- Open Access
Identification of epigenetic modifications that contribute to pathogenesis in therapy-related AML: Effective integration of genome-wide histone modification with transcriptional profiles
© Yang et al.; licensee BioMed Central Ltd. 2015
- Published: 29 May 2015
Therapy-related, secondary acute myeloid leukemia (t-AML) is an increasingly frequent complication of intensive chemotherapy. This malignancy is often characterized by abnormalities of chromosome 7, including large deletions or chromosomal loss. A variety of studies suggest that decreased expression of the EZH2 gene located at 7q36.1 is critical in disease pathogenesis. This histone methyltransferase has been implicated in transcriptional repression through modifying histone H3 on lysine 27 (H3k27). However, the critical target genes of EZH2 and their regulatory roles remain unclear.
To characterize the subset of EZH2 target genes that might contribute to t-AML pathogenesis, we developed a novel computational analysis to integrate tissue-specific histone modifications and genome-wide transcriptional regulation. Initial integrative analysis utilized a novel "seq2gene" strategy to explore largely the target genes of chromatin immuneprecipitation sequencing (ChIP-seq) enriched regions. By combining seq2gene with our Phenotype-Genotype-Network (PGNet) algorithm, we enriched genes with similar expression profiles and genomic or functional characteristics into "biomodules".
Initial studies identified SEMA3A (semaphoring 3A) as a novel oncogenic candidate that is regulated by EZH2-silencing, using data derived from both normal and leukemic cell lines as well as murine cells deficient in EZH2. A microsatellite marker at the SEMA3A promoter has been associated with chemosensitivity and radiosensitivity. Notably, our subsequent studies in primary t-AML demonstrate an expected up-regulation of SEMA3A that is EZH2-modulated. Furthermore, we have identified three biomodules that are co-expressed with SEMA3A and up-regulated in t-AML, one of which consists of previously characterized EZH2-repressed gene targets. The other two biomodules include MAPK8 and TATA box targets. Together, our studies suggest an important role for EZH2 targets in t-AML pathogenesis that warrants further study.
These developed computational algorithms and systems biology strategies will enhance the knowledge discovery and hypothesis-driven analysis of multiple next generation sequencing data, for t-AML and other complex diseases.
- computational biology
The significance of non-coding DNA regulators in human disease has drawn increasing attention. For example, human non-coding regions contain collections of transcription factor binding sites and other regulatory elements called "cis-regulatory" regions. These cis-regulatory elements are sufficient to activate transcription in a defined spatial and temporal expression domain . Cis-regulation can occur on either side of a target transcript and regulators can reside far from their regulatory targets . However, identifying cis-regulatory elements and their domain-specific targets remains a major challenge for current computational biology. To address the challenge, we here perform a "sequence-regulator-network" study to integrate information from histone modification and transcriptional regulation. This method both generates and validates genomic hypotheses, and could have a broad impact in studying regulatory mechanisms of gene expression in systems biology. Here, we select therapy-related acute myeloid leukemia (t-AML) as a clinically significant context to apply the method.
T-AML, including therapy-related myelodysplastic syndrome, accounts for approximately 10 to 20 percent of myeloid malignancies . T-AML complicates conventional chemoradiotherapies that are used to treat a variety of primary malignancies and is associated with a uniformly poor prognosis, with a median survival of six months . Complete loss of chromosome 7 (-7) and 5 or partial deletion involving the long arm of chromosome 7 (del7q) are highly recurrent chromosomal aberrations in AML and t-AML.
Specific interest has focused on the link between common chromosome 7 abnormalities and the location of the EZH2 gene, the histone methyltransferase enhancer of Zeste homologue 2 present at position 7q36.1 . Not surprisingly, EZH2 expression is significantly reduced in -7/del7q patients with myeloid disorders when compared with healthy controls . What remains an enigma is the recently reported dual role of EZH2 in malignant cell development. EZH2 is a component of the polycomb group complex, which is vital for hematopoietic cell development. In normal cells, EZH2 suppresses its targets through depositing the histone modification mark H3K27me3 (trimethylation on lysine 27 of histone H3) . In several epithelial cancers, overexpression of wild-type EZH2 has been found to promote tumor progression or metastasis [7, 8]. However, inactive mutated EZH2 or its low expression in myeloid malignancy contributes to tumorigenesis by suppressing differentiation, thus directing cells toward a leukemic stem cell state [5, 9, 10]. Conflictingly, Xu et al. reported that EZH2 overexpression was associated with poor patient outcome in myeloid disorders and chemotherapy reduced expression of EZH2 .
