Transcriptomic heterogeneity of the prostate cancer
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE183889 | GEO | 2021/09/12
REPOSITORIES: GEO
Similar Datasets
Project description:Prostate cancer is a leading cause of death among men but its genomic characterization and best therapeutic strategy are still under debate. The Genomic Fabric Paradigm (GFP) considers the transcriptome as a multi-dimensional mathematical object subjected to a dynamic set of expres-sion correlations among the genes. Here, GFP is applied to gene expression profiles of three (one primary, and two secondary) cancer nodules and the surrounding normal tissue from a surgically removed prostate tumor. GFP was used to determine the regulation and rewiring of the P53 signaling, apoptosis, prostate cancer and several other pathways involved in survival and pro-liferation of the cancer cells. Genes responsible for the block of differentiation, evading apoptosis, immortality, insensitivity to anti-growth signals, proliferation, resistance to chemotherapy and sustained angiogenesis were found as differently regulated in the three cancer nodules with re-spect to the normal tissue. The analysis indicates that even histo-pathologically equally graded cancer nodules from the same tumor have substantially different transcriptomic organizations, raising legitimate questions about the validity of meta-analyses comparing large populations of healthy and cancer humans. The study suggests that GFP may provide a personalized alternative to the biomarkers’ approach of cancer genomics.
2021-03-12 | GSE168718 | GEO
Project description:A comprehensive expression analysis of Wnt signaling genes was performed in a panel of prostate cancer cell lines and tissue specimens using TaqMan low density arrays. The effect of DNA methylation on gene expression was investigated using DNMT inhibitor 5-Aza-CdR. Tissue specimens from a range of disease states were used to represent the stepwise progression of prostate cancer, including benign prostatic hyperplasia (BPH), histologically benign epithelium adjacent to tumor (HB), pre-invasive high-grade prostatic intraepithelial neoplasia (HGPIN) and primary localized tumors categorized into low- and high-grade disease. Fifteen Wnt signaling related genes were idenified with significantly altered expression in prostate cancer; the majority of which were upregulated in tumors. Notably, histologically benign tissue from men with prostate cancer appeared more similar to tumour (r=0.76) than to BPH (r=0.57) (P<0.001). Overall, the expression profile was highly similar between tumors of high (≥7) and low (≤6) Gleason scores. Pharmacological demethylation of PC-3 cells reactivated 38 genes (≥2-fold); 40% of which inhibit Wnt signaling.
2011-11-10 | GSE33557 | GEO
Project description:A comprehensive expression analysis of Wnt signaling genes was performed in a panel of prostate cancer cell lines and tissue specimens using TaqMan low density arrays. The effect of DNA methylation on gene expression was investigated using DNMT inhibitor 5-Aza-CdR. Tissue specimens from a range of disease states were used to represent the stepwise progression of prostate cancer, including benign prostatic hyperplasia (BPH), histologically benign epithelium adjacent to tumor (HB), pre-invasive high-grade prostatic intraepithelial neoplasia (HGPIN) and primary localized tumors categorized into low- and high-grade disease. Fifteen Wnt signaling related genes were idenified with significantly altered expression in prostate cancer; the majority of which were upregulated in tumors. Notably, histologically benign tissue from men with prostate cancer appeared more similar to tumour (r=0.76) than to BPH (r=0.57) (P<0.001). Overall, the expression profile was highly similar between tumors of high (M-bM-^IM-%7) and low (M-bM-^IM-$6) Gleason scores. Pharmacological demethylation of PC-3 cells reactivated 38 genes (M-bM-^IM-%2-fold); 40% of which inhibit Wnt signaling. qPCR gene expression profiling using TLDA Human Wnt Gene set v1.0 microfluidic card (Applied Biosystems, Foster City, CA).The TLDA consisted of 4 identical 96-gene sets preconfigured in a 384-well format. Three different malignant cell lines were profiled LNCaP, 22Rv1 and PC3 (+/- 5-Aza-2M-bM-^@M-^YDeoxycytidine). One benign cell line was included for comparative puposes: PWR1E. Patient samples were obtained from FFPE tissue sections from men who underwent radical prostatectomy or transrectal resection of the prostate. To overcome the limited amount of RNA obtained from FFPE tissues, RNA samples were pooled. Five different pools were generated: high grade prostate cancer (Gleason score M-bM-^IM-%7), low grade prostate cancer (Gleason score M-bM-^IM-$6), HGPIN, HB and BPH. Each pool consisted of DNase-treated total RNA (100ng), isolated from microdissected tissue from 4 individual cases, selected on the basis of similar histological and clinical features and previous epigenetic characterization in our laboratory. Two Biological replicates for each pool were prepared. The high capacity cDNA Archive Kit (Applied Biosystems) was used to reverse transcribe pooled RNA (400ng). TaqManM-BM-. reactions were performed in duplicate on a 7900HT Sequence Detection System. RQ data from TLDAs were analyzed using Real Time StatMinerM-BM-. v3.1 (Integromics, Granada, Spain).
