Identification of Cellular Pathways Mediating Progression of Monoclonal Gammopathy (MGUS) to Multiple Myeloma (MM) Using Gene Expression Profiling (GEP).

Blood ◽  
2008 ◽  
Vol 112 (11) ◽  
pp. 1673-1673
Author(s):  
Wee-Joo Chng ◽  
Gaofeng Huang ◽  
Peter Leif Bergsagel ◽  
Rafael Fonseca

Abstract Events mediating transformation from pre-malignant MGUS to MM is currently not well defined. Recurrent genetic abnormalities such as t(11;14), t(4;14), hyperdiploidy and chromosome 13 deletion are already present in MGUS at relatively similar frequency to MM. The unified deregulation of D-type cyclins is also already present in MGUS. Previous GEP studies have revealed little differences between MGUS and MM. A more recent study using a large patient cohort and new generation Affymetrix genechip identifies an MGUS signature but the functional and biological significance underlying this signature is unknown. In the current study, we analyze a cohort of 22 MGUS and 101 MM from the Mayo Clinic with GEP performed on Affymetrix U133A genechip using gene set enrichment analysis, a method that analyze differentially expressed gene between 2 phenotypes of interest in the context of published or curated genesets that represent specific biological, chemical, or molecular perturbation to cells. This method increases the sensitivity of identifying low but significant changes in gene expression. We made further modification to the original method that allows assessment in individual samples rather than the average across a phenotype further increasing the specificity of the output. In this analysis, 313 genesets were significant enriched for genes over-expressed in MM compared to MGUS, representing potential activated pathways that mediate transformation. When MM samples and genesets were clustered using the enrichment score for each genesets and samples, 4 cluster of genesets emerged, one including a number of MYC genesets, one including a number of cell cycle related genesets, one including genesets related to metabolic activity and another including a number of IFN related genesets. Further dissection of these correlated genesets to identify common enriched genes (leading edge genes) led to identification of a MYC core signature, tRNA core signature, Proteosome core signature and metabolic core signature which are highly correlated. From known literature and biology, it is likely that MYC activation leads to downstream activation of protein synthesis (tRNA signature), degradation (proteasome signature), and metabolic pathway (metabolic signature). This is verified by GEP results from in vitro modulation of MYC activation in cell lines. In addition, a cell cycle core signature and IFN core signature was identified. Activation of IFN and MYC core signatures accounts for almost 90% of MM patients. The remaining patients have a metabolic signature without MYC or IFN activation. The activation of the different core signatures is significantly correlated with certain TC classes when assessed by Chi-Square test. IFN activation is significantly correlated with D1 subtypes and negatively correlated with D2 subtypes. MYC activation is negatively correlated with t(11;14). Similar patterns were observed in a validation dataset of 351 MM patients from UAMS (GSE2677) with GEP performed on the U133plus2.0 chip. These results are validated at the protein level using IHC on TMA of the Mayo cohort. The activation of the IFN and MYC pathway may represent predominant mechanism in MGUS to MM progression.

2017 ◽  
Vol 35 (4_suppl) ◽  
pp. 302-302
Author(s):  
Namrata Vijayvergia ◽  
Suraj Peri ◽  
Karthik Devarajan ◽  
Jianming Pei ◽  
Yulan Gong ◽  
...  

302 Background: NETs lack mutations in the “classical” signaling pathways but share mutations in regulators of gene expression (Jiao; 2011). We compared gene expression in PD & WD NETs to identify novel targets and biomarkers of differentiation. Methods: High quality RNA, extracted from paraffin blocks of deidentified NETs under an IRB-approved protocol, was profiled using a 770 gene panel (nCounter PanCancer pathway, Nanostring Technologies). The resulting data was used to identify the differentially expressed genes between PD and WD NETs using limma software (Ritchie; 2015). Gene Set Enrichment Analysis (Subramanian; 2005) identified differential pathway enrichment by calculating a Normalized Enrichment Score (NES). Results: Analysis of 16 PD and 23 WD NET samples identified 154 genes as extreme outliers ( > 2 fold up/downregulation between the subtypes). Compared to WD NETS, drug targets of interest overexpressed in PD NETs were histone lysine methyltransferase EZH2, and a cell cycle regulator CHEK1 (6.5x and 8.1x, respectively, p < 0.001). In contrast, serine/threonine protein kinase PAK 3 was upregulated in WD (10.6x, p < 0.001). These and other biomarkers will be further validated by immunolabeling of tissue sections. We also found differential enrichment of canonical pathways in PD versus WD NETs (table). Conclusions: Extreme outlier transcripts identified in PD & WD NETs support investigation of inhibitors of EZH2 (e.g. EPZ6438) and CHEK1 (e.g. LY2606368) in PD and PAK3(e.g. FRAX597) in WD NETs. Genes involved in cell cycle regulation and DNA repair in PD NETs and calcium / G protein coupled receptor signaling in WD NET account for biological differences between the 2 molecular subtypes and warrant future investigation as classifiers for NETs. Our findings provide mechanistic insights into the biology of NET and targets for therapy with direct clinical implications.[Table: see text]


Blood ◽  
2009 ◽  
Vol 114 (22) ◽  
pp. 834-834
Author(s):  
Wee-Joo Chng ◽  
Gaofeng Huang ◽  
Siok-Bian Ng ◽  
Marta Chesi ◽  
Leif Bergsagel ◽  
...  

Abstract Abstract 834 Multiple myeloma (MM) is an incurable bone marrow cancer. Events mediating transformation from the pre-malignant monoclonal gammopathy of undetermined significance (MGUS) to MM is unknown. We analyzed 2 gene expression datasets generated on the Affymetrix U133 platform. The test set consisted of 22 MGUS and 101 MM (GEO Accession GSE6477) and the validation set 50 MGUS and 351 MM (GEO Accession GSE2658 and GSE5900). The gene expression profiles of MM were compared to MGUS using gene-set enrichment analysis. Genes over-expressed in MM were enriched for cell cycle, proliferation and MYC activation gene-sets. We dissected the relationship between MYC and cell cycle, and identified a MYC activation signature dissociated from proliferation. We validated our MYC signature in publicly available mouse and human cell line gene expression dataset (GEO Accession GSE3151 and GSE3158 respectively), showing specific expression of our MYC signature in cell lines forced to expressed MYC but not when over-expressing other oncogene such as E2F, HER2. In these analyses, we noted that tumors with RAS mutation also consistently express this signature. Applying this signature to the test dataset, we showed that MYC is activated in 60% of myeloma but none of MGUS. This pattern is reproduced in an independent validation dataset. In order to validate the hypothesis that RAS mutation may also lead to the MYC activation signature, we correlated RAS mutation with MYC activation as measured by a MYC index calculated from the median expression of genes that constitute the MYC activation signature, and showed that almost all cases with RAS mutation have a high MYC index. Together with samples with very high expression of MYC mRNA corresponding to those with MYC translocations, these 2 mechanisms account for 67% of cases with MYC activation. To further confirm MYC activation, we performed immunohistochemistry using a validated MYC antibody together with CD138 double-staining. We can clearly identify CD138 positive plasma cells with and without nuclear MYC expression and found that nuclear expression of MYC, a marker of MYC activation, correlated strongly with the MYC signature, therefore providing clear evidence that MYC activation is present in majority of newly diagnosed MM but not in MGUS. MYC activation is not very well correlated with proliferation as assessed by the plasma cell labeling index. Among newly diagnosed myeloma patients with plasma cell labeling index less than 1, those with nuclear MYC expression have significantly shorter survival than those without (Median survival 77.7 months versus 37.9 months, log-rank p-value 0.04). Multiple pathways converge on MYC activation, which is a common transforming event in MM associated with poor prognosis. MYC nuclear staining by IHC can be a useful clinical surrogate. Targeting MYC can be a chemoprevention strategy. Disclosures: Fonseca: Amgen: Consultancy; Medtronic: Consultancy; Celgene: Consultancy; Bristol Mayor Squibs: Consultancy; Genzyme: Consultancy; Otsuka: Consultancy.


PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11342
Author(s):  
Qingqing Wang ◽  
Xiaoyan Yu ◽  
Zhewen Zheng ◽  
Fengxia Chen ◽  
Ningning Yang ◽  
...  

Background Hepatocellular carcinoma (HCC) is one of the deadliest tumors. The majority of HCC is detected in the late stage, and the clinical results for HCC patients are poor. There is an urgent need to discover early diagnostic biomarkers and potential therapeutic targets for HCC. Methods The GSE87630 and GSE112790 datasets from the Gene Expression Omnibus (GEO) database were downloaded to analyze the differentially expressed genes (DEGs) between HCC and normal tissues. R packages were used for Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses of the DEGs. A Search Tool for Retrieval of Interacting Genes (STRING) database was used to develop a protein-protein interaction (PPI) network, and also cytoHubba, Molecular Complex Detection (MCODE), EMBL-EBI, CCLE, Gene Expression Profiling Interactive Analysis (GEPIA), and Oncomine analyses were performed to identify hub genes. Gene expression was verified with a third GEO dataset, GSE25097. The Cancer Genome Atlas (TCGA) database was used to explore the correlations between the hub genes and clinical indexes of HCC patients. The functions of the hub genes were enriched by gene set enrichment analysis (GSEA), and the biological significance of the hub genes was explored by real-time polymerase chain reaction (qRT-PCR), western blot, immunofluorescence, CCK-8, colony formation, Transwell and flow cytometry assays with loss-of-function experiments in vitro. Results Centromere protein N (CENPN) was screened as a hub gene affecting HCC tumorigenesis. Evaluation by Cox regression showed that a high level of CENPN expression was an independent danger variable for poor prognosis of HCC. GSEA showed that high CENPN expression was linked to the following pathways: liver cancer subclass proliferation, cell cycle, p53 signaling pathway, Rb1 pathway, positive regulation of cell cycle G1/S phase transition, and DNA damage response signal transduction by p53 class moderators. Further cell experiments showed that knocking down CENPN expression decreased the proliferation and colony-forming abilities of HepG2 and Huh7 cells as well as Ki67 expression in these cell lines. The cell cycle was arrested in G1 phase, which is consistent with previous experiments on CENPN downregulation., but neither migration nor invasion were significantly affected. Western blot results revealed that the expression of p53, p27, p21, CDK4, cyclin D1, CDK2, cyclin E, pRb, E2F1 and c-myc decreased after CENPN knockdown, but there was no significant change in total Rb levels. In addition, CENPN-knockdown cells subjected to irradiation showed significantly enhanced of γ-H2AX expression and reduced colony formation. Conclusion CENPN functions as an oncogene in HCC and may be a therapeutic target and promising prognostic marker for HCC.


PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11273
Author(s):  
Lei Yang ◽  
Weilong Yin ◽  
Xuechen Liu ◽  
Fangcun Li ◽  
Li Ma ◽  
...  

