scholarly journals Comparison and Identification of Estrogen-Receptor Related Gene Expression Profiles in Breast Cancer of Different Ethnic Origins

2008 ◽  
Vol 1 ◽  
pp. BCBCR.S626 ◽  
Author(s):  
Hsiao-Wei Chen ◽  
Hsuan-Cheng Huang ◽  
Yi-Shing Lin ◽  
King-Jen Chang ◽  
Wen-Hung Kuo ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Estrogen Receptor ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Microarray Experiment ◽  
Breast Cancer Patients ◽  
Related Gene ◽  
Increased Risk ◽  
Ethnic Origins

The interactions between genetic variants in estrogen receptor (ER) have been identified to be associated with an increased risk of breast cancer. Available evidence indicates that genetic variance within a population plays a crucial role in the occurrence of breast cancer. Thus, the comparison and identification of ER-related gene expression profiles in breast cancer of different ethnic origins could be useful for the development of genetic variant cancer therapy. In this study, we performed microarray experiment to measure the gene expression profiles of 59 Taiwanese breast cancer patients; and through comparative bioinformatics analysis against published U.K. datasets, we revealed estrogen-receptor (ER) related gene expression between Taiwanese and British patients. In addition, SNP databases and statistical analysis were used to elucidate the SNPs associated with ER status. Our microarray results indicate that the expression pattern of the 65 genes in ER+ patients was dissimilar from that of the ER- patients. Seventeen mutually exclusive genes in ER-related breast cancer of the two populations with more than one statistically significant SNP in genotype and allele frequency were identified. These 17 genes and their related SNPs may be important in population-specific ER regulation of breast cancer. This study provides a global and feasible approach to study population-unique SNPs in breast cancer of different ethnic origins.

scholarly journals Impact of 70-Gene Signature Use on Adjuvant Chemotherapy Decisions in Patients With Estrogen Receptor–Positive Early Breast Cancer: Results of a Prospective Cohort Study

2017 ◽  
Vol 35 (24) ◽  
pp. 2814-2819 ◽  
Author(s):  
Anne Kuijer ◽  
Marieke Straver ◽  
Bianca den Dekker ◽  
Annelotte C.M. van Bommel ◽  
Sjoerd G. Elias ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Estrogen Receptor ◽  
Early Stage ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Gene Signature ◽  
Early Stage Breast Cancer ◽  
Er Positive ◽  
Test Result

Purpose Gene-expression profiles increasingly are used in addition to conventional prognostic factors to guide adjuvant chemotherapy (CT) decisions. The Dutch guideline suggests use of validated gene-expression profiles in patients with estrogen receptor (ER) –positive, early-stage breast cancer without overt lymph node metastases. We aimed to assess the impact of a 70-gene signature (70-GS) test on CT decisions in patients with ER-positive, early-stage breast cancer. Patients and Methods In a prospective, observational, multicenter study in patients younger than 70 years old who had undergone surgery for ER-positive, early-stage breast cancer, physicians were asked whether they intended to administer adjuvant CT before deployment of the 70-GS test and after the test result was available. Results Between October 1, 2013, and December 31, 2015, 660 patients, treated in 33 hospitals, were enrolled. Fifty-one percent of patients had pT1cN0, BRII, HER2-Neu-negative breast cancer. On the basis of conventional clinicopathological characteristics, physicians recommended CT in 270 (41%) of the 660 patients and recommended withholding CT in 107 (16%) of the 660 patients. For the remaining 43% of patients, the physicians were unsure and unable to give advice before 70-GS testing. In patients for whom CT was initially recommended or not recommended, 56% and 59%, respectively, were assigned to a low-risk profile by the 70-GS (κ, 0.02; 95% CI, -0.08 to 0.11). After disclosure of the 70-GS test result, the preliminary advice was changed in 51% of patients who received a recommendation before testing; the definitive CT recommendation of the physician was in line with the 70-GS result in 96% of patients. Conclusion In this prospective, multicenter study in a selection of patients with ER-positive, early-stage breast cancer, 70-GS use changed the physician-intended recommendation to administer CT in half of the patients.

Genomic Prediction of Locoregional Recurrence After Mastectomy in Breast Cancer

2006 ◽  
Vol 24 (28) ◽  
pp. 4594-4602 ◽  
Author(s):  
Skye H. Cheng ◽  
Cheng-Fang Horng ◽  
Mike West ◽  
Erich Huang ◽  
Jennifer Pittman ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Cancer Patients ◽  
Expression Profiles ◽  
Eligible Patient ◽  
Gene Expression Profiles ◽  
Breast Cancer Patients ◽  
Predictive Index ◽  
Validation Data ◽  
Data Set

