Analyzing the regulation of metabolic pathways in human breast cancer

BackgroundTumor therapy mainly attacks the metabolism to interfere the tumor's anabolism and signaling of proliferative second messengers. However, the metabolic demands of different cancers are very heterogeneous and depend on their origin of tissue, age, gender and other clinical parameters. We investigated tumor specific regulation in the metabolism of breast cancer.MethodsFor this, we mapped gene expression data from microarrays onto the corresponding enzymes and their metabolic reaction network. We used Haar Wavelet transforms on optimally arranged grid representations of metabolic pathways as a pattern recognition method to detect orchestrated regulation of neighboring enzymes in the network. Significant combined expression patterns were used to select metabolic pathways showing shifted regulation of the aggressive tumors.ResultsBesides up-regulation for energy production and nucleotide anabolism, we found an interesting cellular switch in the interplay of biosynthesis of steroids and bile acids. The biosynthesis of steroids was up-regulated for estrogen synthesis which is needed for proliferative signaling in breast cancer. In turn, the decomposition of steroid precursors was blocked by down-regulation of the bile acid pathway.ConclusionWe applied an intelligent pattern recognition method for analyzing the regulation of metabolism and elucidated substantial regulation of human breast cancer at the interplay of cholesterol biosynthesis and bile acid metabolism pointing to specific breast cancer treatment.

[1]  Jelle J. Goeman,et al.  A global test for groups of genes: testing association with a clinical outcome , 2004, Bioinform..

[2]  E. Pitman SIGNIFICANCE TESTS WHICH MAY BE APPLIED TO SAMPLES FROM ANY POPULATIONS III. THE ANALYSIS OF VARIANCE TEST , 1938 .

[3]  Yoshihiro Yamanishi,et al.  KEGG for linking genomes to life and the environment , 2007, Nucleic Acids Res..

[4]  A. Nobel,et al.  Concordance among Gene-Expression – Based Predictors for Breast Cancer , 2011 .

[5]  Van,et al.  A gene-expression signature as a predictor of survival in breast cancer. , 2002, The New England journal of medicine.

[6]  Terry L. Smith,et al.  Tamoxifen for early breast cancer: an overview of the randomised trials. Early Breast Cancer Trialists' Collaborative Group. , 1998, Lancet.

[7]  Mike Clarke,et al.  Tamoxifen for early breast cancer: an overview of the randomised trials , 1998, The Lancet.

[8]  G. V. Ommen,et al.  Medical genomics , 2001, European Journal of Human Genetics.

[9]  J. Abita,et al.  Inhibition of proliferation and induction of monocytic differentiation in HL60 human promyelocytic leukemia cells treated with bile acids In vitro , 1994, International journal of cancer.

[10]  O. Feron,et al.  Pyruvate into lactate and back: from the Warburg effect to symbiotic energy fuel exchange in cancer cells. , 2009, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[11]  Catalin C. Barbacioru,et al.  The balance of reproducibility, sensitivity, and specificity of lists of differentially expressed genes in microarray studies , 2008, BMC Bioinformatics.

[12]  Noriaki Ohuchi,et al.  Sex steroid-producing enzymes in human breast cancer. , 2005, Endocrine-related cancer.

[13]  Jozef Spychala Regulation and function of ecto-5’-nucleotidase and adenosine in cancer , 2003 .

[14]  J. Nielsen,et al.  Uncovering transcriptional regulation of metabolism by using metabolic network topology. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[15]  C. Gespach,et al.  Bile acids and derivatives, their nuclear receptors FXR, PXR and ligands: role in health and disease and their therapeutic potential. , 2008, Anti-cancer agents in medicinal chemistry.

[16]  Peng Huang,et al.  Inhibition of glycolysis in cancer cells: a novel strategy to overcome drug resistance associated with mitochondrial respiratory defect and hypoxia. , 2005, Cancer research.

[17]  J. Collins,et al.  A network biology approach to prostate cancer , 2007, Molecular systems biology.

[18]  Kathleen Marchal,et al.  Evaluation of time profile reconstruction from complex two-color microarray designs , 2008, BMC Bioinformatics.

[19]  S. Bonhoeffer,et al.  Cooperation and Competition in the Evolution of ATP-Producing Pathways , 2001, Science.

