This article has Open Peer Review reports available.
Explorative data analysis of MCL reveals gene expression networks implicated in survival and prognosis supported by explorative CGH analysis
© Blenk et al; licensee BioMed Central Ltd. 2008
Received: 02 November 2007
Accepted: 16 April 2008
Published: 16 April 2008
Mantle cell lymphoma (MCL) is an incurable B cell lymphoma and accounts for 6% of all non-Hodgkin's lymphomas. On the genetic level, MCL is characterized by the hallmark translocation t(11;14) that is present in most cases with few exceptions. Both gene expression and comparative genomic hybridization (CGH) data vary considerably between patients with implications for their prognosis.
We compare patients over and below the median of survival. Exploratory principal component analysis of gene expression data showed that the second principal component correlates well with patient survival. Explorative analysis of CGH data shows the same correlation.
On chromosome 7 and 9 specific genes and bands are delineated which improve prognosis prediction independent of the previously described proliferation signature. We identify a compact survival predictor of seven genes for MCL patients. After extensive re-annotation using GEPAT, we established protein networks correlating with prognosis. Well known genes (CDC2, CCND1) and further proliferation markers (WEE1, CDC25, aurora kinases, BUB1, PCNA, E2F1) form a tight interaction network, but also non-proliferative genes (SOCS1, TUBA1B CEBPB) are shown to be associated with prognosis. Furthermore we show that aggressive MCL implicates a gene network shift to higher expressed genes in late cell cycle states and refine the set of non-proliferative genes implicated with bad prognosis in MCL.
The results from explorative data analysis of gene expression and CGH data are complementary to each other. Including further tests such as Wilcoxon rank test we point both to proliferative and non-proliferative gene networks implicated in inferior prognosis of MCL and identify suitable markers both in gene expression and CGH data.
Mantle cell lymphomas (MCL) make up about 6% of all cases of non-Hodgkin's lymphomas. They occur at any age from the late 30s to old age, are more common in the over 50 years old population and three times more common in men than in women. Morphologically, MCL is characterized by a monomorphic lymphoid proliferation of cells that resemble centrocytes. MCL is associated with a poor prognosis and remains incurable with current chemotherapeutic approaches. Despite response rates of 50–70% with many regimens, the disease typically relapses and progresses after chemotherapy. The median survival time is approximately 3 years (range, 2–5 y); the 10-year survival rate is only 5–10%.
The characteristic translocation t(11;14) leads to overexpression of Cyclin D1 in the tumor cells which therefore comprises an excellent marker in the diagnostic setting . The present study is an effort to improve molecular insights and markers of the disease [2–6] to improve the diagnosis and potential therapeutic strategies. We used gene expression data from 71 cyclin D1-positive patients and coupled these to data on their corresponding chromosomal aberrations (n = 71). We found molecular markers in addition to cyclin D1 and characteristic antigens (shared with blood cells from which the tumor may develop) CD5, CD20 and FMC7 with the aim to better delineate the regulatory network regulated differently in MCL.
Starting from the proliferation signature  we compare long and short living patients subgroups "s" (survivor, above median of survival) and "b" (bad prognosis, below median of survival). Exploratory analysis of gene expression and CGH data shows new genes differentiating both subgroups, proliferation associated genes and non-proliferative genes. For clinical application a seven gene predictor is derived from these gene markers, distinguishing patients with good or bad survival prognosis. A Wilcoxon rank sum test on CGH data identifies specific changes on chromosome 9 and 7.
Data and Materials
MCL gene expression data (n = 71) were obtained from cDNA arrays containing genes preferentially expressed in lymphoid cells or genes known or presumed to be part of cancer development or immune function ("Lymphochip" microarrays ; data have been deposited at NCBI's Gene Expression Omnibus data repository under GEO series accession number GSE10793. We give also the resulting gene expression ratios [see Additional file 5] and the prognosis assigned to patients [see Additional file 6]. The dataset is completed by comparative genomic hybridization (CGH) data for each patient (n = 71). The samples were collected from cyclin D1-positive patients of several hospitals in the "Lymphoma and Leukemia Molecular Profiling Project" (LLMPP) .