These conflicting observations about EZH2 suggest a context-specific regulatory mechanism, which may be explained by plastic epigenetic modification . Histone methylation is an important epigenetic modification in chromatin. The histone modification mark H3K27me3 reflects EZH2-involved Polycomb-mediated repression, and the deposition of H3K27me3 is development- stage-, and tissue-specific . Thus, we studied the presence of H3K27me3 to understand the conflicting roles of EZH2 -- as either an oncogene or a tumor suppressor in different tumors. However, abnormal EZH2-associated regulation to particular target genes remains unclear in leukemia, specifically in t-AML.
Using ChIP-seq (Chromatin Immunoprecipitation Sequencing) of histone marks and other regulatory proteins, researchers can perform genome-wide searches for intergenic functional elements (including promoters and enhancers), but might also identify non-enhancers with similar signatures . Therefore in this study, we evaluated whether the selective regions control the developmental expression of the target genes using transcriptomic measurements. To identify EZH2 target genes and their functional regions in t-AML, we developed a novel computational integrative analysis with histone modification of H3K27me3 and gene expression.
In the proposed integrative analysis, there are three distinguishing features: 1) a novel "seq2gene" strategy links genomic regions to more neighboring coding genes on both sides, 2) selective transcriptional and epigenetic data mining between cells, and 3) the prediction of "biomodules". The seq2gene strategy links genomic regions to a broad range of neighboring genes rather than the nearest one. The rationale is that enhancers can target long-range DNA targets, and often multiple enhancers (five or more) target the same genes . The strategy then incorporates epigenetic regulatory patterns that differ between cell lines with disease-specific transcript alterations in t-AML. Finally, the selected target gene (seed) is associated with a group of genes, the "biomodule" that share similar expression patterns and genomic or functional characteristics, using our PGNet algorithm .
This integrative "sequence-regulator-network" study revealed SEMA3A (semaphoring 3A) as a novel target of EZH2-silencing in t-AML. The fact that EZH2 and SEM3A3 are inversely expressed in vivo is supported by previous data in mouse haematopoietic stem cells and human prostate cancer [16, 17]. We predict that the loss of EZH2 silencing on SEMA3A augments sensitivity to both chemo- and radiotherapy, and thus may contribute to therapy-related AML pathogenesis. We also show that a group of SEMA3A-coexpressed genes, including HOXA11, are up-regulated in t-AML and have been reported as EZH2 targets. We expect further validation both in vitro and in vivo.
Identification of SEMA3A, a loss-of-EZH2-mediated silencing gene in leukemia
To systematically screen functional elements of histone modified EZH2 targets in leukemia, we used the data in the Encyclopedia of DNA Elements (ENCODE, http://genome.ucsc.edu/ENCODE/) Project . We identified 104,370 genomic regions that are enriched for both EZH2 and H3K27me3 in the leukemia cell line (K562) and 53,360 regions in the lymphoblastoid cell line (GM12878). The large number of enriched regions (peaks) suggests substantial downstream effects of EZH2 repression. EZH2 occupancy and presence of H3K27me3 at promoters directly silences the transcription of targeted genes, which has been observed in leukemia and other tumors [19, 20]. Given this, we predict that genes adjacent to these non-coding regions are EZH2 repressive target candidates.
To focus on EZH2-mediated coding-gene silencing that is specific to leukemia, we compared the candidates in leukemia with lymphoblastoids. Only around 10% of the identified ~17,400 EZH2 repressed coding-gene candidates are disease-specific, resulting in 1624 genes specific to lymphoblastoid but not leukemia (Figure 1C). Gene Ontology enrichment analysis suggests a loss of leukemia-specific repressive control on the molecular function termed "hematopoietin/interferon-class (D200 domain) cytokine receptor binding" (GO:0005126, FDR = 0.0017, count = 12), reflecting a cell quiescence-involved, generic cancer metastatic mechanism [22, 23].
We subsequently identified SEMA3A as an EZH2 repressive target of interest. This identification is derived from sequence-based analysis and transcriptional evidence (Figure 1D). From the ChIP-seq peaks in the lymphoblastoid cell line, we observed EZH2 occupancy and presence of H3K27me3 adjacent to the transcription start site of SEMA3A and 20 other genes (Additional file: Table S1). These 21 genes significantly over-represent genes highly expressed in prostate cancer cells after knockdown of EZH2  (p = 0.009, OR = 5.8) and genes down-regulated in fibroblasts expressing mutant forms of ERCC3 after UV irradiation  (P = 0.0046, OR = 7.1). As ERCC3 could help increase the sensitivity of cancer to radiation therapy, loss of EZH2-repression of these ERCC3 targets in t-AML indicates an increased radiosensitivity. Specifically, the transcriptional expression of SEMA3A is negatively EZH2-dependent in both human cancer and mouse model in vivo. For example, Merchan et al. generated mouse models that allow gain-of-function of Ezh2 in the haematopoietic system , and we identified a 2.2-fold decrease of Sema3a expression in Ezh2+ mice compared with wild-types (Q-value = 0.05, the limma test ). However, in the leukemia cell line, a peak with both EZH2 and H3K27me3 enrichment has not been observed in the human genomic region within 150kbp distance to SEMA3A, suggesting a leukemia-specific loss of EZH2-silencing on SEMA3A.
