Transcriptional Profiling Identifies Genes Differentially Regulated by the BCR/ABL Fusion Oncogene.

Blood ◽  
2004 ◽  
Vol 104 (11) ◽  
pp. 1537-1537 ◽  
Author(s):  
Alexander Kohlmann ◽  
Claudia Schoch ◽  
Susanne Schnittger ◽  
Sylvia Merk ◽  
Martin Dugas ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differentially Expressed Genes ◽  
Target Genes ◽  
Kinase Inhibitor ◽  
Protein Biosynthesis ◽  
Lymphoblastic Leukemia ◽  
Principal Component ◽  
Response To Therapy ◽  
Differentially Expressed ◽  
Hoxa Cluster

Abstract The translocation t(9;22) is associated with chronic myeloid leukemia (CML) and also occurs in 30% of adult acute lymphoblastic leukemia (ALL). In this study, we analyzed differential gene expression using microarrays to determine if upregulation or downregulation of specific genes may explain the distinct phenotypes. Enriched monoculear cells from 218 adult patients were hybridized to Affymetrix U133 set (A+B) microarrays (discovery set). Resulting lists of differentially expressed genes were further analyzed in an independent set of 110 patients hybridized to U133 Plus 2.0 microarrays (validation set). In a first analysis ALL with t(9;22) (n=34 discovery, n=6 validation) and CML (n=75 discovery, n=49 validation) were included. Various unsupervised data analysis algorithms, e.g. hierarchical clustering and principal component analysis, clearly separated both types of t(9;22) leukemias from each other. A supervised approach, i.e. t-test statistics followed by false discovery rate estimation, identified genes that were significantly differentially expressed. Using the top differentially expressed genes in a classification algorithm (SVM) >97% of the samples were correctly assigned to their classes, both in the discovery and the validation cohort. This set of genes was further examined by pathway analysis (Ingenuity software). Numerous networks point at clear biological differences between both t(9;22) types. Higher expressed genes in CML were connected to networks related to leukotriene metabolism, immune response, integrin signaling, non-selective vesicle transport, or humoral defense mechanisms. This reflects the underlying transcriptional profile of granulation of promyelocytes in CML in contrast to the non-granulated immature ALL blasts. The aggressiveness of acute leukemic blasts is visualized by several pathways where genes with higher expression in t(9;22) positive ALL were aggregated to networks with cellular functions of DNA metabolism and replication, cell cycle progression, and protein biosynthesis. Next analyses were performed to mine for common t(9;22) target genes. CML samples were compared against an equal number of AML with normal karyotype, and t(9;22) ALL against an equal number of c-ALL/Pre-B-ALL without t(9;22). Then both lists of differentially expressed genes were compared for overlapping probe sets. Here, no statistically significant differentially expressed genes were identified as consistently associated with the presence of t(9;22) across the two lineages. In contrast, using a similar strategy where ALL and AML with t(11q23)/MLL were grouped together and were analyzed against various non-MLL positive leukemia subtypes it is possible to identify common t(11q23)/MLL target genes, e.g. a overexpressed HOXA cluster gene signature. This leads to the hypothesis that both types of t(9;22) leukemias, despite an identical underlying chromosomal aberration, trigger different genes involved in BCR/ABL-dependent leukemogenesis. Thus, depending on the cellular background, i.e. myeloid or lymphoid, translocation t(9;22) results in two types of leukemias with fundamental differences in gene expression, clinical course, and the time and quality of response to therapy which is demonstrated also if a BCR/ABL-specific tyrosine kinase inhibitor (e.g. imatinib mesylate) is administered.

The NOD/SCID Xenograft Model Provides Clinically-Relevant Insights Into Glucocorticoid-Induced Gene Expression In Childhood B-Cell Precursor Acute Lymphoblastic Leukemia (ALL)

Blood ◽  
2010 ◽  
Vol 116 (21) ◽  
pp. 2493-2493
Author(s):  
Vivek A Bhadri ◽  
Mark J Cowley ◽  
Warren Kaplan ◽  
Richard B Lock
Keyword(s):  
Gene Expression ◽  
Acute Lymphoblastic Leukemia ◽  
Mouse Model ◽  
B Cell ◽  
Differential Expression ◽  
Differentially Expressed Genes ◽  
Lymphoblastic Leukemia ◽  
Xenograft Model ◽  
Differentially Expressed ◽  
Cell Precursor

