Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

SNPs of aromatase predict long-term survival and aromatase inhibitor toxicity in patients with early breast cancer: a biomarker analysis of the GIM4 and GIM5 trials

ABSTRACT: Aromatase SNPs Predict Survival and aromatase inhibitor Toxicity in Breast Cancer

PROVIDER: PRJEB66306 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Similar Datasets

SNP of Aromatase Predict Long-term Survival and Aromatase Inhibitor Toxicity in Patients with Early Breast Cancer: A Biomarker Analysis of the GIM4 and GIM5 Trials.

Project description:PurposeIn estrogen receptor-positive (ER+) breast cancer, single-nucleotide polymorphisms (SNP) in the aromatase gene might affect aromatase inhibitors (AI) metabolism and efficacy. Here, we assessed the impact of SNP on prognosis and toxicity of patients receiving adjuvant letrozole.Experimental designWe enrolled 886 postmenopausal patients in the study. They were treated with letrozole for 2 to 5 years after taking tamoxifen for 2 to 6 years, continuing until they completed 5 to 10 years of therapy. Germline DNA was genotyped for SNP rs4646, rs10046, rs749292, and rs727479. Log-rank test and Cox model were used for disease-free survival (DFS) and overall survival (OS). Cumulative incidence (CI) of breast cancer metastasis was assessed through competing risk analysis, with contralateral breast cancer, second malignancies and non-breast cancer death as competing events. CI of skeletal and cardiovascular events were assessed using DFS events as competing events. Subdistribution HR (sHR) with 95% confidence intervals were calculated through Fine-Gray method.ResultsNo SNP was associated with DFS. Variants rs10046 [sHR 2.03, (1.04-2.94)], rs749292 [sHR 2.11, (1.12-3.94)], and rs727479 [sHR 2.62, (1.17-5.83)] were associated with breast cancer metastasis. Three groups were identified on the basis of the number of these variants (0, 1, >1). Variant-based groups were associated with breast cancer metastasis (10-year CI 2.5%, 7.6%, 10.7%, P = 0.035) and OS (10-year estimates 96.5%, 93.0%, 89.6%, P = 0.030). Co-occurrence of rs10046 and rs749292 was negatively associated with 10-year CI of skeletal events (3.2% vs. 10%, P = 0.033). A similar association emerged between rs727479 and cardiovascular events (0.3% vs. 2.1%, P = 0.026).ConclusionsSNP of aromatase gene predict risk of metastasis and AI-related toxicity in ER+ early breast cancer, opening an opportunity for better treatment individualization.

| S-EPMC10722129 | biostudies-literature

The gene expression of aromatase-inhibitor-resistant cell line

2014-04-01 | GSE51390 | GEO

Applications of zebrafish embryo models to predict developmental toxicity for agrochemical product development

Project description:Applications of zebrafish embryo models to predict developmental toxicity for agrochemical product development

| PRJNA1070348 | ENA

CHARACTERIZING TOXICITY PATHWAYS OF FLUOXETINE TO PREDICT ADVERSE OUTCOMES IN ADULT PIMEPHALES PROMELAS

Project description:CHARACTERIZING TOXICITY PATHWAYS OF FLUOXETINE TO PREDICT ADVERSE OUTCOMES IN ADULT PIMEPHALES PROMELAS

| PRJNA758289 | ENA

Lacrimal Gland Gene expression in Aromatase Knockout Mice and Wild Type Controls

Project description:The Effect of Aromatase Knockout on Gene Expression in the Mouse Lacrimal and Meibomoan Gland. Keywords: Aromatase Knockout vs Wild Type Control

2009-02-01 | GSE5878 | GEO

The gene expression of aromatase-inhibitor-resistant cell line

Project description:To identify the genes which have relation to aromatase-inhibitor-resistance, we compared the gene expression of aromatase-inhibitor-resistant cell, A3, with that of parent cell line, T-47D. Because A3 showed remarkable cell proliferation by 1nM Dihydrotestosterone(DHT), cells were cultured with DHTand bicalutamide for 24h, and total RNA was extracted.

