ATHENA: Identifying interactions between different levels of genomic data associated with cancer clinical outcomes using grammatical evolution neural network
© Kim et al.; licensee BioMed Central Ltd. 2013
Received: 5 November 2013
Accepted: 27 November 2013
Published: 20 December 2013
Gene expression profiles have been broadly used in cancer research as a diagnostic or prognostic signature for the clinical outcome prediction such as stage, grade, metastatic status, recurrence, and patient survival, as well as to potentially improve patient management. However, emerging evidence shows that gene expression-based prediction varies between independent data sets. One possible explanation of this effect is that previous studies were focused on identifying genes with large main effects associated with clinical outcomes. Thus, non-linear interactions without large individual main effects would be missed. The other possible explanation is that gene expression as a single level of genomic data is insufficient to explain the clinical outcomes of interest since cancer can be dysregulated by multiple alterations through genome, epigenome, transcriptome, and proteome levels. In order to overcome the variability of diagnostic or prognostic predictors from gene expression alone and to increase its predictive power, we need to integrate multi-levels of genomic data and identify interactions between them associated with clinical outcomes.
Here, we proposed an integrative framework for identifying interactions within/between multi-levels of genomic data associated with cancer clinical outcomes using the Grammatical Evolution Neural Networks (GENN). In order to demonstrate the validity of the proposed framework, ovarian cancer data from TCGA was used as a pilot task. We found not only interactions within a single genomic level but also interactions between multi-levels of genomic data associated with survival in ovarian cancer. Notably, the integration model from different levels of genomic data achieved 72.89% balanced accuracy and outperformed the top models with any single level of genomic data.
Understanding the underlying tumorigenesis and progression in ovarian cancer through the global view of interactions within/between different levels of genomic data is expected to provide guidance for improved prognostic biomarkers and individualized therapies.
Cancer, a complex disease of somatic mutations and regulation abnormalities, causes substantial gene expression changes in its tumor cell. Expression of oncogenes or tumor suppressor genes promotes the malignant phenotype of cancer cells or inhibits cell division, development, or survival of cancer cell. Gene expression profiles have been broadly used in cancer research as a diagnostic or prognostic signature for the clinical outcome prediction such as stage, grade, metastatic status, recurrence, and patient survival, in addition to potentially improving patient management[2–6]. In terms of translational bioinformatics, accurate outcome prediction based on the molecular signature can be used clinically to choose the best of several available therapies for a cancer patient. For example, a high risk patient may be advised to select a more radical therapy.
However, emerging evidence shows that gene expression-based prediction varies between independent data sets and little is known about the accuracy of gene expression-based prediction model with distinguished pathologic and clinical predictors[7, 8]. One possible explanation of this effect is that previous studies were focused on identifying genes with large main effects associated with clinical outcomes. Thus, non-linear interactions, which can be a candidate of synthetic lethal interactions, without large main effects would be missed. The other possible explanation is that gene expression as a single level of genomic data is insufficient to elucidate the clinical outcome since cancer can be dysregulated by multiple alterations through genome, epigenome, transcriptome, and proteome levels.
Recently, the emerging data generation of genomic data has provided unprecedented opportunities to investigate the global view of complex mechanisms between multi-layers of genomic data. The Cancer Genome Atlas (TCGA) is a large-scale collaborative initiative to improve the understanding of cancer using meta-dimensional genomic data. The TCGA research network recently published many notable papers on several cancers concerning an interim analysis of DNA sequencing, copy number, DNA methylation, miRNA, and gene expression data[11–15]. The International Cancer Genome Consortium (ICGC) is another multidisciplinary collaborative initiative to characterize a comprehensive description of genomic, transcriptomic and epigenomic abnormalities in 50 different cancer types. While the TCGA and ICGC open many opportunities to deepen the knowledge of the molecular basis of cancer[16–19], it is particularly important to integrate different levels of genomic data at hand for providing an enhanced global view on interplays between them.