High-expression of SEMA3A may contribute to t-AML pathogenesis by augmenting chemosensitivity and radiosensitivity
We observed the loss of EZH2 and H3K27me3 enrichment on the SEMA3A promoter in leukemia (Figure 1C-D) and hypothesized that this loss rescues SEMA3A expression and facilitates leukemogenesis after chemo- or radiotherapy.
Six studies pertaining to CD34+ cells in t-AML and normal controls.
t-MDS/tAML CD34+ progenitor (BM), -5/del5q
t-MDS/tAML CD34+ progenitor (BM), -7/del7q
t-MDS/tAML CD34+ progenitor (BM), -5/del5q or -7/del7q
t-MDS/tAML CD34+ progenitor (BM), normal 5 and normal 7
normal progenitor (CD34+, BM)
normal HSC+ (CD34+CD133+, BM)
normal HSC+ (Lin-CD34+CD38-, PB)
normal HSC+ (Lin-CD34+CD38loCD36-, PB)
normal HSC+ (Lin-CD34+CD38-CD90+, PB or BM)
normal HSC+ (Lin-CD34+CD38-CD90+CD45RA-, PB)
normal HSC+ (Lin-D34+CD38-CD90+CD45RA-, BM)
The stem cell self-renewal HOX gene family has been described as a major downstream target of EZH2. Unlike HOXA11, the other four HOX family genes (HOXA3, HOXA4, HOXA5, and HOXA9) are down-regulated when comparing -7/del7q t-AML cells against controls (Q-value<0.001, fold change (FC)<0.4, Figure 2C). Of note, sublethally irradiated Hoxa9-/- mice exhibited prolonged suppression of hematopoiesis and developed persistent pancytopenia , indicating an enhanced sensitivity to ionizing irradiation in t-AML cells with deficient HOXA9.
Genes co-regulated with SEMA3A in t-AML are enriched in EZH2 repressed targets
Three functional and transcriptional biomodules.
Fisher's exact test
Genes up-regulated in vascular smooth muscle cells (VSMC) by MAPK8 (JNK1)
Genes with promoter regions [-2kb,2kb] around transcription start site containing the motif TATAAA which matches annotation for TAF TATA
Genes up-regulated in PC3 cells (prostate cancer) after knockdown of EZH2 by RNAi.
The chromatin immunoprecipitation sequencing (ChIP-seq) peaks for EZH2 occupancy and presence of H3K27me3 were downloaded from the Encyclopedia of DNA Elements (ENCODE, Release 3, hg19 assembly) . We focused on human blood cell lines: K562 from leukemia and GM12878 from lymphoblastoid cell of a female donor.
We collected published expression levels of CD34+ progenitor cells from 28 t-AML patients and 24 healthy control samples (Table 1) [39–43]. Samples from patients with breast cancer or lymphoma before the development of t-AML were excluded from the control group [39, 40].
Functional gene-sets were defined by the MsigDB database .
Identifying target genes of transcription regulators or histone marks
Analyzing transcriptomic data
Data pre-processing. The normalized expression profiles were downloaded from the Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/) or ArrayExpress (http://www.ebi.ac.uk/arrayexpress/). The raw data from the Qian's dataset was processed using the global rank-invariant normalization (GRSN) . Gene expression profiles were log2-transformed if authors hadn't already done so. When collapsing probes to genes for each dataset, probes with the same Entrez IDs were collapsed to the maximum mean expression per gene using an empirically recommended method  and the Bioconductor annotation package biomaRt (v2.18.0), resulting in a set of 8442 human Entrez genes measured across all cohorts. The batch effects due to multi-datasets were removed by an empirical Bayes method using the Bioconductor package sva [26, 47] (Additional file: Fig. S1). Then a smaller data space of 4221 genes was considered for the following analysis by keeping the half of genes with the highest interquartile range .
Step 1 to build biomodules: transcriptional association. To identify the potential upstream regulators or down-stream targets of the genes that drive the tumorigenesis in t-AML, we applied the PGNet method . PGNet evaluates the similarity of the gene orders between two independently ordered lists. Specifically, we used PGNet to compare genome-wide correlated expressions with a seed gene and the differential expressions between two sample groups and yielded a regulatory network of genes that are mutually associated. To identify the potential downstream regulatory targets of SEMA3A, we inputted it as a seed gene together with the phenotype information (t-AML: n = 28, normal control: n = 24) into the PGNet system to infer a regulatory network . Differential expression (DE) was estimated using the Bioconductor package Limma  and co-expression (CE) was evaluated by the Pearson correlation test on the log-transformed data. The resulting p-values were adjusted by a Q-value for false discovery rate in the multiple testing problem [50, 51]. The significance of the similarity between the two statistics (coefficient and fold change) was estimated using an empirical p-value based on permutation (n = 1000), using the Bioconductor package OrderedList . We also tested the differential expression between -q/del(7q) t-AML (n = 8) and normal controls to determine whether a regulatory biomodule for t-AML is consistent despite loss of chromosome 7.
