Transcription profiling of human prostate cancer samples
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
SUBMITTER: Yipeng Wang
PROVIDER: E-GEOD-8218 | biostudies-arrayexpress |
REPOSITORIES: biostudies-arrayexpress
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Project description:Prostate cancer gene expression profiles were studied in this project. A total RNA from 148 prostate sample with various amount of different cell types were hybridized to Affymetrix U133A arrays. The percentage of different cell types vary considerably among samples and were determined by pathologist. Cell type specific genes can be determined by linear regression using the methods of Stuart et al, PNAS, 2004. Keywords: disease state analysis
2007-06-22 | GSE8218 | GEO
Project description:Over one million prostate biopsies are performed in the U.S. every year. However, pathology examination is not definitive in a significant percentage of cases due limited diagnostic tumor. We have observed that the microenvironment of prostate tumor cells exhibits numerous differential gene expression changes and have asked whether such information can be used to distinguish “tumor” from “nontumor”. We initially compared expression analysis data (Affymetrix U133plus2) from 18 volunteer biopsy specimens to 17 specimens containing largely tumor-adjacent stroma and identified 964 significant (p_adj < 0.01 and B > 0) expression changes. These genes were filtered to eliminate possible aging-related genes and genes expressed in tumor cells > 10% of the stroma cell expression level leading to 23 candidate genes (28 Affymetrix probe sets). A classifier based on the 28 probe sets was tested on 289 independent cases, including 195 tumor-bearing cases, 99 nontumor cases (normal biopsies, normal autopsies, remote stroma as well as pure tumor adjacent stroma) all with accuracies >85%, sensitivities >90% and specificities >85%. These results indicate that the prostate cancer microenvironment exhibits reproducible changes useful for categorization as “tumor” and “nontumor”. For inquires please contact dmercola@uci.edu. Experiment Overall Design: Prostate cancer gene expression profiles were studied in this project. Total RNA from 154 prostate sample with various amount of different cell types were hybridized to Affymetrix U133Plus2 array. The percentage of different cell types were determined by a pathologist.
2009-09-11 | E-GEOD-17951 | biostudies-arrayexpress
Project description:used to identify differences between tissues from patients undergoing surgery for BPH with unresolved symptoms compared to incidental BPH from patients with prostate cancer
2015-01-27 | GSE65343 | GEO
Project description:The cyclin D1 oncogene encodes the regulatory subunit of a holoenzyme that phosphorylates and inactivates the Rb protein and promotes progression through G1 to S phase of the cell cycle. Several prostate cancer cell lines and a subset of primary prostate cancer samples have increased cyclin D1 protein expression. However, the relationship between cyclin D1 expression and prostate tumor progression has yet to be clearly characterized. This study examined the effects of manipulating cyclin D1 expression in either human prostatic epithelial or stromal cells using a tissue recombination model. The data showed that overexpression of cyclin D1 in the initiated BPH-1 cell line increased cell proliferation rate, but did not elicit tumorigenicity in vivo. However, overexpression of cyclin D1 in Normal Prostate Fibroblasts (NPF) that were subsequently recombined with BPH-1 did induce malignant transformation of the epithelial cells. The present study also showed that recombination of BPH-1 + cyclin D1 overexpressing fibroblasts (NPF cyclin D1) resulted in permanent malignant transformation of epithelial cells (BPH-1 NPF-cyclin D1 cells) similar to that seen with Carcinoma Associated Fibroblasts (CAFs). Microarray analysis showed that the expression profiles between CAFs and NPF cyclin D1 cells were highly concordant including cyclin D1 upregulation. These data indicated that the tumor-promoting activity of cyclin D1 may be tissue-specific. Keywords: cyclin D1; stromal-epithelial interactions; prostate cancer; cDNA microarray NPF cyclin D1 cells were generated from NPFs, which were isolated from two different patient samples; CAFs were isolated from two different patient samples as well. RNA was isolated from NPFs, CAFs, and NPF cyclin D1 cells using total RNA isolation kit (Qiagen). Custom spotted cDNA microarrays were constructed as previously described (True, L., Coleman, I., Hawley, S., Huang, C. Y., Gifford, D., Coleman, R., Beer, T. M., Gelmann, E., Datta, M., Mostaghel, E., Knudsen, B., Lange, P., Vessella, R., Lin, D., Hood, L., and Nelson, P. S. A molecular correlate to the Gleason grading system for prostate adenocarcinoma. Proc Natl Acad Sci U S A, 103: 10991-10996, 2006) using a non-redundant set of 6,700 prostate-derived cDNA clones identified from the Prostate Expression Data Base (PEDB), a public sequence repository of expressed sequence tag data derived from human prostate cDNA libraries. Total RNA was amplified through one round of linear amplification using the MessageAmp aRNA kit (Ambion, Austin, TX). Sample quality and quantification was assessed by agarose gel electrophoresis and absorbance at A260. Cy3 and Cy5 labeled cDNA probes were made from 4 M-BM-5g of amplified RNA. Two NPF cyclin D1 and two CAF samples (labeled with Cy3) were hybridized head-to-head with an NPF control sample labeled with Cy5. Probes were hybridized competitively to microarrays under a coverslip for 16 h at 63M-BM-0C. Fluorescent array images were collected for both Cy3 and Cy5 by using a GenePix 4000B fluorescent scanner, and image intensity data were gridded and extracted using GenePix Pro 4.1 software. Differences in gene expression between NPF cyclin D1/NPF and CAF/NPF groups were determined using a two-sample t-test with Significance Analysis of Microarrays (SAM) software (http://www-stat.stanford.edu/_tibs/SAM/) with a False Discovery Rate (FDR) of M-bM-^IM-$ 10% considered significant (Tusher, PNAS, 2001). Similarities in gene expression between NPF cyclin D1/NPF and CAF/NPF groups were determined using a one-sample t-test in SAM with an FDR of M-bM-^IM-$ 0.1% considered significant. These results were reduced to unique genes by eliminating all but the highest scoring clones for each gene. A Pearson correlation coefficient was calculated in Excel to assess the strength of the linear relationship between NPF cyclin D1/NPF and CAF/NPF average log2 ratios.