Purpose This study aims to explore gene expression profiles that are associated with locoregional (LR) recurrence in breast cancer after mastectomy. Patients and Methods A total of 94 breast cancer patients who underwent mastectomy between 1990 and 2001 and had DNA microarray study on the primary tumor tissues were chosen for this study. Eligible patient should have no evidence of LR recurrence without postmastectomy radiotherapy (PMRT) after a minimum of 3-year follow-up (n = 67) and any LR recurrence (n = 27). They were randomly split into training and validation sets. Statistical classification tree analysis and proportional hazards models were developed to identify and validate gene expression profiles that relate to LR recurrence. Results Our study demonstrates two sets of gene expression profiles (one with 258 genes and the other 34 genes) to be of predictive value with respect to LR recurrence. The overall accuracy of the prediction tree model in validation sets is estimated 75% to 78%. Of patients in validation data set, the 3-year LR control rate with predictive index more than 0.8 derived from 34-gene prediction models is 91%, and predictive index 0.8 or less is 40% (P = .008). Multivariate analysis of all patients reveals that estrogen receptor and genomic predictive index are independent prognostic factors that affect LR control. Conclusion Using gene expression profiles to develop prediction tree models effectively identifies breast cancer patients who are at higher risk for LR recurrence. This gene expression–based predictive index can be used to select patients for PMRT.

scholarly journals Comparison of gene expression profiles predicting progression in breast cancer patients treated with tamoxifen

2008 ◽  
Vol 113 (2) ◽  
pp. 275-283 ◽  
Author(s):  
Marleen Kok ◽  
Sabine C. Linn ◽  
Ryan K. Van Laar ◽  
Maurice P. H. M. Jansen ◽  
Teun M. van den Berg ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Cancer Patients ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Breast Cancer Patients

scholarly journals Integrative analysis of DNA methylation and gene expression profiles identified potential breast cancer-specific diagnostic markers

2020 ◽  
Vol 40 (5) ◽  
Author(s):  
Xinhua Liu ◽  
Yonglin Peng ◽  
Ju Wang
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Dna Methylation ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
The Cancer Genome Atlas ◽  
Diagnostic Model ◽  
Breast Cancer Patients ◽  
Gene Expressions ◽  
Normal Tissues

Abstract Breast cancer is a common malignant tumor among women whose prognosis is largely determined by the period and accuracy of diagnosis. We here propose to identify a robust DNA methylation-based breast cancer-specific diagnostic signature. Genome-wide DNA methylation and gene expression profiles of breast cancer patients along with their adjacent normal tissues from the Cancer Genome Atlas (TCGA) were obtained as the training set. CpGs that with significantly elevated methylation level in breast cancer than not only their adjacent normal tissues and the other ten common cancers from TCGA but also the healthy breast tissues from the Gene Expression Omnibus (GEO) were finally remained for logistic regression analysis. Another independent breast cancer DNA methylation dataset from GEO was used as the testing set. Lots of CpGs were hyper-methylated in breast cancer samples compared with adjacent normal tissues, which tend to be negatively correlated with gene expressions. Eight CpGs located at RIIAD1, ENPP2, ESPN, and ETS1, were finally retained. The diagnostic model was reliable in separating BRCA from normal samples. Besides, chromatin accessibility status of RIIAD1, ENPP2, ESPN and ETS1 showed great differences between MCF-7 and MDA-MB-231 cell lines. In conclusion, the present study should be helpful for breast cancer early and accurate diagnosis.

scholarly journals Hormone Receptor-status Prediction in Breast Cancer Using Gene Expression Profiles and Their Macroscopic Landscape

2020 ◽  
Author(s):  
Seokhyun Yoon ◽  
Hye Sung Won ◽  
Keunsoo Kang ◽  
Kexin Qiu ◽  
Woong June Park ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Hormone Receptor ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Receptor Expression ◽  
The Cancer Genome Atlas ◽  
Breast Cancer Patients ◽  
Intrinsic Subtypes ◽  
Receptor Status

AbstractThe cost of next-generation sequencing technologies is rapidly declining, making RNA-seq-based gene expression profiling (GEP) an affordable technique for predicting receptor expression status and intrinsic subtypes in breast cancer (BRCA) patients. Based on the expression levels of co-expressed genes, GEP-based receptor-status prediction can classify clinical subtypes more accurately than can immunohistochemistry (IHC). Using data from the cancer genome atlas TCGA BRCA and METABRIC datasets, we identified common predictor genes found in both datasets and performed receptor-status prediction based on these genes. By assessing the survival outcomes of patients classified using GEP- or IHC-based receptor status, we compared the prognostic value of the two methods. We found that GEP-based HR prediction provided higher concordance with the intrinsic subtypes and a stronger association with treatment outcomes than did IHC-based hormone receptor (HR) status. GEP-based prediction improved the identification of patients who could benefit from hormone therapy, even in patients with non-luminal BRCA. We also confirmed that non-matching subgroup classification affected the survival of BRCA patients and that this could be largely overcome by GEP-based receptor-status prediction. In conclusion, GEP-based prediction provides more reliable classification of HR status, improving therapeutic decision making for breast cancer patients.

Identification of circadian-related gene expression profiles in entrained breast cancer cell lines

2016 ◽  
Vol 33 (4) ◽  
pp. 392-405 ◽  
Author(s):  
Miguel A. Gutiérrez-Monreal ◽  
Victor Treviño ◽  
Jorge E. Moreno-Cuevas ◽  
Sean-Patrick Scott
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Cell Lines ◽  
Cancer Cell ◽  
Breast Cancer Cell ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cancer Cell Lines ◽  
Breast Cancer Cell Lines ◽  
Related Gene
Sign in / Sign up

Export Citation Format

Share Document