Development of gene expression signatures for breast cancer stem cells (SP cells)
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ABSTRACT:
ORGANISM(S): Homo sapiens
SUBMITTER: zi m
PROVIDER: S-ECPF-GEOD-26677 | biostudies-other |
REPOSITORIES: biostudies-other
Similar Datasets
Project description:To further compare gene expression profile between breast cancer stem cells (SP cells) and non-SP cells, we have employed illumina GEX microarray as a discovery platform to identify gene differential expression between SP with non-SP cells.
2011-01-19 | GSE26677 | GEO
Project description:To further compare gene expression profile between breast cancer stem cells (SP cells) and non-SP cells, we have employed illumina GEX microarray as a discovery platform to identify gene differential expression between SP with non-SP cells. SP analysis and sorting were done using a FACSVantage SE.The breast cancer cells were sorted into SP and non-SP cells.Total RNA was isolated from the FACS-sorted SP or non-SP cells, and gene expression signiture was detected by microarray.
2011-01-19 | E-GEOD-26677 | biostudies-arrayexpress
Project description:To clarify and compare gene expression profile of each cell line, we have employed microarray expression profiling. The expression of many genes was similar in MM231-D3H2LN-GFP-BM2 cells parental cells, MM231-D3H2LN-GFP cells; however, the expression of genes related to the cell cycle was decreased in the BM2 cells compared with the parental cells. We also confirmed that the global gene expression patterns of the CD44+ and CD44- MM231-D3H2LN-GFP-BM2 cells were similar, although some genes which encode proteins that breast cancer cell dormancy, such as SRC and ERBB2 were increased and decreased, respectively, in the CD44- population. The gene expression of breast cancer cells metastatic to bone marrow and those with or without expression of cancer stem cell marker were measured.
| S-ECPF-GEOD-57921 | biostudies-other
Project description:Development of gene expression signatures for ovarian cancer stem cells
| PRJNA438122 | ENA
Project description:BACKGROUND: During the last years, several groups have identified prognostic gene expression signatures with apparently similar performances. However, signatures were never compared on an independent population of untreated breast cancer patients, where risk assessment was computed using the original algorithms and microarray platforms. RESULTS: We compared three gene expression signatures, the 70-gene, the 76-gene and the Gene expression Grade Index (GGI) signatures, in terms of predicting distant metastasis free survival (DMFS) for the individual patient. To this end, we used the previously published TRANSBIG independent validation series of node-negative untreated primary breast cancer patients. We observed agreement in prediction for 135 of 198 patients (68%) when considering the three signatures. When comparing the signatures two by two, the agreement in prediction was 71% for the 70- and 76-gene signatures, 76% for the 76-gene signature and the GGI, and 88% for the 70-gene signature and the GGI. The three signatures had similar capabilities of predicting DMFS and added significant prognostic information to that provided by the classical parameters. CONCLUSION: Despite the difference in development of these signatures and the limited overlap in gene identity, they showed similar prognostic performance, adding to the growing evidence that these prognostic signatures are of clinical relevance.
| S-EPMC2533026 | biostudies-literature
Project description:Tumor endothelial cells (TEC) play an indispensible role in tumor growth and metastasis although much of the detailed mechanism still remains elusive. In this study we characterized and compared the global gene expression profiles of TECs and control ECs isolated from human breast cancerous tissues and reduction mammoplasty tissues respectively by single cell RNA sequencing (scRNA-seq). Based on the qualified scRNA-seq libraries that we made, we found that 1302 genes were differentially expressed between these two EC phenotypes. Both principal component analysis (PCA) and heat map-based hierarchical clustering separated the cancerous versus control ECs as two distinctive clusters, and MetaCore disease biomarker analysis indicated that these differentially expressed genes are highly correlated with breast neoplasm diseases. Gene Set Enrichment Analysis software (GSEA) enriched these genes to extracellular matrix (ECM) signal pathways and highlighted 127 ECM-associated genes. External validation verified some of these ECM-associated genes are not only generally overexpressed in various cancer tissues but also specifically overexpressed in colorectal cancer ECs and lymphoma ECs. In conclusion, our data demonstrated that ECM-associated genes play pivotal roles in breast cancer EC biology and some of them could serve as potential TEC biomarkers for various cancers.