Step 2 to build biomodules: genomic or functional enrichment. The over-representation of functional gene-sets among the identified biomodule was evaluated using a conditional hypergeometric distribution test (Additional file: Fig. S2). Note that only the true background, the common genes within MSigDB and those covered by the experiment, were used for the test. We used a threshold of Q-value<0.001 and count>5 for significance over MSigDB defined functional genesets .
The ENCODE project increasingly produces genomic data on transcription factor binding, chromatin structure and histone modification. Interpreting transcriptional regulation in relation to chromatin modifications has been recognized as a powerful strategy to discover and understand intergenic regulatory elements (reviewed by Kellis et al. ). However, ChIP-seq enrichment for chromatin modification and differential expression for transcriptional control may provide complementary information. Thus, the simple overlap strategy, that we here used, may discover only limited targets . We expect additional strategies to reveal more candidates than just a single locus to elucidate genomic function in human biology and diseases. To address this challenge, we piloted the application of the PGNet algorithm to build genomic and functional related as well transcriptionally associated gene-sets (biomodules). The identified biomodule of EZH2-suppressed targets that up-regulated in t-AML in conjunction with SEMA3A, in turn, evaluated our hypothesis that SEMA3A is a critical EZH2-silencing target.
The reduced EZH2 expression in t-AML parallels reduced expression observed in primary AML or pre-leukemia, which was previously found in 78% of patients carrying either EZH2 inactive mutation or -7/del7q involving the EZH2 locus . Importantly, primary AML patients who have lower EZH2 expression (either spliceosomal mutants or -7/del7q) show decreased H3K27 trimethylation and increased chromatin relaxation at specific gene loci accompanied by higher transcriptional activity . Using the proposed "sequence-regulator-network" strategy, we identified SEMA3A as a new such gene target that loses epigenetically modified EZH2 silencing in t-AML. The identified gene locus covers a radiation sensitivity mark revealed by genome-wide association study . However, HOXA9 was found to be overexpressed in cases of either EZH2 mutations or -7/del7q when compared to EZH2 wild-type , which differs from the observation in our t-AML samples. This observation indicates a commonly reduced histone modification and alternative leukemogenic regulation on HOXA9 between t-AML and primary AML. Both involve EZH2 and potentially some of its DNA-binding cofactors. We expect further validation both in vitro and in vivo.
A literature review suggests two possible mechanisms to explain why this epigenetically modified up-regulation of SEMA3A contributes to pathogenesis in therapy-related AML. The first could be a reduction of DNA repair capacity, given that Sema3A suppresses angiogenesis and migration in mice models [54, 55] and thus triggers the sensitivity of leukemic cells to apoptosis signal , possibly via a MAPK8 regulated pathway (Figure 4C). On the other hand, a previous study found that Sema3A counteracted chemotherapy-induced activation of epithelial-mesenchymal transition (EMT) by improving cancer tissue oxygenation and extending the vascular normalization . Therefore, the second mechanism could be the production of cancer stem cells (CSCs), given that cells that undergo EMT gain stem cell-like properties .
In summary, we predicted EZH2-silencing targets and their functions in t-AML by performing a novel computational integrative analysis. The analysis incorporates chromatin-based epigenetic regulation patterns in different cell lines with transcriptional expression alteration pertaining to t-AML. This integrative analysis promises to reveal novel functional elements in a complex and versatile regulatory system behind target gene selection and their tissue-specific expression.
We thank Joshua Stevens-Stein for his careful proof-reading. We acknowledge the assistance of Lorenzo Pesce for running BEAGLE supported by the NIH under grant [1S10OD018495-01].
Publication of this article has been funded by the National Institutes of Health [R21 CA167305].
This article has been published as part of BMC Medical Genomics Volume 8 Supplement 2, 2015: Selected articles from the 4th Translational Bioinformatics Conference and the 8th International Conference on Systems Biology (TBC/ISB 2014). The full contents of the supplement are available online at http://www.biomedcentral.com/bmcmedgenomics/supplements/8/S2.
