Transcriptional profiling of primary prostate tumor in metastatic hormonesensitive prostate cancer and association with clinical outcomes: correlative analysis of the E3805 CHAARTED study
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PROVIDER: PRJNA833082 | ENA |
REPOSITORIES: ENA
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Project description:To identify genes with the potential target of differentially expressed miRNA in metastatic prostate cancer, we have employed whole genome microarray expression profiling. Transplantable metastatic versus a non-metastatic prostate cancer xenograft line, both derived from one patient’s primary cancer, were developed via sub-renal capsule grafting of cancer tissue into NOD/SCID mice. The same RNA samples from both lines were also used for miRNA sequencing. Overlapped genes of predicted targets of differentially expressed miRNAs and differentially expressed in microarray platform showed potential markers of combination of miRNA and gene in metastatic prostate cancer. Gene expression in metastatic/non-metastatic prostate cancer was measured. Same total RNA samples were used for small RNA library construction for miRNA sequencing. This submission represents the gene expression component of the study.
2012-03-11 | E-GEOD-28029 | biostudies-arrayexpress
Project description:An integrative analysis of this compendium of proteomic alterations and transcriptomic data was performed revealing only 48-64% concordance between protein and transcript levels. Importantly, differential proteomic alterations between metastatic and clinically localized prostate cancer that mapped concordantly to gene transcripts served as predictors of clinical outcome in prostate cancer as well as other solid tumors. Keywords: prostate cancer progression 13 individual benign prostate, primary and metastatic prostate cancer samples and 6 pooled samples from benign,primary or metastatic prostate cancer tissues.
2005-09-20 | E-GEOD-3325 | biostudies-arrayexpress
Project description:Accurate prediction of prostate cancer clinical courses remains elusive. In this study, we performed whole genome copy number analysis on leukocytes of 273 prostate cancer patients using Affymetrix SNP6.0 chip. Copy number variations (CNV) were found across all chromosomes of the human genome. An average of 152 CNV fragments per genome was identified in the leukocytes from prostate cancer patients. The size distributions of CNV in the genome of leukocytes were highly correlative with prostate cancer aggressiveness. A prostate cancer outcome prediction model was developed based on large size ratio of CNV from the leukocyte genomes. This prediction model generated an average prediction rate of 75.2%, with sensitivity of 77.3% and specificity of 69.0% for prostate cancer recurrence. When combined with Nomogram and the status of fusion transcripts, the average prediction rate was improved to 82.5% with sensitivity of 84.8% and specificity of 78.2%. In addition, the leukocyte prediction model was 62.6% accurate in predicting short prostate specific antigen doubling time. When combined with Gleason’s grade, Nomogram and the status of fusion transcripts, the prediction model generated a correct prediction rate of 77.5% with 73.7% sensitivity and 80.1% specificity. To our knowledge, this is the first study showing that CNVs in leukocyte genomes are predictive of clinical outcomes of a human malignancy.
2017-02-09 | GSE70650 | GEO
Project description:To identify genes with the potential target of differentially expressed miRNA in metastatic prostate cancer, we have employed whole genome microarray expression profiling. Transplantable metastatic versus a non-metastatic prostate cancer xenograft line, both derived from one patient’s primary cancer, were developed via sub-renal capsule grafting of cancer tissue into NOD/SCID mice. The same RNA samples from both lines were also used for miRNA sequencing. Overlapped genes of predicted targets of differentially expressed miRNAs and differentially expressed in microarray platform showed potential markers of combination of miRNA and gene in metastatic prostate cancer.
