Association Between Gene Expression Profiles and Commonly Mutated Genes In The Hematopoietic Stem Cells Of Patients With Myelodysplastic Syndromes

Blood ◽  
2013 ◽  
Vol 122 (21) ◽  
pp. 2779-2779 ◽  
Author(s):  
Andrea Pellagatti ◽  
Moritz Gerstung ◽  
Elli Papaemmanuil ◽  
Luca Malcovati ◽  
Aristoteles Giagounidis ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differentially Expressed Genes ◽  
Gene Expression Data ◽  
Expression Profiles ◽  
Gene Mutations ◽  
Gene Expression Profiles ◽  
Differentially Expressed ◽  
Expression Data ◽  
Common Gene ◽  
Mutational Status

Abstract A particular profile of gene expression can reflect an underlying molecular abnormality in malignancy. Distinct gene expression profiles and deregulated gene pathways can be driven by specific gene mutations and may shed light on the biology of the disease and lead to the identification of new therapeutic targets. We selected 143 cases from our large-scale gene expression profiling (GEP) dataset on bone marrow CD34+ cells from patients with myelodysplastic syndromes (MDS), for which matching genotyping data were obtained using next-generation sequencing of a comprehensive list of 111 genes involved in myeloid malignancies (including the spliceosomal genes SF3B1, SRSF2, U2AF1 and ZRSR2, as well as TET2, ASXL1and many other). The GEP data were then correlated with the mutational status to identify significantly differentially expressed genes associated with each of the most common gene mutations found in MDS. The expression levels of the mutated genes analyzed were generally lower in patients carrying a mutation than in patients wild-type for that gene (e.g. SF3B1, ASXL1 and TP53), with the exception of RUNX1 for which patients carrying a mutation showed higher expression levels than patients without mutation. Principal components analysis showed that the main directions of gene expression changes (principal components) tend to coincide with some of the common gene mutations, including SF3B1, SRSF2 and TP53. SF3B1 and STAG2 were the mutated genes showing the highest number of associated significantly differentially expressed genes, including ABCB7 as differentially expressed in association with SF3B1 mutation and SULT2A1 in association with STAG2 mutation. We found distinct differentially expressed genes associated with the four most common splicing gene mutations (SF3B1, SRSF2, U2AF1 and ZRSR2) in MDS, suggesting that different phenotypes associated with these mutations may be driven by different effects on gene expression and that the target gene may be different. We have also evaluated the prognostic impact of the GEP data in comparison with that of the genotype data and importantly we have found a larger contribution of gene expression data in predicting progression free survival compared to mutation-based multivariate survival models. In summary, this analysis correlating gene expression data with genotype data has revealed that the mutational status shapes the gene expression landscape. We have identified deregulated genes associated with the most common gene mutations in MDS and found that the prognostic power of gene expression data is greater than the prognostic power provided by mutation data. AP and MG contributed equally to this work. JB and PJC are co-senior authors. Disclosures: No relevant conflicts of interest to declare.

scholarly journals Connecting gene expression data from connectivity map and in silico target predictions for small molecule mechanism-of-action analysis

2015 ◽  
Vol 11 (1) ◽  
pp. 86-96 ◽  
Author(s):  
Aakash Chavan Ravindranath ◽  
Nolen Perualila-Tan ◽  
Adetayo Kasim ◽  
Georgios Drakakis ◽  
Sonia Liggi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ligand Binding ◽  
Gene Expression Data ◽  
Mechanism Of Action ◽  
In Silico ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Expression Data ◽  
Connectivity Map ◽  
Action Analysis

Integrating gene expression profiles with certain proteins can improve our understanding of the fundamental mechanisms in protein–ligand binding.

scholarly journals Generation of patterns from gene expression data by assigning confidence to differentially expressed genes

2000 ◽  
Vol 16 (8) ◽  
pp. 685-698 ◽  
Author(s):  
E. Manduchi ◽  
G. R. Grant ◽  
S. E. McKenzie ◽  
G. C. Overton ◽  
S. Surrey ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differentially Expressed Genes ◽  
Gene Expression Data ◽  
Differentially Expressed ◽  
Expression Data

Building Gene Networks by Analyzing Gene Expression Profiles

2019 ◽  
pp. 27-44
Author(s):  
Crescenzio Gallo
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Gene Networks ◽  
Dna Microarrays ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Expression Data ◽  
Gene Expressions ◽  
Over Time

The possible applications of modeling and simulation in the field of bioinformatics are very extensive, ranging from understanding basic metabolic paths to exploring genetic variability. Experimental results carried out with DNA microarrays allow researchers to measure expression levels for thousands of genes simultaneously, across different conditions and over time. A key step in the analysis of gene expression data is the detection of groups of genes that manifest similar expression patterns. In this chapter, the authors examine various methods for analyzing gene expression data, addressing the important topics of (1) selecting the most differentially expressed genes, (2) grouping them by means of their relationships, and (3) classifying samples based on gene expressions.