Most of the statistical analyses were performed using the "Genome Expression Pathway Analysis Tool" (GEPAT). This is a web-based platform for annotation (allowing also extensive re-annotation of the data), analysis and visualization of microarray gene expression data  including genomic, proteomic and metabolic features.
For identification of differentially expressed genes, GEPAT uses the "limma" package which offers moderate t-statistics [11, 12]. It fits linear models on the gene expression values of each gene with respect to the groups which are compared. After that empirical Bayes shrinkage of the standard errors is performed. Due to its robustness the method can be applied to experiments with a small number of samples. To correct for multiple testing it offers three options, we chose the method by Benjamini and Hochberg .
For identifying all protein-protein network interactions GEPAT uses the "Search Tool for the Retrieval of Interacting Genes/Proteins" (STRING) . The STRING database comprises known and predicted protein-protein interactions. The interaction information arises from genomic context, experiments, other databases, coexpression and textmining.
For explorative correspondence analysis and principal component analysis, functions from the R package "Modern Applied Statistics with S" (MASS) was applied . A constrained or canonical correspondence analysis (CCA)  was performed using the vegan package .
The Wilcoxon rank sum test , a non-parametric statistical test, was applied to the CGH data. It tests here each of the chosen bands against the null hypothesis that there is no statistically significant difference between our proposed two MCL patients "b" and "s". The R package "survival" is used to calculate all Cox regression hazard models [19, 20]. It examines the correlation between the given measurements and the survival data. For the exploratory analysis of the CGH data as well as for the new predictor of MCL overall survival, we used the Wald test to determine the significance of the association between the model and the outcome.
Exploratory analysis and lymphoma prognosis
In the correspondence analysis plot [see Additional file 1], the four bands mentioned before attract most patients of the subgroup "b" and the 1st factor axis separates almost completely the two groups. Bands 9q33 and 9q34, are located relatively far away from the remaining ones. In Figure 2 the second principal component groups almost all the "b" – patients near the four bands 9p24, 9p23, 9p22, 9p21 with vectors of similar length and similar direction. The vectors of 9q33 and 9q34 include along their lengths almost all "s" samples. These results indicate that these six bands of chromosome 9 correlate with good and bad survival between patients. The principal component 1 is an interesting main component, carrying 51% of the variance, but non-trivial to link to a known phenotype (we investigated different possibilities including sex differences, cancer sub-types, patient accrual and correlation with different gene signatures).
Further exploratory data analysis was performed to merge the survival time and the CGH data by the Cox regression hazard model. A univariate Cox regression hazard model was performed on all available bands of the CGH data of all 71 patients. The mentioned four bands of chromosome 9 delivered amongst others the most significant results. The resulting bands are "9p24", "9p23", "9p22", "9p21", "9q31" and "9q32". These comprise the first four bands found on chromosome 9 by the analyses before.
A compact predictor of survival with seven genes
Exploratory analysis pointed to differences between longer and shorter living MCL patients, but rather than forming two distinct subgroups, the patients constitute a coherent continuum. Therefore, the results of the exploratory analysis above were not additionally confirmed by classification tools. However, the differences in gene expression above and below the median of survival correlate well with different gene signatures identified before (proliferation signature) as well as with the new ones described in our study (non-proliferative signatures, see below). To improve survival predictions we further searched with univariate Cox regression hazard analysis for highly significant genes, which correlate strongly with the overall survival time. The cox regression was applied to all data points. However, the first 50 MCL samples served as training set for classification by gene signatures and the remaining data (21 patients) for validation. The idea was here to have a large training data set, but still keep a third of the available data for validation.
A four gene predictor with the genes CDC2, ASPM, tubulin-α and CENP-F reported in  could not be tested, as after reannotation by GEPAT , mapping of CENP-F seemed uncertain. Predictors with 4, 5 or 6 genes delivered not the same predictive power as the proliferation signature  (data not shown). The prediction power was calculated from the correct classification and misclassification for patients over or below the median of survival for 69 patients (the two patients with the median value were excluded).
