scholarly journals Analysis of Patterns of Gene Expression Variation within and between Ethnic Populations in Pediatric B-ALL

2013 ◽  
Vol 12 ◽  
pp. CIN.S11831 ◽  
Author(s):  
Chindo Hicks ◽  
Lucio Miele ◽  
Tejaswi Koganti ◽  
LaFarra Young-Gaylor ◽  
Deidre Rogers ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Networks ◽  
Lymphoblastic Leukemia ◽  
The United States ◽  
Expression Data ◽  
Expression Levels ◽  
Ethnic Populations ◽  
Racial Ethnic ◽  
Key Pathways ◽  
Gene Expression Levels

B-Precursor acute lymphoblastic leukemia (B-ALL) is the most common childhood cancer. Although 80% of B-ALL patients are able to be cured, significant challenges persist. Significant disparities in clinical outcomes and mortality rates exist between racial/ ethnic populations. The objective of this study was to determine whether gene expression levels significantly differ between ethnic populations. We compared gene expression levels between four ethnic populations (Whites, Blacks, Hispanics, and Asians) in the United States. Additionally, we performed network and pathway analysis to identify gene networks and pathways. Gene expression data involved 198 samples distributed as follows: 126 Whites, 51 Hispanics, 13 Blacks, and 8 Asians. We identified 300 highly significantly ( P < 0.001) differentially expressed genes between the four ethnic populations. Among the identified genes included the genes PHF6, BRD3, CRLF2, and RNF135 which have been implicated in pediatric B-ALL. We identified key pathways implicated in B-ALL including the PDGF, PI3/AKT, ERBB2-ERBB3, and IL-15 signaling pathways.

scholarly journals Pathway based factor analysis of gene expression data produces highly heritable phenotypes that associate with age

2015 ◽  
Author(s):  
Andrew Anand Brown ◽  
Zhihao Ding ◽  
Ana Viñuela ◽  
Dan Glass ◽  
Leopold Parts ◽  
...  
Keyword(s):  
Gene Expression ◽  
Factor Analysis ◽  
Gene Expression Data ◽  
Biological Knowledge ◽  
Expression Data ◽  
Expression Levels ◽  
Biologically Relevant ◽  
Kegg Pathways ◽  
Analysis Methods ◽  
Gene Expression Levels

Statistical factor analysis methods have previously been used to remove noise components from high dimensional data prior to genetic association mapping, and in a guided fashion to summarise biologically relevant sources of variation. Here we show how the derived factors summarising pathway expression can be used to analyse the relationships between expression, heritability and ageing. We used skin gene expression data from 647 twins from the MuTHER Consortium and applied factor analysis to concisely summarise patterns of gene expression, both to remove broad confounding influences and to produce concise pathway-level phenotypes. We derived 930 "pathway phenotypes" which summarised patterns of variation across 186 KEGG pathways (five phenotypes per pathway). We identified 69 significant associations of age with phenotype from 57 distinct KEGG pathways at a stringent Bonferroni threshold (P<5.38E-5). These phenotypes are more heritable (h^2=0.32) than gene expression levels. On average, expression levels of 16% of genes within these pathways are associated with age. Several significant pathways relate to metabolising sugars and fatty acids, others with insulin signalling. We have demonstrated that factor analysis methods combined with biological knowledge can produce more reliable phenotypes with less stochastic noise than the individual gene expression levels, which increases our power to discover biologically relevant associations. These phenotypes could also be applied to discover associations with other environmental factors.

Bayesian functional mixed-effects models with grouped smoothness for analyzing time-course gene expression data

2020 ◽  
Vol 15 ◽  
Author(s):  
Shangyuan Ye ◽  
Ye Liang ◽  
Bo Zhang
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Time Course ◽  
Mixed Effects ◽  
Mixed Effects Model ◽  
Penalized Splines ◽  
Expression Data ◽  
Expression Levels ◽  
The Mean ◽  
Gene Expression Levels

Objective: As a result of the development of microarray technologies, gene expression levels of thousands of genes involved in a given biological process can be measured simultaneously, and it is important to study their temporal behavior to understand their mechanisms. Since the dependence between gene expression levels over time for a given gene is often too complicated to model parametrically, sparse functional data analysis has received an increasing amount of attention for analyzing such data. Methods: We propose a new functional mixed-effects model for analyzing time-course gene expression data. Specifically, the model groups individual functions with heterogeneous smoothness. The proposed method utilizes the mixed-effects model representation of penalized splines for both the mean function and the individual functions. Given noninformative or weakly informative priors, Bayesian inference on the proposed models was developed, and Bayesian computation was implemented by using Markov chain Monte Carlo methods. Results: The performance of our new model was studied by two simulation studies and illustrated using a yeast cell cycle gene expression dataset. Simulation results suggest that our proposed methods can outperform the previously used methods in terms of the mean integrated squared error. The yeast gene expression data application suggests that the proposed model with two latent groups should be used on this dataset.