2007-08-31 | E-GEOD-6936 | biostudies-arrayexpress
Project description:In this study we performed transcriptional profiling of transurethral resections of hormone resistant prostate cancer and compared it with benign prostatic hyperplasia (BPH), untreated localized prostate cancer and hormone sensitive prostate cancer. Experiment Overall Design: Each sample (= Array) is derived from a different patient with the following subsequent disease states: 3x BPH, 7x localized prostate cancer samples, 2 horomone sensitive prostate cancer samples, one locally advanced prostate cancer, 4x hormone resistant prostate cancer early stage and 3x hormone resistant prostate cancer late stage. Experiment Overall Design: Platform was Affymetrix
2008-06-13 | E-GEOD-5377 | biostudies-arrayexpress
Project description:The understanding of common prostatic disorders has been restricted by both cellular heterogeneity and the scarcity of established cell lines, although organoid technology, based on primary cultures, promises much for the future. In particular, little is known about the aetiology of benign prostatic hyperplasia (BPH), for which culture of single cell types might not accurately not reflect the stromal and epithelial overgrowths observed in tissues. To address the applicability of primary cell culture models of prostate disease, we compared cell type-specific mRNA expression patterns in BPH tissues and primary basal cells cultured from the same transurethral biopsies.
2017-05-09 | E-MTAB-5647 | biostudies-arrayexpress
Project description:Objective: Benign prostatic hyperplasia (BPH) is a common disease in men over 50 years old, which seriously reduces the quality of life of patients. At present, the transcriptome changes of gene expression profile in benign prostatic hyperplasia have not been explored to a great extent. Methods: In this study, single-cell RNA sequencing was performed on the isolated prostate tissue of BPH mice, and its cell types were identified and enriched. In addition, we constructed the cell communication network and sequence analysis, revealing its receptor-ligand pair and its transmission signals with other cells. Results: The results revealed the changes in 11 cell subpopulations including B cells, DC, Endothelial cells, Epithelial cells, Fibroblast & Myofibroblast, Macrophage, Mast cells, Satellite cells, Schwann cells, Smooth Muscle cells, and T Cell & NK & NKT in the microenvironment at various stages of the occurrence and development of benign prostatic hyperplasia, and updated the understanding of Epithelial cells, Fibroblast & Myofibroblast cell markers. Conclusion: Our study promotes the understanding of cellular dynamic changes and potential molecular mechanisms in the development of BPH.
2022-09-09 | GSE212770 | GEO
Project description:Cell surface proteomes were performed by amine-reactive cell surface biotinylation method for 20 prostate cancer and 8 BPH promary cells.
2023-02-07 | PXD031476 | JPOST Repository
Project description:used to identify differences between tissues from patients undergoing surgery for BPH with unresolved symptoms compared to incidental BPH from patients with prostate cancer cohorts are based on surgical distinction
2015-01-27 | E-GEOD-65343 | biostudies-arrayexpress
Project description:There is an unmet need for improved diagnostic testing and risk prediction for cases of prostate cancer (PCa) to improve care and reduce overtreatment of indolent disease. Here we have analysed the serum proteome and lipidome of 262 study participants by liquid chromatography-mass spectrometry, including participants with a diagnosis of PCa, benign prostatic hyperplasia (BPH), or otherwise healthy volunteers, with the aim of improving biomarker specificity. Although a two-class machine learning model was able to separate PCa from controls with sensitivity of 0.82 and specificity of 0.95, adding BPH resulted in a statistically significant decline in specificity for prostate cancer to 0.76, with half of BPH cases being misclassified by the model as PCa. A small number of biomarkers differentiating between BPH and prostate cancer were identified, including proteins in MAP Kinase pathways, as well as in lipids containing oleic acid; these may offer a route to greater specificity. These results highlight, however, that whilst there are opportunities for machine learning, these will only be achieved by use of appropriate training sets that include confounding comorbidities, especially when calculating the specificity of a test.
2024-01-26 | PXD046530 | Pride