Integrated Transcriptomic Analysis of Necrosis-related Gene in Diffuse Gliomas

2019 ◽  
Vol 80 (04) ◽  
pp. 240-249
Author(s):  
Jiajia Wang ◽  
Jie Ma
Keyword(s):  
Gene Expression ◽  
High Risk ◽  
Differentially Expressed Genes ◽  
Expression Profiles ◽  
Prognostic Index ◽  
Gene Expression Profiles ◽  
Risk Groups ◽  
Low Risk ◽  
Differentially Expressed ◽  
Low Grade

Glioblastoma multiforme (GBM), an aggressive brain tumor, is characterized histologically by the presence of a necrotic center surrounded by so-called pseudopalisading cells. Pseudopalisading necrosis has long been used as a prognostic feature. However, the underlying molecular mechanism regulating the progression of GBMs remains unclear. We hypothesized that the gene expression profiles of individual cancers, specifically necrosis-related genes, would provide objective information that would allow for the creation of a prognostic index. Gene expression profiles of necrotic and nonnecrotic areas were obtained from the Ivy Glioblastoma Atlas Project (IVY GAP) database to explore the differentially expressed genes.A robust signature of seven genes was identified as a predictor for glioblastoma and low-grade glioma (GBM/LGG) in patients from The Cancer Genome Atlas (TCGA) cohort. This set of genes was able to stratify GBM/LGG and GBM patients into high-risk and low-risk groups in the training set as well as the validation set. The TCGA, Repository for Molecular Brain Neoplasia Data (Rembrandt), and GSE16011 databases were then used to validate the expression level of these seven genes in GBMs and LGGs. Finally, the differentially expressed genes (DEGs) in the high-risk and low-risk groups were subjected to gene ontology enrichment, Kyoto Encyclopedia of Genes and Genomes pathway, and gene set enrichment analyses, and they revealed that these DEGs were associated with immune and inflammatory responses. In conclusion, our study identified a novel seven-gene signature that may guide the prognostic prediction and development of therapeutic applications.

scholarly journals GEDS: A Gene Expression Display Server for mRNAs, miRNAs and Proteins

Cells ◽  
2019 ◽  
Vol 8 (7) ◽  
pp. 675 ◽  
Author(s):  
Xia ◽  
Liu ◽  
Zhang ◽  
Guo
Keyword(s):  
Gene Expression ◽  
Cell Lines ◽  
Gene Expression Data ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cancer Cell Line ◽  
Tissue Expression ◽  
The Cancer Genome Atlas ◽  
Expression Data ◽  
Protein Levels

High-throughput technologies generate a tremendous amount of expression data on mRNA, miRNA and protein levels. Mining and visualizing the large amount of expression data requires sophisticated computational skills. An easy to use and user-friendly web-server for the visualization of gene expression profiles could greatly facilitate data exploration and hypothesis generation for biologists. Here, we curated and normalized the gene expression data on mRNA, miRNA and protein levels in 23315, 9009 and 9244 samples, respectively, from 40 tissues (The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GETx)) and 1594 cell lines (Cancer Cell Line Encyclopedia (CCLE) and MD Anderson Cell Lines Project (MCLP)). Then, we constructed the Gene Expression Display Server (GEDS), a web-based tool for quantification, comparison and visualization of gene expression data. GEDS integrates multiscale expression data and provides multiple types of figures and tables to satisfy several kinds of user requirements. The comprehensive expression profiles plotted in the one-stop GEDS platform greatly facilitate experimental biologists utilizing big data for better experimental design and analysis. GEDS is freely available on http://bioinfo.life.hust.edu.cn/web/GEDS/.

scholarly journals Genomewide Expression Profile Analysis of the Candida glabrata Pdr1 Regulon

2010 ◽  
Vol 10 (3) ◽  
pp. 373-383 ◽  
Author(s):  
Kelly E. Caudle ◽  
Katherine S. Barker ◽  
Nathan P. Wiederhold ◽  
Lijing Xu ◽  
Ramin Homayouni ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differentially Expressed Genes ◽  
Candida Glabrata ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Differentially Expressed ◽  
Resistant Isolate ◽  
Content Type ◽  
Activating Mutations ◽  
Zinc Cluster