Abstract Abstract 2493 Introduction. Glucocorticoids (GC) such as prednisolone (Pred) and dexamethasone (Dex) are critical drugs in multi-agent chemotherapy protocols used to treat acute lymphoblastic leukemia (ALL). The NOD/SCID ALL xenograft mouse model is a clinically relevant model in which the mice develop a systemic leukemia which retains the fundamental biological characteristics of the original disease. Here we report the results of a study evaluating the NOD/SCID xenograft model to investigate GC-induced gene expression. Methods. Cells from a GC-sensitive xenograft derived from a child with B-cell precursor ALL were inoculated into NOD/SCID mice. Engraftment, defined as the proportion of human vs mouse CD45+ cells in the peripheral blood, was monitored by serial weekly tail-vein sampling. When engraftment levels reached >50%, the mice were randomised and treated with either dexamethasone 15 mg/kg or vehicle control by intraperitoneal injection, and harvested at 0, 8, 24 or 48 h thereafter. The 48 hour groups received a second dose of vehicle or Dex at 24 hours. At the defined timepoints, the mice were euthanized and lymphoblasts harvested from the spleen. RNA was extracted, amplified and hybridised onto Illumina WG-6 V3 chips. The data was pre-processed using variance-stabilisation transformation, and quantile normalisation. Differential expression was determined using limma by comparing all treated groups to time 0, with the positive False Discovery Rate correction for multiple testing. Hierarchical clustering was used to compare groups to each other. The stability of results when reducing the number of replicates was assessed using the Recovery Score method. Functional analysis was performed using gene set enrichment analysis (GSEA) and comparison to publicly available microarray data using parametric GSEA. Results. The 8 hour Dex-treated timepoint had the most number of significantly differentially expressed genes (see Table), with fewer observed at the 24 and 48 hour Dex-treated timepoints. There was minimal significant differential gene expression across the time-matched controls. At the 8 hour timepoint, ZBTB16, a known GC-induced gene, was the most significantly upregulated gene. Other significantly differentially expressed genes included TSC22D3 and SOCS1, both downstream targets of the glucocorticoid receptor (upregulated), and BCL-2 and C-MYC (downregulated). GSEA at 8 hours revealed a significant upregulation of catabolic pathways and downregulation of pathways associated with cell proliferation, particularly C-MYC. GSEA at 24 hours revealed enrichment of pathways associated with NF-kB. Replicate analysis revealed that at the 8 hour Dex treated timepoint, a dataset with high signal and differential expression, using data from 3 replicates instead of 4 resulted in excellent recovery scores of >0.9. However at other timepoints with less signal very poor recovery scores were obtained using 3 replicates. We compared our data to publicly available datasets of GC-induced genes in ALL (Schmidt et al, Blood 2006; Rainer et al, Leukemia 2009) using parametric GSEA, which revealed that the 8 hour gene expression data obtained from the NOD/SCID xenograft model clustered with data from primary patient samples (Schmidt) rather than the cell line data (Rainer). The 24 and 48 hour datasets clustered separately from all other datasets by this method, reflecting fewer and predominantly downregulated gene expression at these timepoints. Conclusions: The NOD/SCID xenograft mouse model provides a reproducible experimental model system in which to investigate clinically-relevant mechanisms of GC-induced gene regulation in ALL; the 8 hour timepoint provides the highest number of significantly differentially expressed genes; time-matched controls are redundant and excellent recovery scores can be obtained with 3 replicates. Disclosures: No relevant conflicts of interest to declare.

scholarly journals Combined Analysis of Expression Profiles in a Mouse Model and Patients Identified BHMT2 as a New Regulator of Lipid Metabolism in Metabolic-Associated Fatty Liver Disease

2021 ◽  
Vol 9 ◽  
Author(s):  
Yongqiang Ma ◽  
Zhi Tan ◽  
Qiang Li ◽  
Wenling Fan ◽  
Guangshun Chen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Lipid Metabolism ◽  
Liver Disease ◽  
Fatty Liver ◽  
Differentially Expressed Genes ◽  
Fatty Liver Disease ◽  
Target Genes ◽  
Differentially Expressed ◽  
Overlapping Genes ◽  
Geo Database

