Profiles of prostate cancer cell lines
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA343724 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR2063510.fastq.gz | Fastqsanger.gz | |||
| SRR2063520.fastq.gz | Fastqsanger.gz | |||
| SRR2063530.fastq.gz | Fastqsanger.gz | |||
| SRR2063511.fastq.gz | Fastqsanger.gz | |||
| SRR2063521.fastq.gz | Fastqsanger.gz |
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Project description:Prostate cancer (PCa) is a major public health issue in industrialized countries, mainly because of patients’ relapse by castration-refractory disease after androgen ablation. PCa progresses through a series of clinical states characterized by the extent of the disease, the hormonal status (castration-sensitive or castration-resistant) and the presence or absence of metastases. Progression to castration-resistant prostate cancer (CRPCa) involves several mechanisms such as ligand-independent androgen receptor activation and adaptive up-regulation of anti-apoptotic genes. CRPCa is highly aggressive and incurable, jeopardizing the patient’s quality of life and lifespan. Identifying the molecular events responsible for the progression to CRPCa is essential to avoid its development and design specific therapies. Despite the existing treatment guidelines for PCa and novel clinical trials for CRPCa, major challenges still remain for identifying specific CRPCa biomarkers and therapeutic targets for effective tailored therapy design for these patients. In this context, molecular profiling of human PCa may importantly contribute to the identification of new disease-specific biomarkers in order to improve PCa early diagnosis, prognosis and disease course and to predict strategies that help develop novel therapies. System-wide approaches such as transcriptomics and proteomics are nowadays applied to monitor molecular variations at the cellular level. Proteomics approaches offer a great potential for the discovery of novel biomarkers and the identification of new therapeutic agents by accurate quantification of proteins. Several studies have been published in the past where different proteomics approaches were used to identify PCa proteome. In the present study we analyzed the PCa proteome from several PCa cell lines representing different hormonal status of the disease to identify potential biomarkers differentially expressed in CRPCa.
2020-01-14 | PXD004992 | Pride
Project description:Prostate cancer (PCa) is a major public health issue in industrialized countries, mainly because of patients’ relapse by castration-refractory disease after androgen ablation. PCa progresses through a series of clinical states characterized by the extent of the disease, the hormonal status (castration-sensitive or castration-resistant) and the presence or absence of metastases. Progression to castration-resistant prostate cancer (CRPCa) involves several mechanisms such as ligand-independent androgen receptor activation and adaptive up-regulation of anti-apoptotic genes. CRPCa is highly aggressive and incurable, jeopardizing the patient’s quality of life and lifespan. Identifying the molecular events responsible for the progression to CRPCa is essential to avoid its development and design specific therapies. Despite the existing treatment guidelines for PCa and novel clinical trials for CRPCa, major challenges still remain for identifying specific CRPCa biomarkers and therapeutic targets for effective tailored therapy design for these patients. In this context, molecular profiling of human PCa may importantly contribute to the identification of new disease-specific biomarkers in order to improve PCa early diagnosis, prognosis and disease course and to predict strategies that help develop novel therapies. System-wide approaches such as transcriptomics and proteomics are nowadays applied to monitor molecular variations at the cellular level. Proteomics approaches offer a great potential for the discovery of novel biomarkers and the identification of new therapeutic agents by accurate quantification of proteins. Several studies have been published in the past where different proteomics approaches were used to identify PCa proteome. In the present study we analyzed the PCa proteome from several PCa cell lines representing different hormonal status of the disease to identify potential biomarkers differentially expressed in CRPCa.
2020-01-14 | PXD004970 | Pride
Project description:Genome wide DNA methylation profiling of androgen-sensitive and –refractory prostate cancer cells. The Illumina Infinium HumanMethylation450 Beadchip was used to obtain DNA methylation profiles across approximately 480.000 CpGs in Prostate cancer cell lines showing different sensitivity to hormonal treatments. Samples included the androgen receptor negative cell lines PC3 and DU145, the androgen sensitive cell line LNCaP and the LNCaP abl cell line expressing androgen receptor but refractory prostate cancer cell line to hormonal treatments.
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Project description:Abstract: BACKGROUND: The aim of this study was to characterize gene expression and DNA copy number profiles in androgen sensitive (AS) and androgen insensitive (AI) prostate cancer cell lines on a genome-wide scale. METHODS: Gene expression profiles and DNA copy number changes were examined using DNA microarrays in eight commonly used prostate cancer cell lines. Chromosomal regions with DNA copy number changes were identified using cluster along chromosome (CLAC). RESULTS: There were discrete differences in gene expression patterns between AS and AI cells that were not limited to androgen-responsive genes. AI cells displayed more DNA copy number changes, especially amplifications, than AS cells. The gene expression profiles of cell lines showed limited similarities to prostate tumors harvested at surgery. CONCLUSIONS: AS and AI cell lines are different in their transcriptional programs and degree of DNA copy number alterations. This dataset provides a context for the use of prostate cancer cell lines as models for clinical cancers. Set of arrays organized by shared biological context, such as organism, tumors types, processes, etc. Keywords: Logical Set
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Project description:Abstract: BACKGROUND: The aim of this study was to characterize gene expression and DNA copy number profiles in androgen sensitive (AS) and androgen insensitive (AI) prostate cancer cell lines on a genome-wide scale. METHODS: Gene expression profiles and DNA copy number changes were examined using DNA microarrays in eight commonly used prostate cancer cell lines. Chromosomal regions with DNA copy number changes were identified using cluster along chromosome (CLAC). RESULTS: There were discrete differences in gene expression patterns between AS and AI cells that were not limited to androgen-responsive genes. AI cells displayed more DNA copy number changes, especially amplifications, than AS cells. The gene expression profiles of cell lines showed limited similarities to prostate tumors harvested at surgery. CONCLUSIONS: AS and AI cell lines are different in their transcriptional programs and degree of DNA copy number alterations. This dataset provides a context for the use of prostate cancer cell lines as models for clinical cancers. Set of arrays organized by shared biological context, such as organism, tumors types, processes, etc. Keywords: Logical Set
2006-01-12 | GSE4017 | GEO