Gene Expression Profiles in Acute Myeloid Leukemia (AML): From Diagnosis to Prognosis.

Blood ◽  
2005 ◽  
Vol 106 (11) ◽  
pp. 2996-2996
Author(s):  
Sanggyu Lee ◽  
Jianjun Chen ◽  
Goulin Zhou ◽  
Run Shi ◽  
Masha Kocherginsky ◽  
...  
Keyword(s):  
Gene Expression ◽  
De Novo ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Major Change ◽  
Molecular Signature ◽  
Human Leukemia ◽  
Prognostic Information ◽  
Myeloid Progenitor Cells ◽  
Novel Transcripts

Abstract Chromosome translocations are among the most common genetic abnormalities in human leukemia. The abnormally expressed genes from each translocation can be used to identify specific markers for clinical diagnosis of each translocation. Microarrays have identified genes differentially expressed in different translocations but the results between laboratories are not always compatible. We used SAGE to quantitate gene expression in bone marrow(BM) samples from 22 patients with four types of AML, [de novo AML M2 with t(8;21), AML M3 or M3V with t(15;17), AML M4Eo with inv(16), AML M5 with t(9;11) or secondary t(9;11)].We made SAGE libraries from CD15+ leukemic myeloid progenitor cells, collecting over 106 SAGE tags, of which 209,486 were unique tags; 136,010 were known genes and ESTs, and 73,476 were novel transcripts. SAGE tags for further analysis were selected based on a 5-fold difference between patient’s samples and normal CD15+ BM; they were also statistically significantly different at the 5% level. Using these strict criteria, we identified 2,381 unique tags, of which 2,053 were known genes and ESTs, and 328 were novel transcripts that were either specific for each translocation or were common(55) SAGE tags for all 4 translocations. The major change in all translocations was a decrease in expression in leukemia cells compared with normal cells; the decrease was least in the t(8;21) cells. Changes in expression of these known genes, which fall into different gene ontology functional categories, varied by translocation. Those associated with macromolecular biosynthesis, transport and transcription were most altered in the t(8;21); those related to defense response and apoptosis were altered in the t(15;17); cell proliferation genes were most affected by the t(9;11). From this analysis, we identified the functional molecular signature of each translocation. We designed a custom microarray to validate our SAGE data analysis. Our initial microarray contained 349 probes including 212 known genes, 61 ESTs, 28 novel sequences based on our data and 48 genes reported by others. We have now included 65 additional probes that appeared to be correlated with survival. Using 63 samples with the four translocations [16 inv(16), 4 t(9;11), 20 t(15;17), 4 t(8;21) and 19 other translocations], we are validating which genes provide a robust, reproducible “fingerprint” for each translocation, for all translocations, and which ones provide reliable information related to prognosis and survival. Our results will provide new insights into genes that collaborate with each translocation to lead to a fully leukemic phenotype as well as which genes appear to provide valid prognostic information.

Gene Expression Profiles in Acute Myeloid Leukemias (AML): A Novel Approach Using SAGE and Custom Microarray.

Blood ◽  
2004 ◽  
Vol 104 (11) ◽  
pp. 197-197
Author(s):  
Sanggyu Lee ◽  
JianJun Chen ◽  
Guolin Zhou ◽  
Edward Touma ◽  
Run Shi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Lines ◽  
Intracellular Signaling ◽  
De Novo ◽  
Expression Profiles ◽  
Cell Surface Receptor ◽  
Molecular Signature ◽  
Human Leukemia ◽  
Novel Transcripts ◽  
Sage Data