In order to overcome the variability of diagnostic or prognostic predictors from gene expression data and to increase its predictive power, we need to integrate multi-levels of genomic data and identify interactions between them associated with clinical outcomes. Interactions within a single genomic level such as gene-gene interaction, miRNA-miRNA interaction, or protein-protein interaction have been known to be associated with cancer susceptibility, progression, and treatment[9, 20–23]. In addition, interactions between multi-levels of genomic data such as miRNA-target gene interaction, copy number-gene interaction, or methylation-gene interaction are also associated with cell development, stress response, apoptosis, proliferation, and tumorigenesis[24–26]. However, to the best of our knowledge, there is no systematic approach to identify interactions within/between different levels of genomic data for cancer clinical outcome prediction.
In this study, we proposed an integrative framework for identifying not only interactions within a single genomic level but also interactions between multi-levels of genomic data associated with cancer clinical outcomes using the grammatical evolution neural networks. In order to highlight the validity of the proposed framework, ovarian cancer data from TCGA was used as a pilot task. Serous cystadenocarcinoma is the most prevalent form of ovarian cancer, and is the 5th leading cause of cancer mortality in women in the United States. Understanding the underlying biology and molecular pathogenesis in ovarian cancer survival through the global view of interactions between different levels of genomic data is expected to provide guidance for improved prognostic biomarkers and individualized therapies.
Agilent SurePrint G3 human CGH microarray kit 1x1M
Infinium human methylation27 BeadChip
27,578 CpG loci
Agilent human miRNA microarray Rel2.0
Affymetrix HT human genome U133 array plate set
Analysis Tool for Heritable and Environmental Network Associations (ATHENA)
We have developed ATHENA, a multi-functional software package, designed to perform the three main functions essential to determine the meta-dimensional models of complex disease: (1) performing feature/variable selections from categorical or continuous independent variables; (2) modelling main and interaction effects that explain or predict categorical or continuous clinical outcomes; (3) interpreting the significant models for use in further translational bioinformatics[30–32]. ATHENA contains filtering components, modelling components, and an evolutionary computing approach based on a machine technique to generate complex models. The current version of ATHENA has two different computational evolution modelling methods, Grammatical Evolution Symbolic Regression (GESR) and Grammatical Evolution Neural Networks (GENN).
Grammatical Evolution Neural Networks (GENN)
In order to identify non-linear interactions between genomic features with small/large main effects, various computational methods have been introduced such as the multi-factor dimensionality reduction (MDR)[33, 34]. However, MDR performs an exhaustive analysis of every possible combination of interacting loci to generate multi-locus predictor models. The search spaces of all n-wise interacting features will increase exponentially when integrating with different levels of genomic data. Thus, stochastic methods employing evolutionary computing approaches have been developed and demonstrated to utilize the full dimensionality of the data without exhaustively searching all possible combinations of variables that influence complex traits[31, 35, 36].
The original dataset is divided into 5 equal groups for 5-fold cross-validation (4/5 for training and 1/5 for testing dataset).
Training begins by generating a random population of binary strings initialized to be functional ANNs. The total population is divided into demes as sub-populations across a user-defined number of CPUs for parallelization.
The ANNs in the population are evaluated using the training data and the fitness (balanced classification accuracy) for each model is recorded. A new population is generated as the solutions with the highest fitness are selected for crossover and reproduction.
Step 3 is repeated for a pre-defined number of generations. Migration of best solutions occurs between demes every n-number of generations, as specified by user.
The overall best solution across generations is tested using the remaining 1/5 test dataset and fitness is recorded.
Steps 2-5 are repeated four more times, each time using a different 4/5 of the data for training and 1/5 for testing. The best model is defined as the model identified the most over all five cross-validations.