The genes of the survival predictor
Official full name
Centromeric protein E
Cell division cycle protein 20 homolog
Cell division control protein 2 homolog
Baculoviral IAP repeat-containing protein 5
Abnormal spindle-like microcephaly-associated protein
IGF-II mRNA-binding protein 3
Taken together, these results show that the seven gene predictor is able to distinguish patient prognosis as well as the complete proliferation signature, but with less effort.
Protein networks and interactions differently regulated in good and bad prognosis tumors
Most significant genes separating good (s) and bad (b) prognosis
Genes separating good (s) and bad (b) prognosis not associated with cell cycle and proliferation association
CGH data reveals new genes implicated in MCL outcome
The best "s" and "b" separating genes of chromosome 9 bands 9p24, 9p21, 9q33, and 9q34
Official full name
Heat shock 70 kDa protein 5
Protein phosphatase 6, catalytic subunit
Pre-B-cell leukemia homeobox 3
Prostaglandin-endoperoxide synthase 1
Quiescin Q6 sulfhydryl oxidase 2
Several different marker genes and events have been proposed for MCL, e.g the translocation t(11;14)(q13;q32) , immunohistochemically  and Repp86 proteins as a proliferation markers  and increased levels of cyclin D1.
The present study consolidates gene expression and CGH data regarding MCL subgroups with good or bad prognosis to an overall picture. These subgroups are indicated and confirmed by exploratory analyses. This picture shows as yet unknown relations and differences between patients from these groups.
Correspondence analysis is an unsupervised tool to project high dimensional data into lower dimensional subspaces. Surprisingly, its second component separates well the shorter and longer living patients according to the median of survival. This result is in close agreement with the median of the outcome predictor score derived by the proliferation signature  as a discriminator.
A new predictor of survival with similar predictive power as the proliferation signature of 20 genes  was developed requiring gene expression values of only seven genes. With the key genes CDC20, HPRT1 and CDC2 the seven-gene-predictor matches with three genes from the 20 genes proliferation signature. Moreover, the four genes CENPE, BIRC5, ASPM and IGF2BP3 add to its predictive power and are associated with chromosome movement, inhibition of apoptosis and tumors. It was shown that a four gene predictor (CDC2, ASPM, tubulin-alpha, CENP-F)  is also able to predict length of survival with high statistical significance. Besides the fact, that the proliferation signature is more efficient and powerful than the four gene model, our model meets extensive re-annotation of the genes through the clone IDs.
These CGH data support the association of alterations in chromosomal regions and outcome of MCL patients.
Gene expression analysis comparing long and short surviving patients delivered cell cycle related genes and their protein-protein interactions. A dense interaction network differently regulated in good or bad prognosis includes CDC2 and interaction partners for cell cycle control and proliferation (CCND1, CDK4, MYC and E2F1; CDC25, WEE1, AURKB, AURKA, BUB1, PCNA, FOS, JUN and MYBL2). However, we identified furthermore non proliferation genes differentially implicated in MCL prognosis such as SOCS1 and CEBPB.
The Wilcoxon rank sum test revealed relations between the bands 9p24, 9p23, 9p22 and 9p21 and the difference between the longer and shorter living patients. Investigation of those bands regarding most significant differentially expressed genes revealed a cluster of genes with properties such as "differentiation blocking", "anti apoptotic" and "apoptosis inducing". Supporting our finding, the band 9p21 was suggested be implicated in MCL patient outcome . Some bands of chromsome 7 identified further expression differences somewhat weaker associated with the outcome. As the annotation and properties of embedded genes are not completely known, further data are required to better explain the relation between gene functions and survival. CGH data may improve the power of gene expression based predictors . Besides others, the band 9p21 was associated with a poor clinical outcome, which affirms our finding.
Our study extends these CGH results in two ways: (i) exploratory analysis shows here for the first time, that in fact CGH data alone can predict prognosis in MCL, (ii) CGH data point here directly to several genes regulated differently in good or bad prognosis patients.
