scholarly journals Gene Set Enrichment Analysis Unveils the Mechanism for the Phosphodiesterase 4B Control of Glucocorticoid Response in B-cell Lymphoma

2011 ◽  
Vol 17 (21) ◽  
pp. 6723-6732 ◽  
Author(s):  
Sang-Woo Kim ◽  
Deepak Rai ◽  
Ricardo C.T. Aguiar
Blood ◽  
2011 ◽  
Vol 118 (21) ◽  
pp. 2833-2833
Author(s):  
Xiao J. Yan ◽  
Daniel Kalenscher ◽  
Erin Boyle ◽  
Sophia Yancopoulos ◽  
Rajendra N Damle ◽  
...  

Abstract Abstract 2833 Introduction: In chronic lymphocytic leukemia (CLL), clonally expanded CD5+ B lymphocytes eventually overwhelm healthy immune cells, hindering normal immune function. To determine mechanisms fueling this expansion, gene expression data were gathered by microarray analysis of cells from CLL patients. Samples were grouped based on Ki-67 expression, an indicator of proliferation. To determine mechanisms correlating with B-cell proliferation and impacting on CLL B-cell biology, microarray profiles were compared using Gene Set Enrichment Analysis (GSEA) [Subramanian A, et al. PNAS 2005]. Methods: Samples were analyzed for intracellular expression of Ki-67 by flow cytometry and divided into 2 groups based on Ki-67 expression (cutoff at 5%). RNA was then purified from CD5+CD19+ CLL cells and gene expression microarray assays were performed using Illumina HumanHT12 beadchips. GSEA was carried out using a library of signatures by Dr. Louis Staudt [Shaffer AL, et al. Immunol Rev 2006] containing 305 gene sets encompassing 13, 564 genes biased towards hematopoietic signatures. Results: Of 61 cases, 14 were Ki-67high and 47 were Ki-67low. When time-to-first-treatment (TTFT) was compared between the groups, Ki67high patients had significantly shorter TTFT (2.76 yrs) compared to Ki-67low patients (23.46 yrs; P<0.0001). By GSEA, we determined 255/285 gene sets were upregulated in the Ki-67high group with 50 gene sets significantly enriched at a false discovery rate (FDR) <25%. For the Ki-67low group, 30/285 gene sets were upregulated with only one significant at FDR <25%. IGHV unmutated CLL (U-CLL) was enriched in only one gene set, termed CLLUNMUT-1, while mutated CLL (M-CLL) was only enriched in CLLMUT-1. CD38high and CD38low subsets were similarly enriched in these two gene sets, with 4 additional gene sets in the CD38high group, including MYD88UP-4 and IFN-2. Of the 50 significantly enriched gene sets in the Ki-67high group, 17 relate to signaling pathways, 16 to cellular differentiation, 6 to cellular processes, 4 to transcription factor targets, and the remaining 7 relate to cancer. Of these, the percentage of the signaling component is up 13% from its representation in the original Staudt library. The top 5 gene sets enriched in the Ki-67high group are: upregulated U-CLL compared to M-CLL (CLLUNMUT-1), myeloid tissue compared to other tissues (MYELOID-1), T cell cytokine induced proliferation (TCYTUP-8), BCR crosslinking CLL B cells (CLLBCRUP-1) and BDCA4+ dendritic cells compared to other hematopoietic cells (DC-1). The total number of genes enriched in these 50 sets is 769, with 217 genes shared in two or more gene sets. Twenty genes were enriched in the CLL BCR signature, CLLBCRUP-1 [Herishanu Y, et al. Blood 2011]. Of these, WARS, IRF4, MX1, OAS1, and NAMPT are also enriched in the T cell cytokine induced and T cell activation signatures. Only one gene set was enriched in the Ki-67low group, CLLMUT-1, upregulated in M-CLL compared to U-CLL. CD274 (PD-L1) was consistently elevated in the Ki-67low group in all the patients, irrespective of IGHV mutation status. Discussion: The observed GSEA profiles in Ki-67high patients correlated with gene signatures biased towards BCR signaling, signal transduction, and hematopoietic cancer, consistent with the Ki-67high group containing more (recently) proliferating cells influenced at least in part by BCR signaling. The profiles also suggest that additional cells (T lymphocytes and dendritic cells) may be involved. It is notable these gene sets were not observed for CLL patients subgrouped by IGHV mutation status or by CD38, and that these other subsets did not show as pronounced a distinction by GSEA profiling. Disclosures: No relevant conflicts of interest to declare.