Metabolic associated fatty liver disease (MAFLD) is associated with obesity, type 2 diabetes mellitus, and other metabolic syndromes. Farnesoid X receptor (FXR, NR1H4) plays a prominent role in hepatic lipid metabolism. This study combined the expression of liver genes in FXR knockout (KO) mice and MAFLD patients to identify new pathogenic pathways for MAFLD based on genome-wide transcriptional profiling. In addition, the roles of new target genes in the MAFLD pathogenic pathway were also explored. Two groups of differentially expressed genes were obtained from FXR-KO mice and MAFLD patients by transcriptional analysis of liver tissue samples. The similarities and differences between the two groups of differentially expressed genes were analyzed to identify novel pathogenic pathways and target genes. After the integration analysis of differentially expressed genes, we identified 134 overlapping genes, many of which have been reported to play an important role in lipid metabolism. Our unique analysis method of comparing differential gene expression between FXR-KO mice and patients with MAFLD is useful to identify target genes and pathways that may be strongly implicated in the pathogenesis of MAFLD. The overlapping genes with high specificity were screened using the Gene Expression Omnibus (GEO) database. Through comparison and analysis with the GEO database, we determined that BHMT2 and PKLR could be highly correlated with MAFLD. Clinical data analysis and RNA interference testing in vitro confirmed that BHMT2 may a new regulator of lipid metabolism in MAFLD pathogenesis. These results may provide new ideas for understanding the pathogenesis of MAFLD and thus provide new targets for the treatment of MAFLD.

Genomic Analysis of High Hyperdiploid Acute Lymphoblastic Leukemia and Hyperdiploid Multiple Myeloma Suggests Differential Gene Dosage Effect on Expression and Provide Clues to Preferential Selection of Recurrently Trisomic Chromosomes.

Blood ◽  
2005 ◽  
Vol 106 (11) ◽  
pp. 3006-3006
Author(s):  
Wee-Joo Chng ◽  
Scott Van Wier ◽  
Gregory Ahmann ◽  
Tammy Price-Troska ◽  
Kim Henderson ◽  
...  
Keyword(s):  
Gene Expression ◽  
Signal Transduction ◽  
Multiple Myeloma ◽  
Protein Synthesis ◽  
Differentially Expressed Genes ◽  
Gene Dosage ◽  
Lymphoblastic Leukemia ◽  
Differentially Expressed ◽  
Expression Data ◽  
Gene Dosage Effect

Abstract Hyperdiploid (>48 chromosomes) multiple myeloma (H-MM) and high hyperdiploid (>50 chromosomes) acute lymphoblastic leukemia (H-ALL) are characterized by aneuploidy and multiple recurrent trisomies (chromosome 3,5,7,9,11,15,19 for H-MM and chromosomes X,4,6,10,14,17,18,21 for H-ALL). Little is known about the oncogenic events, consequences of the trisomies and reasons for the different recurrent trisomies. In an attempt to answer these questions, we undertook a combined gene expression and network/pathway analysis approach. Gene expression data was generated using the Affymetrix U133A chip (Affymetrix, Santa Clara, Ca) for 53 H-MM and 37 non-hyperdiploid MM (NH-MM) cases using CD138-enriched plasma-cell RNA. Gene expression data using the same chip for ALL was obtained from previous published data (Ross ME et al Blood2004; 104: 3679–3687). Analysis was performed using Genespring 7 (Agilent Technologies, Palo Alto, CA). By comparing the median expression of all genes on each chromosome between H-MM/H-ALL and their non-hyperdiploid counterparts (NH-MM and NH-ALL) for the 23 chromosomes (excluding Y), one can clearly identify the commonly trisomic chromosomes in H-ALL and H-MM. However, the relationship of gene expression was highly variable for H-MM and NH-MM as compared to H-ALL and NH-ALL which tended to have expression ratios close to 1 for the non-trisomic chromosomes. Sixty-nine percent of the differentially expressed genes generated by ANOVA analysis (p<0.001) in H-ALL were on the commonly trisomic chromosomes and were upregulated whereas the corresponding figure in H-MM is 40%. These similarities and differences probably reflect both an overall gene dosage effect and the different complexities of the karyotypes of H-MM and H-ALL compared to NH-MM and NH-ALL respectively (MM karyotypes are more complex, hence difference between H and NH-MM is greater and less confined to the trisomic chromosomes). We next performed network analysis using a curated web-based software (MetaCore, GeneGo Inc, St Joseph, MI) using the 2 sets of differentially expressed genes. Majority of genes differentially expressed in H-MM are involved in mRNA translation/protein synthesis whereas the genes differentially expressed in H-ALL were mainly involved in signal transduction. Therefore the transcriptional program that characterize the difference between H and NH-MM/ALL seem to recapitulate normal cellular function: protein synthesis in the mature secretory plasma cells and signal transduction in response to cytokines in a differentiating early-B cell. However, due to the concurrent deregulation of many genes on these trisomic chromosomes, these and other cellular programs are deregulated resulting in malignant transformation. We also intersected the 2 lists of differentially expressed genes to find genes that are up- or downregulated in both H-MM and H-ALL relative to the NH tumors. Thirteen genes including interferon response genes (TNFSF10, MX1, ZNF185) and transcription factors like RUNX1 were upregulated, whereas 13 genes including a cancer testis antigen gene (MAGED4) were downregulated in both H-MM and H-ALL. These genes may point to common oncogenic mechanisms.