Abstract Chromosome translocations are among the most common genetic abnormalities in human leukemia. Each translocation may affect a different pair of genes. The abnormally expressed genes that result from the different translocations provide a rich source for identifying specific markers for clinical diagnosis of each translocation. Microarrays have identified genes differentially expressed in different translocations but the results between laboratories are not always compatible. We used SAGE to quantitate gene expression in bone marrow (BM) samples from 22 patients with four types of AML, namely de novo AML M2 with t(8;21), AML M3 or M3V with t(15;17), AML M4Eo with inv(16), AML M5 with t(9;11) or secondary t(9;11).We generated SAGE libraries from CD15+ leukemic myeloid progenitor cells, collecting over 106 SAGE tags, of which 209,486 were unique tags; 136,010 were known genes and ESTs, and 73,476 were novel transcripts. SAGE tags for further analysis were selected based on a 5-fold difference between patients’ samples and normal CD15+ BM; they were also statistically significantly different at the 5 % level. Using these strict criteria, we identified 1,571 unique tags, of which 1,405 were known genes and ESTs, and 166 were novel transcripts that were either specific for each translocation or were common for all four translocations. Changes in expression of these known genes which fall into different gene ontogeny functional categories varied by translocation. For example, those associated with macromolecular biosynthesis, transport and transcription were most altered in the t(8;21); those related to defense response and apoptosis were altered in the t(15;17); cell proliferation genes were most affected by the t(9;11). Cell surface receptor signaling, intracellular signaling and RNA processing were altered in treatment related but not in de novo t(9;11). From this analysis, we identified the functional molecular signature of each translocation. We designed a custom microarray to validate our SAGE data analysis. Our initial pilot microarray experiment with 96 genes that were specific for each translocation or common for all translocations used mononuclear cells from normal and patient BM and translocation cell lines, ME-1, THP-1, Mono Mac-6, Kasumi 1, NB-4; the array data from BM matched the SAGE data for 48-75 % of genes and the majority of cell lines, except ME-1, matched at least 70 % with the SAGE results for the appropriate translocation. We have now designed a full-scale microarray that contains over 400 probes including 250 known genes, 61 ESTs, 45 novel sequences and 48 genes reported by others. We will test at least 100 patients’ samples with the four translocations to validate which genes provide a robust, reproducible “fingerprint” for each translocation and for all translocations. We will correlate our microarray data with age, sex, race, response to treatment, survival and other mutations (FLT3, MLL ITD, etc) to identify any transcripts that might reliably define these categories. Our results will provide new insights into genes that collaborate with each translocation to lead to a fully leukemic phenotype.

scholarly journals Gammaherpesvirus Lytic Gene Expression as Characterized by DNA Array

2002 ◽  
Vol 76 (12) ◽  
pp. 6244-6256 ◽  
Author(s):  
Joo Wook Ahn ◽  
Kenneth L. Powell ◽  
Paul Kellam ◽  
Dagmar G. Alber
Keyword(s):  
Gene Expression ◽  
De Novo ◽  
Expression Profiles ◽  
Cdna Array ◽  
Gene Expression Profiles ◽  
Hierarchical Cluster ◽  
Murine Gammaherpesvirus ◽  
Transcription Profiles ◽  
A Genome

ABSTRACT Gammaherpesviruses are associated with a number of diseases including lymphomas and other malignancies. Murine gammaherpesvirus 68 (MHV-68) constitutes the most amenable animal model for this family of pathogens. However experimental characterization of gammaherpesvirus gene expression, at either the protein or RNA level, lags behind that of other, better-studied alpha- and beta-herpesviruses. We have developed a cDNA array to globally characterize MHV-68 gene expression profiles, thus providing an experimental supplement to a genome that is chiefly annotated by homology. Viral genes started to be transcribed as early as 3 h postinfection (p.i.), and this was followed by a rapid escalation of gene expression that could be seen at 5 h p.i. Individual genes showed their own transcription profiles, and most genes were still being expressed at 18 h p.i. Open reading frames (ORFs) M3 (chemokine-binding protein), 52, and M9 (capsid protein) were particularly noticeable due to their very high levels of expression. Hierarchical cluster analysis of transcription profiles revealed four main groups of genes and allowed functional predictions to be made by comparing expression profiles of uncharacterized genes to those of genes of known function. Each gene was also categorized according to kinetic class by blocking de novo protein synthesis and viral DNA replication in vitro. One gene, ORF 73, was found to be expressed with α-kinetics, 30 genes were found to be expressed with β-kinetics, and 42 genes were found to be expressed with γ-kinetics. This fundamental characterization furthers the development of this model and provides an experimental basis for continued investigation of gammaherpesvirus pathology.

scholarly journals De novo characterization of the Lycium ruthenicum transcriptome and analysis of its digital gene expression profiles during fruit development and ripening

2017 ◽  
Vol 69 (1) ◽  
pp. 181-190 ◽  
Author(s):  
Yong Peng ◽  
Huiqin Ma ◽  
Shangwu Chen
Keyword(s):  
Gene Expression ◽  
Developmental Stages ◽  
De Novo ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Digital Gene Expression ◽  
Orthologous Group ◽  
Functional Studies ◽  
Lycium Ruthenicum