2012-03-11 | GSE28029 | GEO
Project description:Background Metastatic hormone-naïve prostate cancer (mHNPC) is an infrequent form of this tumour type that is characterized by metastasis at the time of diagnosis and accounts for 50% of prostate cancer-related deaths. Despite the extensive characterization of localized and metastatic castration resistant prostate cancer (mCRPC), the molecular characteristics of mHNPC remain largely unexplored. Results Here we provide the first extensive transcriptomics characterization of mHNPC. We generated discovery and validation bulk and single-cell RNA-Seq datasets and performed integrative computational analysis in combination with experimental studies. Our results provide unprecedented evidence of the distinctive transcriptional profile of mHNPC and identify stroma remodelling as a predominant feature of these tumours. Importantly, we discover a central role for the transcription factor SOX11 in triggering a heterotypic communication that is associated to the acquisition of metastatic properties. Conclusions Our study will constitute an invaluable resource for a profound understanding of mHNPC that can influence patient management.
2025-05-07 | PXD063665 | Pride
Project description:Prostate cancer is characterized by heterogeneity in the clinical course that often does not to correlate with morphologic features of the tumor. Metastasis reflects the most adverse outcome of prostate cancer, and to date there are no reliable morphologic features or serum biomarkers that can reliably predict which patients are at higher risk of developing metastatic disease. Understanding the differences in the biology of metastatic and organ confined primary tumors is essential for developing new prognostic markers and therapeutic targets. Using Affymetrix oligonucleotide arrays, we analyzed gene expression profiles of 24 androgen-ablation resistant metastatic samples obtained from 4 patients and a previously published dataset of 64 primary prostate tumor samples. Differential gene expression was analyzed after removing potentially uninformative stromal genes, addressing the differences in cellular content between primary and metastatic tumors. The metastatic samples are highly heterogeneous in expression; however, differential expression analysis shows that 415 genes are upregulated and 364 genes are downregulated at least 2 fold in every patient with metastasis. The expression profile of metastatic samples reveals changes in expression of a unique set of genes representing both the androgen ablation related pathways and other metastasis related gene networks such as cell adhesion, bone remodeling and cell cycle. The differentially expressed genes include metabolic enzymes, transcription factors such as Forkhead Box M1 (FoxM1) and cell adhesion molecules such as Osteopontin (SPP1). We hypothesize that these genes have a role in the biology of metastatic disease and that they represent potential therapeutic targets for prostate cancer. Experiment Overall Design: Using Affymetrix oligonucleotide arrays, we analyzed gene expression profiles of 24 androgen-ablation resistant metastatic samples obtained from 4 patients and a previously published dataset of 64 primary prostate tumor samples. Differential gene expression was analyzed after removing potentially uninformative stromal genes, addressing the differences in cellular content between primary and metastatic tumors.
2008-06-15 | E-GEOD-6605 | biostudies-arrayexpress
Project description:Gene expression study of the ECOG 3805 randomized controlled trial
2022-05-01 | GSE201805 | GEO
Project description:Prostate cancer is characterized by heterogeneity in the clinical course that often does not to correlate with morphologic features of the tumor. Metastasis reflects the most adverse outcome of prostate cancer, and to date there are no reliable morphologic features or serum biomarkers that can reliably predict which patients are at higher risk of developing metastatic disease. Understanding the differences in the biology of metastatic and organ confined primary tumors is essential for developing new prognostic markers and therapeutic targets. Using Affymetrix oligonucleotide arrays, we analyzed gene expression profiles of 24 androgen-ablation resistant metastatic samples obtained from 4 patients and a previously published dataset of 64 primary prostate tumor samples. Differential gene expression was analyzed after removing potentially uninformative stromal genes, addressing the differences in cellular content between primary and metastatic tumors. The metastatic samples are highly heterogeneous in expression; however, differential expression analysis shows that 415 genes are upregulated and 364 genes are downregulated at least 2 fold in every patient with metastasis. The expression profile of metastatic samples reveals changes in expression of a unique set of genes representing both the androgen ablation related pathways and other metastasis related gene networks such as cell adhesion, bone remodeling and cell cycle. The differentially expressed genes include metabolic enzymes, transcription factors such as Forkhead Box M1 (FoxM1) and cell adhesion molecules such as Osteopontin (SPP1). We hypothesize that these genes have a role in the biology of metastatic disease and that they represent potential therapeutic targets for prostate cancer. Keywords: disease state analysis
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