GENN parameter settings
Number of demes (CPUs)
Number of generations
Number of migrations
Probability of crossover
Probability of mutation
Results and discussion
GENN modelling for single level of genomic data
Integration with different levels of genomic data
Significance test of the performances between the integration model and the model with single level of genomic data
Comparison between models
Integration vs. miRNA
Integration vs. methylation
Integration vs. gene expression
Integration vs. CNA
Five miRNAs, hsa-miR-7-1*, hsa-miR-300, hsa-miR-148a*, hsa-miR-32, and hsa-miR-190, were found in the GENN models associated with survival in ovarian cancer. In general, the aberrant miRNA expression provides substantial consequences for the progression of tumorigenesis. The miRNAs, hsa-miR-7-1*, hsa-miR-148a*, and hsa-miR-32, from the model were found to be a prognostic indicator in several cancers[19, 41, 42]. Synergistic regulations between miRNAs through either targeting same genes or co-operating of targeted genes are thought to be important to understand the mechanisms of complex post-transcriptional regulations since complex diseases such as cancer are affected by several miRNAs rather than a single miRNA. In addition, we found possible interactions between genomic loci, 13q14.2, 18q23, 19q12, and 6p22.3, which are associated with survival in ovarian cancer. Identifying interactions between altered genomic loci is a prerequisite to detect any common pathways that may be deregulated through the alterations in gene copy number, suggesting co-operative or complementation effect related to the tumorigenesis[43, 44].
Even though models from miRNA and CNA data showed additive effects, the models from methylation and gene expression data showed complex and non-linear interactions between genomic features associated with survival. In terms of epigenetic regulation, DNA methylation can serve to regulate expression of oncogene or tumor suppressor gene in cancer. Recently, ‘epigenetic epistatic interactions’ have been regarded to place important constrains on the evolution of gene expression that affects disease phenotype. The non-linear interactions of methylation of genes, PRMT3, CHN1, HDHD3, SDC2, C12orf75, RXFP2, and GLB1, might contribute on the survival in ovarian cancer rather than the single methylation of a specific gene. Several genes including SDC2 as a methylation cluster are involved in activation of TGF-beta pathway in ovarian cancer. A role for the insulin-relaxin family of peptides including INSL3 and its receptor RXFP2 in several cancers has been reported[47, 48]. Similarly, complex interactions of genes, TEX264, SFXN3, CD2AP, GPR64, and ABR, might act on crucial role in molecular pathogenesis, progression, and prognosis of ovarian cancer through the expression.
In this study, we addressed the issue of integrating meta-dimensional genomic data and identifying complex interactions in order to overcome the variability of diagnostic or prognostic predictors from any single level of genomic data and to increase its predictive power. Here, we proposed an integrative framework for identifying interactions within/between multi-levels of genomic data associated with cancer clinical outcomes based on the grammatical evolution neural networks. GENN, an efficient evolutionary computing approach, has been shown to be powerful in genetic association studies and meta-dimensional analysis of phenotypes of interest and has been proven superior compared to other methods in term of prediction accuracy[31, 32, 36, 38].
In order to demonstrate the utility of the proposed framework, ovarian cancer data from TCGA was used as a pilot task. We found not only interactions within a single genomic level but also interactions between multi-levels of genomic data associated with survival in ovarian cancer. Notably, the meta-dimensional model outperformed the model with single level of genomic data only. Taken together these results suggest that meta-dimensional model will lead us to an enhanced global view on interplays since different levels of genomic data might affect the cancer phenotype through either partly independent or partly complementary fashion. Understanding the underlying tumorigenesis and progression in ovarian cancer through the global view on interactions within/between different levels of genomic data is expected to provide guidance for improved prognostic biomarkers and individualized therapies. For instance, these models could be a candidate of synthetic lethal interaction, which is a new way in the context of anticancer therapy.
One of the limitations in the current study is that the final meta-dimensional model was obtained using variables from the best model of each genomic dataset. Thus, there will be a possibility to miss the interactions between different levels of genomic data, which were not selected in the best model because of small effect within a single genomic level. Another limitation of our analysis is the modeling techniques do not specifically identify conditional relationships, which are likely to be ubiquitous in meta-dimensional data. For example, if miRNA affect expression level of its target gene, which, in turn, affected the phenotype, methods such as GENN are more likely to identify either miRNA or gene expression, but not both. Bayesian networks could model these types of relationships in a more informative manner. Future improvement to ATHENA will include incorporating Bayesian networks to allow for the generation of more interpretable meta-dimensional models. Moreover, even though the current study was set for the classification problem between short-term and long-term survival, GENN is also able to predict continuous clinical outcomes. However, continuous survival data could not be directly used in GENN due to the context of censored data. In addition, in the current implementation of GENN and in evolutionary algorithm in general, the norm is to select the best model in the final solution because it has higher accuracy than all of the other models. However, there might be multiple different good models and selection based on accuracy alone has its limitations. To overcome this limitation, Pareto optimization can be incorporated in the next iteration of GENN. Pareto optimization is a multi-objective optimization method that aims to maximize or minimize multiple objectives. In our case, through minimizing the model size and the error, it will produce an array of equally good models that are not dominated by other models. Pareto optimization will allow us to find multiple interactions in cancer. We leave these investigations about the alternative way of integration, capturing the conditional relationship, predicting continuous survival data, and Pareto optimization as our future works. Another interesting direction for further works would be the integration with biological knowledge as a knowledge-driven approach.