Lycium ruthenicum Murr., which belongs to the family Solanaceae, is a resource plant for Chinese traditional medicine and nutraceutical foods. In this study, RNA sequencing was applied to obtain raw reads of L. ruthenicum fruit at different stages of ripening, and a de novo assembly of its sequence was performed. Approximately 52.45 million 100-bp paired-end raw reads were generated from the samples by deep RNA-seq analysis. These short reads were assembled to obtain 164814 contigs, and the contigs were assembled into 84968 non-redundant unigenes using the Trinity method. Assembled sequences were annotated with gene descriptions, gene ontology, clusters of orthologous group and KEGG (Kyoto Encyclopedia of Genes and Genomes)pathway terms. Digital gene expression analysis was applied to compare gene-expression patterns at different fruit developmental stages. These results contribute to existing sequence resources for Lycium spp. during the fruit-ripening stages, which is valuable for further functional studies of genes involved in L. ruthenicum fruit nutraceutical quality.

scholarly journals Transcriptional profiling enables molecular classification of adrenocortical tumours

2009 ◽  
Vol 161 (1) ◽  
pp. 141-152 ◽  
Author(s):  
Cecilia Laurell ◽  
David Velázquez-Fernández ◽  
Kristina Lindsten ◽  
Christofer Juhlin ◽  
Ulla Enberg ◽  
...  
Keyword(s):  
Gene Expression ◽  
Clinical Symptoms ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Differentially Expressed ◽  
Molecular Signature ◽  
Survival Prediction ◽  
Cdna Clones ◽  
Hormonal Activity ◽  
Adrenocortical Tumours

ObjectiveTumours in the adrenocortex are common human tumours. Malignancy is however, rare, the yearly incidence being 0.5–2 per million inhabitants, but associated with a very aggressive behaviour. Adrenocortical tumours are often associated with altered hormone production with a variety of clinical symptoms. The aggressiveness of carcinomas together with the high frequency of adenomas calls for a deeper understanding of the underlying biological mechanisms and an improvement of the diagnostic possibilities.MethodsMicroarray gene expression analysis was performed in tumours of adrenocortex with emphasis on malignancy as well as hormonal activity. The sample set consisted of 17 adenomas, 11 carcinomas and 4 histological normal adrenocortexes. RNA from these was hybridised according to a reference design on microarrays harbouring 29 760 human cDNA clones. Confirmation was performed with quantitative real time-PCR and western blot analysis.ResultsUnsupervised clustering to reveal relationships between samples based on the entire gene expression profile resulted in two subclusters; carcinomas and non-cancer specimens. A large number of genes were accordingly found to be differentially expressed comparing carcinomas to adenomas. Among these were IGF2, FGFR1 and FGFR4 in growth factor signalling the most predominant and also the USP4, UBE2C and UFD1L in the ubiquitin-proteasome pathway. Moreover, two subgroups of carcinomas were identified with different survival outcome, suggesting that survival prediction can be made on the basis of gene expression profiles. Regarding adenomas with aldosterone overproduction, OSBP and VEGFB were among the most up-regulated genes compared with the other samples.ConclusionsAdrenocortical carcinomas are associated with a distinct molecular signature apparent in their gene expression profiles. Differentially expressed genes were identified associated with malignancy, survival as well as hormonal activity providing a resource of candidate genes for an exploration of possible drug targets and diagnostic and prognostic markers.

Co-Expression of BAALC with ERG, TCF4, and CD133 Indicates a Shared Pathway in Acute Myeloid and Lymphoblastic Leukemia.

Blood ◽  
2005 ◽  
Vol 106 (11) ◽  
pp. 2755-2755 ◽  
Author(s):  
Claudia D. Baldus ◽  
Michael Radmacher ◽  
Guido Marcucci ◽  
Dieter Hoelzer ◽  
Eckhard Thiel ◽  
...  
Keyword(s):  
Gene Expression ◽  
De Novo ◽  
Expression Profiles ◽  
Lymphoblastic Leukemia ◽  
Gene Expression Profiles ◽  
Newly Diagnosed ◽  
Time Points ◽  
Normal Cytogenetics ◽  
Acute Myeloid

