DNA Methylation in Breast Cancers: Differences based on Estrogen Receptor Status and Recurrence
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ABSTRACT:
PROVIDER: PRJNA482117 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:DNA methylation plays a role in the etiology of primary breast cancers. We analyzed paired primary and second breast tumors to elucidate the role of methylation in recurrence. Methylation profiles from paired primary and second breast tumors of 23 women were assessed using the HumanMethylation450 BeadChip. Twelve women had ERpos primary and second tumors, five had estrogen receptor negative (ERneg) primary and second tumors, and six had an ERpos primary tumor but an ERneg second tumor. Stratifying tumors by occurrence revealed that the greater methylation previously associated with ERpos tumors, is more pronounced in primary tumors than in second tumors. Further, ERneg second tumors are more methylated than ERpos second tumors among women who had ERpos primary tumors. Pathway analyses using gene lists generated from comparisons of methylation in ERpos primary tumors from the paired sets with ERpos tumors from 6 women without recurrences, identified differences between groups based on the ER status of the second tumor. Hypermethylated genes of significantly enriched pathways were differentially associated with survival. DNA methylation profiles of ERpos primary breast tumors support the development and use of tumor methylation profiles for stratifying women with breast cancer both for prognosis and therapy.
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Project description:Fine needle aspiration biopsies (FNABs) of breast cancers were taken before and after surgeries from 16 patients. The cDNA microarray data were used to determine the gene expression profile responding to patient's clinical finding and tumor's pathological changes. A gene profile was generated as Estrogen Receptor Gene Signature (ERGS). The ERGS was verified in a reference dataset and correlated with patient's prognosis significantly. Keywords: Breast cancer, fine needle biopsy, estrogen receptor, prognostic gene signature
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Project description:Estrogen protects neurotransmission transcriptome during status epilepticus
| PRJNA421197 | ENA
Project description:Fine needle aspiration biopsies (FNABs) of breast cancers were taken before and after surgeries from 16 patients. The cDNA microarray data were used to determine the gene expression profile responding to patient's clinical finding and tumor's pathological changes. A gene profile was generated as Estrogen Receptor Gene Signature (ERGS). The ERGS was verified in a reference dataset and correlated with patient's prognosis significantly. Keywords: Breast cancer, fine needle biopsy, estrogen receptor, prognostic gene signature Dye-swap technical replicates were included both FNABs taken before and after surgeries for every patient, then the four replicated array data per patient were combined for analysis.
2009-07-01 | E-GEOD-12080 | biostudies-arrayexpress
Project description:The prognosis of a patient with Estrogen Receptor (ER) and/or Progesterone Receptor (PR)-positive breast cancer is highly variable. Therefore, we developed a gene-expression based outcome predictor for ER+ and/or PR+ (i.e. Luminal) breast cancer patients using biological properties of the tumors. First, we identified estrogen-regulated genes using the ER+ MCF-7 breast cancer cell line treated with estrogen. The estrogen-induced gene set was then used to hierarchically cluster a training set of 65 ER+ and/or PR+ tumors into 2 group, which showed survival differences (p=0.0004). Next, supervised analyses based upon these two groups was performed and identified 822 genes that optimally defined these two groups, with the poor prognosis Group IIE tumors showing a proliferation signature and high expression of anti-apoptosis genes and the good outcome Group IE showing the high expression of estrogen and GATA3-induced genes. Centroids were created for each group and applied to ER+ and/or PR+ tumors from three published datasets. For all datasets, Kaplan-Meier survival analyses showed a statistically significant difference in Relapse-Free Survival (and Overall) between Group IE and IIE tumors. Multivariate Cox analysis of the largest test dataset also showed that this predictor was adding independent information. This study provides new biological information concerning differences within Luminal/ER+ breast cancers and a means of predicting long term outcomes in ER+ and/or PR+ breast cancer patients. Keywords: other
2005-12-01 | GSE2740 | GEO
