scholarly journals LSOSS: Detection of Cancer Outlier Differential Gene Expression

2010 ◽  
Vol 5 ◽  
pp. BMI.S5175 ◽  
Author(s):  
Yupeng Wang ◽  
Romdhane Rekaya
Keyword(s):  
Gene Expression ◽  
Differential Gene Expression ◽  
Profile Analysis ◽  
Gene Activation ◽  
Gene Signature ◽  
Human Breast ◽  
Disease Group ◽  
Activation Patterns ◽  
Cancer Data ◽  
Differential Gene

Detection of differential gene expression using microarray technology has received considerable interest in cancer research studies. Recently, many researchers discovered that oncogenes may be activated in some but not all samples in a given disease group. The existing statistical tools for detecting differentially expressed genes in a subset of the disease group mainly include cancer outlier profile analysis (COPA), outlier sum (OS), outlier robust t-statistic (ORT) and maximum ordered subset t-statistics (MOST). In this study, another approach named Least Sum of Ordered Subset Square t-statistic (LSOSS) is proposed. The results of our simulation studies indicated that LSOSS often has more power than previous statistical methods. When applied to real human breast and prostate cancer data sets, LSOSS was competitive in terms of the biological relevance of top ranked genes. Furthermore, a modified hierarchical clustering method was developed to classify the heterogeneous gene activation patterns of human breast cancer samples based on the significant genes detected by LSOSS. Three classes of gene activation patterns, which correspond to estrogen receptor (ER)+, ER– and a mixture of ER+ and ER–, were detected and each class was assigned a different gene signature.

PADGE: analysis of heterogeneous patterns of differential gene expression

2007 ◽  
Vol 32 (1) ◽  
pp. 154-159 ◽  
Author(s):  
Li Li ◽  
Amitabha Chaudhuri ◽  
John Chant ◽  
Zhijun Tang
Keyword(s):  
Gene Expression ◽  
Differential Gene Expression ◽  
Web Application ◽  
Statistical Power ◽  
Profile Analysis ◽  
Expression Profiles ◽  
Differentially Expressed ◽  
Cancer Genes ◽  
Data Set ◽  
Differential Gene

We have devised a novel analysis approach, percentile analysis for differential gene expression (PADGE), for identifying genes differentially expressed between two groups of heterogeneous samples. PADGE was designed to compare expression profiles of sample subgroups at a series of percentile cutoffs and to examine the trend of relative expression between sample groups as expression level increases. Simulation studies showed that PADGE has more statistical power than t-statistics, cancer outlier profile analysis (COPA) (Tomlins SA, Rhodes DR, Perner S, Dhanasekaran SM, Mehra R, Sun XW, Varambally S, Cao X, Tchinda J, Kuefer R, Lee C, Montie JE, Shah RB, Pienta KJ, Rubin MA, Chinnaiyan AM. Science 310: 644–648, 2005), and kurtosis (Teschendorff AE, Naderi A, Barbosa-Morais NL, Caldas C. Bioinformatics 22: 2269–2275, 2006). Application of PADGE to microarray data sets in tumor tissues demonstrated its utility in prioritizing cancer genes encoding potential therapeutic targets or diagnostic markers. A web application was developed for researchers to analyze a large gene expression data set from heterogeneous biological samples and identify differentially expressed genes between subsets of sample classes using PADGE and other available approaches. Availability: http://www.cgl.ucsf.edu/Research/genentech/padge/ .

scholarly journals Effects of Sample Size on Differential Gene Expression, Rank Order and Prediction Accuracy of a Gene Signature

PLoS ONE ◽  
2013 ◽  
Vol 8 (6) ◽  
pp. e65380 ◽  
Author(s):  
Cynthia Stretch ◽  
Sheehan Khan ◽  
Nasimeh Asgarian ◽  
Roman Eisner ◽  
Saman Vaisipour ◽  
...  
Keyword(s):  
Gene Expression ◽  
Sample Size ◽  
Differential Gene Expression ◽  
Prediction Accuracy ◽  
Rank Order ◽  
Gene Signature ◽  
Differential Gene

scholarly journals Identification of an IL-22-Dependent Gene Signature as a Pharmacodynamic Biomarker

2021 ◽  
Vol 22 (15) ◽  
pp. 8205
Author(s):  
Julie Rae ◽  
Jason Hackney ◽  
Kevin Huang ◽  
Mary Keir ◽  
Ann Herman
Keyword(s):  
Gene Expression ◽  
Differential Gene Expression ◽  
Expression Patterns ◽  
Gene Expression Pattern ◽  
Gene Signature ◽  
Intestinal Cell ◽  
Epithelial Barrier Function ◽  
Stat3 Phosphorylation ◽  
Differential Gene ◽  
Ht 29

Interleukin-22 (IL-22) plays a role in epithelial barrier function and repair, and may provide benefits in conditions like inflammatory bowel disease. However, limited human data are available to assess the clinical effect of IL-22 administration. This study used a human intestinal cell line to identify an IL-22-dependent gene signature that could serve as a pharmacodynamic biomarker for IL-22 therapy. The response to IL-22Fc (UTTR1147A, an Fc-stabilized version of IL-22) was assessed in HT-29 cells by microarray, and the selected responsive genes were confirmed by qPCR. HT-29 cells demonstrated dose-dependent increases in STAT3 phosphorylation and multiple gene expression changes in response to UTTR1147A. Genes were selected that were upregulated by UTTR1147A, but to a lesser extent by IL-6, which also signals via STAT3. IL-1R1 was highly upregulated by UTTR1147A, and differential gene expression patterns were observed in response to IL-22Fc in the presence of IL-1β. An IL-22-dependent gene signature was identified that could serve as a pharmacodynamic biomarker in intestinal biopsies to support the clinical development of an IL-22 therapeutic. The differential gene expression pattern in the presence of IL-1β suggests that an inflammatory cytokine milieu in the disease setting could influence the clinical responses to IL-22.

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