Gene expression profiling of breast cancer patients treated with docetaxel, doxorubicin, and cyclophosphamide within the GEPARTRIO trial: HER-2, but not topoisomerase II alpha and microtubule-associated protein tau, is highly predictive of tumor response

The Breast ◽  
2007 ◽  
Vol 16 (1) ◽  
pp. 86-93 ◽  
Author(s):  
A. Rody ◽  
T. Karn ◽  
R. Gätje ◽  
A. Ahr ◽  
C. Solbach ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Cancer Patients ◽  
Expression Profiling ◽  
Topoisomerase Ii ◽  
Tumor Response ◽  
Breast Cancer Patients ◽  
Topoisomerase Ii Alpha ◽  
Protein Tau ◽  
Her 2

scholarly journals Expression and Prognostic Characteristics of m6 A RNA Methylation Regulators in Breast Cancer

2020 ◽  
Vol 11 ◽  
Author(s):  
Bo Zhang ◽  
Yanlin Gu ◽  
Guoqin Jiang
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Cancer Patients ◽  
High Expression ◽  
Cancer Gene ◽  
Breast Cancer Patients ◽  
Rna Methylation ◽  
Kaplan Meier ◽  
Her 2 ◽  
Kaplan Meier Plotter

PurposeN6-methyladenosine (m6A) is the most prevalent modification in mRNA methylation which has a wide effect on biological functions. This study aims to figure out the efficacy of m6A RNA methylation regulator-based biomarkers with prognostic significance in breast cancer.Patients and MethodsThe 23 RNA methylation regulators were firstly analyzed through ONCOMINE, then relative RNA-seq transcriptome and clinical data of 1,096 breast cancer samples and 112 normal tissue samples were acquired from The Cancer Gene Atlas (TCGA) database. The expressive distinction was also showed by the Gene Expression Omnibus (GEO) database. The gene expression data of m6A RNA regulators in human tissues were acquired from the Genotype-Tissue Expression (GTEx) database. The R v3.5.1 and other online tools such as STRING, bc-GeneExminer v4.5, Kaplan-Meier Plotter were applied for bioinformatics analysis.ResultsResults from ONCOMINE, TCGA, and GEO databases showed distinctive expression and clinical correlations of m6A RNA methylation regulators in breast cancer patients. The high expression of YTHDF3, ZC3H13, LRPPRC, and METTL16 indicated poor survival rate in patients with breast cancer, while high expression of RBM15B pointed to a better survival rate. Both univariate and multivariate Cox regression analyses revealed that age and risk scores were related to overall survival (OS). Univariate analysis also delineated that stage, tumor (T) status, lymph node (N) status, and metastasis (M) status were associated with OS. From another perspective, Kaplan-Meier Plotter platform showed that the relatively high expression of YTHDF3 and LRPPRC and the relatively low expression of RBM15B, ZC3H13, and METTL16 in breast cancer patients had worse Relapse-Free Survival (RFS). Breast Cancer Gene-Expression Miner v4.5 showed that LRPPRC level was negatively associated with ER and PR expression, while METTL16, RBM15B, ZC3H13 level was positively linked with ER and PR expression. In HER-2 (+) breast cancer patients, the expression of LRPPRC, METTL16, RBM15B, and ZC3H13 were all lower than the HER-2 (−) group.ConclusionThe significant difference in expression levels and prognostic value of m6A RNA methylation regulators were analyzed and validated in this study. This signature revealed the potential therapeutic value of m6A RNA methylation regulators in breast cancer.

scholarly journals Population-based study of the effect of gene expression profiling on adjuvant chemotherapy use in breast cancer patients under the age of 65 years

Cancer ◽  
2015 ◽  
Vol 121 (22) ◽  
pp. 4062-4070 ◽  
Author(s):  
Arnold L. Potosky ◽  
Suzanne C. O'Neill ◽  
Claudine Isaacs ◽  
Huei-Ting Tsai ◽  
Calvin Chao ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Adjuvant Chemotherapy ◽  
Gene Expression Profiling ◽  
Cancer Patients ◽  
Expression Profiling ◽  
Population Based ◽  
Breast Cancer Patients ◽  
Population Based Study

Breast cancer patients stratification by microarray-based gene expression profiling from FFPET samples

2009 ◽  
Vol 27 (15_suppl) ◽  
pp. e22041-e22041
Author(s):  
C. Lih ◽  
Y. Li ◽  
L. Trinh ◽  
S. Chien ◽  
X. Wu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Gene Expression Profiling ◽  
Cancer Patients ◽  
Expression Profiling ◽  
Gene Expression Signature ◽  
Biological Data ◽  
Clinical Samples ◽  
Breast Cancer Patients ◽  
Disease Stratification

e22041 Background: Microarrays have been used to monitor global genes expression and have aided the identification of novel biomarkers for patients stratification and drug response prediction . To date there has been limited application of microarray- based gene expression analysis to formalin fixed paraffin embedded tissues (FFPET). FFPE tissues are the most commonly available clinical samples with documented clinical information for retrospective clinical analysis. However, FFPET RNA has proven to be an obstacle for microarray analysis because of low yield and compromised RNA integrity. Methods: Using a novel RNA amplification method, Single Primer Isothermal Amplification (SPIA, NuGEN Inc, San Carlos, CA), we amplified FFPET RNA, hybridized amplified, and labeled cDNA onto Affymetrix HG U133plus2 GeneChips. Results: We found that SPIA amplification successfully overcomes the problems of poor quality of FFPET RNA, and produced informative biological data. Comparing the gene expression data from 5 different types of FFPET archival cancer samples (breast, lung, ovarian, colon, and melanoma), we demonstrated that gene expression signatures clearly distinguish the tissue of origin. Further, from an analysis of 91 FFPET samples comprised of ER+, HER2+, triple negative breast cancer patients, and normal breast tissue, we have identified a 103 gene signature that distinguishes the intrinsic sub-types of breast cancer. Finally, the accuracy of gene expression measured by microarray was verified by real time PCR quantitation of the ERBB2 gene, resulting in a significant correlation (R = 0.88). Conclusions: We have demonstrated the feasibility of global gene expression profiling using RNA extracted from FFPET and have shown that a gene expression signature can stratify patient samples into different subtypes of disease. This study paves the way to identify novel molecular biomarkers for disease stratification and therapy response from archival FFPET samples, leading to the goals of personalized medicine. No significant financial relationships to disclose.

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