Even though the current study is limited to the prediction of short-term/long-term survival in ovarian cancer as a base task, the proposed framework can be applied to other clinical outcomes such as stage, recurrence, metastasis, grade, etc. Furthermore, it can be applied to other cancer types in order to identify the cancer-specific or common interactions among cancer types. With abundance in multi-omics data and clinical data from TCGA or ICGC, our proposed framework will be valuable for explaining novel tumorigenesis, eventually leading to more effective screening strategies and therapeutic targets in many types of cancer. ATHENA can be downloaded from http://ritchielab.psu.edu/ritchielab/software/.
This work was funded by NIH grant 5R01 LM010040-03 and NHLBI grant 2U01 HL065962-10. In addition, we gratefully acknowledge the TCGA Consortium and all its members for the TCGA Project initiative, for providing sample, tissues, data processing and making data and results available.
- Croce CM: Oncogenes and cancer. N Engl J Med. 2008, 358 (5): 502-511. 10.1056/NEJMra072367.View ArticlePubMedGoogle Scholar
- Pomeroy SL, Tamayo P, Gaasenbeek M, Sturla LM, Angelo M, McLaughlin ME, Kim JY, Goumnerova LC, Black PM, Lau C: Prediction of central nervous system embryonal tumour outcome based on gene expression. Nature. 2002, 415 (6870): 436-442. 10.1038/415436a.View ArticlePubMedGoogle Scholar
- Yeoh EJ, Ross ME, Shurtleff SA, Williams WK, Patel D, Mahfouz R, Behm FG, Raimondi SC, Relling MV, Patel A: Classification, subtype discovery, and prediction of outcome in pediatric acute lymphoblastic leukemia by gene expression profiling. Cancer Cell. 2002, 1 (2): 133-143. 10.1016/S1535-6108(02)00032-6.View ArticlePubMedGoogle Scholar
- Shipp MA, Ross KN, Tamayo P, Weng AP, Kutok JL, Aguiar RC, Gaasenbeek M, Angelo M, Reich M, Pinkus GS: Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning. Nat Med. 2002, 8 (1): 68-74. 10.1038/nm0102-68.View ArticlePubMedGoogle Scholar
- Beer DG, Kardia SL, Huang CC, Giordano TJ, Levin AM, Misek DE, Lin L, Chen G, Gharib TG, Thomas DG: Gene-expression profiles predict survival of patients with lung adenocarcinoma. Nat Med. 2002, 8 (8): 816-824.PubMedGoogle Scholar
- van't Veer LJ, Dai H, van de Vijver MJ, He YD, Hart AA, Mao M, Peterse HL, van der Kooy K, Marton MJ, Witteveen AT: Gene expression profiling predicts clinical outcome of breast cancer. Nature. 2002, 415 (6871): 530-536. 10.1038/415530a.View ArticleGoogle Scholar
- Ntzani EE, Ioannidis JP: Predictive ability of DNA microarrays for cancer outcomes and correlates: an empirical assessment. Lancet. 2003, 362 (9394): 1439-1444. 10.1016/S0140-6736(03)14686-7.View ArticlePubMedGoogle Scholar
- Michiels S, Koscielny S, Hill C: Prediction of cancer outcome with microarrays: a multiple random validation strategy. Lancet. 2005, 365 (9458): 488-492. 10.1016/S0140-6736(05)17866-0.View ArticlePubMedGoogle Scholar
- Kaelin WG: The concept of synthetic lethality in the context of anticancer therapy. Nat Rev Cancer. 2005, 5 (9): 689-698. 10.1038/nrc1691.View ArticlePubMedGoogle Scholar
- Hanash S: Integrated global profiling of cancer. Nat Rev Cancer. 2004, 4 (8): 638-644. 10.1038/nrc1414.View ArticlePubMedGoogle Scholar