After careful re-annotation of involved genes we found two subgroups of MCL patients which were found and supported by exploratory analysis of gene expression values and CGH data, network analysis and literature mining. We obtained an improved classification of MCL regarding prognosis. Differentially expressed genes formed a tight protein interaction network of kinases. A seven gene predictor appeared as an easy to measure prognosis indicator for clinical use. The Wilcoxon rank sum test as well as PCA was applied successfully to a CGH data set in this study. Both identify bands on chromosome 9. Following the indicated bands, we found differentially expressed MCL related genes.
We thank the State of Bavaria for support (IZKF B-36; ENB Lead Structures of Cell Function) and DFG (SFB688 TP A2).
- Bogner C, Peschel C, Decker T: Targeting the proteasome in mantle cell lymphoma: A promising therapeutic approach. Leuk Lymphoma. 2006, 47: 195-205.View ArticlePubMedGoogle Scholar
- Argatoff LH, Connors JM, Klasa RJ, Horsman DE, Gascoyne RD: Mantle cell lymphoma: a clinicopathologic study of 80 cases. Blood. 1997, 89: 2067-2078.PubMedGoogle Scholar
- Bosch F, Lopez-Guillermo A, Campo E, Ribera JM, Conde E, Piris MA, Vallespi T, Woessner S, Montserrat E: Mantle cell lymphoma: presenting features, response to therapy, and prognostic factors. Cancer. 1998, 82: 567-575.View ArticlePubMedGoogle Scholar
- Raty R, Franssila K, Joensuu H, Teerenhovi L, Elonen E: Ki-67 expression level, histological subtype, and the International Prognostic Index as outcome predictors in mantle cell lymphoma. Eur J Haematol. 2002, 69: 11-20.View ArticlePubMedGoogle Scholar
- Velders GA, Kluin-Nelemans JC, De Boer CJ, Hermans J, Noordijk EM, Schuuring E, Kramer MH, Van Deijk WA, Rahder JB, Kluin PM, Van Krieken JH: Mantle-cell lymphoma: a population-based clinical study. J Clin Oncol. 1996, 14: 1269-1274.PubMedGoogle Scholar
- Rosenwald A, Wright G, Wiestner A, Chan WC, Connors JM, Campo E, Gascoyne RD, Grogan TM, Müller-Hermelink HK, Smeland EB, Chiorazzi M, Giltnane JM, Hurt EM, Zhao H, Averett L, Henrickson S, Yang L, Powell J, Wilson WH, Jaffe ES, Simon R, Klausner RD, Montserrat E, Bosch F, Greiner TC, Weisenburger DD, Sanger WG, Dave BJ, Lynch JC, Vose J, Armitage JO, Fisher RI, Miller TP, LeBlanc M, Ott G, Kvaloy S, Holte H, Delabie J, Staudt LM: The proliferation gene expression signature is a quantitative integrator of oncogenic events that predicts survival in mantle cell lymphoma. Cancer Cell. 2003, 3: 185-197.View ArticlePubMedGoogle Scholar
- Alizadeh A, Eisen M, Davis RE, Ma C, Sabet H, Tran T, Powell JI, Yang L, Marti GE, Moore DT, Hudson JR, Chan WC, Greiner T, Weisenburger D, Armitage JO, Lossos I, Levy R, Botstein D, Brown PO, Staudt LM: The lymphochip: a specialized cDNA microarray for the genomic-scale analysis of gene expression in normal and malignant lymphocytes. Cold Spring Harb Symp Quant Biol. 1999, 64: 71-78.View ArticlePubMedGoogle Scholar
- Weniger M, Engelmann JC, Schultz J: Genome Expression Pathway Analysis Tool–analysis and visualization of microarray gene expression data under genomic, proteomic and metabolic context. BMC Bioinformatics. 2007, 8: 179-View ArticlePubMedPubMed CentralGoogle Scholar
