Jun Yu Similar Datasets
Project description:Recently, multicellular spheroids were isolated from a well-established epithelial ovarian cancer cell line, OVCAR-3, and were propagated in vitro. These spheroid derived cells displayed numerous hallmarks of cancer stem cells, which are chemo and radioresistant cells thought to be a significant cause of cancer recurrence and resultant mortality. Gene set enrichment analysis of expression data from the OVCAR-3 cells and the spheroid-derived putative cancer stem cells identified several metabolic pathways enriched in differentially expressed genes. Before this, there had been little previous knowledge or investigation of systems-scale metabolic differences between cancer cells and cancer stem cells, and no knowledge of such differences in ovarian cancer stem cells. To determine if there were substantial metabolic changes corresponding with these transcriptional differences, we used two-dimensional gas chromatography coupled to mass spectrometry to measure the metabolite profiles of the two cell lines. These two cell lines exhibited significant metabolic differences in both intracellular and extracellular metabolite measurements. Principal components analysis, an unsupervised dimensional reduction technique, showed complete separation between the two cell types based on their metabolite profiles. Pathway analysis of intracellular metabolomics data revealed close overlap with metabolic pathways identified from gene expression data, with four out of six pathways found enriched in gene-level analysis also enriched in metabolite-level analysis. Some of those pathways contained multiple metabolites that were individually statistically significantly different between the two cell lines, with one of the most broadly and consistently different pathways, arginine and proline metabolism, suggesting an interesting hypothesis about cancerous and stem-like metabolic phenotypes in this pair of cell lines. Overall, we demonstrate for the first time that metabolism in an ovarian cancer stem cell line is distinct from that of more differentiated isogenic cancer cells, supporting the potential importance of metabolism in the differences between cancer cells and cancer stem cells.
2015-01-08 | MTBLS152 | MetaboLights
Project description:To identify proteins interacting with STAMBP, we performed co-immunoprecipitation (CoIP) assays using pancreatic cancer cells. Specifically, SW1990 cells stably overexpressing either the empty vector control or HA-tagged STAMBP (HA-STAMBP) were subjected to CoIP with an anti-HA antibody to enrich HA-associated proteins. Proteins specifically interacting with HA-STAMBP were then identified by comparative proteomic analysis between the two cell lines. Mass spectrometry-based proteomic services were provided by Shanghai Omicsspace Biotech.
2026-02-13 | PXD071368 | Pride
Project description:Metastatic (as well as tumor) microenvironments contain both cancer-promoting as well as cancer restraining factors. The balance between these opposing forces determines the fate of cancer cells that disseminate to secondary organ sites. In search for microenvironmental drivers or inhibitors of metastasis, we identified, in a previous study, the beta subunit of hemoglobin (HBB) as a lung-derived antimetastatic factor. In the present study, exploring mechanisms leading to melanoma brain metastasis, we discovered that brain-derived factors restrain proliferation and induce apoptosis and necrosis of brain-metastasizing melanoma cells. Employing various purification procedures, we identified a heterodimer composed of hemoglobin alpha and beta chains that performs these anti-metastatic functions. Neither the alpha nor the beta subunit alone were inhibitory. An alpha/beta chain dimer chemically purified from human hemoglobin inhibited the cell viability of primary melanomas, melanoma brain metastasis (MBM), and breast cancer cell lines. The dimer-induced DNA damage, cell cycle arrest at the SubG1 phase, apoptosis, and significant necrosis in four MBM cell lines. Proteomic analysis of dimer-treated MBM cells revealed that the dimer downregulates the expression of BRD4, GAB2, and IRS2 proteins playing crucial roles in cancer cell sustainability and progression.
2023-03-08 | GSE225459 | GEO
Project description:AP-MS approach to identify MANF-interacting proteins from HEK293 and INS1 cell lines. Proteins were identified and MS1 quantified using Andromeda search engine and MaxQuant proteomics software 92. Thermo .raw files were searched against the human or rat component of the UniProt-database complemented with trypsin, BSA, GFP and tag sequences
2020-10-21 | MSV000086342 | MassIVE
Project description:Protein microarray was used to identify proteins with elevated interactions with serum autoantibodies in a responding patient with rhabdomyosarcoma before and after multiple doses of HER2 CAR T cell therapy. Elevated signals were observed for multiple proteins interacting with serum autoantibodies following multiple doses of HER2-CAR T cell treatment when compared to pre-treatment serum.