Expression Level Of A Coagulation Factor Gene, F13A1 Defines Characteristic Subpopulations Of Childhood Acute Lymphoblastic Leukemia

Blood ◽  
2013 ◽  
Vol 122 (21) ◽  
pp. 4888-4888
Author(s):  
Csongor Kiss ◽  
Katalin Gyurina ◽  
Gábor Kiss ◽  
Silvia Bresolin ◽  
Zsuzsa Hevessy ◽  
...  
Keyword(s):  
Gene Expression ◽  
Flow Cytometry ◽  
Acute Lymphoblastic Leukemia ◽  
B Cell ◽  
Tyrosine Kinases ◽  
Lymphoblastic Leukemia ◽  
Coagulation Factor ◽  
Expression Level ◽  
Expression Levels ◽  
Gene Expression Levels

Abstract Using multiparameter flow cytometry, Western blot, ELISA and laser scanning microscopy, leukemic B-cell progenitor lymphoblasts were identified as a novel expression site of coagulation factor XIII subunit A (FXIIIA) (Kiss et al Thromb Haemost 2006). Three-year overall survival (OS) of children with FXIIIA-positive acute lymphoblastic leukemia (ALL) was significantly higher (87%) than 3-yr OS of patients with FXIIIA-negative ALL (65%). Here we report on gene expression profiles (GEP) associated with FXIIIA-positive vs. FXIIIA–negative childhood (ch)ALL. Finnally, the expression levels of F13A1 gene in cytogenetic subgroups were investigated. GEP of 11 FXIIIA-positive and 9 FXIIIA-negative samples of patients treated at the Department of Hematology-Oncology, University of Debrecen was investigated using HG-U 133 plus 2.0 Affymetrix Platform Array. Expression sequence tags (EST) characterized by expression levels with at least 2 logs difference among FXIIIA-positive vs. FXIIIA-negative samples were selected and validated with real-time quantitative PCR. Extending the analysis, we have searched the database containing GEPs of 317 children with ALL treated at the Division of Hemato-Oncology, Department of Women’s and Children’s Health, University of Padova, using R package and Partek Genomic Suite softwares. FXIIIA-positive and FXIIIA-negative samples exhibited a characteristically different GEP. In the FXIIIA-positive samples one of the overexpressed genes was F13A1. FXIIIA-negative samples contained two characteristic groups of overexpressed genes. One of these consisted of genes participating in B-cell development: EBF1, IKZF1 and PAX5. The second set consisted of genes encoding for tyrosine kinases: JAK1, JAK3, NC07523, NC08002, NC06523, NC08345 and for serine-threonine kinase NC00027. In contrast, FXIIIA-positive samples contained only two overexpressed tyrosine kinases: C-KIT and JAK2. A wide variation of F13A1 expression levels of the 317 Padova patients was observed. Since expression level of FXIIIA protein of these patients has not been determined, we have arbitrarily defined F13A1 gene expression levels below 106 as “low” and gene expression levels exceeding 109 as “high”. Considering these two groups, we have investigated the distribution of F13A1 expression among the known cytogenetic subgroups of ALL. Low F13A1 expression was prevalent among “B-other” samples, high F13A1 expression was associated with t(1;19). The pattern of overexpressed ESTs, accumulation of low F13A1 expressing samples in the “B-other” cytogenetic group of ALL and the unfavorable disease outcome of FXIIIA-negative cases may suggest a possible overlap between FXIIIA-negative ALL and BCR-ABL1-like ALL identified recently in the “B-other” group. The nature and extent of this overlap will be investigated prospectively in the BFM ALL-IC 2009 clinical trial. In contrast, high expression levels of F13A1 accumulated preferentially within the t(1;19) genetic group, associated with a good prognosis. Detection of FXIIIA expression by flow cytometry may offer an easy and non-expensive tool for defining new prognostic subpopulations of ALL. Grant support TÁMOP 4.2.2.A-11/1/KONV-2012-0025 project (CK, KG, HZ, IG, IS and JK). The project is co-financed by the European Union and the European Social Fund and the AIRC (Associazione Italiana Ricerca su Cancro; SB) project. Disclosures: No relevant conflicts of interest to declare.

scholarly journals Clinical significance of the ABCB1 and ABCG2 gene expression levels in acute lymphoblastic leukemia