- Gentleman R, Carey V, Bates M, Bolstad B, Dettling M, Dudoit S, Ellis B, Gautier L, Ge Y, Gentry J, Hornik K, Hothorn T, Huber W, Iacus S, Irizarry R, Leisch F, Li C, Maechler M, Rossini AJ, Sawitzki G, Smith C, Smyth G, Tierney L, Yang JYH, Zhang J: Bioconductor: open software development for computational biology and bioinformatics. Genome Biology. 2004, 5: R80-View ArticlePubMedPubMed CentralGoogle Scholar
- R Development Core Team: A language and environment for statistical computing. 2007, R Foundation for Statistical Computing, Vienna, AustriaGoogle Scholar
- Smyth GK: Limma: linear models for microarray data. Bioinformatics and Computational Biology Solutions using R and Bioconductor. Edited by: Gentleman R, Carey V, Dudoit S, Irizarry R, Huber W. 2005, New York: Springer, 397-420.View ArticleGoogle Scholar
- Smyth GK: Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Statistical applications in genetics and molecular biology. 2004, 3 (Article3):Google Scholar
- 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: 125-133.Google Scholar
- von Mering C, Jensen LJ, Snel B, Hooper SD, Krupp M, Foglierini M, Jouffre N, Huynen MA, Bork P: STRING: known and predicted protein-protein associations, integrated and transferred across organisms. Nucleic Acids Research. 2005, D433-437. 33 DatabaseGoogle Scholar
- Venables WN, Ripley BD: Modern Applied Statistics with S. 2002, Springer, FourthView ArticleGoogle Scholar
- Ter Braak CJF: Canonical correspondence analysis: a new eigenvector technique for multivariate direct gradient analysis. Ecology. 1986, 67: 1167-1179.View ArticleGoogle Scholar
- Oksanen J, Kindt R, Legendre P, O'Hara RB: vegan: Community Ecology Package. 2007, R package version 1.8–4, [http://cran.r-project.org/]Google Scholar
- Wilcoxon F: Individual Comparisons by Ranking Methods. Biometrics Bulletin. 1945, 1: 80-83.View ArticleGoogle Scholar
- Andersen P, Gill R: Cox's regression model for counting processes, a large sample study. Annals of Statistics. 1982, 10: 1100-1120.View ArticleGoogle Scholar
- Therneau T, Grambsch P, Fleming T: Martingale based residuals for survival models. Biometrika. 1990, 77: 147-160.View ArticleGoogle Scholar
- Norbury C, Nurse P: Cyclins and cell cycle control. Curr Biol. 1991, 1: 23-24.View ArticlePubMedGoogle Scholar
- Norbury C, Nurse P: Animal cell cycles and their control. Annu Rev Biochem. 1992, 61: 441-470.View ArticlePubMedGoogle Scholar
- Sethi N, Monteagudo MC, Koshland D, Hogan E, Burke DJ: The CDC20 gene product of Saccharomyces cerevisiae, a beta-transducin homolog, is required for a subset of microtubule-dependent cellular processes. Mol Cell Biol. 1991, 11: 5592-5602.View ArticlePubMedPubMed CentralGoogle Scholar
- Yen TJ, Li G, Schaar BT, Szilak I, Cleveland DW: CENP-E is a putative kinetochore motor that accumulates just before mitosis. Nature. 1992, 359: 536-539.View ArticlePubMedGoogle Scholar
- Ambrosini G, Adida C, Altieri DC: A novel anti-apoptosis gene, survivin, expressed in cancer and lymphoma. Nat Med. 1997, 3: 917-921.View ArticlePubMedGoogle Scholar
