Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Identification of differentially expressed circRNAs in prostate cancer of different clinical stages by RNA sequencing

ABSTRACT: Nine samples of prostate cancer and three samples of benign prostatic hyperplasia were selected (circRNA expression profiles were analyzed by circRNA-seq. A total of 13047 circRNAs were identified in these tumor tissues, which were widely distributed in all chromosomes. Analysis of variance showed that prostate cancer was significantly dysregulated in 605 cases compared to BPH tissue (log2 (fold change) > 1, p < 0.05). Our study undoubtedly provide a new insight in investigating the initiation of prostate cancer.

ORGANISM(S): Homo sapiens

PROVIDER: GSE212215 | GEO | 2023/12/06

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Similar Datasets

Transcription profiling of human hormone resistant prostate cancer

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

Gene expression profiling of hormone resistant prostate cancer

2007-07-01 | GSE5377 | GEO

Transcription profiling of human prostatic stromal cells cultured from diseased vs normal tissues

Project description:To obtain a comprehensive view of the transcriptional programs in prostatic stromal cells of different histological/pathological origin, we profiled 18 adult human stromal cell cultures from normal transition zone (TZ), normal peripheral zone (PZ), benign prostatic hyperplasia (BPH), and prostate cancer (CA) using cDNA microarrays.

2006-11-06 | E-SMDB-3867 | biostudies-arrayexpress

Molecular analyses of prostate tumors for diagnosis of malignancy on Fine-Needle Aspiration biopsies

Project description:Prostate cancer (PCa) is a common cancer and remains the second leading cause of cancer-associated mortality in men. To investigate the involvement of differentially expressing genes in PCa with deregulated pathways to allow earlier diagnosis of the disease, transcriptomic analyses of differential expression genes in Fine-Needle Aspiration (FNA) biopsies were for discrimination of PCa from benign prostatic hyperplasia (BPH). The RNA samples were extracted from four PCa biopsy samples and four BPH biopsy controls for microarray profiling and performed in Affymetrix Human U133 Plus 2 arrays for gene expression profiling analysis. Microarray data were analyzed using GeneSpring GX 10 (Agilent).On average, we detected expression of 47,000 transcripts.Under the criteria fold change > 2 or < 0.5, we obtained 1819 differential expressed genes(DEGs).Hierarchy cluster analysis also indicated that the 8 samples were distributed into two clusters, 4 PCa samples in one cluster and 4 BPH samples in another cluster.Then,qRT-PCR validation of the DEGs in PCa tissue and prostate cancer cells.The results revealed that grouping was reasonable and the data can be directly applied to further analysis.

2017-10-11 | GSE104749 | GEO

C11orf52 as a new regulator of nanomechanical properties and cell migration in prostate cancer cells

Project description:In this study, puncture biopsies samples of patients with benign prostatic hyperplasia (BPH), medium-risk and high-risk prostate cancer were collected for RNA-seq analysis. GO analysis showed that DEGs in the medium and high risk groups were significantly enriched in processes such as extracellular region and cell motility and migration. In addition, the BPH and medium-risk groups were significantly enriched in processes such as extracellular region, developmental process and ion transport. The BPH and high-risk groups were significantly enriched in processes such as extracellular region, cell motility, migration and immune system process. In this study, differentially expressed genes associated with risk classes were identified.

2022-11-09 | GSE217260 | GEO

The effect of high fat diet on mouse prostate development

Project description:Steroid 5α reductase 2 (SRD5A2) is the predominant enzyme responsible for prostatic development and growth. Despite the introduction of steroid 5α-reductase inhibitors (5ARI) for benign prostatic hyperplasia (BPH), the progression of LUTS is only slowed by 34% with 5ARI-response. Previous literature establishes link between obesity and BPH progression. We hypothesize that high fat diet is associated with prostate development.

2024-03-03 | GSE260450 | GEO

Homo sapiens

Project description:CNV Identification: Prostate Cancer (PCa) vs Benign Prostatic Hyperplasia (BPH) Human DNA Samples

| PRJNA315774 | ENA

Multi-omic diagnostics of prostate cancer in the presence of benign prostatic hyperplasia

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

Distinctive gene expression of prostatic stromal cells cultured from diseased versus normal tissues

2006-11-10 | GSE6250 | GEO

Real-time quantitative PCR analysis of Wnt signalling in human prostate cancer

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

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data