- Wittkopp PJ, Kalay G: Cis-regulatory elements: molecular mechanisms and evolutionary processes underlying divergence. Nat Rev Genet. 2012, 13 (1): 59-69. 10.1038/nri3362.View ArticleGoogle Scholar
- Visel A, Rubin EM, Pennacchio LA: Genomic views of distant-acting enhancers. Nature. 2009, 461 (7261): 199-205. 10.1038/nature08451.View ArticlePubMedPubMed CentralGoogle Scholar
- Godley LA, Larson RA: Therapy-related myeloid leukemia. Semin Oncol. 2008, 35 (4): 418-429. 10.1053/j.seminoncol.2008.04.012.View ArticlePubMedPubMed CentralGoogle Scholar
- Ernst T, Chase AJ, Score J, Hidalgo-Curtis CE, Bryant C, Jones AV, Waghorn K, Zoi K, Ross FM, Reiter A, et al: Inactivating mutations of the histone methyltransferase gene EZH2 in myeloid disorders. Nat Genet. 2010, 42 (8): 722-726. 10.1038/ng.621.View ArticlePubMedGoogle Scholar
- Jerez A, Sugimoto Y, Makishima H, Verma A, Jankowska AM, Przychodzen B, Visconte V, Tiu RV, O'Keefe CL, Mohamedali AM, et al: Loss of heterozygosity in 7q myeloid disorders: clinical associations and genomic pathogenesis. Blood. 2012, 119 (25): 6109-6117. 10.1182/blood-2011-12-397620.View ArticlePubMedPubMed CentralGoogle Scholar
- Cao R, Wang L, Wang H, Xia L, Erdjument-Bromage H, Tempst P, Jones RS, Zhang Y: Role of histone H3 lysine 27 methylation in Polycomb-group silencing. Science. 2002, 298 (5595): 1039-1043. 10.1126/science.1076997.View ArticlePubMedGoogle Scholar
- Varambally S, Dhanasekaran SM, Zhou M, Barrette TR, Kumar-Sinha C, Sanda MG, Ghosh D, Pienta KJ, Sewalt RG, Otte AP, et al: The polycomb group protein EZH2 is involved in progression of prostate cancer. Nature. 2002, 419 (6907): 624-629. 10.1038/nature01075.View ArticlePubMedGoogle Scholar
- Kleer CG, Cao Q, Varambally S, Shen R, Ota I, Tomlins SA, Ghosh D, Sewalt RG, Otte AP, Hayes DF, et al: EZH2 is a marker of aggressive breast cancer and promotes neoplastic transformation of breast epithelial cells. Proc Natl Acad Sci USA. 2003, 100 (20): 11606-11611. 10.1073/pnas.1933744100.View ArticlePubMedPubMed CentralGoogle Scholar
- Khan SN, Jankowska AM, Mahfouz R, Dunbar AJ, Sugimoto Y, Hosono N, Hu Z, Cheriyath V, Vatolin S, Przychodzen B, et al: Multiple mechanisms deregulate EZH2 and histone H3 lysine 27 epigenetic changes in myeloid malignancies. Leukemia : official journal of the Leukemia Society of America, Leukemia Research Fund, UK. 2013, 27 (6): 1301-1309.View ArticleGoogle Scholar
- Ohm JE, McGarvey KM, Yu X, Cheng L, Schuebel KE, Cope L, Mohammad HP, Chen W, Daniel VC, Yu W, et al: A stem cell-like chromatin pattern may predispose tumor suppressor genes to DNA hypermethylation and heritable silencing. Nat Genet. 2007, 39 (2): 237-242. 10.1038/ng1972.View ArticlePubMedPubMed CentralGoogle Scholar
- Xu F, Li X, Wu L, Zhang Q, Yang R, Yang Y, Zhang Z, He Q, Chang C: Overexpression of the EZH2, RING1 and BMI1 genes is common in myelodysplastic syndromes: relation to adverse epigenetic alteration and poor prognostic scoring. Annals of hematology. 2011, 90 (6): 643-653. 10.1007/s00277-010-1128-5.View ArticlePubMedGoogle Scholar
- Zardo G, Cimino G, Nervi C: Epigenetic plasticity of chromatin in embryonic and hematopoietic stem/progenitor cells: therapeutic potential of cell reprogramming. Leukemia : official journal of the Leukemia Society of America, Leukemia Research Fund, UK. 2008, 22 (8): 1503-1518. 10.1038/leu.2008.141.View ArticleGoogle Scholar
- Arnold CD, Gerlach D, Stelzer C, Boryn LM, Rath M, Stark A: Genome-wide quantitative enhancer activity maps identified by STARR-seq. Science. 2013, 339 (6123): 1074-1077. 10.1126/science.1232542.View ArticlePubMedGoogle Scholar
- de Laat W, Duboule D: Topology of mammalian developmental enhancers and their regulatory landscapes. Nature. 2013, 502 (7472): 499-506. 10.1038/nature12753.View ArticlePubMedGoogle Scholar