- TCGA Network: Comprehensive molecular characterization of human colon and rectal cancer. Nature. 2012, 487 (7407): 330-337. 10.1038/nature11252.View ArticleGoogle Scholar
- TCGA Network: Comprehensive genomic characterization of squamous cell lung cancers. Nature. 2012, 489 (7417): 519-525. 10.1038/nature11404.View ArticleGoogle Scholar
- TCGA Network: Comprehensive molecular portraits of human breast tumours. Nature. 2012, 490 (7418): 61-70. 10.1038/nature11412.View ArticleGoogle Scholar
- TCGA Network: Integrated genomic analyses of ovarian carcinoma. Nature. 2011, 474 (7353): 609-615. 10.1038/nature10166.View ArticleGoogle Scholar
- TCGA Network: Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature. 2008, 455 (7216): 1061-1068. 10.1038/nature07385.View ArticleGoogle Scholar
- Hudson TJ, Anderson W, Artez A, Barker AD, Bell C, Bernabe RR, Bhan MK, Calvo F, Eerola I, International Cancer Genome Consortium: International network of cancer genome projects. Nature. 2010, 464 (7291): 993-998. 10.1038/nature08987.View ArticlePubMedGoogle Scholar
- Kim D, Shin H, Song YS, Kim JH: Synergistic effect of different levels of genomic data for cancer clinical outcome prediction. J Biomed Inform. 2012, 45 (6): 1191-1198. 10.1016/j.jbi.2012.07.008.View ArticlePubMedGoogle Scholar
- Noushmehr H, Weisenberger DJ, Diefes K, Phillips HS, Pujara K, Berman BP, Pan F, Pelloski CE, Sulman EP, Bhat KP: Identification of a CpG island methylator phenotype that defines a distinct subgroup of glioma. Cancer Cell. 2010, 17 (5): 510-522. 10.1016/j.ccr.2010.03.017.View ArticlePubMedPubMed CentralGoogle Scholar
- Srinivasan S, Patric IR, Somasundaram K: A ten-microRNA expression signature predicts survival in glioblastoma. PLoS One. 2011, 6 (3): e17438-10.1371/journal.pone.0017438.View ArticlePubMedPubMed CentralGoogle Scholar
- Harris CC: Protein-protein interactions for cancer therapy. Proc Natl Acad Sci USA. 2006, 103 (6): 1659-1660. 10.1073/pnas.0510948103.View ArticlePubMedPubMed CentralGoogle Scholar
- Alshalalfa M: MicroRNA Response Elements-Mediated miRNA-miRNA Interactions in Prostate Cancer. Adv Bioinformatics. 2012, 2012: 839837-PubMedPubMed CentralGoogle Scholar
- Xu J, Li CX, Li YS, Lv JY, Ma Y, Shao TT, Xu LD, Wang YY, Du L, Zhang YP: MiRNA-miRNA synergistic network: construction via co-regulating functional modules and disease miRNA topological features. Nucleic Acids Res. 2011, 39 (3): 825-836. 10.1093/nar/gkq832.View ArticlePubMedGoogle Scholar
- Kessler JD, Kahle KT, Sun T, Meerbrey KL, Schlabach MR, Schmitt EM, Skinner SO, Xu Q, Li MZ, Hartman ZC: A SUMOylation-dependent transcriptional subprogram is required for Myc-driven tumorigenesis. Science. 2012, 335 (6066): 348-353. 10.1126/science.1212728.View ArticlePubMedGoogle Scholar
- Lu J, Clark AG: Impact of microRNA regulation on variation in human gene expression. Genome Res. 2012, 22 (7): 1243-1254. 10.1101/gr.132514.111.View ArticlePubMedPubMed CentralGoogle Scholar