Abstract The human gene BAALC (Brain And Acute Leukemia, Cytoplasmic) is a molecular marker of hematopoietic progenitor cells and is aberrantly expressed in subsets of acute myeloid (AML) and lymphoblastic (ALL) leukemias. High mRNA expression levels of BAALC have been shown to adversely impact outcome in newly diagnosed AML patients (pts) with normal cytogenetics. To gain insight into the functional role of BAALC and its significance to normal hematopoiesis and leukemogenesis we compared gene expression profiles of normal CD34+ progenitors with those of AML and ALL blasts (using oligonucleotide microarrays; HG-U133 plus 2.0, Affymetrix, Santa Clara, CA). First we explored the regulation of BAALC expression during lineage specific maturation of in vitro differentiated human CD34+ bone marrow cells selected from healthy individuals. Microarray analyses were carried out using CD34+ cells stimulated in vitro with EPO, TPO, or G/GM-CSF to induce lineage-specific differentiation. At day 0 of culture and at three different time points during differentiation (days 4, 7, 11) cells were harvested, and if necessary purified by immunomagnetic beads and used for microarray studies. Experiments of all lineages and time points were done in triplicates. A total of 276 genes were identified showing similar changes in expression (with downregulation during differentiation) as BAALC at the three time points in all lineages with a correlation coefficient of R>0.95. This set of 276 BAALC co-expressed genes was investigated in an AML expression dataset generated from 51 adult pts with newly diagnosed de novo AML and normal cytogenetics (Cancer and Leukemia Group B). After exclusion of probesets expressed in fewer than 20% of pt samples, 21 probesets representing 14 named genes 6 of which are known to be involved in AML (BAALC, CD34, CD133, SOX4, ERG, SEPT6) and 4 implicated in lymphoid development (TCF4, SH2D1A, ITM2A, ITM2C) were found to be overexpressed (a significance level of P=0.01 was used) in pts of the highest third compared to pts of the lowest third of BAALC expression values as measured by real-time RT-PCR. We next applied these same 21 BAALC co-expressed probesets to an ALL expression dataset generated from 66 adult pts with newly diagnosed standard risk B-lineage precursor ALL (from the German ALL GMALL study group). A BAALC specific cluster uncovered 7 probesets representing 4 different co-expressed genes: BAALC, CD133, and the transcription factors ERG and TCF4. Thus, applying a BAALC specific expression signature to AML and ALL gene expression profiles revealed 3 genes (CD133, ERG, TCF4), which are highly associated with BAALC in myeloid and lymphoid blasts. Interestingly in non-malignant lymphoid and myeloid cells the oncogeneic ETS transcription factor ERG has shown specificity to immature cells, while its mechanistical role in leukemogenesis remains unknown. ERG and TCF4 may directly regulate BAALC and indicate a specific pathway implicated in leukemogenesis, while co-expression of CD133 and BAALC suggests shared stem cell characteristics. Functional studies are in progress to further explore these findings.

scholarly journals Differential gene expression profiles of invasive and non-invasive non-functioning pituitary adenomas based on microarray analysis

2010 ◽  
Vol 17 (2) ◽  
pp. 361-371 ◽  
Author(s):  
Françoise Galland ◽  
Ludovic Lacroix ◽  
Patrick Saulnier ◽  
Philippe Dessen ◽  
Geri Meduri ◽  
...  
Keyword(s):  
Gene Expression ◽  
Pituitary Adenomas ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Molecular Signature ◽  
Specific Gene ◽  
Tumor Invasiveness ◽  
Non Invasive ◽  
Qrt Pcr ◽  
Non Functioning Pituitary Adenomas

Non-functioning pituitary adenomas (NFPAs) may be locally invasive. Markers of invasiveness are needed to guide patient management and particularly the use of adjuvant radiotherapy. To examine whether invasive NFPAs display a specific gene expression profile relative to non-invasive tumors, we selected 40 NFPAs (38 of the gonadotroph type) and classified them as invasive (n=22) or non-invasive (n=18) on the basis of magnetic resonance imaging and surgical findings. We then performed pangenomic analysis with the 44k Agilent human whole genome expression oligonucleotide microarray in order to identify genes with differential expression between invasive and non-invasive NFPAs. Candidate genes were then tested in qRT-PCR. Prediction class analysis showed that the expression of 346 genes differed between invasive and non-invasive NFPAs (P<0.001), of which 233 genes were up-regulated and 113 genes were down-regulated in invasive tumors. On the basis of Ingenuity networks and the degree of up- or down-regulation in invasive versus non-invasive tumors, 35 genes were selected for expression quantification by qRT-PCR. Overexpression of only four genes was confirmed, namely IGFBP5 (P=0.02), MYO5A (P=0.04), FLT3 (P=0.01), and NFE2L1 (P=0.02). At the protein level, only myosin 5A (MYO5A) immunostaining was stronger in invasive than in non-invasive NFPAs. Molecular signature allows to differentiate ‘grossly’ invasive from non-invasive NFPAs. The product of one of these genes, MYO5A, may be a useful marker of tumor invasiveness.