Hematology ◽  
2016 ◽  
Vol 22 (5) ◽  
pp. 286-291 ◽  
Author(s):  
I. Olarte Carrillo ◽  
C. Ramos Peñafiel ◽  
E. Miranda Peralta ◽  
E. Rozen Fuller ◽  
J. J. Kassack Ipiña ◽  
...  
Keyword(s):  
Gene Expression ◽  
Acute Lymphoblastic Leukemia ◽  
Clinical Significance ◽  
Lymphoblastic Leukemia ◽  
Expression Levels ◽  
Abcg2 Gene ◽  
Gene Expression Levels

Inference of genetic networks using random forests: Assigning different weights for gene expression data

2019 ◽  
Vol 17 (04) ◽  
pp. 1950015 ◽  
Author(s):  
Shuhei Kimura ◽  
Masato Tokuhisa ◽  
Mariko Okada
Keyword(s):  
Gene Expression ◽  
Time Series ◽  
Gene Expression Data ◽  
Genetic Network ◽  
Expression Data ◽  
Informative Gene ◽  
Inference Method ◽  
Expression Levels ◽  
Time Series Experiment ◽  
Gene Expression Levels

In using gene expression levels for genetic network inference, we believe that two measurements that are similar to each other are less informative than two measurements that differ from each other. Given, for example, that gene expression levels measured at two adjacent time points in a time-series experiment are often similar to each other, we assume that each measurement in the time-series experiment will be less informative than each measurement in a steady-state experiment. Based on this idea, we propose a new inference method that relies heavily on informative gene expression data. Through numerical experiments, we prove that the quality of an inferred genetic network is slightly improved by heavily weighting informative gene expression data. In this study, we develop a new method by modifying the existing random-forest-based inference method to take advantage of its ability to analyze both time-series and static gene expression data. The idea we propose can be similarly applied to many of the other existing inference methods, as well.

Supervised classification for gene network reconstruction

2003 ◽  
Vol 31 (6) ◽  
pp. 1497-1502 ◽  
Author(s):  
L.A. Soinov
Keyword(s):  
Gene Expression ◽  
Gene Networks ◽  
Supervised Classification ◽  
Biochemical Network ◽  
Expression Data ◽  
Clustering Methods ◽  
Boolean Models ◽  
Experimental Conditions ◽  
Expression Levels ◽  
Mathematical Techniques

One of the central problems of functional genomics is revealing gene expression networks – the relationships between genes that reflect observations of how the expression level of each gene affects those of others. Microarray data are currently a major source of information about the interplay of biochemical network participants in living cells. Various mathematical techniques, such as differential equations, Bayesian and Boolean models and several statistical methods, have been applied to expression data in attempts to extract the underlying knowledge. Unsupervised clustering methods are often considered as the necessary first step in visualization and analysis of the expression data. As for supervised classification, the problem mainly addressed so far has been how to find discriminative genes separating various samples or experimental conditions. Numerous methods have been applied to identify genes that help to predict treatment outcome or to confirm a diagnosis, as well as to identify primary elements of gene regulatory circuits. However, less attention has been devoted to using supervised learning to uncover relationships between genes and/or their products. To start filling this gap a machine-learning approach for gene networks reconstruction is described here. This approach is based on building classifiers – functions, which determine the state of a gene's transcription machinery through expression levels of other genes. The method can be applied to various cases where relationships between gene expression levels could be expected.

scholarly journals A Sparse and Low-Rank Regression Model for Identifying the Relationships Between DNA Methylation and Gene Expression Levels in Gastric Cancer and the Prediction of Prognosis

Genes ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 854
Author(s):  
Yishu Wang ◽  
Lingyun Xu ◽  
Dongmei Ai
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Dna Methylation ◽  
Regression Model ◽  
The Cancer Genome Atlas ◽  
Low Rank ◽  
Expression Levels ◽  
Rank Regression ◽  
Prediction Of Prognosis ◽  
Gene Expression Levels

DNA methylation is an important regulator of gene expression that can influence tumor heterogeneity and shows weak and varying expression levels among different genes. Gastric cancer (GC) is a highly heterogeneous cancer of the digestive system with a high mortality rate worldwide. The heterogeneous subtypes of GC lead to different prognoses. In this study, we explored the relationships between DNA methylation and gene expression levels by introducing a sparse low-rank regression model based on a GC dataset with 375 tumor samples and 32 normal samples from The Cancer Genome Atlas database. Differences in the DNA methylation levels and sites were found to be associated with differences in the expressed genes related to GC development. Overall, 29 methylation-driven genes were found to be related to the GC subtypes, and in the prognostic model, we explored five prognoses related to the methylation sites. Finally, based on a low-rank matrix, seven subgroups were identified with different methylation statuses. These specific classifications based on DNA methylation levels may help to account for heterogeneity and aid in personalized treatments.

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