Transcription profiling of human and mouse breast cancers
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
SUBMITTER: Andreas Klein
PROVIDER: E-GEOD-6772 | biostudies-arrayexpress |
REPOSITORIES: biostudies-arrayexpress
Publications
Comparison of gene expression data from human and mouse breast cancers: identification of a conserved breast tumor gene set.
Klein Andreas A Wessel Ralf R Graessmann Monika M Jürgens Martin M Petersen Iver I Schmutzler Rita R Niederacher Dieter D Arnold Norbert N Meindl Alfons A Scherneck Siegfried S Seitz Susanne S Graessmann Adolf A
International journal of cancer 20070801 3
The aim of our work was to establish a database for breast cancer gene expression data in order to compare human and mouse breast cancer. We identified human and mouse homologues genes and compared the expression profile of 24 human breast tumors with 6 WAP-SVT/t breast tumors (WAP-SVT/t animals, line 8). Our studies confirmed the heterogeneity in gene expression of human as well as mouse breast cancer cells. However, 63 genes were found to be differentially expressed (upregulated: 40; downregul ...[more]
Similar Datasets
Project description:Microarray studies revealed that as a first hit, SV40 T/t-antigen causes deregulation of 462 genes in mammary gland cells (ME-cells) of WAP-SVT/t transgenic animals. The majority of deregulated genes are cell-proliferation specific and Rb-E2F dependent, causing ME-cell proliferation and gland hyperplasia but not breast cancer formation. In the breast tumor cells, a further 207 genes are differentially expressed, most of them belonging to the cell communication category. In tissue culture, breast tumor cells frequently switch off WAP-SVT/t transgene expression and regain the morphology and growth characteristics of normal-ME-cells, although the tumor-revertant cells are aneuploid and only 114 genes regain the expression level of normal-ME-cells. The profile of retransformants shows that only 38 deregulated genes appear to be tumor-relevant and that none of them is considered to be a typical breast cancer gene. Experiment Overall Design: We have analyzed nine mammary gland tissue segments from three normal NMRI, three WAP-SVT/t mice on the first day of lactation and from three WAP-SVT/t breast cancers (583-tumor 1, 585-tumor 2, 597-tumor 3) as well as three distinct breast cancer derived cell lines (ME-A cells, revertant-ME-B cells and ME-B-T/t cells).
2009-04-25 | E-GEOD-6246 | biostudies-arrayexpress
Project description:The aim of our work was the comparison of human and mouse gene expression data and to identify a conserved breast tumor gene set. The results encourage the usefulness of transgenic mice as a model for human breast cancer formation and therapy. Experiment Overall Design: The aim of our work was to establish a database for breast cancer gene expression data in order to compare human and mouse breast cancer. We identified human and mouse homologues genes and compared the expression profile of 24 human breast tumors with six WAP-SVT/t breast tumors (WAP-SVT/t animals, line 8). Our studies confirmed the heterogeneity in gene expression of human as well as mouse breast cancer cells. However, 63 genes were found to be differentially expressed (upregulated: 40; downregulated: 23 genes) in at least 75% of the breast tumors of both species.
2008-06-14 | E-GEOD-6581 | biostudies-arrayexpress
Project description:Metastasis is responsible for the majority of deaths in a variety of cancer types, including breast cancer. Although several factors or biomarkers have been identified to predict the outcome of patients with breast cancer, few studies have been conducted to identify metastasis-associated biomarkers. Quantitative iTRAQ proteomics analysis was used to detect differentially expressed proteins between lymph node metastases and their paired primary tumor tissues from 23 patients with metastatic breast cancer. Immunohistochemistry was performed to validate the expression of two upregulated (EpCAM, FADD) and two downregulated (NDRG1, αB-crystallin) proteins in 190 paraffin-embedded tissue samples. These four proteins were further analyzed for their correlation with clinicopathological features in 190 breast cancer patients. We identified 637 differentially regulated proteins (397 upregulated and 240 downregulated) in lymph node metastases compared with their paired primary tumor tissues. Furthermore, bioinformatics analysis using GEO profiling confirmed the difference in the expression of EpCAM between metastases and primary tumors tissues. Two upregulated (EpCAM, FADD) and two downregulated (NDRG1, αB-crystallin) proteins were associated with the progression of breast cancer. Obviously, EpCAM plays a role in the metastasis of breast cancer cells to the lymph node. We further identified αB-crystallin as an independent biomarker to predict lymph node metastasis and the outcome of breast cancer patients.
