Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Transcriptome analysis of Newly Diagnosed Type 2 Diabetes Subjects identifies genes to predict Metformin drug Response

ABSTRACT: Transcriptome analysis of Newly Diagnosed Type 2 Diabetes Subjects identifies genes to predict Metformin drug Response

PROVIDER: PRJNA642130 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	SRR12096015.fastq.gz	Fastqsanger.gz
	SRR12096016.fastq.gz	Fastqsanger.gz
	SRR12096017.fastq.gz	Fastqsanger.gz
	SRR12096018.fastq.gz	Fastqsanger.gz
	SRR12096019.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 30

Similar Datasets

Transcriptome analysis of Newly Diagnosed Type 2 Diabetes Subjects identifies genes to predict Metformin drug Response

Project description:Aims: Metformin is a widely used, primary drug of choice to treat individuals with type 2 diabetes (T2D). Clinically, inter-individual variability of drug response is of significant concern. The targets and precise mechanisms of action for metformin is still under interrogation. In the present study, a whole transcriptome analysis was performed with an intent to identify predictive biomarkers of metformin response in T2D individuals. Methods: Twenty drug naïve individuals with T2D and ten healthy controls were enrolled. T2D individuals were administrated with metformin monotherapy and were defined as responders and non-responders based on the changes in their glycated haemoglobin (HbA1c) over a period of three months. Poly(A) RNA Seq was performed using Ion Torrent Proton platform and differentially expressed genes were identified from the transcriptome profiles. The gene prioritization, enrichment analysis, drug-gene interactions, disease gene association analysis were performed using various tools and databases. A correlation analysis was also performed between gene expression and change in HbA1c levels after 3 months of metformin therapy. Results: The expression analysis highlighted differentially expressed genes in metformin responders, non-responders and healthy individuals. The gene prioritization and enrichment analysis highlighted potential targets for predicting metformin response (n=35) as well as T2D disease (n=14). The drug-gene interactions did not show any direct association with diabetes drugs. However, the expression of GDF15, TWISTNB and RPL36A genes showed maximum correlation with change in HbA1c levels after 3 months of metformin therapy. The disease gene association analysis highlighted rs113805659 in MAGI2 gene to be linked with T2D in African Americans. Conclusion: The results of the present study provide evidence for the perturbed transcriptome and pathways associated with metformin drug response in T2D individuals.

2022-06-30 | GSE153315 | GEO

Expression data from 4 newly diagnosed multiple myeloma patients and 4 healthy subjects

Project description:It has been found that miRNA is related to the diagnosis and prognosis of multiple myeloma. In this project, we We used microarrays to detect the expression of miRNA in bone marrow of newly diagnosed multiple myeloma patients and healthy subjects, screened out differentially expressed miRNAs, and studied its role in multiple myeloma.

2020-09-03 | GSE157324 | GEO

Expression data from 4 newly diagnosed multiple myeloma patients and 4 healthy subjects

Project description:Expression data from 4 newly diagnosed multiple myeloma patients and 4 healthy subjects

| PRJNA660944 | ENA

Single cell analysis of newly diagnosed AML patients

Project description:Single cell analysis of newly diagnosed AML patients

| PRJNA1000621 | ENA

Single cell analysis of newly diagnosed AML patients

Project description:Acute Myeloid Leukemia (AML) is a heterogenous disease characterized by immature blasts at different maturation stage. We used single cell sequencing technique to analyze newly diagnosed AML patients.

2023-12-18 | GSE239721 | GEO

Intratumoral heterogeneity and longitudinal changes in gene expression predict differential drug sensitivity in newly diagnosed and recurrent glioblastoma

Project description:We investigated longitudinal transcriptomic patterns associated with glioblastoma (GB) recurrence by integrative approach utilizing multisampling strategy, RNA sequencing and complementary investigations of tumor tissues and GB stem cells. In total, 128 tissue samples of 44 tumors including 23 first diagnosed, 19 recurrent and 2 secondary recurrent GBs were analyzed in parallel with 27 primary cultures of GB stem cells. We found that intratumoral transcriptomic heterogeneity is an intrinsic characteristic that is conserved in newly diagnosed and recurrent GBs and captured in GB stem cells. We revealed a high degree of concordance between GB tumor tissues and stem cells in the longitudinal transcriptomic changes associated with tumor recurrence. We used gene expression data to model the efficacy of 130 anti-cancer drugs. For the recurrent tumor tissues and isolated GB stem cells we showed dramatically reduced simulated sensitivities to the first line chemotherapeutic temozolomide. In turn, several therapeutics including immune checkpoint inhibitors were predicted to be more effective in the recurrence setting. Our results strongly suggest that the spectrum of potentially effective drugs may differ between newly diagnosed and recurrent glioblastomas and provides a transcriptional rationale for the lack of significant therapeutic benefit from temozolomide in patients with recurrent GB.

2020-03-01 | GSE139533 | GEO

A Gene Expression-Based Risk Stratification Model for Newly Diagnosed Multiple Myeloma

Project description:We recently defined a gene expression-based signature of high-risk multiple myeloma; this predictive signature was developed with and independently validated for newly diagnosed patients treated with high dose therapy and stem cell rescue. Here we use Phase 3 clinical trial data to show that this signature also predicts short survival in relapsed disease treated with single agent bortezomib or high dose dexamethasone. In addition, a survival signature derived with relapsed myeloma samples identified newly diagnosed patients with short survival. Taken together these data suggest that a similar biology underlies poor outcome in both newly diagnosed and relapsed myeloma and provide strong evidence that the high-risk signature is a powerful tool to identify patients who are candidates for new therapeutic regimens. Keywords: Model validation

2011-08-20 | GSE8991 | GEO

Intratumoral heterogeneity and longitudinal changes in gene expression predict differential drug sensitivity in newly diagnosed and recurrent glioblastoma

Project description:Intratumoral heterogeneity and longitudinal changes in gene expression predict differential drug sensitivity in newly diagnosed and recurrent glioblastoma

| PRJNA580196 | ENA

A Gene Expression-Based Risk Stratification Model for Newly Diagnosed Multiple Myeloma

2011-08-19 | E-GEOD-8991 | biostudies-arrayexpress

Differential gene expression profile of newly diagnosed versus relapse acute promyelocytic leukemia

Project description:The aim of this study was to gain insight into the potential mechanism of resistance to arsenic trioxide and to identify genes that are modulated in the malignant promyelocytes at the time of relapse in APL patients. We analyzed the gene expression profile by the whole genome microarray of primary promyelocytes obtained from 8 newly diagnosed and 8 at relapse patients. Agilent one-color experiment,Organism: Human ,Agilent Whole Genome Human 4x44k (AMADID: 014850) , Labeling kit: Agilent Quick-Amp labeling Kit (p/n5190-0442) newly diagnosed versus relapse acute promyelocytic leukemia

2013-08-02 | E-GEOD-42030 | 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