- Yang X, Huang Y, Chen JL, Xie J, Sun X, Lussier YA: Mechanism-anchored profiling derived from epigenetic networks predicts outcome in acute lymphoblastic leukemia. BMC Bioinformatics. 2009, 10 (Suppl 9): S6-10.1186/1471-2105-10-S9-S6.View ArticlePubMedPubMed CentralGoogle Scholar
- Nuytten M, Beke L, Van Eynde A, Ceulemans H, Beullens M, Van Hummelen P, Fuks F, Bollen M: The transcriptional repressor NIPP1 is an essential player in EZH2-mediated gene silencing. Oncogene. 2008, 27 (10): 1449-1460. 10.1038/sj.onc.1210774.View ArticlePubMedGoogle Scholar
- Herrera-Merchan A, Arranz L, Ligos JM, de Molina A, Dominguez O, Gonzalez S: Ectopic expression of the histone methyltransferase Ezh2 in haematopoietic stem cells causes myeloproliferative disease. Nature communications. 2012, 3: 623-View ArticlePubMedGoogle Scholar
- Consortium EP: The ENCODE (ENCyclopedia Of DNA Elements) Project. Science. 2004, 306 (5696): 636-640.View ArticleGoogle Scholar
- Gao SB, Xu B, Ding LH, Zheng Q, Zhang L, Zheng QF, Li SH, Feng ZJ, Wei J, Yin ZY, et al: The functional and mechanistic relatedness of the EZH2 and menin in hepatocellular carcinoma. Journal of hepatology. 2014Google Scholar
- Chen J, Li J, Han Q, Sun Z, Wang J, Wang S, Zhao RC: Enhancer of zeste homolog 2 is overexpressed and contributes to epigenetic inactivation of p21 and phosphatase and tensin homolog in B-cell acute lymphoblastic leukemia. Exp Biol Med (Maywood). 2012, 237 (9): 1110-1116. 10.1258/ebm.2012.012075.View ArticleGoogle Scholar
- Heintzman ND, Ren B: Finding distal regulatory elements in the human genome. Current opinion in genetics & development. 2009, 19 (6): 541-549. 10.1016/j.gde.2009.09.006.View ArticleGoogle Scholar
- Vashisht S, Bagler G: An approach for the identification of targets specific to bone metastasis using cancer genes interactome and gene ontology analysis. PLoS One. 2012, 7 (11): e49401-10.1371/journal.pone.0049401.View ArticlePubMedPubMed CentralGoogle Scholar
- Chechlinska M, Siwicki JK, Gos M, Oczko-Wojciechowska M, Jarzab M, Pfeifer A, Jarzab B, Steffen J: Molecular signature of cell cycle exit induced in human T lymphoblasts by IL-2 withdrawal. BMC Genomics. 2009, 10: 261-10.1186/1471-2164-10-261.View ArticlePubMedPubMed CentralGoogle Scholar
- da Costa RM, Riou L, Paquola A, Menck CF, Sarasin A: Transcriptional profiles of unirradiated or UV-irradiated human cells expressing either the cancer-prone XPB/CS allele or the noncancer-prone XPB/TTD allele. Oncogene. 2005, 24 (8): 1359-1374. 10.1038/sj.onc.1208288.View ArticlePubMedGoogle Scholar
- Smyth G: limma: Linear Models for Microarray Data. Bioinformatics and Computational Biology Solutions Using R and Bioconductor. Edited by: Gentleman R, Carey V, Huber W, Irizarry R, Dudoit S. 2005, New York: Springer VerlagGoogle Scholar
- Johnson WE, Li C, Rabinovic A: Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics. 2007, 8 (1): 118-127. 10.1093/biostatistics/kxj037.View ArticlePubMedGoogle Scholar
- Greshock J, Nathanson K, Martin AM, Zhang L, Coukos G, Weber BL, Zaks TZ: Cancer cell lines as genetic models of their parent histology: analyses based on array comparative genomic hybridization. Cancer Res. 2007, 67 (8): 3594-3600. 10.1158/0008-5472.CAN-06-3674.View ArticlePubMedGoogle Scholar
- Jazaeri AA, Awtrey CS, Chandramouli GV, Chuang YE, Khan J, Sotiriou C, Aprelikova O, Yee CJ, Zorn KK, Birrer MJ, et al: Gene expression profiles associated with response to chemotherapy in epithelial ovarian cancers. Clin Cancer Res. 2005, 11 (17): 6300-6310. 10.1158/1078-0432.CCR-04-2682.View ArticlePubMedGoogle Scholar
- Choi CH, Choi JJ, Park YA, Lee YY, Song SY, Sung CO, Song T, Kim MK, Kim TJ, Lee JW, et al: Identification of differentially expressed genes according to chemosensitivity in advanced ovarian serous adenocarcinomas: expression of GRIA2 predicts better survival. Br J Cancer. 2012, 107 (1): 91-99. 10.1038/bjc.2012.217.View ArticlePubMedPubMed CentralGoogle Scholar
- Michikawa Y, Suga T, Ishikawa A, Hayashi H, Oka A, Inoko H, Iwakawa M, Imai T: Genome wide screen identifies microsatellite markers associated with acute adverse effects following radiotherapy in cancer patients. BMC medical genetics. 2010, 11: 123-10.1186/1471-2350-11-123.View ArticlePubMedPubMed CentralGoogle Scholar
- Cooper GM, Stone EA, Asimenos G, Program NCS, Green ED, Batzoglou S, Sidow A: Distribution and intensity of constraint in mammalian genomic sequence. Genome Res. 2005, 15 (7): 901-913. 10.1101/gr.3577405.View ArticlePubMedPubMed CentralGoogle Scholar
- Kellis M, Wold B, Snyder MP, Bernstein BE, Kundaje A, Marinov GK, Ward LD, Birney E, Crawford GE, Dekker J, et al: Defining functional DNA elements in the human genome. Proc Natl Acad Sci USA. 2014, 111 (17): 6131-6138. 10.1073/pnas.1318948111.View ArticlePubMedPubMed CentralGoogle Scholar
- Lawrence HJ, Christensen J, Fong S, Hu YL, Weissman I, Sauvageau G, Humphries RK, Largman C: Loss of expression of the Hoxa-9 homeobox gene impairs the proliferation and repopulating ability of hematopoietic stem cells. Blood. 2005, 106 (12): 3988-3994. 10.1182/blood-2005-05-2003.View ArticlePubMedPubMed CentralGoogle Scholar
- Yang X, Sun X: Meta-analysis of several gene lists for distinct types of cancer: a simple way to reveal common prognostic markers. BMC Bioinformatics. 2007, 8: 118-10.1186/1471-2105-8-118.View ArticlePubMedPubMed CentralGoogle Scholar
- Lottaz C, Yang X, Scheid S, Spang R: OrderedList--a bioconductor package for detecting similarity in ordered gene lists. Bioinformatics. 2006, 22 (18): 2315-2316. 10.1093/bioinformatics/btl385.View ArticlePubMedGoogle Scholar
- Yoshimura K, Aoki H, Ikeda Y, Fujii K, Akiyama N, Furutani A, Hoshii Y, Tanaka N, Ricci R, Ishihara T, et al: Regression of abdominal aortic aneurysm by inhibition of c-Jun N-terminal kinase. Nat Med. 2005, 11 (12): 1330-1338. 10.1038/nm1335.View ArticlePubMedGoogle Scholar
- Yuan F, Xu Z, Yang M, Wei Q, Zhang Y, Yu J, Zhi Y, Liu Y, Chen Z, Yang J: Overexpressed DNA polymerase iota regulated by JNK/c-Jun contributes to hypermutagenesis in bladder cancer. PLoS One. 2013, 8 (7): e69317-10.1371/journal.pone.0069317.View ArticlePubMedPubMed CentralGoogle Scholar
- Mikkelsen TS, Ku M, Jaffe DB, Issac B, Lieberman E, Giannoukos G, Alvarez P, Brockman W, Kim TK, Koche RP, et al: Genome-wide maps of chromatin state in pluripotent and lineage-committed cells. Nature. 2007, 448 (7153): 553-560. 10.1038/nature06008.View ArticlePubMedPubMed CentralGoogle Scholar
- Qian Z, Fernald AA, Godley LA, Larson RA, Le Beau MM: Expression profiling of CD34+ hematopoietic stem/ progenitor cells reveals distinct subtypes of therapy-related acute myeloid leukemia. Proc Natl Acad Sci USA. 2002, 99 (23): 14925-14930. 10.1073/pnas.222491799.View ArticlePubMedPubMed CentralGoogle Scholar
- Li L, Li M, Sun C, Francisco L, Chakraborty S, Sabado M, McDonald T, Gyorffy J, Chang K, Wang S, et al: Altered hematopoietic cell gene expression precedes development of therapy-related myelodysplasia/acute myeloid leukemia and identifies patients at risk. Cancer Cell. 2011, 20 (5): 591-605. 10.1016/j.ccr.2011.09.011.View ArticlePubMedPubMed CentralGoogle Scholar
- Gentles AJ, Plevritis SK, Majeti R, Alizadeh AA: Association of a leukemic stem cell gene expression signature with clinical outcomes in acute myeloid leukemia. JAMA. 2010, 304 (24): 2706-2715. 10.1001/jama.2010.1862.View ArticlePubMedPubMed CentralGoogle Scholar
- Eppert K, Takenaka K, Lechman ER, Waldron L, Nilsson B, van Galen P, Metzeler KH, Poeppl A, Ling V, Beyene J, et al: Stem cell gene expression programs influence clinical outcome in human leukemia. Nat Med. 2011, 17 (9): 1086-1093. 10.1038/nm.2415.View ArticlePubMedGoogle Scholar
