In-depth membrane proteomic study of breast cancer tissues
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo Sapiens (human)
SUBMITTER: Takeshi Tomonaga
PROVIDER: PXD005163 | JPOST Repository | Mon Oct 17 00:00:00 BST 2016
REPOSITORIES: jPOST
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| F032432.mzid | Mzid | |||
| F032433.mzid | Mzid | |||
| F032434.mzid | Mzid | |||
| F032435.mzid | Mzid | |||
| F032436.mzid | Mzid |
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Project description:Proteomics on Human breast cancer tissue samples. 9 patients with high risk or 9 patiens low risk MammaPrint breast cancer. Membran fraction. Proteins were reduced, alkylated, digested with Lys-C and Trypsin, and labelled with iTRAQ4plex: 114; high risk; 115; low risk.
2013-01-08 | PXD000066 | Pride
Project description:Human breast cancer tissue samples from 18 patients with high-risk or low-risk MammaPrint breast cancer were analyzed. The membrane fractions were enriched, and the extracted proteins were reduced, alkylated, digested with Lys-C and Trypsin, and labelled with iTRAQ4plex: 114; high risk; 115; low risk.
For this reanalysis, MS Datasets were processed for peak picking with MaxQuant v1.6.2.10, and the generated peak lists were searched against human entries of UniProt/Swiss-Prot release 2018_10 using three search engines: MaxQuant v1.6.2.10, X!Tandem v2015.04.01.1, and Comet 2015.02 rev. 0. The decoy database was generated by reversing sequences from the database. Then, the sum of the total number of matched b/y ions and the total number of amino acids comprising the peptide sequence tag were counted to score the reliability of peptide identification independent of these search engines. Finally, the 1% false discovery rate was controlled by the target-decoy approach with this score.
2020-05-26 | RPXD018475 | JPOST Repository
Project description:Introduction: The prognosis for patients with breast tumor metastases to brain is extremely poor. Identification of prognostic molecular markers of the metastatic process is critical for designing therapeutic modalities for reducing the occurrence of metastasis. Although ubiquitously present in most human organs, calcium-activated potassium (BK) channel is significantly upregulated in breast cancer cells. In this study we investigated the role of KCNMA1 gene, which encodes α subunit of KCa channels (BK channels) in breast cancer metastasis and invasion. Methods: We performed Global exon array to study the expression of KCNMA1 in metastatic breast cancer in brain, compared its expression in primary breast cancer and breast cancers metastatic to other organs, and validated the findings by RT-PCR. Immunohistochemistry was performed to study the expression and localization of α subunit of KCa channel protein in primary and metastatic breast cancer tissues and breast cancer cell lines. We performed matrigel invasion, transendothelial migration and membrane potential assays in established lines of normal breast cells (MCF-10A), non-metastatic breast cancer (MCF-7), non-brain metastatic breast cancer cells (MDA-MB-231), and brain-specific metastatic breast cancer cells (MDA-MB-361) to study whether KCa channel inhibition attenuates breast tumor invasion and metastasis using KCNMA1 knockdown with siRNA and biochemical inhibition with IBTX (Iberiotoxin). Results: The Global exon array and RT-PCR showed higher KCNMA1 expression in metastatic breast cancer in brain compared to metastatic breast cancers in other organs. Our results clearly show that metastatic breast cancer cells exhibit increased BK channel activity, leading to greater invasiveness and transendothelial migration, both of which could be attenuated by blocking KCNMA1. Conclusion: Determining the relative abundance of BK channel over expression in breast cancer metastatic to brain and the mechanism of its action in brain metastasis will provide a unique opportunity to identify and differentiate between low grade breast tumors that are at high risk for metastasis from those at low risk for metastasis. This distinction would in turn allow for the appropriate and efficient application of effective treatments while sparing patients with low risk for metastasis from the toxic side effects of chemotherapy.
