scholarly journals Source of Dietary Fat in Pig Diet Affects Adipose Expression of Genes Related to Cancer, Cardiovascular, and Neurodegenerative Diseases

Genes ◽  
2019 ◽  
Vol 10 (12) ◽  
pp. 948
Author(s):  
Maria Oczkowicz ◽  
Tomasz Szmatoła ◽  
Małgorzata Świątkiewicz
Keyword(s):  
Differentially Expressed Genes ◽  
Beef Tallow ◽  
Rapeseed Oil ◽  
Fluid Shear Stress ◽  
Coconut Oil ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Differentially Expressed ◽  
Coagulation Cascade ◽  
P Value

It has been known for many years that excessive consumption of saturated fats has proatherogenic properties, contrary to unsaturated fats. However, the molecular mechanism covering these effects is not fully understood. In this paper, we aimed to identify differentially expressed genes (DEGs) using RNA-sequencing, following feeding pigs with different sources of fat. After comparison of adipose samples from three dietary groups (rapeseed oil (n = 6), beef tallow (n = 5), coconut oil (n = 5)), we identified 29 DEGs (adjusted p-value < 0.05, fold change > 1.3) between beef tallow and rapeseed oil and 2 genes between coconut oil and rapeseed oil groups. No differentially expressed genes were observed between coconut oil and beef tallow groups. Almost all 29 DEGs between rapeseed oil and beef tallow groups are connected to neurodegenerative, cardiovascular diseases, or cancer (e.g., PLAU, CYBB, NCF2, ZNF217, CHAC1, CTCFL). Functional analysis of these genes revealed that they are associated with fluid shear stress response, complement and coagulation cascade, ROS signaling, neurogenesis, and regulation of protein binding and protein catabolic processes. Furthermore, gene set enrichment analysis (GSEA) of the whole datasets from all three comparisons suggests that both beef tallow and coconut oil may trigger changes in the expression level of genes crucial in the pathogenesis of civilization diseases.

scholarly journals Bioinformatics-based Screening of Key Genes for Saponin Metabolism in Quinoa

2021 ◽  
Author(s):  
Chengang Guo ◽  
Zhimin wei ◽  
Wei Lyu ◽  
Yanlou Geng
Keyword(s):  
Differentially Expressed Genes ◽  
Principal Component ◽  
Enrichment Analysis ◽  
Hierarchical Cluster ◽  
Gene Set Enrichment Analysis ◽  
Differentially Expressed ◽  
Differential Analysis ◽  
Rna Seq ◽  
Protein Coding ◽  
Key Genes

Abstract Quinoa saponins have complex, diverse and evident physiologic activities. However, the key regulatory genes for quinoa saponin metabolism are not yet well studied. The purpose of this study was to explore genes closely related to quinoa saponin metabolism. In this study, the significantly differentially expressed genes in yellow quinoa were firstly screened based on RNA-seq technology. Then, the key genes for saponin metabolism were selected by gene set enrichment analysis (GSEA) and principal component analysis (PCA) statistical methods. Finally, the specificity of the key genes was verified by hierarchical clustering. The results of differential analysis showed that 1654 differentially expressed genes were achieved after pseudogenes deletion. Therein, there were 142 long non-coding genes and 1512 protein-coding genes. Based on GSEA analysis, 116 key candidate genes were found to be significantly correlated with quinoa saponin metabolism. Through PCA dimension reduction analysis, 57 key genes were finally obtained. Hierarchical cluster analysis further demonstrated that these key genes can clearly separate the four groups of samples. The present results could provide references for the breeding of sweet quinoa and would be helpful for the rational utilization of quinoa saponins.

scholarly journals Expression Signatures of Long Noncoding RNAs in Left Ventricular Noncompaction

2021 ◽  
Vol 8 ◽  
Author(s):  
Qingshan Tian ◽  
Hanxiao Niu ◽  
Dingyang Liu ◽  
Na Ta ◽  
Qing Yang ◽  
...  
Keyword(s):  
Differentially Expressed Genes ◽  
Noncoding Rnas ◽  
Enrichment Analysis ◽  
Left Ventricular ◽  
Long Noncoding Rnas ◽  
Gene Set Enrichment Analysis ◽  
Differentially Expressed ◽  
Control Group ◽  
Left Ventricular Noncompaction ◽  
Ventricular Noncompaction

Long noncoding RNAs have gained widespread attention in recent years for their crucial role in biological regulation. They have been implicated in a range of developmental processes and diseases including cancer, cardiovascular, and neuronal diseases. However, the role of long noncoding RNAs (lncRNAs) in left ventricular noncompaction (LVNC) has not been explored. In this study, we investigated the expression levels of lncRNAs in the blood of LVNC patients and healthy subjects to identify differentially expressed lncRNA that develop LVNC specific biomarkers and targets for developing therapies using biological pathways. We used Agilent Human lncRNA array that contains both updated lncRNAs and mRNAs probes. We identified 1,568 upregulated and 1,141 downregulated (log fold-change &gt; 2.0) lncRNAs that are differentially expressed between LVNC and the control group. Among them, RP11-1100L3.7 and XLOC_002730 are the most upregulated and downregulated lncRNAs. Using quantitative real-time reverse transcription polymerase chain reaction (RT-QPCR), we confirmed the differential expression of three top upregulated and downregulated lncRNAs along with two other randomly picked lncRNAs. Gene Ontology (GO) and KEGG pathways analysis with these differentially expressed lncRNAs provide insight into the cellular pathway leading to LVNC pathogenesis. We also identified 1,066 upregulated and 1,017 downregulated mRNAs. Gene set enrichment analysis (GSEA) showed that G2M, Estrogen, and inflammatory pathways are enriched in differentially expressed genes (DEG). We also identified miRNA targets for these differentially expressed genes. In this study, we first report the use of LncRNA microarray to understand the pathogenesis of LVNC and to identify several lncRNA and genes and their targets as potential biomarkers.

scholarly journals Prediction of molecular markers of bovine mastitis by meta-analysis of differentially expressed genes using combined p-value and robust rank aggregation

2021 ◽  
Author(s):  
Anushri Umesh ◽  
Praveen Kumar Guttula ◽  
Mukesh Kumar Gupta
Keyword(s):  
Molecular Markers ◽  
Differentially Expressed Genes ◽  
Meta Analysis ◽  
Mirna Gene ◽  
Bovine Mastitis ◽  
Rank Aggregation ◽  
Gene Set Enrichment Analysis ◽  
Differentially Expressed ◽  
P Value ◽  
Expression Levels

Bovine mastitis causes significant economic loss to the dairy industry by affecting milk quality and quantity. E.coli and S.aureus are the two common mastitis-causing bacteria among the consortia of mastitis pathogens, wherein E.coli is an opportunistic environmental pathogen, and S.aureus is a contagious pathogen. This study was designed to predict molecular markers of bovine mastitis by meta-analysis of differentially expressed genes (DEG) in E.coli or S.aureus infected mammary epithelial cells (MECs) using p-value combination and robust rank aggregation (RRA) methods. High throughput transcriptome of bovine (MECs, infected with E.coli or S.aureus, were analyzed, and correlation of z-scores were computed for the expression datasets to identify the lineage profile and functional ontology of DEGs. Key pathways enriched in infected MECs were deciphered by Gene Set Enrichment Analysis (GSEA), following which combined p-value and RRA were used to perform DEG meta-analysis to limit type I error in the analysis. The miRNA-Gene networks were then built to uncover potential molecular markers of mastitis. Lineage profiling of MECs showed that the gene expression levels were associated with mammary tissue lineage. The up-regulated genes were enriched in immune-related pathways whereas down-regulated genes influenced the cellular processes. GSEA analysis of DEGs deciphered the involvement of Toll-like receptor (TLR), and NF- Kappa B signalling pathway during infection. Comparison after meta-analysis yielded with genes ZC3H12A, RND1 and MAP3K8 having significant expression levels in both E.coli and S.aureus dataset and on evaluating miRNA-Gene network 7 pairs were common to both sets identifying them as potential molecular markers.