Gene Expression Profiling Differentiates Childhood Acute Lymphoblastic Leukemia in Down Syndrome Versus Non-Down Syndrome Patients.

Blood ◽  
2008 ◽  
Vol 112 (11) ◽  
pp. 1203-1203
Author(s):  
Karen R. Rabin ◽  
Jinhua Wang ◽  
Anna Tsimelzon ◽  
Debra Morrison ◽  
Amos S. Gaikwad ◽  
...  
Keyword(s):  
Gene Expression ◽  
Down Syndrome ◽  
Acute Lymphoblastic Leukemia ◽  
Oxidative Phosphorylation ◽  
Differentially Expressed Genes ◽  
Expression Profiling ◽  
Lymphoblastic Leukemia ◽  
Gene Expression Signature ◽  
Differentially Expressed ◽  
P Values

Abstract Children with Down syndrome (DS) and acute lymphoblastic leukemia (ALL) form a unique biological subset. These patients have generally inferior outcomes in many studies, and an increased incidence of treatment-related toxicities. Cases of DS ALL have a much lower frequency of recurrent prognostically significant chromosomal abnormalities than cases of ALL in the general pediatric population. Global gene expression profiling provides an opportunity to gain insights into pathogenesis and potential therapeutic targets in DS ALL. We performed microarray analysis of RNA from bone marrow samples obtained at diagnosis in 30 DS ALL and 24 non-DS ALL cases using the Affymetrix Human Genome U133 Plus 2.0 array. Unsupervised hierarchical clustering separated cases into two main groups, one of which included 21 of 30 DS samples (Fisher’s exact test, p = 0.013), suggesting inherent biologic similarities. Non-DS samples clustered according to known cytogenetic features. Consistent with recently published data, a subset (13%) of our DS ALL cases were found to have JAK2 mutations. Cases of DS ALL bearing JAK2 mutations did not form a distinct subcluster, suggesting that the JAK2 pathway may be dysregulated via other events in cases of DS ALL with wild-type JAK2. Two-sample comparison of DS versus non-DS ALL cases demonstrated differential expression of 513 genes with p values <0.001 (Figure 1). Oxidative phosphorylation pathway genes were most over-represented among differentially expressed genes, with 35 of 115 genes in this pathway demonstrating down-regulation in DS compared to non-DS ALL (Bonferroni corrected p value < 1 ×10−9), including several cytochrome c oxidase and ubiquinone subunits. Our data indicate that DS ALL blasts may utilize oxidative phosphorylation to a lesser extent than non-DS ALL, a feature which could be exploited therapeutically. The top 513 genes differentially expressed in DS versus non-DS ALL (Benjamini-Hochberg corrected p values < 0.001) are displayed in a heatmap where genes relatively overexpressed in DS ALL are depicted in yellow, and relatively underexpressed in DS ALL in red. DS ALL cases are indicated by red circles and non-DS ALL cases by white circles. Four DS ALL cases bearing a JAK2 mutation at arginine 683 are indicated by black stars. Figure 1. Gene expression signature of top differentially expressed genes in Down syndrome (DS) versus non-Down syndrome acute lymphoblastic leukemia (ALL). Figure 1. Gene expression signature of top differentially expressed genes in Down syndrome (DS) versus non-Down syndrome acute lymphoblastic leukemia (ALL).