Project description:TP53 mutations are a poor prognostic factor in breast cancers. This study sets out to identify the gene set that determine expression signature of the TP53 status (TP53 signature) and to correlate it with clinical outcome. Using comprehensive expression analysis and DNA sequencing of the TP53 gene in 38 Japanese breast cancer patients, we have isolated a gene set of 33 genes from differentially expressed genes in the learning set (n=26), depending on the TP53 status. Predictive accuracy of TP53 status by gene expression profile was 83.3% in the test set (n=12). As independent external datasets, two published datasets were introduced for validation of TP53 status prediction (251 Swedish samples) and survival analysis (both the Swedish and 295 Dutch samples). TP53 signature has the ability to predict recurrence-free survival (RFS) of 29 stage I and II Japanese breast cancers (log rank, P = 0.032), and RFS, overall survival of two independently published datasets (log rank, both P < 0.0001). Multivariate analysis has shown an independent and significant prognostic factor with a hazard ratio (HR) for recurrence and survival in two external datasets (P < 0.0001). The TP53 signature is also a strong prognostic factor in the subgroups: estrogen-receptor positive, lymph node (LN) positive and negative, intermediate/high risk in St. Gallen criteria, and high risk in National Cancer Institute (NCI) criteria (log rank, P < 0.0001). TP53 signature is a reliable and independent predictor of the outcome of disease in operated breast cancer. Keywords: Tumor sample comparison
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Project description:Gene expression profiles were generated from 199 primary breast cancer patients. Samples 1-176 were used in another study, GEO Series GSE22820, and form the training data set in this study. Sample numbers 200-222 form a validation set. This data is used to model a machine learning classifier for Estrogen Receptor Status. RNA was isolated from 199 primary breast cancer patients. A machine learning classifier was built to predict ER status using only three gene features.
2013-01-01 | E-GEOD-29210 | biostudies-arrayexpress
Project description:Gene expression profiles were generated from 199 primary breast cancer patients. Samples 1-176 were used in another study, GEO Series GSE22820, and form the training data set in this study. Sample numbers 200-222 form a validation set. This data is used to model a machine learning classifier for Estrogen Receptor Status.
2013-01-01 | GSE29210 | GEO
Project description:The objective of this study is to identify a gene set to predict recurrence of colorectal cancer (CRC) patients. We generated RNA-seq data of 110 primary CRC samples and identified significant genes associated with recurrence of CRC. Through diverse statistical methods including generalized linear model likelihood ratio test, significant 10 genes were identified. In the validation cohorts, a risk classifier consisting of the 10 genes was an independent risk factor in colorectal cancers.
2019-10-29 | GSE107422 | GEO
Project description:Signatures of Oncogenic Pathway Deregulation in Human Cancers. The ability to define cancer subtypes, recurrence of disease, and response to specific therapies using DNA microarray-based gene expression signatures has been demonstrated in multiple studies. Such data is also of substantial importance to the analysis of cellular signaling pathways central to the oncogenic process. With this focus, we have developed a series of gene expression signatures that reliably reflect the activation status of several oncogenic pathways. When evaluated in several large collections of human cancers, these gene expression signatures identify patterns of pathway deregulation in tumors, and clinically relevant associations with disease outcomes. Combining signature-based predictions across several pathways identifies coordinated patterns of pathway deregulation that distinguish between specific cancers and tumor sub-types. Clustering tumors based on pathway signatures further defines prognosis in respective patient subsets, demonstrating that patterns of oncogenic pathway deregulation underlie the development of the oncogenic phenotype and reflect the biology and outcome of specific cancers. Furthermore, predictions of pathway deregulation in cancer cell lines are shown to coincide with sensitivity to therapeutic agents that target components of the pathway, underscoring the potential for such pathway prediction to guide the use of targeted therapeutics. Experiment Overall Design: RNA was extracted from frozen tissue of primary lung tumors for gene array analysis.
2008-06-12 | E-GEOD-3141 | biostudies-arrayexpress