scholarly journals Meta-analysis of Transcriptomic Data Reveals Pathophysiological Modules Involved with Atrial Fibrillation

2020 ◽  
Author(s):  
Rodrigo Haas Bueno ◽  
Mariana Recamonde-Mendoza
Keyword(s):  
Atrial Fibrillation ◽  
Differentially Expressed Genes ◽  
Complex Disease ◽  
Meta Analysis ◽  
Differential Expression Analysis ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
P Value

Atrial fibrillation (AF) is a complex disease and affects millions of people around the world. The biological mechanisms that are involved with AF are complex and still need to be fully elucidated. Therefore, we performed a meta-analysis of transcriptome data related to AF to explore these mechanisms aiming at more sensitive and reliable results. Public transcriptomic datasets were downloaded, analyzed for quality control, and individually pre-processed. Differential expression analysis was carried out for each individual dataset, and the results were meta-analytically aggregated using the r-th ordered p-value method. We analyzed the final list of differentially expressed genes through network analysis, namely topological and modularity analysis, and functional enrichment analysis. The meta-analysis of transcriptomes resulted in 589 differentially expressed genes, whose protein-protein interaction network presented 11 hubs-bottlenecks and four main identified functional modules. These modules were enriched for, respectively, 23, 54, 33, and 53 biological pathways involved with the pathophysiology of AF, especially with the disease's structural and electrical remodeling processes. Stress of the endoplasmic reticulum, protein catabolism, oxidative stress, and inflammation are some of the enriched processes. Among hubs-bottlenecks genes, which are highly connected and probably have a key role in regulating these processes, we found HSPA5, ANK2, CTNNB1, and VWF. Further experimental investigation of our findings may shed light on the pathophysiology of the disease and contribute to the identification of new therapeutic targets and treatments.

scholarly journals Identifying the p65-Dependent Effect of Sulforaphene on Esophageal Squamous Cell Carcinoma Progression via Bioinformatics Analysis

2020 ◽  
Vol 22 (1) ◽  
pp. 60
Author(s):  
Sichong Han ◽  
Zhe Wang ◽  
Jining Liu ◽  
Qipeng Yuan
Keyword(s):  
Squamous Cell Carcinoma ◽  
Esophageal Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Differentially Expressed Genes ◽  
Squamous Cell ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Dependent Manner

Understanding the mechanism by which sulforaphene (SFE) affects esophageal squamous cell carcinoma (ESCC) contributes to the application of this isothiocyanate as a chemotherapeutic agent. Thus, we attempted to investigate SFE regulation of ESCC characteristics more deeply. We performed gene set enrichment analysis (GSEA) on microarray data of SFE-treated ESCC cells and found that differentially expressed genes are enriched in TNFα_Signaling_via_the_NFκB_Pathway. Coupled with the expression profile data from the GSE20347 and GSE75241 datasets, we narrowed the set to 8 genes, 4 of which (C-X-C motif chemokine ligand 10 (CXCL10), TNF alpha induced protein 3 (TNFAIP3), inhibin subunit beta A (INHBA), and plasminogen activator, urokinase (PLAU)) were verified as the targets of SFE. RNA-sequence (RNA-seq) data of 182 ESCC samples from The Cancer Genome Atlas (TCGA) were grouped into two phenotypes for GSEA according to the expression of CXCL10, TNFAIP3, INHBA, and PLAU. The enrichment results proved that they were all involved in the NFκB pathway. ChIP-seq analyses obtained from the Cistrome database indicated that NFκB-p65 is likely to control the transcription of CXCL10, TNFAIP3, INHBA, and PLAU, and considering TNFAIP3 and PLAU are the most significantly differentially expressed genes, we used chromatin immunoprecipitation-polymerase chain reaction (ChIP-PCR) to verify the regulation of p65 on their expression. The results demonstrated that SFE suppresses ESCC progression by down-regulating TNFAIP3 and PLAU expression in a p65-dependent manner.