CEBPα Is a Transcriptional Repressor of T-Cell Related Genes Explaining the Myeloid/T-Lymphoid Features of CEBPα-Silenced AML

Blood ◽  
2011 ◽  
Vol 118 (21) ◽  
pp. 554-554
Author(s):  
Erdogan Taskesen ◽  
Roberto Avellino ◽  
Meritxell AlberichJorda ◽  
Daniel G. Tenen ◽  
Jeroen Ridder de ◽  
...  
Keyword(s):  
Gene Expression ◽  
Bone Marrow ◽  
T Cell ◽  
Differentially Expressed Genes ◽  
Target Genes ◽  
Differentially Expressed ◽  
Lymphoid Leukemia ◽  
Hematopoietic Stem ◽  
Knock Out ◽  
Molecular Abnormalities

Abstract Abstract 554 Acute myeloid leukemia (AML) is a heterogeneous disease of different subtypes characterized by distinct cytogenetic or molecular abnormalities. We recently identified a previously unrecognized subtype with a unique epigenetic feature, i.e. silencing of the gene that encodes CCAAT-enhancer binding protein alpha (CEBPα) by DNA hypermethylation (denoted as the CEBPαsilenced group). The leukemic blast cells of these patients express myeloid as well as T-lymphoid markers. Moreover, gene expression and DNA methylation profiling put these leukemias in between AML and T-lymphoid leukemia (T-ALL). CEBPα is a transcription regulator that is essential for normal neutrophil development. We hypothesize that methylation and consequently silencing of the gene encoding CEBPα is abnormal and an important hit in the transformation of this unique leukemia subtype. The mechanism by which CEBPα silencing plays a role in transformation of cells with myeloid/T-lymphoid features is the aim of our study. We carried out gene expression profiling of the CEBPαsilenced group (n=10) and compared gene expression levels to normal CD34+ bone marrow cells (n=11) and the remaining AML group (n=506) using a three-way ANOVA and a post-hoc test (tukey-kramer method). We detected 686 differentially expressed genes with P < 0.05 after multiple testing. Of these 686 genes, 288 were up- and 401 down-regulated in the CEBPαsilenced group. We next asked the question which of those genes might be bona fide CEBPα targets and whether expression has been altered as the result of CEBPα silencing. We transduced estrogen-inducible C/EBPα in 32D cells and carried out ChIP-on-chip analysis using ER specific antibodies. The analysis yielded a collection of 529 CEBPα target genes that are significant enriched for C/EBPα binding (P < 0.05) using Hypergeometric Analysis of Tiling-arrays (HAT). We hypothesized that the direct target genes of CEBPα, derived from the 32D model system, were also present among deregulated genes in CEBPαsilenced human AMLs. We therefore overlaid the detected direct binding targets of CEBPα in 32D cells, with the differentially expressed genes in the CEBPαsilenced group and identified 49 overlapping genes (P=1×10−7) as putative direct targets. Among these 49 genes, 25 were down-regulated and 24 were upregulated in the primary CEBPαsilenced AML group. Both groups of genes were highly enriched for the CEBPα consensus binding sequence, i.e. 16/25 and 20/24 promoter regions respectively. The 25 downregulated genes, among which ACSL1, MYCT1 or Slc7a11, represent targets that are most likely normally activated by CEBPα, but are not transcribed in CEBPαsilenced human AMLs due to the absence of CEBPα. Among the genes that were upregulated in CEBPαsilenced leukemias are BCL2, CCR9, CEBPG or CD47. These putative target genes are repressed in CEBPα expressing cells and activated when CEBPα is silenced. This observation suggests that the transcription factor CEBPα may also acts as a repressor of gene transcription. We therefore studied the expression of two of those genes, i.e. CEBPG and CCR9 in Lin-Sca+Kit+ (LSK) bone marrow hematopoietic stem cells from wild type versus conditional Mx-Cre/CEBPα knock-out mice. Similar to what we observed in human CEBPαsilenced leukemias, we found that these two genes were switched off in LSK cells from CEBPα knock-out animals. Ingenuity pathway analysis of the 24 upregulated genes detected high enrichment (P < 0.001) of pathways involved with T-cell development. Our data clearly suggest that CEBPα may act as repressor of T-cell related genes through direct promoter interaction. We therefore propose that silencing of CEBPα by promoter hypermethylation is one of the transforming events that driving towards mixed myeloid/T-lymphoid leukemia. Disclosures: No relevant conflicts of interest to declare.

scholarly journals Screening and Identification of Differentially Expressed Genes Expressed among Left and Right Colon Adenocarcinoma

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Jing Han ◽  
Xue Zhang ◽  
Yang Yang ◽  
Li Feng ◽  
Gui-Ying Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differentially Expressed Genes ◽  
Kinase Inhibitor ◽  
Colon Adenocarcinoma ◽  
Interaction Network ◽  
Differentially Expressed ◽  
Right Colon ◽  
Hub Genes ◽  
Expression Levels ◽  
Public Data