2020-06-09 | GSE152029 | GEO
Project description:Two glioblastoma cell lines (LN18 and HS863) were stable transfected with control empty vector (EV) or RNF123-vector (KPC1). The objective of this experiment was to determine NFKB1-targets regulated by RNF123 overexpression in glioblastoma cell lines. To do that LN18 and HS863 cell lines with RNF123 overexpression were compared to control empty vector. In the present study, we utilized the combination of RPPA and RNA-Sequencing. By comparing both datasets, we identified commonly proteins and genes differentially expressed in control versus RNF123-overexpressing cells.
2020-06-17 | GSE131399 | GEO
Project description:Bordel2018 - GSMM for Human Metabolic
Reactions (HMR database)
This model is described in the article:
Constraint based modeling of
metabolism allows finding metabolic cancer hallmarks and
identifying personalized therapeutic windows
Sergio Bordel
Oncotarget. 2018; 9:19716-19729
Abstract:
In order to choose optimal personalized anticancer
treatments, transcriptomic data should be analyzed within the
frame of biological networks. The best known human biological
network (in terms of the interactions between its different
components) is metabolism. Cancer cells have been known to have
specific metabolic features for a long time and currently there
is a growing interest in characterizing new cancer specific
metabolic hallmarks. In this article it is presented a method
to find personalized therapeutic windows using RNA-seq data and
Genome Scale Metabolic Models. This method is implemented in
the python library, pyTARG. Our predictions showed that the
most anticancer selective (affecting 27 out of 34 considered
cancer cell lines and only 1 out of 6 healthy mesenchymal stem
cell lines) single metabolic reactions are those involved in
cholesterol biosynthesis. Excluding cholesterol biosynthesis,
all the considered cell lines can be selectively affected by
targeting different combinations (from 1 to 5 reactions) of
only 18 metabolic reactions, which suggests that a small subset
of drugs or siRNAs combined in patient specific manners could
be at the core of metabolism based personalized treatments.
This model is hosted on
BioModels Database
and identified by:
MODEL1707250000.
To cite BioModels Database, please use:
Chelliah V et al. BioModels: ten-year
anniversary. Nucl. Acids Res. 2015, 43(Database
issue):D542-8.
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.
| MODEL1707250000 | BioModels
Project description:Solid tumors are complex organs comprising neoplastic cells and stroma, yet cancer cell lines remain widely used to study tumor biology, biomarkers and experimental therapy. Here, we performed a fully integrative analysis of global proteomic data comparing human colorectal cancer (CRC) cell lines to primary tumors and normal tissues. We found a significant, systematic difference between cell line and tumor proteomes, with a major contribution from tumor stroma proteomes. Nevertheless, cell lines overall mirrored the proteomic differences observed between tumors and normal tissues, in particular for genetic information processing and metabolic pathways, indicating that cell lines provide a system for the study of the intrinsic molecular programs in cancer cells. Intersection of cell line data with tumor data provided insights into tumor cell specific proteome alterations driven by genomic alterations. Our integration of cell line proteogenomic data with drug sensitivity data highlights the potential of proteomic data in predicting therapeutic response. We identified representative cell lines for the proteomic subtypes of primary tumors, and linked these to drug sensitivity data to identify subtype-specific drug candidates.
2016-12-01 | MSV000080374 | GNPS
Project description:We identified potential PA28γ-interacting proteins in oral squamous cell carcinoma cell lines through co-immunoprecipitation assay.
2023-08-28 | PXD044901 |
Project description:Because most potential molecular markers and targets are proteins, proteomic profiling is expected to yield more direct answers to functional and pharmacological questions than does transcriptional profiling. To aid in such studies, we have developed a protocol for making reverse-phase protein lysate microarrays with larger numbers of spots than previously feasible. Our first application of these arrays was to profiling of the 60 human cancer cell lines (NCI-60) used by the National Cancer Institute to screen compounds for anticancer activity. Each glass slide microarray included 648 lysate spots representing the NCI-60 cell lines plus controls, each at 10 two-fold serial dilutions to provide a wide dynamic range. Mouse monoclonal antibodies and the catalyzed signal amplification system were used for immunoquantitation. The signal levels from the >30,000 data points for our first 52 antibodies were analyzed by using P-SCAN and a quantitative dose interpolation method. Clustered image maps revealed biologically interpretable patterns of protein expression. Among the principal early findings from these arrays were two promising pathological markers for distinguishing colon from ovarian adenocarcinomas. When we compared the patterns of protein expression with those we had obtained for the same genes at the mRNA level by using both cDNA and oligonucleotide arrays, a striking regularity appeared: cell-structure-related proteins almost invariably showed a high correlation between mRNA and protein levels across the NCI-60 cell lines, whereas non-cell-structure-related proteins showed poor correlation. http://discover.nci.nih.gov/host/2003_profiling_abstract.jsp Keywords: Cancer cell line comparison.
2007-02-01 | GSE5501 | GEO