Hedgehog gene expression patterns among intrinsic subtypes of breast cancer: prognostic relevance

2021 ◽  
pp. 153478
Author(s):  
Araceli García-Martínez ◽  
Ariadna Pérez-Balaguer ◽  
Fernando Ortiz-Martínez ◽  
Eloy Pomares-Navarro ◽  
Elena Sanmartín ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Expression Patterns ◽  
Gene Expression Patterns ◽  
Intrinsic Subtypes ◽  
Prognostic Relevance

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.

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