- Beardmore VA, Ahonen LJ, Gorbsky GJ, Kallio MJ: Survivin dynamics increases at centromeres during G2/M phase transition and is regulated by microtubule-attachment and Aurora B kinase activity. J Cell Sci. 2004, 117: 4033-4042.View ArticlePubMedGoogle Scholar
- Bond J, Roberts E, Mochida GH, Hampshire DJ, Scott S, Askham JM, Springell K, Mahadevan M, Crow YJ, Markham AF, Walsh CA, Woods CG: ASPM is a major determinant of cerebral cortical size. Nat Genet. 2002, 32: 316-320.View ArticlePubMedGoogle Scholar
- Mueller-Pillasch F, Lacher U, Wallrapp C, Micha A, Zimmerhackl F, Hameister H, Varga G, Friess H, Buchler M, Beger HG, Vila MR, Adler G, Gress TM: Cloning of a gene highly overexpressed in cancer coding for a novel KH-domain containing protein. Oncogene. 1997, 14: 2729-2733.View ArticlePubMedGoogle Scholar
- Monk D, Bentley L, Beechey C, Hitchins M, Peters J, Preece MA, Stanier P, Moore GE: Characterisation of the growth regulating gene IMP3, a candidate for Silver-Russell syndrome. J Med Genet. 2002, 39: 575-581.View ArticlePubMedPubMed CentralGoogle Scholar
- Nielsen J, Christiansen J, Lykke-Andersen J, Johnsen AH, Wewer UM, Nielsen FC: A family of insulin-like growth factor II mRNA-binding proteins represses translation in late development. Mol Cell Biol. 1999, 19: 1262-1270.View ArticlePubMedPubMed CentralGoogle Scholar
- Aleem E, Kiyokawa H, Kaldis P: Cdc2-cyclin E complexes regulate the G1/S phase transition. Nat Cell Biol. 2005, 7: 831-836.View ArticlePubMedGoogle Scholar
- Malumbres M, Barbacid M: Cell cycle kinases in cancer. Curr Opin Genet Dev. 2007, 17: 60-65.View ArticlePubMedGoogle Scholar
- Peri S, Navarro JD, Amanchy R, Kristiansen TZ, Jonnalagadda CK, Surendranath V, Niranjan V, Muthusamy B, Gandhi TK, Gronborg M, Ibarrola N, Deshpande N, Shanker K, Shivashankar HN, Rashmi BP, Ramya MA, Zhao Z, Chandrika KN, Padma N, Harsha HC, Yatish AJ, Kavitha MP, Menezes M, Choudhury DR, Suresh S, Ghosh N, Saravana R, Chandran S, Krishna S, Joy M, Anand SK, Madavan V, Joseph A, Wong GW, Schiemann WP, Constantinescu SN, Huang L, Khosravi-Far R, Steen H, Tewari M, Ghaffari S, Blobe GC, Dang CV, Garcia JG, Pevsner J, Jensen ON, Roepstorff P, Deshpande KS, Chinnaiyan AM, Hamosh A, Chakravarti A, Pandey A: Development of human protein reference database as an initial platform for approaching systems biology in humans. Genome Res. 2003, 13: 2363-2371.View ArticlePubMedPubMed CentralGoogle Scholar
- Lampson MA, Renduchitala K, Khodjakov A, Kapoor TM: Correcting improper chromosome-spindle attachments during cell division. Nat Cell Biol. 2004, 6: 232-237.View ArticlePubMedGoogle Scholar
- Tang Z, Shu H, Oncel D, Chen S, Yu H: Phosphorylation of Cdc20 by Bub1 provides a catalytic mechanism for APC/C inhibition by the spindle checkpoint. Mol Cell. 2004, 16: 387-397.View ArticlePubMedGoogle Scholar
- Maga G, Hubscher U: Proliferating cell nuclear antigen (PCNA): a dancer with many partners. J Cell Sci. 2003, 116: 3051-3060.View ArticlePubMedGoogle Scholar
- Crosby ME, Almasan A: Opposing roles of E2Fs in cell proliferation and death. Cancer Biol Ther. 2004, 3: 1208-1211.View ArticlePubMedPubMed CentralGoogle Scholar
- Lapeyre B, Bourbon H, Amalric F: Nucleolin, the major nucleolar protein of growing eukaryotic cells: an unusual protein structure revealed by the nucleotide sequence. Proc Natl Acad Sci USA. 1987, 84: 1472-1476.View ArticlePubMedPubMed CentralGoogle Scholar