scholarly journals Gene Expression and Epigenetic Analysis in Relapsed/Refractory Diffuse Large B Cell Lymphoma Provides Insights into Evolution of Treatment Resistance to R-CHOP

Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 26-26
Author(s):  
Manishkumar S. Patel ◽  
Ellen K. Kendall ◽  
Sarah Ondrejka ◽  
Agrima Mian ◽  
Yazeed Sawalha ◽  
...  
Keyword(s):  
Gene Expression ◽  
B Cell ◽  
Differentially Expressed Genes ◽  
Research Funding ◽  
B Cell Lymphoma ◽  
Cell Receptor ◽  
B Cell Receptor ◽  
Gene Set Enrichment Analysis ◽  
Differentially Expressed ◽  
P Value

Background Diffuse large B cell lymphoma (DLBCL) is curable in ~60-70% of patients using standard chemoimmunotherapy, but the prognosis is poor for relapsed/refractory (R/R) DLBCL. Therefore, understanding the underlying molecular mechanisms will facilitate early prediction and effective management of resistance to therapy. Recent studies of paired diagnostic-relapse biopsies from patients have relied on a single "omics" approach, examining either gene expression or epigenetic evolution. Here we present a combined analysis of gene expression and DNA methylation profiles of paired diagnostic-relapse DLBCL biopsies to identify changes responsible for relapse after R-CHOP. Methods Biopsies from 23 DLBCL patients were obtained at the time of diagnosis and relapse following frontline R-CHOP chemoimmunotherapy. The cohort had 18 (78.3%) male patients with median age of 62 (range, 35-86) years and median IPI of 2.5 (range, 1-5). The median time from diagnosis to relapse was 7 (range, 0-57) months. DNA and RNA were extracted simultaneously from formalin-fixed paraffin embedded (FFPE) biopsy samples. DNA methylation levels were measured through Illumina 850k Methylation Array for 22 pairs of diagnostic-relapse biopsies. RNA from diagnostic-relapse paired biopsies from 6 patients was sequenced using Illumina HiSeq4000. Differentially methylated probes were identified using the DMRcate package, and differentially expressed genes were identified using the DESeq2 package. Gene set enrichment analysis was performed using canonical pathway gene sets from MSigDB. Pearson's correlation with a Bonferroni correction to the p-value was used to calculate the correlation between regularized log transformed gene expression counts and methylation beta values. Results In a pairwise comparison of gene expression between diagnostic and R/R biopsy pairs, we found 14 differentially expressed genes (FDR&lt;0.1 & Log2FC&gt;|1|) consistent across all pairs. Compared to gene expression at diagnosis, five genes (CYP1B1, LGR4, ATXN1, CTSC, ZMAT3) were downregulated, and eight genes (ERBB3, CD19, CARD11, MT-RNR2, IGHG3, CCDC88C, ATP2A3, CENPE, and PCNT) were up-regulated in the R/R samples. Many of these genes have been previously implicated in oncogenesis, such as ERBB3, a member of the epidermal growth receptor family. Importantly, some of these genes have known roles in DLBCL biology, such as CD19, a member of the B-cell receptor complex, and CARD11, a gene in which several oncogenic mutations have been identified in DLBCL as a mediator of NF-KB activation. Gene set enrichment analysis revealed overexpression of immune signatures such as cytokine-cytokine receptor interaction, chemokine receptor-chemokine binding, and the IL-12-STAT4 pathway at diagnosis. At relapse, cell cycle, B-cell receptor, and NOTCH signaling pathways were overexpressed. Interestingly, in a pairwise comparison of methylation between diagnostic and R/R biopsy pairs, there were no differentially methylated probes (FDR&lt;0.05), suggesting no coordinated epigenetic evolution between diagnostic and R/R pairs. For biopsy pairs that had both gene expression and methylation data (5 pairs), we correlated gene expression and methylation values. We found that none of the differentially expressed genes between the diagnostic and R/R biopsies were significantly correlated with methylation status (adjusted p-value&lt;0.05). Conclusions By analyzing paired diagnostic and relapse DLBCL biopsies, we found that at the time of relapse, there are significant transcriptomic changes but no significant epigenetic changes when compared to diagnostic biopsies. Activation of B-cell receptor and NOTCH signaling, as well as the loss of immune signaling at relapse, cannot be attributed to coordinated epigenetic changes in methylation. As the epigenetic profile of the biopsies did not consistently evolve, these data emphasize the need for better understanding of the baseline methylation profiles at the time of diagnosis, as well as acquired somatic mutations that may contribute to the emergence of therapeutic resistance. Future studies are needed to focus on how activation of signaling pathways triggered by genomic alterations can be targeted in relapsed/refractory DLBCL. Disclosures Hsi: Seattle Genetics: Consultancy, Honoraria; Miltenyi: Consultancy, Honoraria; Abbvie: Research Funding; Eli Lilly: Research Funding; CytomX: Consultancy, Honoraria. Hill:Takeda: Research Funding; Genentech: Consultancy, Honoraria, Research Funding; Karyopharm: Consultancy, Honoraria, Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Abbvie: Consultancy, Honoraria, Research Funding; Pharmacyclics: Consultancy, Honoraria, Research Funding; Beigene: Consultancy, Honoraria, Research Funding; AstraZenica: Consultancy, Honoraria, Research Funding; Kite, a Gilead Company: Consultancy, Honoraria, Research Funding; Novartis: Consultancy, Honoraria; BMS: Consultancy, Honoraria, Research Funding.