Metabolic and transcriptional patterns accompanying glutamine depletion and repletion in mouse hepatoma cells: a model for physiological regulatory networks

2004 ◽  
Vol 16 (2) ◽  
pp. 247-255 ◽  
Author(s):  
Matthew S. Wong ◽  
R. Michael Raab ◽  
Isidore Rigoutsos ◽  
Gregory N. Stephanopoulos ◽  
Joanne K. Kelleher
Keyword(s):  
Gene Expression ◽  
Regulatory Networks ◽  
Dna Microarrays ◽  
Cell Function ◽  
De Novo ◽  
Expression Profiles ◽  
Mammalian Cell Culture ◽  
Gene Expression Profiles ◽  
Tca Cycle ◽  
Glycolytic Flux

An important objective in postgenomic biology is to link gene expression to function by developing physiological networks that include data from the genomic and functional levels. Here, we develop a model for the analysis of time-dependent changes in metabolites, fluxes, and gene expression in a hepatic model system. The experimental framework chosen was modulation of extracellular glutamine in confluent cultures of mouse Hepa1-6 cells. The importance of glutamine has been demonstrated previously in mammalian cell culture by precipitating metabolic shifts with glutamine depletion and repletion. Our protocol removed glutamine from the medium for 24 h and returned it for a second 24 h. Flux assays of glycolysis, the tricarboxylic acid (TCA) cycle, and lipogenesis were used at specified intervals. All of these fluxes declined in the absence of glutamine and were restored when glutamine was repleted. Isotopomer spectral analysis identified glucose and glutamine as equal sources of lipogenic carbon. Metabolite measurements of organic acids and amino acids indicated that most metabolites changed in parallel with the fluxes. Experiments with actinomycin D indicated that de novo mRNA synthesis was required for observed flux changes during the depletion/repletion of glutamine. Analysis of gene expression data from DNA microarrays revealed that many more genes were anticorrelated with the glycolytic flux and glutamine level than were correlated with these indicators. In conclusion, this model may be useful as a prototype physiological regulatory network where gene expression profiles are analyzed in concert with changes in cell function.

Molecular signature of late-stage human ALS revealed by expression profiling of postmortem spinal cord gray matter

2004 ◽  
Vol 16 (2) ◽  
pp. 229-239 ◽  
Author(s):  
Fernando Dangond ◽  
Daehee Hwang ◽  
Sandra Camelo ◽  
Piera Pasinelli ◽  
Matthew P. Frosch ◽  
...  
Keyword(s):  
Gene Expression ◽  
Spinal Cord ◽  
Gray Matter ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Terminal State ◽  
Molecular Signature ◽  
Fisher Discriminant Analysis ◽  
Sporadic Als

Little is known about global gene expression patterns in the human neurodegenerative disease amyotrophic lateral sclerosis (ALS). To address this, we used high-density oligonucleotide microarray technology to compare expression levels of ∼6,800 genes in postmortem spinal cord gray matter obtained from individuals with ALS as well as normal individuals. Using Fisher discriminant analysis (FDA) and leave-one-out cross-validation (LOOCV), we discerned an ALS-specific signature. Moreover, it was possible to distinguish familial ALS (FALS) from sporadic ALS (SALS) gene expression profiles. Characterization of the specific genes significantly altered in ALS uncovered a pro-inflammatory terminal state. Moreover, we found alterations in genes involved in mitochondrial function, oxidative stress, excitotoxicity, apoptosis, cytoskeletal architecture, RNA transcription and translation, proteasomal function, and growth and signaling. It is apparent from this study that DNA microarray analysis and appropriate bioinformatics can reveal distinct phenotypic changes that underlie the terminal stages of neurodegeneration in ALS.

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