Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Transcriptomic profiling of ccRCC nephrectomy samples by PCR-cDNAseq

ABSTRACT: Transcriptomic profiling of ccRCC nephrectomy samples by PCR-cDNAseq

PROVIDER: PRJNA1011977 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	SRR25880330_1.fastq.gz	Fastqsanger.gz
	SRR25880331_1.fastq.gz	Fastqsanger.gz
	SRR25880332_1.fastq.gz	Fastqsanger.gz
	SRR25880333_1.fastq.gz	Fastqsanger.gz
	SRR25880334_1.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 12

Similar Datasets

Transcriptomic profiling of ccRCC nephrectomy samples by PCR-cDNAseq

Project description:Clear cell renal cell carcinoma (ccRCC) is the most common form of kidney cancer. Following primary tumour resection approximately 30% of patients experience disease recurrence associated with metastasis. To date, long-read RNA sequencing has not been applied to kidney cancer. Here, we used ONT long-read PCR-cDNAseq to profile the transcriptomes of ccRCC archival tumours, 6 of which were from patients who went on to relapse. Our results revealed a loss of immune infiltrate in tumours of patients who relapse. Moreover, thousands of novel isoforms were discovered, including a novel PD-L1 transcript encoding for the soluble version of the protein but having a longer 3'UTR than the currently annotated transcript. Finally, we have identified a novel non-coding gene that was over-expressed in patients who experience recurrence. Our data shows that ONT long-read PCR-cDNAseq can be used in archival tumour samples to comprehensively characterise tumour transcriptomes, and to reveal novel features that would have been missed by short-read RNAseq.

2023-09-04 | GSE242204 | GEO

Transcriptomic profiling of ccRCC nephrectomy samples by Direct RNAseq

Project description:Transcriptomic profiling of ccRCC nephrectomy samples by Direct RNAseq

| PRJNA1010770 | ENA

Transcriptomic profiling of ccRCC nephrectomy samples by Direct RNAseq

Project description:Clear cell renal cell carcinoma (ccRCC) is the most common form of kidney cancer. Following primary tumour resection approximately 30% of patients experience disease recurrence associated with metastasis. To date, long-read RNA sequencing has not been applied to kidney cancer. Here, we used ONT long-read Direct RNA sequencing to profile the transcriptomes of ccRCC archival tumours, 6 of which were from patients who went on to relapse. Our results revealed a loss of immune infiltrate in tumours of patients who relapse. Moreover, thousands of novel isoforms were discovered, including a novel PD-L1 transcript encoding for the soluble version of the protein but having a longer 3'UTR than the currently annotated transcript. Finally, we have identified a novel non-coding gene that was over-expressed in patients who experience recurrence. Our data shows that DRS can be used in archival tumour samples to comprehensively characterise tumour transcriptomes, and to reveal novel features that would have been missed by short-read RNAseq.

2023-09-04 | GSE241932 | GEO

Genome-wide maps of H3K36me3 in ccRCC and RNA-seq of matched nephrectomy samples

Project description:Genome-wide maps of H3K36me3 in ccRCC and RNA-seq of matched nephrectomy samples

| PRJNA284821 | ENA

Clear cell renal cell carcinoma (ccRCC) samples

Project description:To select signatures of ccRCC, 265 ccRCC samples were obtained from the Van Andel Research Institute. Gene expression profiles of 265 samples were determined using the HG-U133_Plus_2 platform.

2017-08-08 | GSE73731 | GEO

Real-time quantitative PCR analysis of ccRCC model rats

Project description:Real-time quantitative PCR analysis of ccRCC model rats

| PRJNA1076576 | ENA

Real-time quantitative PCR analysis of ccRCC model rats II

Project description:Real-time quantitative PCR analysis of ccRCC model rats II

| PRJNA1076582 | ENA

Real-time quantitative PCR analysis of ccRCC model rats III

Project description:Real-time quantitative PCR analysis of ccRCC model rats III

| PRJNA1076583 | ENA

Transcriptomic changes in response to 5/6 or 1/3 nephrectomy

Project description:We report the changes in kidney mRNA abundance in response to 5/6 nephrectomy or 1/3 nephrectomy surgery when compared to sham operated controls.

2022-03-01 | GSE194367 | GEO

Expression profiling of ccRCC samples treated with anti-VEGF therapies reveals distinct markers

Project description:Metastatic renal cell carcinoma (mRCC) patients treated with anti-vascular endothelial growth factor (VEGF) therapies demonstrate promising outcomes but not all patients benefit. Factors that predict response remain to be elucidated. Nephrectomy material from 37 patients with mRCC receiving bevacizumab 6 erlotinib was used for protein and gene expression assessment. Protein lysates were subjected to reverse-phase protein array profiling. RNA extracts were used to carry out gene expression microarray-based profiling. Normalized protein and gene expression data were correlated with overall survival (OS) and progression-free survival (PFS) using univariate Cox hazard model and linear regression. Immunoblotting was carried out to validate the results. High protein levels of AMP-activated protein kinase and low levels of cyclin B1 (CCNB1) were associated with longer OS and PFS. Further validation revealed reduced expression and activation of phosphoinositide 3-kinase (PI3K) pathway components and cell cycle factors in patients with prolonged survival after therapy. Gene expression analysis revealed up-regulation of PI3K- and cell cycle-related pathways in patients with shorter PFS. The OS and PFS of bevacizumab 6 erlotinib-treated patients with renal cell carcinoma were associated with changes in expression of protein and gene expression markers related to PI3K pathway and cell cycle signaling. Expression profiling of ccRCC samples post-treatment (bevacizumab plus erlotinib, or bevacizumab alone). Note: sample 'Jonasch_R06_Tumor_3-27-08_CMM' was not used in the publication (PMID: 20089566).

2011-01-07 | E-GEOD-25698 | biostudies-arrayexpress

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

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