Dataset Information

Expression data from fibroblast growth factor receptor 4 (FGFR4) knock down ovarian cancer cell lines

ABSTRACT: Advanced ovarian cancer is the most lethal gynecologic malignancy in the United States. Currently patients are treated by surgical cytoreductive surgery with the aim of reducing tumor burden to microscopic disease followed by adjuvant combined treatment with a platinum and taxane containing chemotherapy, which affords 80% of patients an initial complete response. However, Abdominal and pelvic recurrence rates are high and response to further chemotherapy is limited. Attempts at introducing biologic therapeutic agents to improve outcome in this disease are ongoing, while prognostic or predictive biomarkers that can stratify patients for treatment are still lacking. Using a 60-mer 22K oligonucleotide-based array comparative genome hybridization (CGH) platform combined with DNA isolated from microdissected tumor tissue samples, Birrer et. al. reported that the amplification of 5q31-35.3 in ovarian cancer cells is a negative prognostic indicator for patients with advanced stage high-grade serous ovarian cancer (HGSC). Further studies showed that fibroblast growth factor 1 (FGF1) located in the amplicon, may be one of the driving genes for ovarian cancer progression (Birrer et. al., 2007). Besides FGF1, located on the same amplicon is one of its receptors fibroblast growth factor receptor 4 (FGFR4), suggesting that it may also be amplified and may be another driving gene involved in ovarian cancer pathogenesis.In this study, we used microarrays to explore and compare gene expression profiles between FGFR4 knock down ovarian cancer cell lines and their corresponding parental cell lines.

ORGANISM(S): Homo sapiens

PROVIDER: GSE34828 | GEO | 2013/12/03

SECONDARY ACCESSION(S): PRJNA150235

REPOSITORIES: GEO

ACCESS DATA

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Project description:Advanced ovarian cancer is the most lethal gynecologic malignancy in the United States. Currently patients are treated by surgical cytoreductive surgery with the aim of reducing tumor burden to microscopic disease followed by adjuvant combined treatment with a platinum and taxane containing chemotherapy, which affords 80% of patients an initial complete response. However, Abdominal and pelvic recurrence rates are high and response to further chemotherapy is limited. Attempts at introducing biologic therapeutic agents to improve outcome in this disease are ongoing, while prognostic or predictive biomarkers that can stratify patients for treatment are still lacking. Using a 60-mer 22K oligonucleotide-based array comparative genome hybridization (CGH) platform combined with DNA isolated from microdissected tumor tissue samples, Birrer et. al. reported that the amplification of 5q31-35.3 in ovarian cancer cells is a negative prognostic indicator for patients with advanced stage high-grade serous ovarian cancer (HGSC). Further studies showed that fibroblast growth factor 1 (FGF1) located in the amplicon, may be one of the driving genes for ovarian cancer progression (Birrer et. al., 2007). Besides FGF1, located on the same amplicon is one of its receptors fibroblast growth factor receptor 4 (FGFR4), suggesting that it may also be amplified and may be another driving gene involved in ovarian cancer pathogenesis.In this study, we used microarrays to explore and compare gene expression profiles between FGFR4 knock down ovarian cancer cell lines and their corresponding parental cell lines. Two high grade serous ovarian cancer cell lines: SKOV3 and OVCA432 were selected for this study. For each of the cell lines, two different siRNA sequences which both target human FGFR4 were used. While a non-target scramble siRNA sequence was used as control. Ovarian cancer cells were transfected with siRNA using lipofectamine (Invitrogen). RNA extraction was performed 72 hours post-transfection, followed by hybridization on Affymetrix microarrays.

Project description:To identify and evaluate the prognostic ability of progression-free survival-related profile for advanced-stage ovarian cancer, advanced-stage serous ovarian cancer tissues from 110 patients who received primary surgery and a platinum/taxane-based chemotherapy were profiled using oligonucleotide microarrays of more than 40,000 transcripts. We first selected 88 genes by a univariate Cox proportional hazard analysis (p<0.01) and next optimized regression coefficients by ridge regression model using 10-fold cross-validation. The prognostic index was independently associated with PFS time compared to other clinical factors in multivariate analysis [hazard ratio (HR), 3.72; 95% confidence interval (CI), 2.66–5.43; p,0.0001]. In an external dataset, multivariate analysis revealed that this prognostic index was significantly correlated with PFS time (HR, 1.54; 95% CI, 1.20–1.98; p = 0.0008). Furthermore, the correlation between the prognostic index and overall survival time was confirmed in the two independent external datasets (log rank test, p = 0.0010 and 0.0008). In multivariable analysis, our prognostic index was independently associated with progression-free survival times compared to other clinical factors (p<0.001). Furthermore, the prognostic ability of our prognostic index was validated in external, publicly available dataset (n = 87), and was proved in multivariate analysis (p = 0.0032). These results suggest that disease progression or recurrence of advanced-stage serous ovarian cancer can be predicted by gene expression profile. The prognostic ability of our index based on the 88-gene expression profile in ridge regression Cox hazard model was shown to be independent of other clinical factors in predicting cancer prognosis across two distinct datasets. Further study will be necessary to improve predictive accuracy of the prognostic index toward clinical application for evaluation of the risk of recurrence in patients with advanced-stage serous ovarian cancer.

Project description:Background: Resistance to platinum-based chemotherapy remains a major impediment in the treatment of serous epithelial ovarian cancer. The objective of this study was to use gene expression profiling to delineate major deregulated pathways and biomarkers associated with the development of intrinsic chemotherapy resistance upon exposure to standard first-line therapy for ovarian cancer. Methods: The study cohort comprised 28 patients divided into two groups based on their varying sensitivity to first-line chemotherapy using progression free survival (PFS) as a surrogate of response. All 28 patients had advanced stage, high-grade serous ovarian cancer, and were treated with the same standard platinum-based chemotherapy. Twelve patient tumors demonstrating relative resistance to platinum chemotherapy corresponding to shorter PFS (< eight months) were compared to sixteen tumors from platinum-sensitive patients (PFS > eighteen months). Whole transcriptome profiling was performed using a Affymetrix high-resolution microarray platform to permit global comparisons of gene expression profiles between tumors from the resistant group and the sensitive group. Results: Microarray data analysis revealed a set of 204 discriminating genes possessing expression levels, which could influence differential chemotherapy response between the two groups. Robust statistical testing was then performed which eliminated a dependence on the normalization algorithm employed, producing a restricted list of differentially regulated genes, and which found IGF1 to be the most strongly differentially expressed gene. Pathway analysis, based on the list of 204 genes, revealed enrichment in genes primarily involved in the IGF1/PI3K/NFκB/ERK gene signalling networks. Conclusions: This study has identified pathway specific prognostic biomarkers possibly underlying a differential chemotherapy response in patients undergoing standard platinum-based treatment of serous epithelial ovarian cancer. Future studies to validate these markers are necessary to apply this knowledge to biomarker-based clinical trials. Total RNA from 12 chemotherapy resistant and 16 sensitive chemotherapy sensitive high-grade serous epithelial ovarian cancer samples was subjected to whole transcriptome profiling using Affymetrix U133 Plus 2.0 arrays

Dataset Information

Expression data from fibroblast growth factor receptor 4 (FGFR4) knock down ovarian cancer cell lines

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets