Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

RNA-seq Validation of MicroRNA Expression Signatures for Precision Melanoma Diagnosis and Prognostication

ABSTRACT: RNA-seq Validation of MicroRNA Expression Signatures for Precision Melanoma Diagnosis and Prognostication

PROVIDER: PRJNA1108602 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	SRR28958800.fastq.gz	Fastqsanger.gz
	SRR28958801.fastq.gz	Fastqsanger.gz
	SRR28958802.fastq.gz	Fastqsanger.gz
	SRR28958803.fastq.gz	Fastqsanger.gz
	SRR28958804.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 120

Similar Datasets

RNA-seq Validation of MicroRNA Expression Signatures for Precision Melanoma Diagnosis and Prognostication

Project description:This study aimed to transition genomic signatures of invasive cutaneous melanoma (MEL38 for diagnosis and MEL12 for prognosis) from Nanostring to a high-throughput RNAseq platform using the gel-free NEXTflex Small RNA Library Prep Kit. We validated against 59 FFPE and 61 plasma samples from patients with melanoma and normal tissue . MEL38 accurately diagnosed invasive melanoma across different sample types and platforms, unaffected by disease stage or data origin. The MEL12 signature significantly stratified patients by survival rates in solid tissues, surpassing previous prognostic models based on DNA, mRNA, or protein analysis, with hazard ratios indicating high risk. In plasma, MEL12 also differentiated survival outcomes. Overall, the study confirms the reliability of MEL38 and MEL12 in predicting melanoma presence and prognosis, demonstrating their superiority and adaptability across specimen types and platforms, marking a substantial advancement in personalized melanoma management.

2024-10-10 | GSE266859 | GEO

RNA-seq validation of microRNA expression signatures for precision melanoma diagnosis and prognostic stratification.

Project description:BackgroundNew diagnostic tools are needed to improve the diagnosis and risk stratification of cutaneous melanoma. Disease-specific microRNA signatures have been previously described via NanoString profiling of solid biopsy tissue and plasma. This study validated these signatures via next-generation sequencing technology and compared their performance against clinical metrics and other published melanoma signatures.MethodsRNA from 64 plasma and 60 FFPE biopsy samples from individuals with invasive melanoma or related benign/control phenotypes was extracted and enriched for microRNA. RNA sequencing was performed to compute MEL38/MEL12 signature scores. The results were evaluated with published NanoString and RNA sequencing datasets, comprising 548 solid tissue samples and 217 plasma samples, to predict disease status and patient outcome.ResultsThe MEL38 diagnostic signature classifies patients into discrete diagnostic groups via RNA sequencing in either solid tissue or plasma (P < 0.001). In solid tissue, the prognostic MEL12 signature stratifies patients into low-, intermediate- and high-risk groups, independent of clinical covariates. The hazard ratios for 10-year overall survival, based on observed survival intervals, were 2.2 (MEL12 high-risk vs low-risk, P < 0.001) and 1.8 (intermediate-risk vs low-risk, P < 0.001), outperforming other published prognostic models. MEL12 also exhibited prognostic significance in the plasma of 42 patients with invasive disease.ConclusionsThe MEL38 and MEL12 signatures can be assessed in either solid tissue or plasma using RNA-seq and are strong predictors of disease state and patient outcome. Compared with other genomic methods, MEL12 was shown to be the strongest predictor of poor prognosis. MicroRNA expression profiling offers objective, accurate genomic information about a patient's likelihood of invasive melanoma and prognosis.

| S-EPMC11515382 | biostudies-literature

Precision diagnosis of neurodevelopmental disorders in Middle Eastern Populations

Project description:Precision diagnosis of neurodevelopmental disorders in Middle Eastern Populations

| PRJNA1221598 | ENA

Precision diagnosis of neurodevelopmental disorders in Middle Eastern Populations

Project description:Precision diagnosis of neurodevelopmental disorders in Middle Eastern Populations

| PRJNA1221597 | ENA

Gene Expression Signatures for the Accurate Diagnosis of Peripheral T-Cell Lymphoma Entities in the Routine Clinical Practice [validation]

Project description:Peripheral T-cell lymphoma (PTCL) includes heterogeneous clinicopathologic entities with numerous diagnostic and treatment challenges. We previously defined robust transcriptomic signatures that distinguish common PTCL entities and identified two novel biologic and prognostic PTCL-not otherwise specified subtypes (PTCL-TBX21 and PTCL-GATA3). We aimed to consolidate a gene expression–based subclassification using formalin-fixed, paraffin-embedded (FFPE) tissues to improve the accuracy and precision in PTCL diagnosis. We assembled a well-characterized PTCL training cohort (n = 105) with gene expression profiling data to derive a diagnostic signature using fresh-frozen tissue on the HG-U133plus2.0 platform (Affymetrix, Inc, Santa Clara, CA) subsequently validated using matched FFPE tissues in a digital gene expression profiling platform (nCounter, NanoString Technologies, Inc, Seattle, WA). Statistical filtering approaches were applied to refine the transcriptomic signatures and then validated in another PTCL cohort (n = 140) with rigorous pathology review and ancillary assays.

2023-02-23 | GSE225758 | GEO

IsomiR Utility in ALS Prognostication (ALS)

Project description:IsomiR Utility in ALS Prognostication (ALS)

| PRJNA1269625 | ENA

IsomiR Utility in ALS Prognostication [control]

Project description:IsomiR Utility in ALS Prognostication [control]

| PRJNA1321739 | ENA

Molecular signatures in peripheral T-cell lymphoma (PTCL)

Project description:Molecular signatures to improve diagnosis in PTCL and prognostication in angioimmunoblastic T-cell lymphoma (AITL). Gene expression profiling of PTCL patient samples was performed to investigate whether molecular signatures can be used to identify distinct entities of PTCL.

2010-02-08 | GSE19069 | GEO

The OncoLoop Network-based Precision Oncology Framework

Project description:We introduce OncoLoop, a highly-generalizable, precision medicine framework to triangulate between available mouse models, human tumors, and large-scale drug perturbational assays with in vivo validation to predict personalized treatment

2022-10-31 | GSE186566 | GEO

Internal validation cohort of breast cancers for development of ClinicoMolecular Triad Classification

Project description:In our early study (PMID: 21939527), we have created a ClinicoMolecular Triad Classification (CMTC) to improve prediction and prognostication of breast cancer by using a training cohort contained 161 breast cancer patients (2003 to 2008). Here, a supplemental internal validation cohort contained 340 breast cancer patients was collected (2008 to 2010) for development of the CMTC.

2014-06-19 | GSE45725 | GEO

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