scholarly journals PODN is a prognostic biomarker and correlated with immune infiltrates in osteosarcoma

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Feng Yao ◽  
Zhao Feng Zhu ◽  
Jun Wen ◽  
Fu Yong Zhang ◽  
Zheng Zhang ◽  
...  
Keyword(s):  
Survival Analysis ◽  
Differentially Expressed Genes ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Differentially Expressed ◽  
Pathway Enrichment Analysis ◽  
Ppi Network ◽  
Potential Biomarker ◽  
Public Data

Abstract Background Osteosarcoma was the most common primary bone malignancy in children and adolescents. It was imperative to identify effective prognostic biomarkers for this cancer. This study was aimed to identify potential crucial genes of osteosarcoma by integrated bioinformatics analysis. Methods Identification of differentially expressed genes from public data gene expression profiles (GSE42352), functional and pathway enrichment analysis, protein–protein interaction (PPI) network construction and module analysis, Cox regression and survival analysis was conducted. Results Totally 17 co-differential genes were found to be differentially expressed. These genes were enriched in biological processes, Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Set Enrichment Analysis (GSEA) pathway of inflammatory immune response. PPI network was constructed with 63 differentially expressed genes that co-existed between the test set and the validation set. The area under the receiver operating characteristic curve (AUC value) was 0.855, which indicated that the expression of PODN had a good diagnostic value for osteosarcoma. Furthermore, Cox regression and survival analysis revealed 5 genes were statistically significant. Conclusions PODN was regarded as a potential biomarker for the diagnosis and prognosis of osteosarcoma, ACTA2, COL6A1, FAP, OLFML2B and COL6A3, can be used as potential prognostic indicators for osteosarcoma.

scholarly journals 140 ASAS-EAAP Exchange Speaker Talk: Effects of different sources of fat in the diet of pigs on the liver transcriptome estimated by 3`RNA-sequencing

2019 ◽  
Vol 97 (Supplement_3) ◽  
pp. 134-134
Author(s):  
Maria Oczkowicz ◽  
Małgorzata Świątkiewicz ◽  
Tomasz Szmatoła ◽  
Artur Gurgul ◽  
Anna Koseniuk ◽  
...  
Keyword(s):  
Cardiovascular Diseases ◽  
Beef Tallow ◽  
Rapeseed Oil ◽  
Coconut Oil ◽  
Coagulation Cascade ◽  
Biological Processes ◽  
Alcoholic Fatty Liver ◽  
Group Iii ◽  
Group I ◽  
Group Ii