| S-EPMC5834272 | biostudies-literature
Project description:Induced pluripotent stem cells (iPSCs) outwardly appear to be indistinguishable from embryonic stem cells (ESCs). A study of gene expression profiles of mouse and human ESCs and iPSCs suggests that, while iPSCs are quite similar to their embryonic counterparts, a recurrent gene expression signature appears in iPSCs regardless of their origin or the method by which they were generated. Upon extended culture, hiPSCs adopt a gene expression profile more similar to hESCs; however, they still retain a gene expression signature unique from hESCs that extends to miRNA expression. Genome-wide data suggested that the iPSC signature gene expression differences are due to differential promoter binding by the reprogramming factors. High-resolution array profiling demonstrated that there is no common specific subkaryotypic alteration that is required for reprogramming and that reprogramming does not lead to genomic instability. Together, these data suggest that iPSCs should be considered a unique subtype of pluripotent cell.
| S-EPMC3448781 | biostudies-literature
Project description:Gene expression of breast cancer stem cells.
| PRJNA635100 | ENA
Project description:To understand the differentiation of ovarian cancer stem cells (CSCs), We derived two phenotypes of CSCs and identified the gene expression profiling. The CSCs were derived from Cp70 ovarian cancer cells and cultured in suspension and examined every day for sphere formation. Spheres were then dissociated and passaged at least eight times in 2 months to generate spheres, which are henceforth referred to as SR cells. The surface of SR-I was smooth regardless of the size, whereas SR-II was morula. SR-I could differentiate into multiple-lineage cell types under specific induction conditions. SR-I spheroids could differentiate to SR-II spheroids through epithelial-mesenchymal transition.The self-renewal was slower for SR-I than for SR-II.
2021-03-13 | GSE111776 | GEO
Project description:Purpose: There is a quest for novel non-invasive diagnostic markers for the detection of breast cancer. The goal of this study is to identify circulating microRNA signatures using a cohort of Asian Chinese breast cancer patients, and to compare microRNA profiles between tumour and serum samples. Experimental design: MicroRNAs from paired breast cancer tumours, normal tissue and serum samples derived from 32 patients were comprehensively profiled using microarrays (1300 microRNAs against tumour and normal tissues) or LNA RT-PCR panels (742 microRNAs against serum samples). Serum samples from healthy individuals (n=22) were also employed as normal controls. Significant serum microRNAs, identified by logistic regression, were validated in an independent set of serum samples from patients (n=82) and healthy controls (n=53). Results: The 20 most significant microRNAs differentially expressed in breast cancer tumours included miR-21, miR-10b, and miR-145, previously shown to be dysregulated in breast cancer. Interestingly, 16 of the 20 most significant microRNAs differentially expressed in serum samples were novel. MiR-1, miR-92a, miR-133a and miR-133b were identified as the most important diagnostic markers, and were successfully validated; receiver operating characteristic curves derived from combinations of these microRNAs exhibited areas under the curves of 0.944-0.946. Only seven microRNAs were overexpressed in both tumours and serum, suggesting that microRNAs may be released into the serum selectively. Conclusion: The clinical employment of microRNA signatures as a non-invasive diagnostic strategy is promising, but should be further validated for different subtypes of breast cancers. "_A" and "_B" are two tissue sections of the same sample; "_1" and "_2" represents 2 runs of the same sample; na = not available All tissue samples were histologically confirmed by a pathologist using hematoxylin and eosin staining of cryosectioned specimens. One tumour sample was rejected due to failure to detect any tumour cells. Except for two samples (with 30% and 40% tumour cells), all tumour tissues employed had a minimum of 60% tumour cells, as estimated microscopically. Overall, the breast cancer tumour samples had an average of 71% tumour cells. The criteria for adjacent normal tissue were absence of tumour cells and presence of epithelial cells. Hence, after histological confirmation, 31 breast cancer tumours and 23 matched normal tissues were employed for microRNA extraction and profiling using microarray.
| S-ECPF-GEOD-42072 | biostudies-other