- Orozco LD, Cokus SJ, Ghazalpour A, Ingram-Drake L, Wang S, van Nas A, Che N, Araujo JA, Pellegrini M, Lusis AJ: Copy number variation influences gene expression and metabolic traits in mice. Hum Mol Genet. 2009, 18 (21): 4118-4129. 10.1093/hmg/ddp360.View ArticlePubMedPubMed CentralGoogle Scholar
- Dudziec E, Gogol-Doring A, Cookson V, Chen W, Catto J: Integrated epigenome profiling of repressive histone modifications, DNA methylation and gene expression in normal and malignant urothelial cells. PLoS One. 2012, 7 (3): e32750-10.1371/journal.pone.0032750.View ArticlePubMedPubMed CentralGoogle Scholar
- Jemal A, Siegel R, Ward E, Hao Y, Xu J, Thun MJ: Cancer statistics, 2009. CA Cancer J Clin. 2009, 59 (4): 225-249. 10.3322/caac.20006.View ArticlePubMedGoogle Scholar
- Cerami E, Gao J, Dogrusoz U, Gross BE, Sumer SO, Aksoy BA, Jacobsen A, Byrne CJ, Heuer ML, Larsson E: The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2012, 2 (5): 401-404. 10.1158/2159-8290.CD-12-0095.View ArticlePubMedGoogle Scholar
- Bild AH, Yao G, Chang JT, Wang Q, Potti A, Chasse D, Joshi MB, Harpole D, Lancaster JM, Berchuck A: Oncogenic pathway signatures in human cancers as a guide to targeted therapies. Nature. 2006, 439 (7074): 353-357. 10.1038/nature04296.View ArticlePubMedGoogle Scholar
- Holzinger ER, Dudek SM, Frase AT, Pendergrass SA, Ritchie MD: ATHENA: the analysis tool for heritable and environmental network associations. Bioinformatics. 2013, epubGoogle Scholar
- Turner SD, Dudek SM, Ritchie MD: ATHENA: A knowledge-based hybrid backpropagation-grammatical evolution neural network algorithm for discovering epistasis among quantitative trait Loci. BioData Mining. 2010, 3 (1): 5-10.1186/1756-0381-3-5.View ArticlePubMedPubMed CentralGoogle Scholar
- Holzinger ER, Dudek SM, Frase AT, Krauss RM, Medina MW, Ritchie MD: ATHENA: a tool for meta-dimensional analysis applied to genotypes and gene expression data to predict HDL cholesterol levels. Pac Symp Biocomput. 2013, 385-396.Google Scholar
- Cordell HJ: Detecting gene-gene interactions that underlie human diseases. Nat Rev Genet. 2009, 10 (6): 392-404. 10.1038/nrg2579.View ArticlePubMedPubMed CentralGoogle Scholar
- Ritchie MD, Hahn LW, Roodi N, Bailey LR, Dupont WD, Parl FF, Moore JH: Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer. Am J Hum Genet. 2001, 69 (1): 138-147. 10.1086/321276.View ArticlePubMedPubMed CentralGoogle Scholar
- Ritchie MD, White BC, Parker JS, Hahn LW, Moore JH: Optimization of neural network architecture using genetic programming improves detection and modeling of gene-gene interactions in studies of human diseases. BMC Bioinforma. 2003, 4: 28-10.1186/1471-2105-4-28.View ArticleGoogle Scholar
- Motsinger-Reif AA, Dudek SM, Hahn LW, Ritchie MD: Comparison of approaches for machine-learning optimization of neural networks for detecting gene-gene interactions in genetic epidemiology. Genet Epidemiol. 2008, 32 (4): 325-340. 10.1002/gepi.20307.View ArticlePubMedGoogle Scholar
- Ritchie MD, Motsinger AA, Bush WS, Coffey CS, Moore JH: Genetic programming neural networks: a powerful bioinformatics tool for human genetics. Appl Soft Comput. 2007, 7 (1): 471-479. 10.1016/j.asoc.2006.01.013.View ArticlePubMedPubMed CentralGoogle Scholar
