Gene Expression Data Explorer
Info Gene counts are sourced from ARCHS4, which provides uniform alignment of GEO samples. You can learn more about ARCHS4 and its pipeline here.
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GROUP CONDITION SAMPLES
breast cancer cell line MCF7
GSM2574347
GSM2574348
Description

Submission Date: Apr 11, 2017

Summary: Metabolic diseases, including type 2 diabetes and obesity are relevant negative prognostic factor in patients with breast cancer (BC). We have investigated the mechanisms through which elevated glucose levels affect tamoxifen sensitivity of estrogen receptor positive (ER+) BC cells. We found that MCF7 BC cell sensitivity to tamoxifen was 2-fold reduced in 25mM glucose (HG), a concentration mimicking hyperglycaemia, compared to 5.5 mM glucose (LG), resembling normal fasting glucose levels in humans. Shifting MCF7 cells from HG to LG ameliorated their responsiveness to tamoxifen. RNA-Sequencing revealed that glucose modified the transcriptome of MCF7 cells. In particular, cell cycle-related genes were affected by glucose. Combining gene specific knockdown and treatment with human recombinant proteins, we identified the Connective Tissue Growth Factor (CTGF) as glucose-induced factor able to reduce MCF7 cell sensitivity to tamoxifen. Moreover, we found that both CTGF expression levels and tamoxifen responsiveness were enhanced co-culturing MCF7 cells with human adipocytes through an Interleukin-8 (IL8)-mediated mechanism. Indeed, IL8 inhibition reduced CTGF levels and rescued tamoxifen sensitivity in MCF7 cells. Interestingly, CTGF immuno-detection in bioptic specimens obtained from women with ER+ BC correlated with distant metastases (P-value = 0.000), hormone therapy resistance (P-value = 0.000), reduced overall (P-value = 0.051) and disease free survival (P-value = 0.000). Thus, glucose affects tamoxifen responsiveness directly modulating CTGF in BC cells, and indirectly promoting the adipocytes' release of IL8. Both CTGF and IL8 may represent potential targets in novel therapeutic strategies to increase tamoxifen sensitivity.

GEO Accession ID: GSE97647

PMID: No Pubmed ID

Description

Submission Date: Apr 11, 2017

Summary: Metabolic diseases, including type 2 diabetes and obesity are relevant negative prognostic factor in patients with breast cancer (BC). We have investigated the mechanisms through which elevated glucose levels affect tamoxifen sensitivity of estrogen receptor positive (ER+) BC cells. We found that MCF7 BC cell sensitivity to tamoxifen was 2-fold reduced in 25mM glucose (HG), a concentration mimicking hyperglycaemia, compared to 5.5 mM glucose (LG), resembling normal fasting glucose levels in humans. Shifting MCF7 cells from HG to LG ameliorated their responsiveness to tamoxifen. RNA-Sequencing revealed that glucose modified the transcriptome of MCF7 cells. In particular, cell cycle-related genes were affected by glucose. Combining gene specific knockdown and treatment with human recombinant proteins, we identified the Connective Tissue Growth Factor (CTGF) as glucose-induced factor able to reduce MCF7 cell sensitivity to tamoxifen. Moreover, we found that both CTGF expression levels and tamoxifen responsiveness were enhanced co-culturing MCF7 cells with human adipocytes through an Interleukin-8 (IL8)-mediated mechanism. Indeed, IL8 inhibition reduced CTGF levels and rescued tamoxifen sensitivity in MCF7 cells. Interestingly, CTGF immuno-detection in bioptic specimens obtained from women with ER+ BC correlated with distant metastases (P-value = 0.000), hormone therapy resistance (P-value = 0.000), reduced overall (P-value = 0.051) and disease free survival (P-value = 0.000). Thus, glucose affects tamoxifen responsiveness directly modulating CTGF in BC cells, and indirectly promoting the adipocytes' release of IL8. Both CTGF and IL8 may represent potential targets in novel therapeutic strategies to increase tamoxifen sensitivity.

GEO Accession ID: GSE97647

PMID: No Pubmed ID

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Precomputed Differential Gene Expression

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Differential Gene Expression Analysis
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Bulk RNA-seq Appyter

This pipeline enables you to analyze and visualize your bulk RNA sequencing datasets with an array of downstream analysis and visualization tools. The pipeline includes: PCA analysis, Clustergrammer interactive heatmap, library size analysis, differential gene expression analysis, enrichment analysis, and L1000 small molecule search.