ABSTRACTThe ABC transportersCandida glabrataCdr1 (CgCdr1), CgPdh1, and CgSnq2 are known to mediate azole resistance in the pathogenic fungusC. glabrata. Activating mutations inCgPDR1, a zinc cluster transcription factor, result in constitutive upregulation of these ABC transporter genes but to various degrees. We examined the genomewide gene expression profiles of two matched azole-susceptible and -resistantC. glabrataclinical isolate pairs. Of the differentially expressed genes identified in the gene expression profiles for these two matched pairs, there were 28 genes commonly upregulated withCgCDR1in both isolate sets includingYOR1,LCB5,RTA1,POG1,HFD1, and several members of theFLOgene family of flocculation genes. We then sequencedCgPDR1from each susceptible and resistant isolate and found two novel activating mutations that conferred increased resistance when they were expressed in a common background strain in whichCgPDR1had been disrupted. Microarray analysis comparing these reengineered strains to their respective parent strains identified a set of commonly differentially expressed genes, includingCgCDR1,YOR1, andYIM1, as well as genes uniquely regulated by specific mutations. Our results demonstrate that while CgPdr1 activates a broad repertoire of genes, specific activating mutations result in the activation of discrete subsets of this repertoire.

Changes in Gene Expression Profiles in Patients with 5q- Syndrome Caused by Lenalidomide Treatment

Blood ◽  
2011 ◽  
Vol 118 (21) ◽  
pp. 5023-5023
Author(s):  
Monika Belickova ◽  
Jaroslav Cermak ◽  
Jitka Vesela ◽  
Eliska Cechova ◽  
Zuzana Zemanova ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differentially Expressed Genes ◽  
Conflicts Of Interest ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Differentially Expressed ◽  
Aurora A ◽  
Genes Expression ◽  
Marrow Stroma ◽  
Cell Niche

Abstract Abstract 5023 A direct effects of lenalidomide on gene expression in 5q- patients was studied using HumanRef-8 v2 Expression BeadChips (Illumina). Expression profiles of 6 patients (before treatment and at the time of the first erytroid response) and 6 healthy controls were investigated from CD14+ monocytes of peripheral blood. Differentially expressed genes were identified by Significance Analysis of Microarrays (SAM). Simultaneously, selected genes (TNF, JUN, IL1) were monitored in the course of treatment using Real-Time PCR with Taqman Gene Expression Assays. A comparison of gene expression levels before and during lenalidomide treatment revealed 97 differentially expressed genes (FC >2; p<0.05) related to following biological processes: immune response (16 genes), inflammatory response (11 genes), response to bacteria (8 genes), anti-apoptosis (7 genes), regulation of MAP kinase activity (5 genes), oxygen transport (4 genes), and regulation of cell proliferation (11 genes). An overexpression of a number of cytokines (e.g. TNF, IL8, IL1B, CCL3L, CXCL2, and TNFAIP3) was detected in patients before treatment, after lenalidomide administration expression of the majority of the up-regulated cytokine genes decreased to the control baseline level. Detected overproduction of the cytokines in 5q- syndrome may lead to an increased apoptosis of hematopoietic progenitor cells and together with excessive oxidative stress may contribute to the damage the hematopoietic niche. In the same manner, untreated patients showed suppressed expression of two genes (CXCR4, CRTAP) which play an important role in the stem cell niche. After treatment, we detected increased expression of these genes. Both the observations might explain favorable effects of lenalidomide on the bone marrow stroma defect seen in 5q- syndrome. On the other hand, a substantial increase of the ARPC1B gene (an activator and a substrate of Aurora A) expression was detected after lenalidomide treatment. Since overexpression of Aurora A leads to polyploidy and chromosomal instability, ARPC1B might play a role in the disease progression observed in some patients treated with lenalidomide. To conclude, described changes in genes expression may contribute to identification of the pathways affected by lenalidomide and to the explanation of some effects of this drug that have not been fully understood yet. Supported by grants NS/9634 MZCR, UHKT2005 00023736, MSM0021620808 and COST EUGESMA Disclosures: No relevant conflicts of interest to declare.