Background Hepatocellular carcinoma (HCC) is considered to be a malignant tumor with a high incidence and a high mortality. Accurate prognostic models are urgently needed. The present study was aimed at screening the critical genes for prognosis of HCC. Methods The GSE25097, GSE14520, GSE36376 and GSE76427 datasets were obtained from Gene Expression Omnibus (GEO). We used GEO2R to screen differentially expressed genes (DEGs). A protein-protein interaction network of the DEGs was constructed by Cytoscape in order to find hub genes by module analysis. The Metascape was performed to discover biological functions and pathway enrichment of DEGs. MCODE components were calculated to construct a module complex of DEGs. Then, gene set enrichment analysis (GSEA) was used for gene enrichment analysis. ONCOMINE was employed to assess the mRNA expression levels of key genes in HCC, and the survival analysis was conducted using the array from The Cancer Genome Atlas (TCGA) of HCC. Then, the LASSO Cox regression model was performed to establish and identify the prognostic gene signature. We validated the prognostic value of the gene signature in the TCGA cohort. Results We screened out 10 hub genes which were all up-regulated in HCC tissue. They mainly enrich in mitotic cell cycle process. The GSEA results showed that these data sets had good enrichment score and significance in the cell cycle pathway. Each candidate gene may be an indicator of prognostic factors in the development of HCC. However, hub genes expression was weekly associated with overall survival in HCC patients. LASSO Cox regression analysis validated a five-gene signature (including CDC20, CCNB2, NCAPG, ASPM and NUSAP1). These results suggest that five-gene signature model may provide clues for clinical prognostic biomarker of HCC.


2020 ◽  
Author(s):  
Xiaomei Lei ◽  
Zhijun Feng ◽  
Xiaojun Wang ◽  
Xiaodong He

Abstract Background. Exploring alterations in the host transcriptome following SARS-CoV-2 infection is not only highly warranted to help us understand molecular mechanisms of the disease, but also provide new prospective for screening effective antiviral drugs, finding new therapeutic targets, and evaluating the risk of systemic inflammatory response syndrome (SIRS) early.Methods. We downloaded three gene expression matrix files from the Gene Expression Omnibus (GEO) database, and extracted the gene expression data of the SARS-CoV-2 infection and non-infection in human samples and different cell line samples, and then performed gene set enrichment analysis (GSEA), respectively. Thereafter, we integrated the results of GSEA and obtained co-enriched gene sets and co-core genes in three various microarray data. Finally, we also constructed a protein-protein interaction (PPI) network and molecular modules for co-core genes and performed Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis for the genes from modules to clarify their possible biological processes and underlying signaling pathway. Results. A total of 11 co-enriched gene sets were identified from the three various microarray data. Among them, 10 gene sets were activated, and involved in immune response and inflammatory reaction. 1 gene set was suppressed, and participated in cell cycle. The analysis of molecular modules showed that 2 modules might play a vital role in the pathogenic process of SARS-CoV-2 infection. The KEGG enrichment analysis showed that genes from module one enriched in signaling pathways related to inflammation, but genes from module two enriched in signaling of cell cycle and DNA replication. Particularly, necroptosis signaling, a newly identified type of programmed cell death that differed from apoptosis, was also determined in our findings. Additionally, for patients with SARS-CoV-2 infection, genes from module one showed a relatively high-level expression while genes from module two showed low-level. Conclusions. We identified two molecular modules were used to assess severity and predict the prognosis of the patients with SARS-CoV-2 infection. In addition, these results provide a unique opportunity to explore more molecular pathways as new potential targets on therapy in COVID 19.


2020 ◽  
Vol 19 ◽  
pp. 117693511989991
Author(s):  
Po-Ming Chen ◽  
Jian-Rong Li ◽  
Chun-Chi Liu ◽  
Feng-Yao Tang ◽  
En-Pei Isabel Chiang