2019-08-29 | PXD013931 | Pride
Project description:Detection of breast cancer (BC) in young women is challenging because mammography, the most common tool for detecting BC, is not effective on the dense breast tissue characteristic of young women. In addition to the limited means for detecting their BC, young women face a transient increased risk of pregnancy-associated BC. As a consequence, reproductively active women could benefit significantly from a tool that provides them with accurate risk assessment and early detection of BC. One potential method for detection of BC is biochemical monitoring of proteins and other molecules in bodily fluids such as serum, nipple aspirate, ductal lavage, tear, urine, saliva and breast milk. Of all these fluids, only breast milk provides access to a large volume of breast tissue, in the form of exfoliated epithelial cells, and to the local breast environment, in the form of molecules in the milk. Thus, analysis of breast milk is a non-invasive method with significant potential for assessing BC risk. Here we analyzed human breast milk by mass spectrometry (MS)-based proteomics to build a biomarker signature for early detection of BC. Ten milk samples from 8 women provided 5 paired-groups (cancer versus control) for analysis of differentially expressed proteins: 2 within woman comparisons (milk from a diseased breast versus a healthy breast of the same woman) and 3 across women comparisons (milk from a woman with cancer versus a woman without cancer). Despite a wide range in the time between milk donation and cancer diagnosis (cancer diagnosis occurred from 1 month before to 24 months after milk donation), the levels of some proteins differed significantly between cancer and control in several of the 5 comparison groups. These pilot data are supportive of the idea that molecular analysis of breast milk will identify proteins informative for early detection and accurate assessment of BC risk, and warrant further research.
2017-12-18 | PXD007066 | Pride
Project description:We applied our previously established Alkyne-A-DSBSO-based in vivo XL-MS analytical platform on two types of breast cancer PDX samples cross-linking. Coupled with the LC-MSn strategy, the experiments enable us to obtain information on cancer-related protein-protein interactions since experimenting on PDX models.
2024-03-20 | PXD046966 | Pride
Project description:Metastasis of breast cancer to other distant organs is fatal to patients. However, few studies have revealed biomarkers associated with distant metastatic breast cancer. Furthermore, the inability of current biomarkers such as HER2, ER and PR, in accurately differentiating between distant metastatic breast cancers from non-distant metastatic ones necessitates the development of novel biomarkers. An integrated proteomics approach that combines filter-aided sample preparation, tandem mass tag labeling (TMT), high pH fractionation, and high resolution MS was applied to acquire in-depth proteome data of distant metastatic breast cancer FFPE tissue. Bioinformatics analyses for gene ontology and signaling pathways using differentially expressed proteins (DEPs) were performed to investigate molecular characteristics of distant metastatic breast cancer. In addition, real-time polymerase chain reaction (RT-PCR) and invasion/migration assays were performed to validate the differential regulation and functional capability of biomarker candidates. A total of 9,459 and 8,760 proteins were identified from the pooled sample set and the individual sample set, respectively. Through our stringent criteria, TUBB2A was selected as a novel biomarker. The metastatic functions of the candidate were subsequently validated. Bioinformatics analysis using DEPs were able to characterize the overall molecular features of distant metastasis as well as investigate the differences across breast cancer subtypes. Our study is the first to explore the distant metastatic breast cancer proteome using FFPE tissue. The depth of our dataset enabled the discovery of novel biomarker and the investigation of proteomic characteristics of distant metastatic breast cancer. The distinct molecular features of breast cancer subtypes were also observed. Our proteomic data has important utility as a valuable resource for the research on distant metastatic breast cancer.
2020-06-05 | PXD016061 | Pride
Project description:Estrogen Receptor subtypes (ERα and ERβ) are transcription factors sharing similar structure, however, they often perform opposite roles in breast cancer’s cell proliferation and tumor progression. Besides the well-characterized genomic actions of ERs upon ligand binding, rapid non-genomic cytoplasmic changes together with the recently discovered ligand-free action of ERs are emerging as key regulators of tumorigenesis. The identification of cytoplasmic interaction partners of unliganded ERα and ERβ may help characterize the molecular basis of the extra-nuclear mechanism of action of these receptors, revealing novel mechanisms to explain their role in breast cancer response or resistance to endocrine therapy. To this aim, in this study, cytoplasmic extracts from stably expressing TAP-ERα and -ERβ MCF-7 cell clones were subjected to interaction proteomics in the absence of estrogen stimulation, leading to the identification of 84 and 142 proteins associated with unliganded ERα and ERβ, respectively. Functional analyses of ER subtype-specific interactomes revealed significant differences in the molecular pathways associated to each receptor in the cytoplasm. This work reports the first identification of the unliganded ERα and ERβ cytoplasmic interactomes in breast cancer cells, providing novel experimental evidence on the non-genomic effects of ERs in the absence of hormonal stimulus.