[20]  A. Tarnawski,et al.  Deoxycholic acid activates beta-catenin signaling pathway and increases colon cell cancer growth and invasiveness. , 2004, Molecular biology of the cell.

[21]  S. Anant,et al.  Characterization of Enantiomeric Bile Acid-induced Apoptosis in Colon Cancer Cell Lines* , 2009, Journal of Biological Chemistry.

[22]  N Keiding,et al.  Risk of acute nonlymphocytic leukemia and preleukemia in patients treated with cyclophosphamide for non-Hodgkin's lymphomas. Comparison with results obtained in patients treated for Hodgkin's disease and ovarian carcinoma with other alkylating agents. , 1985, Annals of internal medicine.

[23]  R. Sampliner,et al.  Reduced bile acid-induced apoptosis in "normal" colorectal mucosa: a potential biological marker for cancer risk. , 1996, Cancer research.

[24]  Laura Biganzoli,et al.  New diagnostics and biological predictors of outcome in early breast cancer , 2009 .

[25]  Eytan Domany,et al.  Outcome signature genes in breast cancer: is there a unique set? , 2004, Breast Cancer Research.

[26]  H. Khamis,et al.  Simple solution to a common statistical problem: interpreting multiple tests. , 2004, Clinical therapeutics.

[27]  Liqiang Chen,et al.  Recent development of IMP dehydrogenase inhibitors for the treatment of cancer. , 2007, Current opinion in drug discovery & development.

[28]  E. Pitman Significance Tests Which May be Applied to Samples from Any Populations , 1937 .

[29]  M. Daly,et al.  PGC-1α-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes , 2003, Nature Genetics.

[30]  M. Guppy,et al.  The role of the Crabtree effect and an endogenous fuel in the energy metabolism of resting and proliferating thymocytes. , 1993, European journal of biochemistry.

[31]  H C Pitot,et al.  Oxaloacetate induces DNA synthesis and mitosis in primary cultured rat hepatocytes in the absence of EGF. , 1993, Biochemical and biophysical research communications.

[32]  M. Mareel,et al.  The role of bile acids in carcinogenesis. , 2001, Mutation research.

[33]  G Weber,et al.  Biochemical pharmacology of acivicin in rat hepatoma cells. , 1982, Biochemical pharmacology.

[34]  Serban Nacu,et al.  Gene expression network analysis and applications to immunology , 2007, Bioinform..

[35]  Fengzhu Sun,et al.  BMC Bioinformatics BioMed Central Methodology article Testing gene set enrichment for subset of genes: Sub-GSE , 2008 .

[36]  J. Bergh,et al.  Definition of clinically distinct molecular subtypes in estrogen receptor-positive breast carcinomas through genomic grade. , 2007, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[37]  Manuel Hidalgo,et al.  Matrix metalloproteinase-2 contributes to cancer cell migration on collagen. , 2005, Cancer research.

[38]  K. Greulich,et al.  Genes of glycolysis are ubiquitously overexpressed in 24 cancer classes. , 2004, Genomics.

[39]  Gene Ontology Consortium The Gene Ontology (GO) database and informatics resource , 2003 .

[40]  Gianluca Bontempi,et al.  Predicting prognosis using molecular profiling in estrogen receptor-positive breast cancer treated with tamoxifen , 2008, BMC Genomics.

[41]  T. Ideker,et al.  Network-based classification of breast cancer metastasis , 2007, Molecular systems biology.

[42]  C. Bonferroni Il calcolo delle assicurazioni su gruppi di teste , 1935 .

[43]  Yudong D. He,et al.  Gene expression profiling predicts clinical outcome of breast cancer , 2002, Nature.

[44]  L. Cantley,et al.  Understanding the Warburg Effect: The Metabolic Requirements of Cell Proliferation , 2009, Science.

[45]  R. Collins,et al.  Polychemotherapy for early breast cancer: an overview of the randomised trials , 1998, The Lancet.

[46]  Kimberly Van Auken,et al.  WormBase: a multi-species resource for nematode biology and genomics , 2004, Nucleic Acids Res..

[47]  Emmanuel Barillot,et al.  Classification of microarray data using gene networks , 2007, BMC Bioinformatics.