2021 ◽  
Vol 11 ◽  
Author(s):  
Lingna Zhou ◽  
Liya Ding ◽  
Yuqi Gong ◽  
Jing Zhao ◽  
Jing Zhang ◽  
...  

Diffuse large B-cell lymphoma (DLBCL) is the most frequent and commonly diagnosed subtype of NHL, which is characterized by high heterogeneity and malignancy, and most DLBCL patients are at advanced stages. The serine/threonine kinase NEK2 (NIMA-related kinase 2), a member of NIMA-related kinase (NEK) family that regulates cell cycle, is upregulated in a variety of malignancies, including diffuse large B-cell lymphoma. However, the role and underlying mechanisms of NEK2 in DLBCL have seldom been discussed. In this study, we identified that NEK2 is upregulated in DLBCL compared to normal lymphoid tissues, and overexpression of NEK2 predicted a worse prognosis of DLBCL patients. Gene set enrichment analysis indicates that NEK2 might participate in regulating glycolysis. Knockdown of NEK2 inhibited growth and glycolysis of DLBCL cells. The interaction between NEK2 and PKM2 was discovered by tandem affinity purification and then was confirmed by immunofluorescence staining, coimmunoprecipitation, and immunoprecipitation. NEK2 bounds to PKM2 and regulates PKM2 abundance via phosphorylation, which increases PKM2 stability. The xenograft tumor model checks the influence of NEK2 on tumor growth in vivo. Thus, NEK2 could be the novel biomarker and target of DLBCL, which remarkably ameliorates the diagnosis and treatment of DLBCL.


2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Sina Abdollahi ◽  
Seyedeh Zahra Dehghanian ◽  
Liang-Yi Hung ◽  
Shiang-Jie Yang ◽  
Dao-Peng Chen ◽  
...  

Abstract Introduction Earlier studies have shown that lymphomatous effusions in patients with diffuse large B-cell lymphoma (DLBCL) are associated with a very poor prognosis, even worse than for non-effusion-associated patients with stage IV disease. We hypothesized that certain genetic abnormalities were associated with lymphomatous effusions, which would help to identify related pathways, oncogenic mechanisms, and therapeutic targets. Methods We compared whole-exome sequencing on DLBCL samples involving solid organs (n = 22) and involving effusions (n = 9). We designed a mutational accumulation-based approach to score each gene and used mutation interpreters to identify candidate pathogenic genes associated with lymphomatous effusions. Moreover, we performed gene-set enrichment analysis from a microarray comparison of effusion-associated versus non-effusion-associated DLBCL cases to extract the related pathways. Results We found that genes involved in identified pathways or with high accumulation scores in the effusion-based DLBCL cases were associated with migration/invasion. We validated expression of 8 selected genes in DLBCL cell lines and clinical samples: MUC4, SLC35G6, TP53BP2, ARAP3, IL13RA1, PDIA4, HDAC1 and MDM2, and validated expression of 3 proteins (MUC4, HDAC1 and MDM2) in an independent cohort of DLBCL cases with (n = 31) and without (n = 20) lymphomatous effusions. We found that overexpression of HDAC1 and MDM2 correlated with the presence of lymphomatous effusions, and HDAC1 overexpression was associated with the poorest prognosis.  Conclusion Our findings suggest that DLBCL associated with lymphomatous effusions may be associated mechanistically with TP53-MDM2 pathway and HDAC-related chromatin remodeling mechanisms.


