up-regulated MicroRNA-181a induces carcinogenesis in Hepatitis B virus-related hepatocellular carcinoma by targeting E2F5

Research article

The off-label use of targeted therapies in sarcomas: the OUTC’S program
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