2015-03-11 | PXD001202 | Pride
Project description:RNA-seq was performed on breast cancer cell lines and primary tumors RNA-seq was performed on 28 breast cancer cell lines, 42 Triple Negative Breast Cancer (TNBC) primary tumors, and 42 Estrogen Receptor Positive (ER+) and HER2 Negative Breast Cancer primary tumors, 30 uninovlved breast tissue samples that were adjacent to ER+ primary tumors, 5 breast tissue samples from reduction mammoplasty procedures performed on patients with no known cancer, and 21 uninvolved breast tissue samples that were adjacent to TNBC primary tumors.
2014-06-11 | E-GEOD-58135 | biostudies-arrayexpress
Project description:Background. Although gene expression arrays have been used to generate molecular predictors of relapse and drug sensitivity in breast cancer, no large study describes genes and exons differentially expressed between breast cancer and benign lesions.<br><br> Methods. One hundred and sixty-five tumour samples were obtained by fine needle aspiration (FNA). cDNA were hybridized on Splice ArrayTM. A nearest centroid prediction rule was developed to classify lesions as malignant or benign on a training set, and its performance evaluated on an independent validation set. A two-way ANOVA model was used to identify probesets that present a differential expression between cancer and benign lesions while adjusting for scan dates. P-values were adjusted for False Discovery Rate.<br><br>Findings. Overall 120 breast cancers and 45 benign lesions were included in the study. A 1228-probeset molecular classifier for breast cancer diagnosis was generated from the training set (n=94). This signature accurately classified all samples (100% accuracy, 95% exact CI: 96-100%). In the validation set (n=71), the molecular predictor accurately classified 68 out of 71 tumours (96%, 95% CI: 88-99%). When the 165 samples were taken into account, 37 858 exon-probesets (5.4%) and 3733 genes (20%) were found to be differentially expressed between malignant and benign conditions (adjusted p-value<0.05). Pathway analyses showed that genes involved in spliceosome assembly were significantly enriched in malignant condition (permutation p=0.002). In the same population of 165 samples, 956 exon-probesets presented both a higher intensity and higher splice index in breast cancer, although located on unchanged genes.<br><br> Interpretation. The present study provides a thorough description of differentially expressed exons between breast cancer and benign lesions, and emphasizes the contribution of spliceosome and alternative transcripts to the molecular portrait of breast malignancy. This allowed the development of a molecular classifier for breast cancer diagnosis using FNA.
2009-12-30 | E-TABM-609 | biostudies-arrayexpress
Project description:Metastasizing breast cancer is an incurable disease. Thus, it is important to find proteins responsible for tumor dissemination, which may be used as biomarkers or treatment targets. In this study, we established the combination of a non-targeted LC-MS/MS discovery and a targeted LC-SRM verification workflow for improved discovery and validation of biomarkers. We collected 80 breast tumors, stratified for estrogen receptor status and development of distant recurrence (DR+/-). After enrichment of N-glycosylated proteins, label-free LC-MS/MS was performed on all of the individual tumors in triplicates. In total, 1515 glycopeptides from 778 proteins were identified, used to create a map of the breast cancer N-glycosylated proteome. Based on the breast cancer N-glycosylated proteome map we constructed a 92-plex targeted label-free LC-SRM panel, enriched for interesting proteins, and analyzed the samples for the selected proteins, resulting in 10 proteins differentially regulated between DR+/DR- tumors. We also compared the LC-SRM results to clinically reported HER2 status. 17 of 18 patients could be classified the same way, demonstrating the clinical accuracy of LC-SRM. In conclusion, we demonstrated the use of a combined non-targeted LC-MS/MS and targeted LC-SRM strategy, at large scale on clinical samples, and identified 10 proteins as potential biomarkers.
2015-06-16 | PXD001685 | Pride