RNA-Sequencing (RNA-Seq), the most commonly used sequencing application tool, is not only a method for measuring gene expression but also an excellent media to detect important structural variants such as single nucleotide variants (SNVs), insertion/deletion (Indels), or fusion transcripts. The Cancer Genome Atlas (TCGA) contains genomic data from a variety of cancer types and also provides the raw data generated by TCGA consortium. p53 is among the top 10 somatic mutations associated with hepatocellular carcinoma (HCC). The aim of the present study was to analyze concordant different gene profiles and the priori defined set of genes based on p53 mutation status in HCC using RNA-Seq data. In the study, expression profile of 11 799 genes on 42 paired tumor and adjacent normal tissues was collected, processed, and further stratified by the mutated versus normal p53 expression. Furthermore, we used a knowledge-based approach Gene Set Enrichment Analysis (GSEA) to compare between normal and p53 mutation gene expression profiles. The statistical significance (nominal P value) of the enrichment score (ES) genes was calculated. The ranked gene list that reflects differential expression between p53 wild-type and mutant genotypes was then mapped to metabolic process by KEGG, an encyclopedia of genes and genomes to assign functional meanings. These approaches enable us to identify pathways and potential target gene/pathways that are highly expressed in p53 mutated HCC. Our analysis revealed 2 genes, the hexokinase 2 ( HK2) and Enolase 1 ( ENO1), were conspicuous of red pixel in the heatmap. To further explore the role of these genes in HCC, the overall survival plots by Kaplan-Meier method were performed for HK2 and ENO1 that revealed high HK2 and ENO1 expression in patients with HCC have poor prognosis. These results suggested that these glycolysis genes are associated with mutated-p53 in HCC that may contribute to poor prognosis. In this proof-of-concept study, we proposed an approach for identifying novel potential therapeutic targets in human HCC with mutated p53. These approaches can take advantage of the massive next-generation sequencing (NGS) data generated worldwide and make more out of it by exploring new potential therapeutic targets.


2021 ◽  
Author(s):  
Yannian Luo ◽  
Juan Xu ◽  
Mingzhen Zhou ◽  
Xiaomei Lei ◽  
Wen Cao ◽  
...  

Abstract Background. Exploring alterations in the host transcriptome following SARS-CoV-2 infection is not only highly warranted to help us understand molecular mechanisms of the disease, but also provide new prospective for screening effective antiviral drugs, finding new therapeutic targets, and evaluating the risk of systemic inflammatory response syndrome (SIRS) early.Methods. We downloaded three gene expression matrix files from the Gene Expression Omnibus (GEO) database, and extracted the gene expression data of the SARS-CoV-2 infection and non-infection in human samples and different cell line samples, and then performed gene set enrichment analysis (GSEA), respectively. Thereafter, we integrated the results of GSEA and obtained co-enriched gene sets and co-core genes in three various microarray data. Finally, we also constructed a protein-protein interaction (PPI) network and molecular modules for co-core genes and performed Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis for the genes from modules to clarify their possible biological processes and underlying signaling pathway. Results. A total of 11 co-enriched gene sets were identified from the three various microarray data. Among them, 10 gene sets were activated, and involved in immune response and inflammatory reaction. 1 gene set was suppressed, and participated in cell cycle. The analysis of molecular modules showed that 2 modules might play a vital role in the pathogenic process of SARS-CoV-2 infection. The KEGG enrichment analysis showed that genes from module one enriched in signaling pathways related to inflammation, but genes from module two enriched in signaling of cell cycle and DNA replication. Particularly, necroptosis signaling, a newly identified type of programmed cell death that differed from apoptosis, was also determined in our findings. Additionally, for patients with SARS-CoV-2 infection, genes from module one showed a relatively high-level expression while genes from module two showed low-level. Conclusions. We identified two molecular modules were used to assess severity and predict the prognosis of the patients with SARS-CoV-2 infection. In addition, these results provide a unique opportunity to explore more molecular pathways as new potential targets on therapy in COVID 19.


2013 ◽  
Vol 7 ◽  
pp. BBI.S12167 ◽  
Author(s):  
Vincenzo Belcastro ◽  
Carine Poussin ◽  
Stephan Gebel ◽  
Carole Mathis ◽  
Walter K. Schlage ◽  
...  

We recently constructed a computable cell proliferation network (CPN) model focused on lung tissue to unravel complex biological processes and their exposure-related perturbations from molecular profiling data. The CPN consists of edges and nodes representing upstream controllers of gene expression largely generated from transcriptomics datasets using Reverse Causal Reasoning (RCR). Here, we report an approach to biologically verify the correctness of upstream controller nodes using a specifically designed, independent lung cell proliferation dataset. Normal human bronchial epithelial cells were arrested at G1/S with a cell cycle inhibitor. Gene expression changes and cell proliferation were captured at different time points after release from inhibition. Gene set enrichment analysis demonstrated cell cycle response specificity via an overrepresentation of proliferation related gene sets. Coverage analysis of RCR-derived hypotheses returned statistical significance for cell cycle response specificity across the whole model as well as for the Growth Factor and Cell Cycle sub-network models.