Abstract The effect of different fats [rapeseed oil (group I n = 6), beef tallow (group II n = 6), coconut oil (group III n = 5)] in the diet of pigs on the liver transcriptomic profile was analyzed using the 3`quant mRNA-sequencing method (Lexogen, Vienna, Austria). In this method the quantification of gene expression is based only on the 3 ‘UTR fragment of genes in contrast to traditional RNA-seq in which the analysis is based on the entire length of the transcripts. Thus, this method is much cheaper and some sources claim that it is also more sensitive. Using this method, we detected approximately 1000 Differentially Expressed genes (DEGs) between samples from the liver of individuals fed with the addition of rapeseed oil when compared to samples from animals receiving beef tallow in the diet (base mean &gt;10, fold change &gt;1.5, p-adjusted&lt; 0.05). More than 700 DEGs and about 100 DEGs were identified in group I vs group III and group II vs group III comparisons respectively. Functional analysis with STRING software revealed 287, and 233 significant Biological Processes in group I vs group II and group I vs group III comparisons respectively. Among genes with altered expression, there were many genes playing a key role in signalling pathways, autophagy, mitophagy, complement and coagulation cascade, cholesterol metabolism, oxidative stress, RNA and protein processing. Significant part of these genes were strongly associated with human diseases: Non Alcoholic Fatty Liver Disease (NAFLD), cancer, Parkinson`s disease, Alzheimer`s disease and cardiovascular diseases. There were many overlapping genes and biological processes in results of comparisons of rapeseed oil group with beef tallow and coconut oil groups, however pathways connected to atherosclerosis and cardiovascular diseases were only observed in group I vs group II comparisons. These results are preliminary and will be validated by qPCR. Table 1. Top 20 DEGs identified in three comparisons.

scholarly journals Integrative bioinformatics analysis for identification of hub genes and pathways responsible for early pathogenesis of retinoblastoma

2020 ◽  
Author(s):  
Aditi Karmakar ◽  
Md. Maqsood Ahamad Khan ◽  
Nidhi Kumari ◽  
Senthil Kumar
Keyword(s):  
Differentially Expressed Genes ◽  
Enrichment Analysis ◽  
Differentially Expressed ◽  
Integrated Analysis ◽  
P Value ◽  
Pathway Enrichment Analysis ◽  
Hub Genes ◽  
Diagnostic Biomarkers ◽  
Appropriate Therapy ◽  
Microarray Datasets

Abstract Background Retinoblastoma (Rb) is the most common childhood malignancy in which intra-ocular tumors developed at a very young age. It is very crucial to detect Rb at an earlier stage and start appropriate therapy to prevent further metastasis. With the recent advancement of multi-omics analysis of microarray data and sophisticated bioinformatics tools, we could possibly identify potential early diagnostic biomarkers and novel therapeutic targets for retinoblastoma. Methods Microarray datasets (DNA methylation-GSE57362, miRNA-GSE7072, & mRNA-GSE110811) were utilized from NCBI-GEO. The GEO2R were employed to discover Differentially Expressed Genes (DEGs) in Retinoblastoma. Further, an integrated analysis of these genes was performed and a co-expression network was formed to identify hub genes. GEPIA server was used to validate these genes which are responsible for the progression of retinoblastoma. Results Differentially expressed genes were identified on the basis of P-value ≤ 0.05 and log2fc ≥ 2. In GSE57362 a total no of 267 genes methylated status, in GSE7072 a total no. of 265 gene targets of miRNAs and in GSE110811 a total no. of 770 genes were shown to be differentially expressed. Further, 10 hub genes, 5 bottleneck genes, and 3 common genes were identified by constructing the co-expression network. Survival analysis was done to validate the identified candidate genes using the GEPIA web server. Pathway enrichment analysis and gene ontology demonstrated that these genes were mostly enriched in biological processes such as regulation of cell proliferation and involved in multiple pathways like p53 pathway, cell cycle, and apoptosis. Conclusion This study suggested that these hub genes possibly will play vital roles in the onset and progression of retinoblastoma and could be serve as potential biomarkers to facilitate the diagnosis and treatment of this disease in the future.