Purpose. Colon adenocarcinoma (COAD) is the third most common malignancy globally and is further categorized as left colon adenocarcinoma (LCOAD) or right colon adenocarcinoma (RCOAD) depending on the location of the primary tumor. The therapeutic outcome and long-term prognosis for patients with COAD are less than satisfactory, and this may be associated with tumor location. Therefore, it is important to investigate the genetic differences in COAD at different sites. Patients and Methods. Public data associated with COAD were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified using R software (version 3.5.3), and functional annotation of DEGs was performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. A protein-protein interaction network was constructed, hub genes were identified and analyzed, and data mining using Gene Expression Profiling Interactive Analysis (GEPIA) was conducted. Results. A total of 286 DEGs were identified between LCOAD and RCOAD. Additionally, 10 hub genes associated with COAD at different locations were screened, namely, CDKN2A, IGF1R, MDM2, SMAD3, SLC2A1, GRM5, PLCB4, FGFR1, UBE2V2, and TNFRSF10B. The expression of cyclin-dependent kinase inhibitor 2A (CDKN2A) and solute carrier family 2 member 1 (SLC2A1) was significantly associated with pathological stage P<0.05. COAD patients with high expression levels of CDKN2A exhibited poorer overall survival (OS) times than those with low expression levels P<0.05. Conclusion. CDKN2A expression was significantly different between LCOAD and RCOAD and was closely related to the prognosis of COAD. It is of great value for further understanding of the pathogenesis of LCOAD and RCOAD.

scholarly journals In Silico Analysis and Characterization of Differentially Expressed Genes to Distinguish Glioma Stem Cells from Normal Neural Stem Cells

2021 ◽  
Author(s):  
Urja Parekh ◽  
Mohit Mazumder ◽  
Harpreet Kaur ◽  
Elia Brodsky
Keyword(s):  
Gene Expression ◽  
Stem Cells ◽  
Neural Stem Cells ◽  
Differentially Expressed Genes ◽  
Expression Patterns ◽  
Principal Component ◽  
Differentially Expressed ◽  
Glioma Stem Cells ◽  
Rna Seq ◽  
Bioinformatics Analyses

AbstractGlioblastoma multiforme (GBM) is a heterogeneous, invasive primary brain tumor that develops chemoresistance post therapy. Theories regarding the aetiology of GBM focus on transformation of normal neural stem cells (NSCs) to a cancerous phenotype or tumorigenesis driven via glioma stem cells (GSCs). Comparative RNA-Seq analysis of GSCs and NSCs can provide a better understanding of the origin of GBM. Thus, in the current study, we performed various bioinformatics analyses on transcriptional profiles of a total 40 RNA-seq samples including 20 NSC and 20 GSC, that were obtained from the NCBI-SRA (SRP200400). First, differential gene expression (DGE) analysis using DESeq2 revealed 358 significantly differentially expressed genes between GSCs and NSCs (padj. value <0.05, log2fold change ±3) with 192 upregulated and 156 downregulated genes in GSCs in comparison to NSCs. Subsequently, exploratory data analysis using the principal component analysis (PCA) based on key significant genes depicted the clear separation between both the groups. Further, the Hierarchical clustering confirmed the distinct clusters of GSC and NSC samples. Eventually, the biological enrichment analysis of the significant genes showed their enrichment in tumorigenesis pathways such as Wnt-signalling, VEGF- signalling and TGF-β-signalling pathways. Conclusively, our study depicted significant differences in the gene expression patterns between NSCs and GSCs. Besides, we also identified novel genes and genes previously unassociated with gliomagenesis that may prove to be valuable in establishing diagnostic, prognostic biomarkers and therapeutic targets for GBM.

scholarly journals Gene Expression and Alternative Splicing Datasets Analyses of MDS with Ring Sideroblasts Highlight Alternative Branchpoint Usage in Genes Involved in Iron Metabolism and Erythropoiesis

Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 1972-1972
Author(s):  
Abderrahmane Bousta ◽  
Sabrina Bondu ◽  
Alexandre Houy ◽  
Nicolas Cagnard ◽  
Carine Lefevre ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differentially Expressed Genes ◽  
Iron Metabolism ◽  
Target Genes ◽  
Differentially Expressed ◽  
P Value ◽  
Rna Seq ◽  
Splice Sites ◽  
Gene Sets ◽  
Ring Sideroblasts