2014-04-01 | E-GEOD-51390 | biostudies-arrayexpress

PREDICT 2

Project description:PREDICT 2

| PRJEB75460 | ENA

Aromatase in adipose tissue exerts osteoprotective function in male mice via phosphorus regulation

Project description:Aromatase contributes to the maintenance of bone mass because male patients with loss-of-function mutations of CYP19A1 exhibit bone loss. Furthermore, treatment with aromatase inhibitor also causes bone loss in men as well as post-menopausal women, in whom serum estrogen level is very low, suggesting that a part of the anabolic effects of testosterone in men is dependent on estradiol (E2) biosynthesized by aromatase in non-gonadal tissues. However, it remains unclear how locally biosynthesized E2 contributes to the maintenance of bone mass. We examined the function of aromatase in local tissues rather than gonad using cell type specific aromatase KO mice. Because osteoblast-specific aromatase KO mice exhibited no bone phenotype, we focused on adipose tissue, which is known as reservoir of steroid hormones, and analyzed the bone phenotypes of adipose tissue-specific aromatase KO mice (AroΔaP2). Sixteen-week-old male AroΔaP2 mice exhibit significantly lower bone mineral density in tibia and femur, especially in trabecular bone compared to the control. Bone histomorphometry showed that AroΔaP2 mice exhibited an osteomalacia-like bone phenotype with increased osteoid volume and width, and decreased osteoclast area and numbers. Moreover, serum PI and renal phosphorus reabsorption were significantly decreased but FGF23 level was decreased in AroΔaP2, suggesting that an osteomalacia-like phenotype in AroΔaP2 was not caused by abnormal FGF23 level. Finally, we analyzed NaPi2a and NaPi2c, a phosphate transporter localized in the kidney, and found that protein levels in renal brush border membrane vesicles were decreased in AroΔaP2. These results indicate that locally biosynthesized estrogens by aromatase in adipocytes can play a significant role in bone mass maintenance via regulate phosphorus reabsorption in kidney by NaPi2.

2024-07-22 | GSE263849 | GEO

High-throughput identification of functional regulatory SNPs in systemic lupus erythematosus

Project description:Genome-wide association studies implicate multiple loci in risk for systemic lupus erythematosus (SLE), but few contain exonic variants, rendering systematic identification of non-coding variants essential to decoding SLE genetics. We utilized SNP-seq and bioinformatic enrichment to interrogate 2180 single-nucleotide polymorphisms (SNPs) from 87 SLE risk loci for potential binding of transcription factors and related proteins from B cells. 52 SNPs that passed initial screening were tested by electrophoretic mobility shift (EMSA) and luciferase reporter assays. To identify binding of transcription factors and/or other nuclear proteins in an allele-determined manner, we employed pulldown using nuclear extract from Daudi cells and silver staining in SNPs that had exhibited allele-specific differential binding by EMSA. Each pulldown product for each allele of the five high-probability SNPs (rs2297550 C/G, rs13213604 C/G, rs276461 T/C, rs9907955 C/T, rs7302634 T/C) was evaluated by mass spectrometry (MS) to identify binding nuclear proteins, yielding a set of candidate proteins for each.

2024-08-02 | PXD048367 | Pride

Rong2020 - Grover-clintox: A classification model to predict the likelihood of failure in clinical trials due to toxicity

Project description:This model has been trained using the GROVER transformer and the Molecule Net dataset ClinTox, the authors trained a classification model to predict the likelihood of failure in clinical trials due to toxicity. The dataset has been built using FDA approved drugs (non-toxic) and a set of drugs that have failed at advanced clinical trial stages. Model Type: Predicitive machine learning model. Model Relevance: Probability that a molecule is approved by the FDA and probability that a molecule shows toxicity in clinical trials. Model Encoded by: Amna Ali (Ersilia) Metadata Submitted in BioModels by: Zainab Ashimiyu-Abdusalam Implementation of this model code by Ersilia is available here: https://github.com/ersilia-os/eos6fza

2024-06-05 | MODEL2406050004 | BioModels

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data