scholarly journals Catechol-O-Methyltransferase Gene Polymorphisms and the Risk of Chemotherapy-Induced Prospective Memory Impairment in Breast Cancer Patients with Varying Tumor Hormonal Receptor Expression

2020 ◽  
Vol 26 ◽  
Author(s):  
Wen Li ◽  
Jingjing Zhao ◽  
Ke Ding ◽  
Herta H. Chao ◽  
Chiang-Shan R. Li ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Prospective Memory ◽  
Gene Polymorphisms ◽  
Memory Impairment ◽  
Receptor Expression ◽  
Breast Cancer Patients ◽  
Hormonal Receptor ◽  
Methyltransferase Gene ◽  
Prospective Memory Impairment

scholarly journals ABCB1 gene polymorphisms and response to chemotherapy in breast cancer patients: A meta-analysis

2017 ◽  
Vol 26 (4) ◽  
pp. 473-482 ◽  
Author(s):  
Adela Madrid-Paredes ◽  
Marisa Cañadas-Garre ◽  
Antonio Sánchez-Pozo ◽  
Manuela Expósito-Ruiz ◽  
Miguel Ángel Calleja-Hernández
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Gene Polymorphisms ◽  
Meta Analysis ◽  
Breast Cancer Patients ◽  
Abcb1 Gene ◽  
Response To Chemotherapy

scholarly journals Prognostic Role of Androgen Receptor Expression in Surgically Resected Early Breast Cancer Patients

2020 ◽  
Vol 23 (2) ◽  
pp. 182 ◽  
Author(s):  
Yaewon Yang ◽  
Ahrum Min ◽  
Kyung-Hun Lee ◽  
Han Suk Ryu ◽  
Tae-Yong Kim ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Androgen Receptor ◽  
Cancer Patients ◽  
Early Breast Cancer ◽  
Receptor Expression ◽  
Androgen Receptor Expression ◽  
Breast Cancer Patients ◽  
Prognostic Role

Prolactin receptor expression as a novel prognostic biomarker for triple negative breast cancer patients

2020 ◽  
Vol 46 ◽  
pp. 151507 ◽  
Author(s):  
Behnaz Motamedi ◽  
Hossain-Ali Rafiee-Pour ◽  
Mohammad-Reza Khosravi ◽  
Amirhosein Kefayat ◽  
Azar Baradaran ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
Prognostic Biomarker ◽  
Prolactin Receptor ◽  
Receptor Expression ◽  
Breast Cancer Patients

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

Cancers ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 1165
Author(s):  
Seokhyun Yoon ◽  
Hye Sung Won ◽  
Keunsoo Kang ◽  
Kexin Qiu ◽  
Woong June Park ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Cancer Patients ◽  
Hormone Receptor ◽  
Expression Profiles ◽  
Receptor Expression ◽  
Luminal Breast Cancer ◽  
Breast Cancer Patients ◽  
Intrinsic Subtypes ◽  
Receptor Status

The 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 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 Breast Invasive Carcinoma (TCGA BRCA) and Molecular Taxonomy of Breast Cancer International Consortium (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 breast cancer. We also confirmed that non-matching subgroup classification affected the survival of breast cancer 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.

Sign in / Sign up

Export Citation Format

Share Document