2009-09-09 | GSE17019 | GEO
Project description:Mufudza2012 - Estrogen effect on the dynamics
of breast cancer
This deterministic model shows the
dynamics of breast cancer with immune response. The effects of
estrogen are incorporated to study its effects as a risk factor for
the disease.
This model is described in the article:
Assessing the effects of
estrogen on the dynamics of breast cancer.
Mufudza C, Sorofa W, Chiyaka
ET.
Comput Math Methods Med 2012; 2012:
473572
Abstract:
Worldwide, breast cancer has become the second most common
cancer in women. The disease has currently been named the most
deadly cancer in women but little is known on what causes the
disease. We present the effects of estrogen as a risk factor on
the dynamics of breast cancer. We develop a deterministic
mathematical model showing general dynamics of breast cancer
with immune response. This is a four-population model that
includes tumor cells, host cells, immune cells, and estrogen.
The effects of estrogen are then incorporated in the model. The
results show that the presence of extra estrogen increases the
risk of developing breast cancer.
This model is hosted on
BioModels Database
and identified by:
BIOMD0000000642.
To cite BioModels Database, please use:
BioModels Database:
An enhanced, curated and annotated resource for published
quantitative kinetic models.
To the extent possible under law, all copyright and related or
neighbouring rights to this encoded model have been dedicated to
the public domain worldwide. Please refer to
CC0
Public Domain Dedication for more information.
2024-09-02 | BIOMD0000000642 | BioModels
Project description:Introduction Mammographic density (MD), as assessed from film screen mammograms, is determined by the relative content of adipose, connective and epithelial tissue in the female breast. In epidemiological studies, a high percentage of MD confers a four to six fold risk elevation of developing breast cancer, even after adjustment for other known breast cancer risk factors. However, the biologic correlates of density are little known. Methods Gene expression analysis using whole genome arrays was performed on breast biopsies from 143 women; 79 women with no malignancy (healthy women) and 64 newly diagnosed breast cancer patients, both included from mammographic centres. Percent MD was determined using a previously validated, computerized method on scanned mammograms. Significance analysis of microarrays (SAM) was performed to identify genes influencing MD and generalized regression models were used to assess the independent contribution from different variables to MD. Results SAM-analysis identified 24 genes differentially expressed between samples from breasts with high and low MD. These genes included three uridine 5'-diphospho-glucuronosyltransferase (UGT) genes and the oestrogen receptor gene (ESR1). These genes were down-regulated in samples with high MD compared to those with low MD. The UGT gene products, which are known to inactivate oestrogen metabolites, were also down-regulated in tumour samples compared to samples from healthy individuals. Several single nucleotide polymorphisms (SNPs) in the UGT genes associated with the expression of UGT and other genes in their vicinity were identified. Conclusions Three UGT enzymes were lower expressed both in breast tissue biopsies from healthy women with high MD and in biopsies from newly diagnosed breast cancers. The association was strongest among young women and women using hormonal therapy. UGT2B10 predicts MD independently of age, hormone therapy and parity. Our results indicate that down-regulation of UGT genes in women exposed to female sex hormones is associated with high MD and might increase the risk of breast cancer. Gene expression analysis of breast biopsies from 143 women, 79 non-cancer (healthy women with no cancer who had a mammogram taken) and 64 breast cancer.
