Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Serum miR-122-5p and miR-206 expression: Non-invasive prognostic biomarkers for renal cell carcinoma

ABSTRACT: Analysis of the small non-coding RNA expression profile in cell-free serum RNA of patients with clear cell renal cell carcinoma (RCC) and patients with benign renal tumors (BRT) in order to identify novel non-invasive biomarkers for patients with RCC. Aims: (1) to compare the expression profile of patients with RCC and BRT. (2) to compare the expression profile of patients with localized (M=0) and metastatic (M=1) RCC.

ORGANISM(S): Homo sapiens

PROVIDER: GSE85699 | GEO | 2017/11/18

SECONDARY ACCESSION(S): PRJNA339130

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Similar Datasets

Serum miR-122-5p and miR-206 expression: non-invasive prognostic biomarkers for renal cell carcinoma.

Project description: Not available

| S-EPMC5781339 | biostudies-literature

miR-34a, miR-122, miR-206 and miR-210 targets identification

Project description:To identify putative novel specific targets of miR-34a, miR-122, miR-206 and miR-210, we overexpressed these miRNAs in human Hela cells by transfecting them with synthetic pre-miRNAs or a synthetic “negative” pre-miRNA as control (miR-Neg1). RNA samples were harvested at 48 hours post-transfection and 2 independent experiments were carried out.

2021-12-20 | GSE33235 | GEO

Global and targeted microRNA expression profiling in clear cell renal cell carcinoma tissues potentially links miR-155-5p and miR-210-3p to both tumorigenesis and recurrence [discovery phase]

Project description:About 30% of renal cell carcinoma (RCC) patients undergoing nephrectomy experience disease recurrence. This study aimed to profile microRNAs (miRNAs) that are dysregulated in clear cell RCC (ccRCC) tumor tissues and predictive of recurrence. The expression levels of 800 miRNAs were assessed in paired tumor and normal tissues from a discovery cohort of 18 ccRCC patients via the NanoString assay platform. MiRNAs found to be differentially expressed were further examined in a validation set of 205 patients using quantitative real-time PCR. Tumor-normal data from 64 patients in The Cancer Genome Atlas were used for external validation. Results showed 28 miRNAs were consistently dysregulated in tumor tissues. Dichotomized analyses indicated patients with high levels of miR-155-5p and miR-210-3p displayed increased risk of ccRCC recurrence (HR, 2.64; 95% CI, 1.49-4.70; P=0.0009 and HR, 1.8; 95% CI, 1.04-3.12; P=0.036, respectively) and shorter median recurrence-free survival times than patients with low levels (log rank test P<0.01). A risk score was generated based on expression levels of miR-155-5p and miR-210-3p, and the trend test was significant (P for trend=0.005). Pathway analysis revealed target genes regulated by miR-155-5p and miR-210-3p were mainly enriched in inflammation-related pathways. In summary, we identified and validated multiple miRNAs dysregulated in ccRCC tissues. MiR-155-5p and miR-210-3p were predictive of ccRCC recurrence pointing to potential utility as biomarkers and underlying biological mechanisms.

2018-10-04 | GSE116251 | 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.

2020-01-06 | BIOMD0000000908 | BioModels

Cancer-associated fibroblasts promote the stemness and progression of renal cell carcinoma via exosomal miR-181d-5p

Project description:The mechanisms underlying the effects of cancer-associated fibroblasts (CAFs) on cancer stemness and tumor progression in renal cell carcinoma (RCC) have not been elucidated yet. In the present study, we found that the enrichment of CAFs was positively associated with tumor progression and cancer stemness in RCC. Further investigation revealed that CAFs could enhance cancer stemness through delivering exosomes to RCC cells, and miR-181d-5p was identified as the critical exosomal miRNA in CAF secreted exosomes by small RNA sequencing and subsequent screening assays. Mechanistically, exosomal miR-181d-5p transferred from CAFs to RCC cells directly suppressed the expression of ring finger protein 43 (RNF43) and activated Wnt/β-catenin signaling pathway, thus promoted cancer stemness and tumor progression. Overexpression of RNF43 strongly suppressed stemness properties and the effects could be reverted by miR-181d-5p. Overall, our findings revealed a crucial mechanism by which CAFs secreted exosomal miRNAs to enhance cancer stemness and thus promote RCC progression, suggesting a new avenue based on CAF secreted miRNAs for more effective targeted therapies.

2022-11-09 | GSE213453 | GEO

Transcriptome analysis of normal kidney, primary and metastasis ccRCC

Project description:Understanding gene expression changes during transformation from normal tissue to primary RCC and then to metastasis is important. Such analysis is pivotal for undertanding biology in renal cancer and also to unearth novel gene targets. Hence, we examined global transcriptome profile of normal renal tissue, primary RCC and metastasis RCC tumors, identifying important RCC biomarkers

2018-10-16 | GSE105261 | GEO

Association of miR-21-5p, miR-122-5p, and miR-320a-3p with 90-Day Mortality in Cardiogenic Shock.

Project description: Not available

| S-EPMC7662780 | biostudies-literature

MiR-223-3p and miR-122-5p as circulating biomarkers for plaque instability.

Project description: Not available

| S-EPMC7269547 | biostudies-literature

Expression of miR-1-3p, miR-16-5p and miR-122-5p as Possible Risk Factors of Secondary Cardiovascular Events.

Project description: Not available

| S-EPMC8391895 | biostudies-literature

Genomic profiling of 19 renal cell carcinoma samples from patients with end-stage renal disease

Project description:In this study, 19 tumor samples from patients with renal cell carcinoma (RCC)-end-stage renal disease (ESRD) were analyzed by array comparative genomic hybridization (array CGH) using the Agilent Whole Human Genome 4× Array. 19 cystic disease samples from patients with RCC-ESRD

2012-04-01 | E-GEOD-33117 | biostudies-arrayexpress

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