2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Zijian Liu ◽  
Jingshu Meng ◽  
Xiaoqian Li ◽  
Fang Zhu ◽  
Tao Liu ◽  
...  

There is a significant difference in prognosis between the germinal center B-cell (GCB) and activated B-cell (ABC) subtypes of diffuse large B-cell lymphoma (DLBCL). However, the signaling pathways and driver genes involved in these disparate subtypes are ambiguous. This study integrated three cohort profile datasets, including 250 GCB samples and 250 ABC samples, to elucidate potential candidate hub genes and key pathways involved in these two subtypes. Differentially expressed genes (DEGs) were identified. After Gene Ontology functional enrichment analysis of the DEGs, protein-protein interaction (PPI) network and sub-PPI network analyses were conducted using the STRING database and Cytoscape software. Subsequently, the Oncomine database and the cBioportal online tool were employed to verify the alterations and differential expression of the 8 hub genes (MME, CD44, IRF4, STAT3, IL2RA, ETV6, CCND2, and CFLAR). Gene set enrichment analysis was also employed to identify the intersection of the key pathways (JAK-STAT, FOXO, and NF-κB pathways) validated in the above analyses. These hub genes and key pathways could improve our understanding of the process of tumorigenesis and the underlying molecular events and may be therapeutic targets for the precise treatment of these two subtypes with different prognoses.


Author(s):  
Zhixing Kuang ◽  
Xun Li ◽  
Rongqiang Liu ◽  
Shaoxing Chen ◽  
Jiannan Tu

BackgroundCachexia is defined as an involuntary decrease in body weight, which can increase the risk of death in cancer patients and reduce the quality of life. Cachexia-inducing factors (CIFs) have been reported in colorectal cancer and pancreatic adenocarcinoma, but their value in diffuse large B-cell lymphoma (DLBCL) requires further genetic research.MethodsWe used gene expression data from Gene Expression Omnibus to evaluate the expression landscape of 25 known CIFs in DLBCL patients and compared them with normal lymphoma tissues from two cohorts [GSE56315 (n = 88) and GSE12195 (n = 136)]. The mutational status of CIFs were also evaluated in The Cancer Genome Atlas database. Based on the expression profiles of 25 CIFs, a single exploratory dataset which was merged by the datasets of GSE10846 (n = 420) and GSE31312 (n = 498) were divided into two molecular subtypes by using the method of consensus clustering. Immune microenvironment between different subtypes were assessed via single-sample gene set enrichment analysis and the CIBERSORT algorithm. The treatment response of commonly used chemotherapeutic drugs was predicted and gene set variation analysis was utilized to reveal the divergence in activated pathways for distinct subtypes. A risk signature was derived by univariate Cox regression and LASSO regression in the merged dataset (n = 882), and two independent cohorts [GSE87371 (n = 221) and GSE32918 (n = 244)] were used for validation, respectively.ResultsClustering analysis with CIFs further divided the cases into two molecular subtypes (cluster A and cluster B) associated with distinct prognosis, immunological landscape, chemosensitivity, and biological process. A risk-prognostic signature based on CCL2, CSF2, IL15, IL17A, IL4, TGFA, and TNFSF10 for DLBCL was developed, and significant differences in overall survival analysis were found between the low- and high-risk groups in the training dataset and another two independent validation datasets. Multivariate regression showed that the risk signature was an independently prognostic factor in contrast to other clinical characteristics.ConclusionThis study demonstrated that CIFs further contribute to the observed heterogeneity of DLBCL, and molecular classification and a risk signature based on CIFs are both promising tools for prognostic stratification, which may provide important clues for precision medicine and tumor-targeted therapy.


Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 36-36
Author(s):  
Hamza Kamran ◽  
Ariz Akhter ◽  
Hassan Rizwan ◽  
Meer-Taher Shahbani-Rad ◽  
Ghaleb Elyamany ◽  
...  

Background:Plasmablastic lymphoma (PBL), is a rare aggressive B-cell lymphoma that shares many overlapping characteristics with activated B-cell type diffuse large B-cell lymphoma (ABC-DLBCL) and multiple myeloma (MM). High expression ofWnt/β-cateninpathway molecules has been linked with several aspects of tumour biology in ABC-DLBCL and MM. In MMWnt/β-cateninplay critical role in chemoresistance, while high FOXP1 in ABC-DLBCL exert poor prognosis through up-regulation of theWnt/β-cateninsignalling pathway. There is strong evidence that enhanced crosstalk between the Wnt/β-catenin andRASpathways impact tumorigenesis and metastasis of cancerous stem cells in various cancers. In breast cancer; targeting theWnt/β-cateninand RAS pathways with small molecular inhibitors have shown effective results. The role of the Wnt/β-catenin signalling pathway and its corresponding linkage toRASsignalling molecules remained unknown in PBL. This pilot study provides preliminary data in relation to expressionof Wnt/β-cateninandRASpathways molecules in PBL in contrast to ABC-DLBCL. Method:FFPE RNA from diagnostic tissue samples in PBL patients (n=31) were compared with ABC-DLBCLs (n=18) patients for keyWnt/β-cateninandRASpathway molecules expression, utilizing nCounter (NanoString Technologies) platform. Qlucore Omics Explorer software was employed with defined criteria (fold change &gt;2.0; p&lt;0.01 and q &lt;0.05) for statistical analysis. Gene Set Enrichment Analysis (GSEA) from 5 publicly available gene data sets was used to analyze the expression of other accompanying pathways. Result:We identified significant differential expression of mRNA related toWnt/β-cateninsignalling between ABC-DLBCL and PBL (Figure 1). Expression ofWnt/β-cateninsignalling inhibitors (CXXC4, SFRP2, and DKK1) were significantly higher among PBL compared to ABC-DLBCL (8.12-3.22 log fold difference). In divergence, molecules linked withWnt/β-cateninsignalling activation were elevated in PBL when compared to ABC-DLBCL (FZD3andWNT10B). The GESA analysis proved that the RAS pathway was significantly up-regulated in PBL patients compared to ABC-DLBCL. In particular, the expression of crucial RAS pathway genes such asNRAS, RAF1, SHC1, andSOS1was significantly up-regulated in PBL patients when compared to ABC-DLBCL patients (Figure 2). Conclusion:Our data suggest that the expression ofWnt/B-catenintarget genes and ligands are enhanced in PBL patients along with the up-regulation of theRASsignalling pathway molecules as compared to ABC-DLBCL. The heightened expression of crucialWnt/B-catenininhibitors does not down-regulate the Wnt/β-catenin signalling. We anticipate that the combined down-regulation of theWnt/ β-cateninandRASpathways by targeting its key members (RAF1, NRAS, FZD3) may serve to contain tumor progression in PBL, hence impacting prognosis. Disclosures Stewart: Gilead:Honoraria;Sandoz:Honoraria;Teva:Honoraria;Amgen:Honoraria;Celgene:Honoraria;Abbvie:Honoraria;Roche:Honoraria;Janssen:Honoraria;Novartis:Honoraria;AstraZeneca:Honoraria.


Blood ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 1472-1472
Author(s):  
Sydney Dubois ◽  
Bruno Tesson ◽  
Pierre-Julien Viailly ◽  
Thierry Molina ◽  
Christiane Copie-Bergman ◽  
...  