- Holzinger ER, Dudek SC, Frase AT, Fridley BL, Chalise P, Ritchie MD: Comparison of methods for meta-dimensional data analysis using in silico and biological data sets. Lect Notes Comput Sci. 2012, 7246: 134-143. 10.1007/978-3-642-29066-4_12.View ArticleGoogle Scholar
- Demsar J: Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res. 2006, 7: 1-30.Google Scholar
- Davis-Dusenbery BN, Hata A: MicroRNA in cancer: the involvement of Aberrant MicroRNA Biogenesis regulatory pathways. Genes Cancer. 2010, 1 (11): 1100-1114. 10.1177/1947601910396213.View ArticlePubMedPubMed CentralGoogle Scholar
- Shohet JM, Ghosh R, Coarfa C, Ludwig A, Benham AL, Chen Z, Patterson DM, Barbieri E, Mestdagh P, Sikorski DN: A genome-wide search for promoters that respond to increased MYCN reveals both new oncogenic and tumor suppressor microRNAs associated with aggressive neuroblastoma. Cancer Res. 2011, 71 (11): 3841-3851. 10.1158/0008-5472.CAN-10-4391.View ArticlePubMedGoogle Scholar
- Yanaihara N, Caplen N, Bowman E, Seike M, Kumamoto K, Yi M, Stephens RM, Okamoto A, Yokota J, Tanaka T: Unique microRNA molecular profiles in lung cancer diagnosis and prognosis. Cancer Cell. 2006, 9 (3): 189-198. 10.1016/j.ccr.2006.01.025.View ArticlePubMedGoogle Scholar
- Gorringe KL, George J, Anglesio MS, Ramakrishna M, Etemadmoghadam D, Cowin P, Sridhar A, Williams LH, Boyle SE, Yanaihara N: Copy number analysis identifies novel interactions between genomic loci in ovarian cancer. PLoS One. 2010, 5 (9): doi: 10.1371/journal.pone.0011408Google Scholar
- Courjal F, Cuny M, Simony-Lafontaine J, Louason G, Speiser P, Zeillinger R, Rodriguez C, Theillet C: Mapping of DNA amplifications at 15 chromosomal localizations in 1875 breast tumors: definition of phenotypic groups. Cancer Res. 1997, 57 (19): 4360-4367.PubMedGoogle Scholar
- Park S, Lehner B: Epigenetic epistatic interactions constrain the evolution of gene expression. Mol Syst Biol. 2013, 9: 645-View ArticlePubMedPubMed CentralGoogle Scholar
- Matsumura N, Huang Z, Mori S, Baba T, Fujii S, Konishi I, Iversen ES, Berchuck A, Murphy SK: Epigenetic suppression of the TGF-beta pathway revealed by transcriptome profiling in ovarian cancer. Genome Res. 2011, 21 (1): 74-82. 10.1101/gr.108803.110.View ArticlePubMedPubMed CentralGoogle Scholar
- Silvertown JD, Summerlee AJ, Klonisch T: Relaxin-like peptides in cancer. Int J Cancer. 2003, 107 (4): 513-519. 10.1002/ijc.11424.View ArticlePubMedGoogle Scholar
- Klonisch T, Bialek J, Radestock Y, Hoang-Vu C, Hombach-Klonisch S: Relaxin-like ligand-receptor systems are autocrine/paracrine effectors in tumor cells and modulate cancer progression and tissue invasiveness. Adv Exp Med Biol. 2007, 612: 104-118. 10.1007/978-0-387-74672-2_8.View ArticlePubMedGoogle Scholar
- Griffiths-Jones S, Saini HK, van Dongen S, Enright AJ: miRBase: tools for microRNA genomics. Nucleic Acids Res. 2008, 36 (Database issue): D154-D158.PubMedGoogle Scholar
- Lee BY, Han JA, Im JS, Morrone A, Johung K, Goodwin EC, Kleijer WJ, DiMaio D, Hwang ES: Senescence-associated beta-galactosidase is lysosomal beta-galactosidase. Aging Cell. 2006, 5 (2): 187-195. 10.1111/j.1474-9726.2006.00199.x.View ArticlePubMedGoogle Scholar
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