- Derenzini M, Sirri V, Trere D, Ochs RL: The quantity of nucleolar proteins nucleolin and protein B23 is related to cell doubling time in human cancer cells. Lab Invest. 1995, 73: 497-502.PubMedGoogle Scholar
- Srivastava M, Pollard HB: Molecular dissection of nucleolin's role in growth and cell proliferation: new insights. FASEB J. 1999, 13: 1911-1922.PubMedGoogle Scholar
- Grinstein E, Shan Y, Karawajew L, Snijders PJ, Meijer CJ, Royer HD, Wernet P: Cell cycle-controlled interaction of nucleolin with the retinoblastoma protein and cancerous cell transformation. J Biol Chem. 2006, 281: 22223-22235.View ArticlePubMedGoogle Scholar
- Akira S, Isshiki H, Sugita T, Tanabe O, Kinoshita S, Nishio Y, Nakajima T, Hirano T, Kishimoto T: A nuclear factor for IL-6 expression (NF-IL6) is a member of a C/EBP family. EMBO J. 1990, 9: 1897-1906.PubMedPubMed CentralGoogle Scholar
- Starr R, Willson TA, Viney EM, Murray LJ, Rayner JR, Jenkins BJ, Gonda TJ, Alexander WS, Metcalf D, Nicola NA, Hilton DJ: A family of cytokine-inducible inhibitors of signalling. Nature. 1997, 387: 917-921.View ArticlePubMedGoogle Scholar
- Li J, Lee AS: Stress induction of GRP78/BiP and its role in cancer. Curr Mol Med. 2006, 6: 45-54.View ArticlePubMedGoogle Scholar
- Stefansson B, Brautigan DL: Protein phosphatase 6 subunit with conserved Sit4-associated protein domain targets IkappaBepsilon. J Biol Chem. 2006, 281: 22624-22634.View ArticlePubMedGoogle Scholar
- Monica K, Galili N, Nourse J, Saltman D, Cleary ML: PBX2 and PBX3, new homeobox genes with extensive homology to the human proto-oncogene PBX1. Mol Cell Biol. 1991, 11: 6149-6157.View ArticlePubMedPubMed CentralGoogle Scholar
- Knoepfler PS, Sykes DB, Pasillas M, Kamps MP: HoxB8 requires its Pbx-interaction motif to block differentiation of primary myeloid progenitors and of most cell line models of myeloid differentiation. Oncogene. 2001, 20: 5440-5448.View ArticlePubMedGoogle Scholar
- Garavito RM, Mulichak AM: The structure of mammalian cyclooxygenases. Annu Rev Biophys Biomol Struct. 2003, 32: 183-206.View ArticlePubMedGoogle Scholar
- Wiese FW, Thompson PA, Warneke J, Einspahr J, Alberts DS, Kadlubar FF: Variation in cyclooxygenase expression levels within the colorectum. Mol Carcinog. 2003, 37: 25-31.View ArticlePubMedGoogle Scholar
- DeWitt DL: Prostaglandin endoperoxide synthase: regulation of enzyme expression. Biochim Biophys Acta. 1991, 1083: 121-134.View ArticlePubMedGoogle Scholar
- Hla T, Ristimaki A, Appleby S, Barriocanal JG: Cyclooxygenase gene expression in inflammation and angiogenesis. Ann N Y Acad Sci. 1993, 696: 197-204.View ArticlePubMedGoogle Scholar
- Herschman HR: Regulation of prostaglandin synthase-1 and prostaglandin synthase-2. Cancer Metastasis Rev. 1994, 13: 241-256.View ArticlePubMedGoogle Scholar
- Wittke I, Wiedemeyer R, Pillmann A, Savelyeva L, Westermann F, Schwab M: Neuroblastoma-derived sulfhydryl oxidase, a new member of the sulfhydryl oxidase/Quiescin6 family, regulates sensitization to interferon gamma-induced cell death in human neuroblastoma cells. Cancer Res. 2003, 63: 7742-7752.PubMedGoogle Scholar