Abstract Introduction Diffuse Large B Cell Lymphoma (DLBCL) is the most common lymphoid malignancy, accounting for 30-40% of all Non Hodgkin Lymphomas. Gene expression profiling (GEP) has identified three main subtypes of DLBCL: Germinal Center B-cell like (GCB), Activated B-Cell like (ABC) and Primary Mediastinal B-cell Lymphoma (PMBL). Recently, Next Generation Sequencing (NGS) has enabled a more detailed characterization of DLBCL mutational profiles. Conventional techniques such as immunohistochemistry (IHC) and FISH are also widely used to describe DLBCL. However, no study has yet performed an integrative analysis of the mutational, gene expression, IHC and FISH profiles of DLBCL, in order to provide a comprehensive view of this disease. Methods 215 patients with de novo DLBCL in the prospective, multicenter and randomized LNH-03B clinical trials led by the LYmphoma Study Association (LYSA) were included in this study. Microarray-based GEP identified 81 ABC, 83 GCB, 18 PMBL and 33 other. Mutational profiles of patients' tumor DNA were established using Lymphopanel NGS, designed to identify mutations in 34 genes important for lymphomagenesis. For each recurrently mutated gene, we applied ROMER (Ritchie, Nucleic Acids Res, 2015) to perform gene set enrichment analysis on differential expression profiles of mutant and wild-type patients, using a multifactorial model accounting for subtype. The gene sets were obtained from the MSIGDB Hallmarks (Subramanian A, PNAS, 2005) and Signaturedb (Schaffer, Immunol Rev, 2006) collections. When possible, IHC was performed for IgM (n=150), MYC (n=140), BCL2 (n=148), BCL6 (n=146), CD10 (n=152), FOXP1 (n=147) and MUM1 (n=152); FISH was performed for MYC (n=131), BCL2 (n=133) and BCL6 (n=131). Results As expected, EZH2 mutations were significantly associated with upregulation of GCB gene expression (p<10-3), as well as downregulation of bivalent genes (p<10-2), H3K27me3 targets (p<10-2) and GSK343 upregulated genes (p=0.02) (Beguelin et al, Cancer Cell, 2013). IHC and FISH data further cemented EZH2 mutations' link to GCB subtype, and particularly the t(14;18)-positive subset (CD10+: OR=3.9 and p=0.01, MUM1-: OR=0.12 and p<10-3, BCL2+: OR=8.1 and p=0.04, BCL2 rearranged: OR=6.1 and p=0.04). BCL2 and CREBBP mutations were also linked to GCB subtype (CD10+ and MUM1-), and BCL2 mutations correlated with double-hit GCB DLBCL (Myc+: OR=6.6 and p<10-2, MYC rearranged: OR=7.6 and p=0.03, BCL2 rearranged: OR=20 and p<10-3). An association between BCL6 translocations and ABC subtype was confirmed, via a correlation with ABC-enriched CD79B mutations (p=0.02), although interestingly not with MYD88 mutations. MYD88 mutations were correlated with an upregulation of genes involved in proliferation or repressed by PRDM1 (FDR=0.04 each), as well as with an upregulation of genes involved in checkpoint controls, such as E2F targets and genes involved in DNA repair (FDR=0.03 each). All MYD88 mutants expressed FOXP1 in IHC (p<10-3) and MYD88 mutations were also correlated with IgM IHC positivity (OR=3.3 and p<10-2). TNFAIP3 mutations, also involved in constitutive NFkB activation, were associated with an upregulation of genes regulated by NFkB in response to TNF (FDR=0.02), as well as with an upregulation of KRAS-activated genes (FDR<10-2). PMBL-enriched mutations in our cohort were frequently associated with IgM and FoxP1 negative IHC, as expected (WHO 2008 and Roschewski, Nat Rev Clin Onc). XPO1 and ITPKB mutations were correlated with JAK-STAT pathway activation in the total cohort, including upregulation of interferon-inducible genes for both gene mutations (FDR=0.02 and FDR=0.08 respectively) and upregulation of BCL6-repressed genes for XPO1 mutations only (FDR=0.02). Interestingly, CD58 mutations were significantly correlated with upregulation of Nfkb pathway target genes (FDR=0.06), perhaps due to their negative impact on CD2 activation and ROS production inhibition. Conclusion The results herein provide steps toward a comprehensive, multi-level overview of DLBCL. We highlight differential gene set expression linked to gene mutation status, as well as driver translocation-associated mutational profiles. By using an integrative analysis approach, this study broadens our understanding of DLBCL subtypes' diverse genetic backgrounds. Disclosures Briere: St. Louis Hospital, Paris, France: Employment. Salles:Celgene Corporation; Roche: Speakers Bureau; Calistoga Pharmaceuticals, Inc.; Celgene Corporation; Genentech, Inc.; Janssen Pharmaceutica Products, L.P.; Roche: Consultancy; Celgene Corporation; Roche and Gilead Sciences: Research Funding.