2010-08-13 | E-GEOD-18672 | biostudies-arrayexpress
Project description:Microarray analysis has been shown to improve risk stratification of breast cancer. Breast tumors analyzed by hierarchical clustering of expression patterns of "intrinsic" genes have been reported to subdivide into at least four molecular subtypes that are associated with distinct patient outcomes. Using a supervised method, a 70-gene expression profile has been identified that predicts the later appearance or absence of clinical metastasis in young breast cancer patients. Here, we show that distant metastases display both the same molecular breast cancer subtype as well as the 70-gene prognosis signature as their primary tumors. Our results suggest that the capacity to metastasize is an inherent feature of most breast cancers. Furthermore, our data imply that poor prognosis breast carcinomas classified either by the intrinsic gene set or the 70 prognosis genes represent distinct disease entities that seem sustained throughout the metastatic process. Keywords: other
2005-11-23 | GSE2741 | GEO
Project description:Introduction Mammographic density (MD), as assessed from film screen mammograms, is determined by the relative content of adipose, connective and epithelial tissue in the female breast. In epidemiological studies, a high percentage of MD confers a four to six fold risk elevation of developing breast cancer, even after adjustment for other known breast cancer risk factors. However, the biologic correlates of density are little known. Methods Gene expression analysis using whole genome arrays was performed on breast biopsies from 143 women; 79 women with no malignancy (healthy women) and 64 newly diagnosed breast cancer patients, both included from mammographic centres. Percent MD was determined using a previously validated, computerized method on scanned mammograms. Significance analysis of microarrays (SAM) was performed to identify genes influencing MD and generalized regression models were used to assess the independent contribution from different variables to MD. Results SAM-analysis identified 24 genes differentially expressed between samples from breasts with high and low MD. These genes included three uridine 5'-diphospho-glucuronosyltransferase (UGT) genes and the oestrogen receptor gene (ESR1). These genes were down-regulated in samples with high MD compared to those with low MD. The UGT gene products, which are known to inactivate oestrogen metabolites, were also down-regulated in tumour samples compared to samples from healthy individuals. Several single nucleotide polymorphisms (SNPs) in the UGT genes associated with the expression of UGT and other genes in their vicinity were identified. Conclusions Three UGT enzymes were lower expressed both in breast tissue biopsies from healthy women with high MD and in biopsies from newly diagnosed breast cancers. The association was strongest among young women and women using hormonal therapy. UGT2B10 predicts MD independently of age, hormone therapy and parity. Our results indicate that down-regulation of UGT genes in women exposed to female sex hormones is associated with high MD and might increase the risk of breast cancer.
2010-08-13 | GSE18672 | GEO
Project description:To screen candidate methylation markers for early detection of breast cancer, we performed methylated-CpG island recovery assay combined with CpG island array on 61982 CpG sites across 4162 genes in 10 breast tumor tissues and 10 non-tumor breast tissues. We detected 70 significantly hypermethylated genes in breast tumor tissues, including many novel hypermethylated genes such as ITGA4, NFIX, OTX2 and FGF12. Direct bisulfite sequencing showed widespread methylations occurred in intragenic regions of WT1, PAX6 and ITGA4 genes and promoter region of OTX2 in breast cancer tissue. COBRA assay in independent tumor and non-tumor samples confirmed that WT1, OTX2 and PAX6 genes were hypermethylated in breast cancer tissues. To explore the relationship between methylation and gene expression, gene expression profiling analysis was performed in 8 breast tumor tissues and 8 non-tumor breast tissues. We found that some hypermethylated genes in breast cancer were not expressed in breast tissues. RT-PCR assay showed that WT1 and PITX2 were only weakly expressed in the breast tumor tissues and weren’t expressed in most non-tumor breast tissues. OTX2 and PAX6 weren’t expressed in both breast tumor tissues and non-tumor tissues. Unpaired experiments, breast cancer tissue vs. normal cancer tissue. Biological replicates: 10 breast cancer tissue replicates, 10 normal breast tissue replicates.
2011-11-11 | E-GEOD-33445 | biostudies-arrayexpress
Project description:Breast cancer was one of the first cancer types where molecular subtyping led to explanation of interpersonal heterogeneity and resulted in improvement of treatment regimen. Several multigene classifiers have been developed and in particular those defining molecular signatures of early breast cancers possess significant prognostic information. Hence since 2014, molecular subtyping of primary breast cancers was implemented as a part of routine diagnostics with direct impact of therapy assignment. In this study, we evaluate direct and potential benefits of molecular subtyping in low-risk breast cancers as well as present the advantages of a robust molecular signature in regard to patient work-up among high-risk breast cancers.
2017-12-01 | E-MTAB-5724 | biostudies-arrayexpress