Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

E-PROT-26

ABSTRACT: Deep proteomic profiling of luminal breast cancer progression

TISSUE(S): Breast, Lymph Node

PROVIDER: E-PROT-26 | ExpressionAtlas | 2025-07-10

REPOSITORIES: ExpressionAtlas

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	E-PROT-26-analysis-methods.tsv	Tabular
	E-PROT-26-configuration.xml	Xml
	E-PROT-26-factors.xml	Xml
	E-PROT-26.condensed-sdrf.tsv	Tabular
	E-PROT-26.idf.txt	Idf

Items per page:

1 - 5 of 12

Similar Datasets

Proteomic maps of breast cancer subtypes

Project description:Systems-wide profiling of breast cancer has so far built on RNA and DNA analysis by microarray and sequencing techniques. Dramatic developments in proteomic technologies now enable very deep profiling of clinical samples, with high identification and quantification accuracy. We analyzed 40 estrogen receptor positive (luminal), Her2 positive and triple negative breast tumors and reached a quantitative depth of more than 10,000 proteins. Comparison to mRNA classifiers revealed multiple discrepancies between proteins and mRNA markers of breast cancer subtypes. These proteomic profiles identified functional differences between breast cancer subtypes, related to energy metabolism, cell growth, mRNA translation and cell-cell communication. Furthermore, we derived a 19-protein predictive signature, which discriminates between the breast cancer subtypes, through Support Vector Machine (SVM)-based classification and feature selection. The deep proteome profiles also revealed novel features of breast cancer subtypes, which may be the basis for future development of subtype specific therapeutics.

2017-03-29 | MSV000080757 | MassIVE

Proteomic maps of breast cancer subtypes

2016-01-05 | PXD002619 | Pride

Proteomic profiling of urinary extracellular vesicles differentiates breast cancer patients from healthy women

Project description:Urinary extracellular vesicles (uEVs) reflect the biological conditions of the producing cells, and can allows us to better understand cancer progression. We have, herein, aimed at quantifying the concentration and at generating the proteomic profile of uEVs in breast cancer (BC) patients as compared to that of healthy controls (CT). The proteomic profiling of the uEVs was facilitated through LC-MS/MS.

2023-12-31 | PXD039122 | JPOST Repository

Homo sapiens

Project description:MicroRNAs expression profiling during breast cancer progression

| PRJNA169159 | ENA

Estrogen receptor alpha ChIP-seq in p53 null mouse mammary gland

Project description:ERα is a major driver for breast cancer initiation and progression.However,the fundamental mechanisms,including global cistromic and genomic transcriptional responses that are required to elicit breast cancer initiation and progression in response to ERα, have not been elucidated. We used chromatin immunoprecipitation followed by deep sequencing (ChIP-seq) to identify estrogen regulated genes that directly recruit ERα in the p53 null mouse mammary gland

2022-02-06 | GSE77977 | GEO

Advancing FFPE proteomics with a spectral library-free method

Project description:Formalin-fixed paraffin-embedded (FFPE) samples are valuable archived bio-specimens of individuals and are commonly used in biomedical research. In this study, we present a rapid, highly sensitive, and reproducible protocol for deep proteomic profiling of FFPE specimens using a spectral library-free approach. This method is applicable for high-throughput proteomic profiling and can be used on various types of specimens. Gene ontology (GO) and functional analysis specifically enriched pathways associated with brain and breast cancer, demonstrating the sensitivity and specificity of the protocol.

2023-03-08 | MSV000091446 | MassIVE

WHIM Gene Expression Data_Update Through WHIM 26

Project description:The objective of this microarray study was to obtain whole genome gene expression data from breast cancer, human to mouse xenograft models and from the primary cancer used for the xenograft, when possible. We also profiled these xenograft breast cancer tumors as they were passaged into additional mice. Whole Genome expression data allows us to monitor gene expression conservation and/or changes that occur from human to mouse, and from the initial mouse graft through subsequent passages. PAM50 molecular subtypes are also processed using the expression data. The xenografts provide in-vivo models for functional and therapeutic studies.

2013-10-10 | GSE41685 | GEO

All the regulated lncRNAs in breast cancer tissues compared to adjacent normal breast tissues

Project description:Circulating transcriptional landscapes between breast cancer tissues and adjacent normal tissues were compared using the Affymetrix Human OE LncRNA Microarray with probes for profiling of 63542 human lncRNAs. Goal was to investigate potential lncRNAs involved in breast cancer progression.

2019-01-26 | GSE125677 | GEO

Genome‐wide analysis reveals miR‐3184‐5p and miR‐181c‐3p as a critical regulator for adipocytes‐associated breast cancer

Project description:The purpose of this study is to identify miRNA expression changes in adipocyte-associated breast cancer microenvironment by using indirect in vitro co-culture system. We performed small RNA small RNA deep sequencing in human breast cancer cells and breast cancer cells co-cultured with mature adipocytes. We identified 98 differentially expressed miRNAs in breast cancer cells co-cultured with adipocytes using small RNA deep sequencing.

2019-08-20 | GSE118342 | GEO

Predicting the evolution of metastatic disease in breast cancer: results from 50 years of autopsies

Project description:This is a genomic analysis of breast cancer metastasis using array based CGH and is part of a large study investigating the patterns and evolution of metastases from breast cancer using autopsy material accumulated over the last 50 years from a single institution. The samples used in the genomic profiling comprise the primary breast tumour and multiple matched metastases from each patient. The data demonstrate both the clonal nature of metastatic progression and the role of clonal evolution during progression.

2013-11-11 | GSE46826 | GEO

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