scholarly journals Comparative expressed sequence hybridisation revealed distinct chromosomal regions of differential gene expression in breast cancer subtypes

2005 ◽  
Vol 7 (S2) ◽  
Author(s):  
I Vanden Bempt ◽  
V Vanhentenrijk ◽  
M Drijkoningen ◽  
C De Wolf-Peeters
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Differential Gene Expression ◽  
Breast Cancer Subtypes ◽  
Cancer Subtypes ◽  
Differential Gene ◽  
Expressed Sequence

Comparative expressed sequence hybridization reveals differential gene expression in morphological breast cancer subtypes

2006 ◽  
Vol 208 (4) ◽  
pp. 486-494 ◽  
Author(s):  
Isabelle Vanden Bempt ◽  
Vera Vanhentenrijk ◽  
Maria Drijkoningen ◽  
Christiane De Wolf-Peeters
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Differential Gene Expression ◽  
Breast Cancer Subtypes ◽  
Cancer Subtypes ◽  
Differential Gene ◽  
Expressed Sequence

scholarly journals bc-GenExMiner 4.5: new mining module computes breast cancer differential gene expression analyses

Database ◽  
2021 ◽  
Vol 2021 ◽  
Author(s):  
Pascal Jézéquel ◽  
Wilfried Gouraud ◽  
Fadoua Ben Azzouz ◽  
Catherine Guérin-Charbonnel ◽  
Philippe P Juin ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Differential Gene Expression ◽  
Molecular Characteristics ◽  
Cancer Subtypes ◽  
Source Data ◽  
Gene Expression Analyses ◽  
Differential Gene ◽  
Validation Tests ◽  
Expression Module

Abstract ‘Breast cancer gene-expression miner’ (bc-GenExMiner) is a breast cancer–associated web portal (http://bcgenex.ico.unicancer.fr). Here, we describe the development of a new statistical mining module, which permits several differential gene expression analyses, i.e. ‘Expression’ module. Sixty-two breast cancer cohorts and one healthy breast cohort with their corresponding clinicopathological information are included in bc-GenExMiner v4.5 version. Analyses are based on microarray or RNAseq transcriptomic data. Thirty-nine differential gene expression analyses, grouped into 13 categories, according to clinicopathological and molecular characteristics (‘Targeted’ and ‘Exhaustive’) and gene expression (‘Customized’), have been developed. Output results are visualized in four forms of plots. This new statistical mining module offers, among other things, the possibility to compare gene expression in healthy (cancer-free), tumour-adjacent and tumour tissues at once and in three triple-negative breast cancer subtypes (i.e. C1: molecular apocrine tumours; C2: basal-like tumours infiltrated by immune suppressive cells and C3: basal-like tumours triggering an ineffective immune response). Several validation tests showed that bioinformatics process did not alter the pathobiological information contained in the source data. In this work, we developed and demonstrated that bc-GenExMiner ‘Expression’ module can be used for exploratory and validation purposes. Database URL: http://bcgenex.ico.unicancer.fr

scholarly journals A resampling-based meta-analysis for detection of differential gene expression in breast cancer

BMC Cancer ◽  
2008 ◽  
Vol 8 (1) ◽  
Author(s):  
Bala Gur-Dedeoglu ◽  
Ozlen Konu ◽  
Serkan Kir ◽  
Ahmet Rasit Ozturk ◽  
Betul Bozkurt ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Differential Gene Expression ◽  
Meta Analysis ◽  
Differential Gene

Epigenetic aspects of triple-negative in patients with breast cancer.

2012 ◽  
Vol 30 (15_suppl) ◽  
pp. 1041-1041
Author(s):  
Joaquina Martínez-Galan ◽  
Sandra Rios ◽  
Juan Ramon Delgado ◽  
Blanca Torres-Torres ◽  
Jesus Lopez-Peñalver ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Dna Methylation ◽  
Triple Negative ◽  
Breast Cancer Subtype ◽  
Regulation Of Gene Expression ◽  
Breast Cancer Subtypes ◽  
Luminal A ◽  
Cancer Subtypes ◽  
Luminal B

1041 Background: Identification of gene expression-based breast cancer subtypes is considered a critical means of prognostication. Genetic mutations along with epigenetic alterations contribute to gene-expression changes occurring in breast cancer. However, the reproducibility of differential DNA methylation discoveries for cancer and the relationship between DNA methylation and aberrant gene expression have not been systematically analysed. The present study was undertaken to dissect the breast cancer methylome and to deliver specific epigenotypes associated with particular breast cancer subtypes. Methods: By using Real Time QMSPCR SYBR green we analyzed DNA methylation in regulatory regions of 107 pts with breast cancer and analyzed association with prognostics factor in triple negative breast cancer and methylation promoter ESR1, APC, E-Cadherin, Rar B and 14-3-3 sigma. Results: We identified novel subtype-specific epigenotypes that clearly demonstrate the differences in the methylation profiles of basal-like and human epidermal growth factor 2 (HER2)-overexpressing tumors. Of the cases, 37pts (40%) were Luminal A (LA), 32pts (33%) Luminal B (LB), 14pts (15%) Triple-negative (TN), and 9pts (10%) HER2+. DNA hypermethylation was highly inversely correlated with the down-regulation of gene expression. Methylation of this panel of promoter was found more frequently in triple negative and HER2 phenotype. ESR1 was preferably associated with TN(80%) and HER2+(60%) subtype. With a median follow up of 6 years, we found worse overall survival (OS) with more frequent ESR1 methylation gene(p>0.05), Luminal A;ESR1 Methylation OS at 5 years 81% vs 93% when was ESR1 Unmethylation. Luminal B;ESR1 Methylation 86% SG at 5 years vs 92% in Unmethylation ESR1. Triple negative;ESR1 Methylation SG at 5 years 75% vs 80% in unmethylation ESR1. HER2;ESR1 Methylation SG at 5 years was 66.7% vs 75% in unmethylation ESR1. Conclusions: Our results provide evidence that well-defined DNA methylation profiles enable breast cancer subtype prediction and support the utilization of this biomarker for prognostication and therapeutic stratification of patients with breast cancer.

scholarly journals Glycan-related gene expression signatures in breast cancer subtypes; relation to survival

2015 ◽  
Vol 9 (4) ◽  
pp. 861-876 ◽  
Author(s):  
Ivan O. Potapenko ◽  
Torben Lüders ◽  
Hege G. Russnes ◽  
Åslaug Helland ◽  
Therese Sørlie ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Breast Cancer Subtypes ◽  
Related Gene ◽  
Cancer Subtypes ◽  
Gene Expression Signatures
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