- Majeti R, Becker MW, Tian Q, Lee TL, Yan X, Liu R, Chiang JH, Hood L, Clarke MF, Weissman IL: Dysregulated gene expression networks in human acute myelogenous leukemia stem cells. Proc Natl Acad Sci USA. 2009, 106 (9): 3396-3401. 10.1073/pnas.0900089106.View ArticlePubMedPubMed CentralGoogle Scholar
- Liberzon A: A Description of the Molecular Signatures Database (MSigDB) Web Site. Methods Mol Biol. 2014, 1150: 153-160.View ArticlePubMedGoogle Scholar
- Pelz CR, Kulesz-Martin M, Bagby G, Sears RC: Global rank-invariant set normalization (GRSN) to reduce systematic distortions in microarray data. BMC Bioinformatics. 2008, 9: 520-10.1186/1471-2105-9-520.View ArticlePubMedPubMed CentralGoogle Scholar
- Miller JA, Cai C, Langfelder P, Geschwind DH, Kurian SM, Salomon DR, Horvath S: Strategies for aggregating gene expression data: the collapseRows R function. BMC Bioinformatics. 2011, 12: 322-10.1186/1471-2105-12-322.View ArticlePubMedPubMed CentralGoogle Scholar
- Leek JT, Scharpf RB, Bravo HC, Simcha D, Langmead B, Johnson WE, Geman D, Baggerly K, Irizarry RA: Tackling the widespread and critical impact of batch effects in high-throughput data. Nat Rev Genet. 2010, 11 (10): 733-739. 10.1038/nrg2825.View ArticlePubMedGoogle Scholar
- Heydebreck Av, Huber W, Gentleman R: Differential Expression with the Bioconductor Project. Encyclopedia of Genetics, Genomics, Proteomics and Bioinformatics. Edited by: Jorde L, Little P, Dunn M, Subramaniam S. 2009, John Wiley & SonsGoogle Scholar
- Yang X, Huang Y, Chen JL, Xie J, Sun X, Lussier YA: Mechanism-Anchored Profiling derived from Epigenetic Networks Predicts Outcome in Acute Lymphoblastic Leukemia. BMC Bioinformatics. 2009Google Scholar
- Storey JD, Tibshirani R: Statistical significance for genomewide studies. Proc Natl Acad Sci USA. 2003, 100 (16): 9440-9445. 10.1073/pnas.1530509100.View ArticlePubMedPubMed CentralGoogle Scholar
- Storey JD, Tibshirani R: Statistical methods for identifying differentially expressed genes in DNA microarrays. Methods Mol Biol. 2003, 224: 149-157.PubMedGoogle Scholar
- Meyer CA, Liu XS: Identifying and mitigating bias in next-generation sequencing methods for chromatin biology. Nat Rev Genet. 2014, 15 (11): 709-721.View ArticlePubMedPubMed CentralGoogle Scholar
- Aerts S, Haeussler M, van Vooren S, Griffith OL, Hulpiau P, Jones SJ, Montgomery SB, Bergman CM, Open Regulatory Annotation C: Text-mining assisted regulatory annotation. Genome Biol. 2008, 9 (2): R31-10.1186/gb-2008-9-2-r31.View ArticlePubMedPubMed CentralGoogle Scholar
- Mishra R, Thorat D, Soundararajan G, Pradhan SJ, Chakraborty G, Lohite K, Karnik S, Kundu GC: Semaphorin 3A upregulates FOXO 3a-dependent MelCAM expression leading to attenuation of breast tumor growth and angiogenesis. Oncogene. 2014Google Scholar
- Nehil M, Paquette J, Tokuyasu T, McCormick F: High mobility group box 1 promotes tumor cell migration through epigenetic silencing of semaphorin 3A. Oncogene. 2013Google Scholar
- Baratti MO, Moreira YB, Traina F, Costa FF, Verjovski-Almeida S, Olalla-Saad ST: Identification of protein-coding and non-coding RNA expression profiles in CD34+ and in stromal cells in refractory anemia with ringed sideroblasts. BMC Med Genomics. 2010, 3: 30-10.1186/1755-8794-3-30.View ArticlePubMedPubMed CentralGoogle Scholar
- Maione F, Capano S, Regano D, Zentilin L, Giacca M, Casanovas O, Bussolino F, Serini G, Giraudo E: Semaphorin 3A overcomes cancer hypoxia and metastatic dissemination induced by antiangiogenic treatment in mice. J Clin Invest. 2012, 122 (5): 1832-1848. 10.1172/JCI58976.View ArticlePubMedPubMed CentralGoogle Scholar
- Mani SA, Guo W, Liao MJ, Eaton EN, Ayyanan A, Zhou AY, Brooks M, Reinhard F, Zhang CC, Shipitsin M, et al: The epithelial-mesenchymal transition generates cells with properties of stem cells. Cell. 2008, 133 (4): 704-715. 10.1016/j.cell.2008.03.027.View ArticlePubMedPubMed CentralGoogle Scholar
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