2019 ◽  
Vol 8 (10) ◽  
pp. 1580 ◽  
Author(s):  
Kyoung Min Moon ◽  
Kyueng-Whan Min ◽  
Mi-Hye Kim ◽  
Dong-Hoon Kim ◽  
Byoung Kwan Son ◽  
...  

Ninety percent of patients with scrub typhus (SC) with vasculitis-like syndrome recover after mild symptoms; however, 10% can suffer serious complications, such as acute respiratory failure (ARF) and admission to the intensive care unit (ICU). Predictors for the progression of SC have not yet been established, and conventional scoring systems for ICU patients are insufficient to predict severity. We aimed to identify simple and robust indicators to predict aggressive behaviors of SC. We evaluated 91 patients with SC and 81 non-SC patients who were admitted to the ICU, and 32 cases from the public functional genomics data repository for gene expression analysis. We analyzed the relationships between several predictors and clinicopathological characteristics in patients with SC. We performed gene set enrichment analysis (GSEA) to identify SC-specific gene sets. The acid-base imbalance (ABI), measured 24 h before serious complications, was higher in patients with SC than in non-SC patients. A high ABI was associated with an increased incidence of ARF, leading to mechanical ventilation and worse survival. GSEA revealed that SC correlated to gene sets reflecting inflammation/apoptotic response and airway inflammation. ABI can be used to indicate ARF in patients with SC and assist with early detection.


2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Mike Fang ◽  
Brian Richardson ◽  
Cheryl M. Cameron ◽  
Jean-Eudes Dazard ◽  
Mark J. Cameron

Abstract Background In this study, we demonstrate that our modified Gene Set Enrichment Analysis (GSEA) method, drug perturbation GSEA (dpGSEA), can detect phenotypically relevant drug targets through a unique transcriptomic enrichment that emphasizes biological directionality of drug-derived gene sets. Results We detail our dpGSEA method and show its effectiveness in detecting specific perturbation of drugs in independent public datasets by confirming fluvastatin, paclitaxel, and rosiglitazone perturbation in gastroenteropancreatic neuroendocrine tumor cells. In drug discovery experiments, we found that dpGSEA was able to detect phenotypically relevant drug targets in previously published differentially expressed genes of CD4+T regulatory cells from immune responders and non-responders to antiviral therapy in HIV-infected individuals, such as those involved with virion replication, cell cycle dysfunction, and mitochondrial dysfunction. dpGSEA is publicly available at https://github.com/sxf296/drug_targeting. Conclusions dpGSEA is an approach that uniquely enriches on drug-defined gene sets while considering directionality of gene modulation. We recommend dpGSEA as an exploratory tool to screen for possible drug targeting molecules.


2011 ◽  
Vol 10 (4) ◽  
pp. 3856-3887 ◽  
Author(s):  
Q.Y. Ning ◽  
J.Z. Wu ◽  
N. Zang ◽  
J. Liang ◽  
Y.L. Hu ◽  
...  

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