Abstract Introduction SF3B1 hotspot mutations are associated with various cancers like uveal melanoma, chronic lymphocytic leukemia and myelodysplastic syndrome with ring sideroblasts (MDS-RS). These mutations affect RNA splicing by the use of alternative branchpoints resulting in an aberrant 3' splice site (ss) selection. RNA-sequencing (RNA-seq) analyzed to quantify exon-exon junctions identified aberrantly spliced transcripts in target genes, and half of them are predicted to be degraded by non-sense mediated decay. For this reason, target genes in SF3B1-mutated MDS remain partially characterized. In the present study, we performed deep RNA-seq analysis of bone marrow mononuclear cells in low/int-1 MDS with SF3B1 mutations to identify aberrant/cryptic splicing events among up or down-regulated gene sets. Methods SF3B1 MUT MDS (n=21) were compared to 6 SF3B1WT cases and 5 controls. Analysis of RNA-seq read count was performed using the Voom method associated with the Limma empirical Bayes analysis pipeline (https://genomebiology.biomedcentral.com/articles/10.1186/gb-2014-15-2-r29). Up or downregulated gene sets were identified using Gene Set Enrichment Analysis (GSEA, false discovery rate<0.1). Gene expression profiling data (Affymetrix Hu2.0) were also available for 26/27 patient samples. TopHat (v2.0.6) was used to align the reads against the human reference genome Hg19 RefSeq (RNA sequences, GRCh37) downloaded from the UCSC Genome Browser (http://genome.ucsc.edu). Read counts for splicing junctions from junctions.bed TopHat output were considered for a differential analysis using DESeq2. Only alternative acceptor splice sites (two or more 3′ss with junctions to the same 5′ss) and alternative donor splice sites (two or more 5′ss with junctions to the same 3′ss) with P-values ≤10−5 (Benjamini-Hochberg) and absolute Log2 (fold change) ≥1 were considered. Results Principal component analysis (PCA) nicely discriminated controls from patients, and patients according to the presence of a SF3B1 mutation. A set of 6971 genes was differently expressed (P- value<0.05) between SF3B1MUT and SF3B1WT cases and allows unsupervised clustering in two separated groups (Fig. 1). Distinct gene sets also discriminated SF3B1MUT or SF3B1WT from controls. Consistent with increased amount of erythroblasts in MDS-RS bone marrows, a set of erythroid genes including several genes involved in hemebiosynthesis pathway (ALAD, UROS, ALAS2, UROD) was significantly enriched in SF3B1MUT samples. Genes selected for their involvement in the core iron-sulfur cluster mitochondrial machinery (FXN, BOLA3, FDXR, GLRX5, ISCA2, NFS1, ISCU), the iron binding and trafficking (SLC25A38, ABCB10, TFR2, SLC25A37, ABCB6, FAM132B, SLC25A39, FTH1) and the cellular iron homeostasis (ACO1, ACO2, GLRX3) were also significantly enriched (FDR<10% and nominal P-value<0.05) when input in GSEA. Moreover, other enriched gene sets were G2M checkpoint, MYC targets, oxidative phosphorylation and E2F targets. All of these observations were similarly obtained when analyzing Affymetrix data. Furthermore SF3B1MUT samples with a K700E substitution harbored a specific pattern of deregulated genes, which allowed the ordering of SF3B1MUT samples according to the type of substitution. As previously reported by AlsafadiS et al (2016), analysis of splice junctions using DESeq2 revealed an overall high level of differences between SF3B1MUT and SF3B1WTsamples. Among more than 540 differentially spliced junctions, more than 80% involved an aberrant acceptor (3'ss) site. As determined by PCA, the top 50 genes associated with relevant aberrant junctions were linked to iron metabolism or erythropoiesis and differentially expressed between SF3B1MUT and SF3B1WTsamples. Conclusion In this study, we combined robust analyses of gene expression and aberrantly spliced transcript expression in MDS with SF3B1 mutation. By comparing SF3B1MUTversus SF3B1WT samples, we identified a set of deregulated genes in which both normally and aberrantly spliced transcripts were detected that could contribute to the physiopathology of MDS-RS. Figure 1 Hierarchical clustering and heatmapshowing differentially expressed genes (P-value<0.05) between SF3B1MUT (n=21, black) and SF3B1WT samples (n=6, grey) Ref. Alsafadi S et al. Nat Commun. 2016 Feb 4;7:10615. Figure 1. Hierarchical clustering and heatmapshowing differentially expressed genes (P-value<0.05) between SF3B1MUT (n=21, black) and SF3B1WT samples (n=6, grey) Ref. Alsafadi S et al. Nat Commun. 2016 Feb 4;7:10615. Disclosures No relevant conflicts of interest to declare.