2011-11-10 | E-GEOD-33557 | biostudies-arrayexpress
Project description:Prostate tumors are among the most heterogeneous of cancers, both histologically and clinically. Microarray expression analysis was used to determine whether global biological differences underlie common pathological features of prostate cancer and to identify genes that might anticipate the clinical behavior of this disease. While no expression correlates of age, serum prostate specific antigen (PSA), and measures of local invasion were found, a set of genes was identified that strongly correlated with the state of tumor differentiation as measured by Gleason score. Moreover, a model using gene expression data alone accurately predicted patient outcome following prostatectomy. These results support the notion that the clinical behavior of prostate cancer is linked to underlying gene expression differences that are detectable at the time of diagnosis.
2015-05-15 | GSE68907 | GEO
Project description:Prostate tumors are among the most heterogeneous of cancers, both histologically and clinically. Microarray expression analysis was used to determine whether global biological differences underlie common pathological features of prostate cancer and to identify genes that might anticipate the clinical behavior of this disease. While no expression correlates of age, serum prostate specific antigen (PSA), and measures of local invasion were found, a set of genes was identified that strongly correlated with the state of tumor differentiation as measured by Gleason score. Moreover, a model using gene expression data alone accurately predicted patient outcome following prostatectomy. These results support the notion that the clinical behavior of prostate cancer is linked to underlying gene expression differences that are detectable at the time of diagnosis. golub-00142 Assay Type: Gene Expression Provider: Affymetrix Array Designs: HG_U95Av2 Organism: Homo sapiens (ncbitax)
2015-05-15 | E-GEOD-68907 | biostudies-arrayexpress
Project description:miRNAs expression of Gleason 3+4 Prostate Cancer
| PRJNA655426 | ENA
Project description:In order to identify methylation changes in prostate cancer, we performed a genome-wide analysis of DNA methylation using Agilent human CpG island arrays. We then chose specific genes to validate methylation both in the same cases as were hybridized to the array (using quantitative EpiTYPER analysis) and in an independent series of prostate cancer samples (using MethyLight quantitative methylation specific PCR). We specifically chose low grade (Gleason score 6 cases) and high grade (Gleason score 8 cases) to discover methylated genes/loci that may be involved in the progression to a higher grade of prostate cancer. We collected 20 specimens consisting of 10 Gleason 6 and 10 Gleason 8 prostate cancers, and compared these to a reference lymphocyte pool (6 age matched, healthy men) to determine cancer associated methylation changes as well as disease progression associated methylation changes. We performed the differential methylation hybridization procedure as described by Yan et al. (Methods, 2002) on each case to enrich for methylated DNA. Each specimen in the reference pool underwent the same enrichment with amplicons being pooled at the end of the procedure. Each prostate cancer case was subsequently co-hybridized to the microarray with the reference pool.
2009-03-26 | E-GEOD-15298 | biostudies-arrayexpress
Project description:Twenty-nine radical prostatectomy samples were laser capture microdissected (LCM) to obtain the most common Gleason patterns (patterns 3, 4, and 5) and matched benign adjacent luminal prostate epithelial cells. We performed cDNA microarrays on matched cancer and adjacent normal samples and identified an 86-gene model capable of distinguishing low Gleason grade (pattern 3) from high Gleason grade (pattern 4 and 5) cancers, which contributes a set of potential targets for modulating the development and progression of the lethal prostate cancer phenotype. Keywords: disease state analysis
2006-07-14 | GSE5132 | GEO
Project description:The identification of novel oncogenic and druggable targets in patient subgroups with poor prognosis may help to develop corresponding targeted therapy approaches. Microarray data analyses of 59 prostate cancer and 39 benign tissue samples revealed major transcriptional differences. More than 5.000 genes were identified to be differentially expressed between matched tumor and benign samples. In the prostate cancer samples we identified 144 differentially expressed associated with Gleason pattern. Illumina microarray experiments were done from of 59 prostate cancer and 39 matched benign tissue samples. We analyzed for differentially expressed genes between tumor and benign tissues, and between tumors with higher Gleason patter (4+3 and higher) against lower Gleason patter (3+4 and lower).
2012-10-19 | E-GEOD-32571 | biostudies-arrayexpress