Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

ABSTRACT: Integrative single-cell analysis of transcriptional and epigenetic regulatory features in the human clear cell renal cell carcinoma

PROVIDER: PRJNA866658 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	SRR20883384_1.fastq.gz	Fastqsanger.gz
	SRR20883384_2.fastq.gz	Fastqsanger.gz
	SRR20883385_1.fastq.gz	Fastqsanger.gz
	SRR20883385_2.fastq.gz	Fastqsanger.gz

Items per page:

1 - 4 of 4

Similar Datasets

Project description:Integrative single-cell analysis of transcriptional and epigenetic regulatory features in the human clear cell renal cell carcinoma

| PRJNA861705 | ENA

Integrative single-cell analysis of transcriptional and epigenetic regulatory features in the human clear cell renal cell carcinoma

Project description:Integrative single-cell analysis of transcriptional and epigenetic regulatory features in the human clear cell renal cell carcinoma

| PRJNA855923 | ENA

Radiomic and transcriptomic analysis for understanding metastatic clear cell renal cell carcinoma

Project description:To understand the relationship of radiomic and transcriptomic features of metastatic clear cell renal cell carcinoma, we performed RNA-sequencing in clear cell renal cell carcinomas (ccRCCs).

2019-09-30 | GSE135091 | GEO

Homo sapiens

Project description:Integrative genomic study of Chinese clear cell renal cell carcinoma reveals features associated with thrombus

| PRJNA596338 | ENA

Analysis of microRNA expression profiles in clear cell papillary renal cell carcinoma compared to normal adjacent tissue.

Project description:Clear cell papillary renal cell carcinoma (CCPRCC) is a low-grade renal neoplasm with morphological characteristics mimicking both clear cell renal cell carcinoma (CCRCC) and papillary renal cell carcinoma (PRCC). However, despite some overlapping features, their morphological, immunohistochemical, and molecular profiles are distinct. To better understand the biology of this tumor, we analyze the miRNA expression profiles of a set of CCPRCC by microarrays.

2017-02-28 | GSE95385 | GEO

Homo sapiens

Project description:Integrative genomic analysis of sporadic clear cell Renal Cell Carcinoma

| PRJNA137871 | ENA

Project description:Single-cell multi-omics analysis reveals regulatory programs in clear cell renal cell carcinoma

| PRJNA768891 | ENA

Loss of 5-hydroxymethylcytosine is linked to gene body hypermethylation in kidney cancer

Project description:Aberrant DNA methylation is common in cancer. To associate DNA methylation with gene function, we performed RNAseq upon tumor tissue and matched normal tissues of two ccRCC (clear cell renal cell carcinoma) patients. To quantify 5mC and 5hmC level in each CG site at genome-wide level, we performed BS-seq and TAB-seq upon tumor tissue and matched normal tissues of two ccRCC (clear cell renal cell carcinoma) patients, respectively. mRNA profiles of tumor and matched normal tissues from two ccRCC patients were generated by deep sequencing, using Hiseq 2000. Single-nucleotide-resolution, whole-genome, 5mC and 5hmC profiles of tumor and matched normal tissues from two ccRCC (clear cell renal cell carcinoma) patients were generated by deep sequencing, using Hiseq 2000.

2016-02-29 | E-GEOD-63183 | biostudies-arrayexpress

Gene expression profiling of 786-O cells with ZHX2 and p65 knockdown

Project description:We are examining the regulatory pathway of ZHX2 and p65 in clear cell renal cell carcinoma cell line 786-O

2018-06-30 | GSE110094 | GEO

dePillis2013 - Mathematical modeling of regulatory T cell effects on renal cell carcinoma treatment

Project description:Mathematical modeling of regulatory T cell effects on renal cell carcinoma treatment Lisette dePillis 1, , Trevor Caldwell 2, , Elizabeth Sarapata 2, and Heather Williams 2, 1. Department of Mathematics, Harvey Mudd College, Claremont, CA 91711 2. Harvey Mudd College, Claremont, CA 91711, United States, United States, United States Abstract We present a mathematical model to study the effects of the regulatory T cells (Treg) on Renal Cell Carcinoma (RCC) treatment with sunitinib. The drug sunitinib inhibits the natural self-regulation of the immune system, allowing the effector components of the immune system to function for longer periods of time. This mathematical model builds upon our non-linear ODE model by de Pillis et al. (2009) [13] to incorporate sunitinib treatment, regulatory T cell dynamics, and RCC-specific parameters. The model also elucidates the roles of certain RCC-specific parameters in determining key differences between in silico patients whose immune profiles allowed them to respond well to sunitinib treatment, and those whose profiles did not. Simulations from our model are able to produce results that reflect clinical outcomes to sunitinib treatment such as: (1) sunitinib treatments following standard protocols led to improved tumor control (over no treatment) in about 40% of patients; (2) sunitinib treatments at double the standard dose led to a greater response rate in about 15% the patient population; (3) simulations of patient response indicated improved responses to sunitinib treatment when the patient's immune strength scaling and the immune system strength coefficients parameters were low, allowing for a slightly stronger natural immune response. Keywords: Renal cell carcinoma, mathematical modeling., sunitinib, immune system, regulatory T cells.

2024-09-02 | BIOMD0000000908 | BioModels

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