scholarly journals PCA via joint graph Laplacian and sparse constraint: Identification of differentially expressed genes and sample clustering on gene expression data

2019 ◽  
Vol 20 (S22) ◽  
Author(s):  
Chun-Mei Feng ◽  
Yong Xu ◽  
Mi-Xiao Hou ◽  
Ling-Yun Dai ◽  
Jun-Liang Shang
Keyword(s):  
Gene Expression ◽  
Differentially Expressed Genes ◽  
Gene Expression Data ◽  
Geometric Structure ◽  
Squamous Carcinoma ◽  
Principal Component ◽  
Graph Laplacian ◽  
Differentially Expressed ◽  
Expression Data ◽  
Sparse Constraint

Abstract Background In recent years, identification of differentially expressed genes and sample clustering have become hot topics in bioinformatics. Principal Component Analysis (PCA) is a widely used method in gene expression data. However, it has two limitations: first, the geometric structure hidden in data, e.g., pair-wise distance between data points, have not been explored. This information can facilitate sample clustering; second, the Principal Components (PCs) determined by PCA are dense, leading to hard interpretation. However, only a few of genes are related to the cancer. It is of great significance for the early diagnosis and treatment of cancer to identify a handful of the differentially expressed genes and find new cancer biomarkers. Results In this study, a new method gLSPCA is proposed to integrate both graph Laplacian and sparse constraint into PCA. gLSPCA on the one hand improves the clustering accuracy by exploring the internal geometric structure of the data, on the other hand identifies differentially expressed genes by imposing a sparsity constraint on the PCs. Conclusions Experiments of gLSPCA and its comparison with existing methods, including Z-SPCA, GPower, PathSPCA, SPCArt, gLPCA, are performed on real datasets of both pancreatic cancer (PAAD) and head & neck squamous carcinoma (HNSC). The results demonstrate that gLSPCA is effective in identifying differentially expressed genes and sample clustering. In addition, the applications of gLSPCA on these datasets provide several new clues for the exploration of causative factors of PAAD and HNSC.

Transcriptome Analysis of Pediatric cALL Versus Normal Fetal B Cells Reveals a Novel Signature of the Malignant Phenotype.

Blood ◽  
2005 ◽  
Vol 106 (11) ◽  
pp. 4352-4352
Author(s):  
Guenther Richter ◽  
Uwe E. Hattenhorst ◽  
Sabine Roessler ◽  
Martin S. Staege ◽  
Gesine Hansen ◽  
...  
Keyword(s):  
B Cells ◽  
Differentially Expressed Genes ◽  
Lymphoblastic Leukemia ◽  
Expression Patterns ◽  
Principal Component ◽  
Differentially Expressed ◽  
Leukemic Cells ◽  
Terminal Deoxynucleotidyl Transferase ◽  
Malignant Phenotype ◽  
Class Prediction

Abstract Differential gene expression profiling of pediatric common acute lymphoblastic leukemia (cALL) versus non-malignant tissues enables identification of aberrantly expressed genes in malignant cells, facilitating discrimination of leukemic from normal cells and possibly revealing specific disease mechanisms. Expression patterns of 29 pediatric cALL samples were analyzed by use of high-density DNA microarrays HG-U133A. Leukemic patients’ bone marrow samples were compared to sorted B cells from cord blood of healthy donors expressing CD19 and CD10 surface antigens. Principal component analysis clearly distinguished leukemia samples from normal controls. Significance analysis of microarrays revealed 723 genes significantly up-regulated, and 617 down-regulated genes in leukemic cells. Independent validation of deregulated genes by RT-PCR was chosen to address enrichment limitations of controls. A comparison to previous publications investigating genetically defined subsets of cALL revealed only 5 – 22% match with our differentially expressed genes. Furthermore, class prediction with only 14 differentially expressed genes correctly classified tumors and controls not included in the training set and hitherto not investigated samples including 3 leukemic tumor lines (NALM-6, CALL-2, 697) as cALL. Interestingly, terminal deoxynucleotidyl-transferase (DNTT) as well as in the context of cALL unknown genes, were found to be the strongest predictive genes for the malignant phenotype signifying the diagnostic value of our approach.

Sign in / Sign up

Export Citation Format

Share Document