Generative Model Based Statistical Analysis of Gene Expression Patterns in Breast Cancer

2004 ◽  
Author(s):  
Zoltan Szallasi
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Statistical Analysis ◽  
Expression Patterns ◽  
Generative Model ◽  
Gene Expression Patterns ◽  
Model Based

Predicting features of breast cancer with gene expression patterns

2007 ◽  
Vol 108 (2) ◽  
pp. 191-201 ◽  
Author(s):  
Xuesong Lu ◽  
Xin Lu ◽  
Zhigang C. Wang ◽  
J. Dirk Iglehart ◽  
Xuegong Zhang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Expression Patterns ◽  
Gene Expression Patterns

scholarly journals SIGN: similarity identification in gene expression

2019 ◽  
Vol 35 (22) ◽  
pp. 4830-4833 ◽  
Author(s):  
Seyed Ali Madani Tonekaboni ◽  
Venkata Satya Kumar Manem ◽  
Nehme El-Hachem ◽  
Benjamin Haibe-Kains
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Open Source ◽  
Supervised Learning ◽  
Cancer Patients ◽  
Biomedical Research ◽  
Expression Patterns ◽  
R Package ◽  
Gene Expression Patterns ◽  
Learning Schemes

Abstract Motivation High-throughput molecular profiles of human cells have been used in predictive computational approaches for stratification of healthy and malignant phenotypes and identification of their biological states. In this regard, pathway activities have been used as biological features in unsupervised and supervised learning schemes. Results We developed SIGN (Similarity Identification in Gene expressioN), a flexible open-source R package facilitating the use of pathway activities and their expression patterns to identify similarities between biological samples. We defined a new measure, the transcriptional similarity coefficient, which captures similarity of gene expression patterns, instead of quantifying overall activity, in biological pathways between the samples. To demonstrate the utility of SIGN in biomedical research, we establish that SIGN discriminates subtypes of breast tumors and patients with good or poor overall survival. SIGN outperforms the best models in DREAM challenge in predicting survival of breast cancer patients using the data from the Molecular Taxonomy of Breast Cancer International Consortium. In summary, SIGN can be used as a new tool for interrogating pathway activity and gene expression patterns in unsupervised and supervised learning schemes to improve prognostic risk estimation for cancer patients by the biomedical research community. Availability and implementation An open-source R package is available (https://cran.r-project.org/web/packages/SIGN/).

Gene Expression Patterns and Profile Changes Pre- and Post-Erlotinib Treatment in Patients with Metastatic Breast Cancer

2005 ◽  
Vol 11 (17) ◽  
pp. 6226-6232 ◽  
Author(s):  
Sherry X. Yang ◽  
Richard M. Simon ◽  
Antoinette R. Tan ◽  
Diana Nguyen ◽  
Sandra M. Swain
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Metastatic Breast Cancer ◽  
Expression Patterns ◽  
Metastatic Breast ◽  
Gene Expression Patterns ◽  
Profile Changes

scholarly journals Integrative analysis of gene expression patterns predicts specific modulations of defined cell functions by estrogen and tamoxifen in MCF7 breast cancer cells

2005 ◽  
Vol 34 (1) ◽  
pp. 61-75 ◽  
Author(s):  
F Gadal ◽  
A Starzec ◽  
C Bozic ◽  
C Pillot-Brochet ◽  
S Malinge ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Cell Cycle ◽  
Data Analysis ◽  
Differential Expression ◽  
Breast Cancer Cells ◽  
Cell Cycle Progression ◽  
Expression Patterns ◽  
Gene Expression Patterns ◽  
Cycle Progression