A prospective study to evaluate the role of gene expression profiles (GEP) in predicting clinical outcome of patients (pts) receiving preoperative chemotherapy for oesophagogastric (OG) cancer

2007 ◽  
Vol 25 (18_suppl) ◽  
pp. 4501-4501
Author(s):  
S. Rao ◽  
D. Cunningham ◽  
M. Benson ◽  
R. Te Poele ◽  
L. Welsh ◽  
...  
Keyword(s):  
Gene Expression ◽  
Clinical Outcome ◽  
Differentially Expressed Genes ◽  
Preoperative Chemotherapy ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Control Measures ◽  
Differentially Expressed ◽  
Outcome Group

4501 Background: Whilst preoperative chemotherapy has demonstrated survival benefit for pts with potentially resectable OG cancer it is not possible to predict the benefit for an individual pt. This study was designed to prospectively correlate GEP with clinical outcome. Methods: Eligible pts were deemed to have resectable disease after staging CT, EUS, and laparoscopy as indicated & following discussion at the multidisciplinary team meeting. All pts received neoadjuvant platinum & fluoropyrimidine based chemotherapy & clinical data were entered prospectively onto a study specific database. GEP were produced from total RNA isolated from snap frozen pre treatment tumour biopsies obtained at baseline endoscopy. Labelled cDNA was hybridised versus a universal human reference using an in house c DNA array of 22,000 clones. Results: Of the pts with adequate follow up accrued between 2002–2005, 35 met the quality control measures for the arrays. Median age=66 yrs (47–83); male=32, female=3; tumour subsites: oesophagus=23, oesophago-gastric junction (OGJ)=12; adenocarcinoma=35; T stage: T 2=3, T3=30, T4=2; N stage: N0=12, N1=23; performance status 0=7, 1=28. Median follow up=938 days. Median overall survival (OS) = 570 days. Prognostic groups were designated according to the median OS (days) of the group: good > median and poor < median. Supervised hierarchical clustering of normalised data revealed significantly differentially expressed genes based on OS (p<0.01) with 2 distinct clusters: a poor outcome group: N= 17 (2yr OS 17.6%) [95% CI: 4.3–38.3], a good outcome group: N=18 (2 yr OS 55%) [95% CI: 30.5–74.8]. Of the differentially expressed genes, those involved in receptor tyrosine kinase signalling & cell growth were amongst the most significantly affected pathways. Conclusions: This novel technique using GEP in tumour biopsies has successfully identified groups of tumours with distinct gene expression profiles that correlate with survival. The approach warrants further validation in a larger cohort. It could facilitate the development of tailored treatment according to individual tumour biology in OG cancer. No significant financial relationships to disclose.

An empirical Bayes method for robust variance estimation in detecting DEGs using microarray data

2017 ◽  
Vol 15 (05) ◽  
pp. 1750020 ◽  
Author(s):  
Na You ◽  
Xueqin Wang
Keyword(s):  
Gene Expression ◽  
Differentially Expressed Genes ◽  
Multiple Testing ◽  
Empirical Bayes ◽  
Variance Estimation ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Differentially Expressed ◽  
Link Function ◽  
Multiple Testing Procedure

The microarray technology is widely used to identify the differentially expressed genes due to its high throughput capability. The number of replicated microarray chips in each group is usually not abundant. It is an efficient way to borrow information across different genes to improve the parameter estimation which suffers from the limited sample size. In this paper, we use a hierarchical model to describe the dispersion of gene expression profiles and model the variance through the gene expression level via a link function. A heuristic algorithm is proposed to estimate the hyper-parameters and link function. The differentially expressed genes are identified using a multiple testing procedure. Compared to SAM and LIMMA, our proposed method shows a significant superiority in term of detection power as the false discovery rate being controlled.

scholarly journals Bayesian log-normal deconvolution for enhanced in silico microdissection of bulk gene expression data

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Bárbara Andrade Barbosa ◽  
Saskia D. van Asten ◽  
Ji Won Oh ◽  
Arantza Farina-Sarasqueta ◽  
Joanne Verheij ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cell Types ◽  
Expression Data ◽  
Cell Type ◽  
Expression Variability ◽  
Variable Nature ◽  
Log Normal

AbstractDeconvolution of bulk gene expression profiles into the cellular components is pivotal to portraying tissue’s complex cellular make-up, such as the tumor microenvironment. However, the inherently variable nature of gene expression requires a comprehensive statistical model and reliable prior knowledge of individual cell types that can be obtained from single-cell RNA sequencing. We introduce BLADE (Bayesian Log-normAl Deconvolution), a unified Bayesian framework to estimate both cellular composition and gene expression profiles for each cell type. Unlike previous comprehensive statistical approaches, BLADE can handle > 20 types of cells due to the efficient variational inference. Throughout an intensive evaluation with > 700 simulated and real datasets, BLADE demonstrated enhanced robustness against gene expression variability and better completeness than conventional methods, in particular, to reconstruct gene expression profiles of each cell type. In summary, BLADE is a powerful tool to unravel heterogeneous cellular activity in complex biological systems from standard bulk gene expression data.

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