Breast Cancer Cell Lines
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE3156 | GEO | 2005/11/07
SECONDARY ACCESSION(S): PRJNA92987
REPOSITORIES: GEO
Similar Datasets
Project description:Multiple breast cancer cell lines with different metastatic capabilities The goal of this study is to identify metastasis related genes in breast cancer We obtained RNA from each cell line using regular Trizol and RNA purification kit. Keywords: Expression profiling by array
2011-01-15 | GSE25972 | GEO
Project description:Multiple breast cancer cell lines with different metastatic capabilities The goal of this study is to identify metastasis related genes in breast cancer We obtained RNA from each cell line using regular Trizol and RNA purification kit. Keywords: Expression profiling by array Multiple breast cancer cell lines with different metastatic capabilities
2011-01-15 | E-GEOD-25972 | biostudies-arrayexpress
Project description:7 breast cancer cell lines were differentially labeled and each competitively hybridized against female genomic DNA using the 32K SMRT Array Keywords: None
2006-02-16 | GSE3106 | GEO
Project description:We analysed aquired chemotherapeutic resistance of two different triple negative breast cancer cell lines BT-549 (Doxorubicin resistance) and MDA-MB-468 (5-Fluorouracil) by comparing the proteome of the parental cell line with the resistant cell line.
2018-02-23 | PXD008522 | Pride
Project description:Breast cancer is the leading type of cancer in women. Breast cancer brain metastasis is considered as an essential issue in breast cancer patients. Membrane proteins play important roles in breast cancer brain metastasis that contributes to the cell adhesion and penetration of blood-brain barrier. To achieve a deeper insight of the mechanism of breast cancer brain metastasis, liquid chromatography tandem mass spectrometry (LC-MS/MS) was performed to analyze the enriched membrane proteomes from six different breast cancer cell lines. Quantitative proteomic data of all cell lines were compared with MDA-MB-231BR which has the specific brain metastasis capacity. 1239 proteins were identified and 990 were quantified with more than 70% of membrane proteins in all cell lines. Each cell line can be separated apart from others in PCA. Ingenuity pathway analysis (IPA) supported the high brain metastatic ability of 231BR and suggested importance of the up-regulation of integrin proteins and down-regulation of EPHA in brain metastasis. 28 proteins were observed unique expression alteration in 231BR. The up-regulation of NPM1, hnRNP Q, hnRNP K and eIF3l and the down-regulation of TUBB4B and TUBB were observed to be associated with the brain metastasis cell line and may contributes to the breast cancer brain metastasis.
2017-05-23 | PXD005719 | Pride
Project description:Three human ER+ breast cancer cell lines--MCF-7, T47-D, BT-474--grown with or without estradiol (E2). Keywords: Cell Line Comparison
2006-01-15 | GSE3529 | GEO
Project description:We studied genes, that are differentially expressed between malignant and normal breast tissue, to find weak spots for anti-cancer therapy development. RNA sequencing of three cell lines was performed: MCF-7 (epithelial breast cancer cell line), BCC (primary breast tumour cell line) and MCF-10A (epithelial breast cell line).
2023-01-30 | GSE223718 | GEO
Project description:Gene expression analysis on growing breast cancer cell lines. Experiment Overall Design: see Bild, A. et.al.
2008-06-12 | E-GEOD-3156 | biostudies-arrayexpress
Project description:<p>Sample collection can significantly affect measurements of relative lipid concentrations in cell line panels, hiding intrinsic biological properties of interest between cell lines. Most quality control steps in lipidomic data analysis focus on controlling technical variation. Correcting for the total amount of biological material remains an additional challenge for cell line panels. Here, we investigated how we can normalize lipidomic data acquired from multiple cell lines to correct for differences in sample biomass.</p><p>We studied how commonly used data normalization and transformation steps during analysis influenced the resulting lipid data distributions. We compared normalization by biological properties such as cell count or total protein concentration, to statistical and data-based approaches, such as median, mean or probabilistic quotient-based normalization and used intraclass correlation to estimate how similarity between replicates changed after normalization.</p><p>Normalizing lipidomic data by cell count improved similarity between replicates, but only for a study with cell lines with similar morphological phenotypes. For cell line panels with multiple morphologies collected over a longer time, neither cell count nor protein concentration was sufficient to increase the similarity of lipid abundances between replicates of the same cell line. Data-based normalizations increased these similarities, but also created artifacts in the data caused by a bias towards the large and variable lipid class of triglycerides. This artifact was reduced by normalizing for the abundance of only structural lipids. We conclude that there is a delicate balance between improving the similarity between replicates and avoiding artifacts in lipidomic data and emphasize the importance of an appropriate normalization strategy in studying biological phenomena using lipidomics.</p><p><br></p><p>__________________________________________</p><p><br></p><p>Rewiring of lipid metabolism is a hallmark of cancer, supporting tumor growth, survival, and therapy resistance. However, lipid metabolic heterogeneity in breast cancer remains poorly understood. In this study, we systematically profiled the lipidome of 52 breast cancer cell lines using liquid chromatography-mass spectrometry to uncover lipidomic signatures associated with tumor subtype, proliferation, and epithelial-to-mesenchymal (EMT) state. A total of 806 lipid species were identified and quantified across 21 lipid classes. The main lipidomic heterogeneity was associated with the EMT state, with lower sphingolipid, phosphatidylinositol and phosphatidylethanolamine levels and higher cholesterol ester levels in aggressive mesenchymal-like cell lines compared to epithelial-like cell lines. In addition, cell lines with higher proliferation rates had lower levels of sphingomyelins and polyunsaturated fatty acid (PUFA) side chains in phospholipids. Next, changes in the lipidome over time were analyzed for three fast-proliferating mesenchymal-like cell lines MDA-MB-231, Hs578T, and HCC38. Triglycerides decreased over time, leading to a reduction in lipid droplet levels, and especially PUFA-containing triglycerides and -phospholipids decreased during proliferation. These findings underscore the role of EMT in metabolic plasticity and highlight proliferation-associated lipid dependencies that may be exploited for therapeutic intervention. In conclusion, our study reveals that EMT-driven metabolic reprogramming is a key factor in lipid heterogeneity in breast cancer, providing new insights into tumor lipid metabolism and potential metabolic vulnerabilities.</p>
2025-08-07 | MTBLS9493 | MetaboLights