To explore the mechanisms whereby estrogen and antiestrogen (tamoxifen (TAM)) can regulate breast cancer cell growth, we investigated gene expression changes in MCF7 cells treated with 17β-estradiol (E2) and/or with 4-OH-TAM. The patterns of differential expression were determined by the ValiGen Gene IDentification (VGID) process, a subtractive hybridization approach combined with microarray validation screening. Their possible biologic consequences were evaluated by integrative data analysis. Over 1000 cDNA inserts were isolated and subsequently cloned, sequenced and analyzed against nucleotide and protein databases (NT/NR/EST) with BLAST software. We revealed that E2 induced differential expression of 279 known and 28 unknown sequences, whereas TAM affected the expression of 286 known and 14 unknown sequences. Integrative data analysis singled out a set of 32 differentially expressed genes apparently involved in broad cellular mechanisms. The presence of E2 modulated the expression patterns of 23 genes involved in anchors and junction remodeling; extracellular matrix (ECM) degradation; cell cycle progression, including G1/S check point and S-phase regulation; and synthesis of genotoxic metabolites. In tumor cells, these four mechanisms are associated with the acquisition of a motile and invasive phenotype. TAM partly reversed the E2-induced differential expression patterns and consequently restored most of the biologic functions deregulated by E2, except the mechanisms associated with cell cycle progression. Furthermore, we found that TAM affects the expression of nine additional genes associated with cytoskeletal remodeling, DNA repair, active estrogen receptor formation and growth factor synthesis, and mitogenic pathways. These modulatory effects of E2 and TAM upon the gene expression patterns identified here could explain some of the mechanisms associated with the acquisition of a more aggressive phenotype by breast cancer cells, such as E2-independent growth and TAM resistance.

scholarly journals 231EXPRESSION PATTERN OF CERTAIN DEVELOPMENTALLY IMPORTANT GENES IN BOVINE NUCLEAR TRANSFER EMBRYOS PRODUCED USING CELL LINES OF DIFFERENT EFFICIENCY

2004 ◽  
Vol 16 (2) ◽  
pp. 236 ◽  
Author(s):  
Z. Beyhan ◽  
N.L. First
Keyword(s):  
Gene Expression ◽  
Statistical Analysis ◽  
Cell Line ◽  
Cell Lines ◽  
Live Birth ◽  
Nuclear Transfer ◽  
Expression Patterns ◽  
Blastocyst Stage ◽  
Gene Expression Patterns ◽  
Donor Cells

Developmental abnormalities associated with the cloning process suggest that reprogramming of donor nuclei into an embryonic state may not be fully completed in most of the cloned animals. One of the areas of interest in this respect is the analysis of gene expression patterns in nuclear transfer embryos to dissect the processes that failed and to develop means to overcome the limitations imposed by these factors. In this study, we investigated the expression patterns of histone deacetylase-1,-2,-3 (HDAC-1,-2,-3), DNA methyltransferase-3A (DNMT3A) and octamer binding protein-4 gene (POU5F1) in donor cells with different cloning efficiencies (low: no-pregnancy, medium: pregnancy but no live birth and high: live birth) and nuclear transfer embryos derived from these cell lines using a real time reverse transcription-polymerase chain reaction (RT-PCR) assay with SYBR green chemistry. Employing standard protocols, we produced nuclear transfer embryos from three different cell lines categorized as having varying efficiencies in supporting development to term. Embryos were collected at morula, blastocyst and hatched blastocyst stages and total RNA was extracted from pools of 4–5 embryos using Absolutely RNA nanoprep kit (Stratagene, La Jolla, CA, USA). Relative level of expression at these stages was analyzed using ΔΔCT method with HH2A as the reference gene and in vitro-fertilized embryos as the control samples. Statistical analysis was performed on ranked expression data employing SAS statistical analysis software procedure ANOVA. Same set of genes were also analyzed on donor cells using standard curve method. All genes investigated were affected by nuclear transfer and followed somewhat altered expression patterns. In general, expression of HDAC genes was elevated especially at the compact morula stage but became comparable to control embryos at the hatched blastocyst stage. DNMT3A expression in NT embryos was lower than in IVF embryos at all stages. POU5F1 transcript levels were also reduced in nuclear transfer embryos at the compact morula and blastocyst stages. The difference, however, disappeared at the hatched blastocyst stage. There was a cell line effect on the expression patterns of all genes investigated. Cell lines efficient in producing offspring tended to resemble control embryos in gene expression patterns compared to inefficient cell lines. These results agree with several studies reporting altered gene expression patterns for certain genes in cloned embryos. Our data also suggest that cell line differences in developmental competency observed in cloning experiments might be related to physiological differences in transcriptional regulation and nuclear remodeling, DNA methylation, and lineage differentiation in embryos cloned from these cell lines.

Sign in / Sign up

Export Citation Format

Share Document