- Katzenberger T, Petzoldt C, Holler S, Mader U, Kalla J, Adam P, Ott MM, Müller-Hermelink HK, Rosenwald A, Ott G: The Ki67 proliferation index is a quantitative indicator of clinical risk in mantle cell lymphoma. Blood. 2006, 107: 3407-View ArticlePubMedGoogle Scholar
- Schrader C, Janssen D, Meusers P, Brittinger G, Siebmann JU, Parwaresch R, Tiemann M: Repp86: a new prognostic marker in mantle cell lymphoma. Eur J Haematol. 2005, 75: 498-504.View ArticlePubMedGoogle Scholar
- Rubio-Moscardo F, Climent J, Siebert R, Piris MA, Martin-Subero JI, Nielander I, Garcia-Conde J, Dyer MJ, Terol MJ, Pinkel D, Martinez-Climent JA: Mantle-cell lymphoma genotypes identified with CGH to BAC microarrays define a leukemic subgroup of disease and predict patient outcome. Blood. 2005, 105: 4445-4454.View ArticlePubMedGoogle Scholar
- Salaverria I, Zettl A, Bea S, Moreno V, Valls J, Hartmann E, Ott G, Wright G, Lopez-Guillermo A, Chan WC, Weisenburger DD, Gascoyne RD, Grogan TM, Delabie J, Jaffe ES, Montserrat E, Müller-Hermelink HK, Staudt LM, Rosenwald A, Campo E: Specific secondary genetic alterations in mantle cell lymphoma provide prognostic information independent of the gene expression-based proliferation signature. J Clin Oncol. 2007, 25: 1216-1222.View ArticlePubMedPubMed CentralGoogle Scholar
- Milde-Langosch K: The Fos family of transcription factors and their role in tumourigenesis. Eur J Cancer. 2005, 41: 2449-2461.View ArticlePubMedGoogle Scholar
- Hartl M, Bader AG, Bister K: Molecular targets of the oncogenic transcription factor jun. Curr Cancer Drug Targets. 2003, 3: 41-55.View ArticlePubMedGoogle Scholar
- Weiss C, Bohmann D: Deregulated repression of c-Jun provides a potential link to its role in tumorigenesis. Cell Cycle. 2004, 3: 111-113.View ArticlePubMedGoogle Scholar
- Eisenman RN: Deconstructing myc. Genes Dev. 2001, 15: 2023-2030.View ArticlePubMedGoogle Scholar
- Marcu KB, Bossone SA, Patel AJ: myc function and regulation. Annu Rev Biochem. 1992, 61: 809-860.View ArticlePubMedGoogle Scholar
- Pelengaris S, Khan M, Evan G: c-MYC: more than just a matter of life and death. Nat Rev Cancer. 2002, 2: 764-776.View ArticlePubMedGoogle Scholar
- Golay J, Cusmano G, Introna M: Independent regulation of c-myc, B-myb, and c-myb gene expression by inducers and inhibitors of proliferation in human B lymphocytes. J Immunol. 1992, 149: 300-308.PubMedGoogle Scholar
- Sala A, Watson R: B-Myb protein in cellular proliferation, transcription control, and cancer: latest developments. J Cell Physiol. 1999, 179: 245-250.View ArticlePubMedGoogle Scholar
- Horstmann S, Ferrari S, Klempnauer KH: Regulation of B-Myb activity by cyclin D1. Oncogene. 2000, 19: 298-306.View ArticlePubMedGoogle Scholar
- Cesi V, Tanno B, Vitali R, Mancini C, Giuffrida ML, Calabretta B, Raschella G: Cyclin D1-dependent regulation of B-myb activity in early stages of neuroblastoma differentiation. Cell Death Differ. 2002, 9: 1232-1239.View ArticlePubMedGoogle Scholar
- Schafer KA: The cell cycle: a review. Vet Pathol. 1998, 35: 461-478.View ArticlePubMedGoogle Scholar
- The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2407/8/106/prepub
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.