2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Suzana Makpol ◽  
Azalina Zainuddin ◽  
Kien Hui Chua ◽  
Yasmin Anum Mohd Yusof ◽  
Wan Zurinah Wan Ngah

The effect ofγ-tocotrienol, a vitamin E isomer, in modulating gene expression in cellular aging of human diploid fibroblasts was studied. Senescent cells at passage 30 were incubated with 70 μM ofγ-tocotrienol for 24 h. Gene expression patterns were evaluated using Sentrix HumanRef-8 Expression BeadChip from Illumina, analysed using GeneSpring GX10 software, and validated using quantitative RT-PCR. A total of 100 genes were differentially expressed (P<0.001) by at least 1.5 fold in response toγ-tocotrienol treatment. Amongst the genes wereIRAK3, SelS, HSPA5, HERPUD1, DNAJB9, SEPR1, C18orf55, ARF4, RINT1, NXT1, CADPS2, COG6, andGLRX5. Significant gene list was further analysed by Gene Set Enrichment Analysis (GSEA), and the Normalized Enrichment Score (NES) showed that biological processes such as inflammation, protein transport, apoptosis, and cell redox homeostasis were modulated in senescent fibroblasts treated withγ-tocotrienol. These findings revealed thatγ-tocotrienol may prevent cellular aging of human diploid fibroblasts by modulating gene expression.


2021 ◽  
Author(s):  
Ya Yang ◽  
Xintan Zhang ◽  
Tingxuan Li ◽  
Yue Zhang ◽  
Xiaoxiao Zuo

Abstract Background: Immune infiltrated genes (IIGs) have been identified to associated with the prognosis of various cancers, but their expression and prognostic significance remain largely unclear in stomach adenocarcinoma (STAD).Methods: Gene expression profiles and clinical data of STAD patients were downloaded from The Cancer Genome Atlas (TCGA) as a training dataset (n = 375) and Gene Expression Omnibus (GEO) databases as a validation dataset (n = 300). Construction of high and low immune cell infiltration groups was performed by single sample gene set enrichment analysis (ssGSEA) and evaluated by ESTIMATE algorithm-derived immune scores. The overlapping differentially expressed genes (DEGs) in tumor vs. normal and Immunity-H vs. Immunity-L were selected as differentially expressed immune infiltrated genes (DEIIGs), which were used to construct DEIIG prognostic signature and its performance was validated using validation dataset. Moreover, the association between clinical data and immune features were explored. Furthermore, ADH4 and ANGPT2 were selected for analyzing their expression and prognostic values in STAD patients.Results: A total of 191 overlapping DEGs, including 6 lnRNAs and 185 mRNA were identified. Consecutively, 9 DEIIG prognostic signature (LINC00843, ADH4, ANGPT2, APOA1, ASLC2, GFRA1, KIAA1549L, MTTP and PROC) were identified as risk signature and Kaplan-Meier curve and ROC curve verified its performance in TCGA and GEO datasets. Total five clinical outcomes (age, pathologic T, radiotherapy, tumor recurrence and prognostic score model status) were identified to be associated with the survival prognosis of STAD patients. The TIMER algorithm revealed that B cell, T cell CD4+, neutrophil, macrophage and myeloid dendritic cell were positively correlated with STAD prognosis, while CD8+ was negatively correlated with STAD prognosis. Additionally, we validated that higher ADH4 and lower ANGPT2 predicted better survival prognosis in STAD patients.Conclusion: We constructed and verified a robust signature of nine DEIIG prognostic signature for the prediction of STAD patient survival.


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