Gene Expression Profiling in Down Syndrome Acute Lymphoblastic Leukemia Identifies Distinct Profiles Associated with CRLF2 Expression Status.

Blood ◽  
2009 ◽  
Vol 114 (22) ◽  
pp. 2389-2389
Author(s):  
Karen R. Rabin ◽  
Jinhua Wang ◽  
Julia Meyer ◽  
Michael G. Loudin ◽  
Deepa Bhojwani ◽  
...  
Keyword(s):  
Gene Expression ◽  
Down Syndrome ◽  
Acute Lymphoblastic Leukemia ◽  
Differentially Expressed Genes ◽  
Lymphoblastic Leukemia ◽  
Gene Expression Signature ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Differentially Expressed ◽  
Gene Set Enrichment

Abstract Abstract 2389 Poster Board II-366 Children with Down syndrome (DS) have a 10 to 20-fold increased risk of developing acute lymphoblastic leukemia (ALL), and they have experienced poorer outcomes on recent major protocols worldwide. The cytogenetic abnormalities which are generally common in childhood ALL and contribute to risk-based treatment assignment are markedly less frequent in children with DS-ALL. Recently, activating mutations in Janus kinase 2 (JAK2) have been identified in approximately 20% of DS-ALL, and interstitial deletions involving cytokine receptor-like factor 2 (CRLF2) in approximately 50% of DS-ALL. Global gene expression profiling may provide insights into the biologic consequences of these molecular lesions. We performed microarray analysis of RNA from diagnostic bone marrow samples in 23 DS-ALL and 26 non-DS ALL cases using the Affymetrix Human Genome U133 Plus 2.0 array. CRLF2 expression was high in 10 of the 23 DS-ALL cases, 3 of which also bore JAK2 mutations, and in a single non-DS ALL case. Unsupervised hierarchical clustering analysis demonstrated clustering of non-DS ALL cases belonging to known cytogenetic subgroups such as E2A-PBX1, MLL rearrangement, and high hyperdiploidy. In contrast, neither DS-ALL cases overall nor the JAK2-mutated or high CRLF2 expressing cases formed a cohesive cluster. Supervised analysis identified 43 genes that were differentially expressed between CRLF2 high versus low cases with a false discovery rate <10%. Several of the most highly differentially expressed genes were validated by quantitative real-time PCR. These included three genes with high expression in CRLF2-high cases: chemokine (C-C motif) ligand 17 (CCL17) (p=0.01), V-yes-1 Yamaguchi sarcoma viral oncogene homolog 1 (YES1) (p=0.007), and Iroquois homeobox 2 (IRX2) (p=0.008); and one gene with expression inversely correlated with CRLF2 expression: dual specificity phosphatase 6 (DUSP6) (p=0.0015). Our findings suggest that DS-ALL does not form a single distinct biologic subgroup, but nearly half of DS-ALL cases are defined by high CRLF2 expression, a substantial enrichment for this lesion compared to its prevalence in non-DS ALL. Identification of downstream pathways may identify opportunities for targeted intervention, including interactions with other cytokines and activation of the JAK-STAT pathway. Figure 1. Gene expression signature of top differentially expressed genes in Down syndrome (DS) acute lymphoblastic leukemia (ALL) cases with high versus low CRLF2 expression. Each column indicates a case, with CRLF2-high cases depicted in gray and CRLF2-low cases in gold. Each row indicates one of the top 100 differentially expressed genes as determined by Gene Set Enrichment Analysis. Figure 1. Gene expression signature of top differentially expressed genes in Down syndrome (DS) acute lymphoblastic leukemia (ALL) cases with high versus low CRLF2 expression. Each column indicates a case, with CRLF2-high cases depicted in gray and CRLF2-low cases in gold. Each row indicates one of the top 100 differentially expressed genes as determined by Gene Set Enrichment Analysis. Disclosures: No relevant conflicts of interest to declare.

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