Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Patient- and Cell Type-Specific Heterogeneity of Metformin Response.

ABSTRACT: Most FDA approved drugs are not equally effective in all patients, suggesting that identification of biomarkers to predict responders to a chemoprevention agent will be needed to stratify patients and achieve maximum benefit. The goal of this study was to investigate both patient specific and cell-context specific heterogeneity of metformin response, using cancer cell lines fibroblast cell lines and induced pluripotent stem cells differentiated into lung epithelial lineages. We performed transcriptome analysis on both patient-derived fibroblast cell lines and cancer cell lines to assess differential metformin response and identify response genes. We found differences in response to metformin treatment across a variety of cell lines and cellular contexts, suggesting heterogeneity that may be patient and cell-type specific. Gene expression profiling and analysis of metformin sensitive and resistant cells identified differentially expressed genes that may be able to stratify patients into metformin responders and non-responders.

ORGANISM(S): Homo sapiens

PROVIDER: GSE95298 | GEO | 2018/05/02

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Homo sapiens

Project description:Patient- and Cell Type-Specific Heterogeneity of Metformin Response.

| PRJNA376586 | ENA

Whole-blood transcriptome profiling reveals signatures of metformin and its therapeutic response

Project description:Metformin, a biguanide agent, is the first-line treatment for type 2 diabetes mellitus due to its glucose-lowering effect. Despite its wide application in the treatment of multiple health conditions, the glycemic response to metformin is highly variable, emphasizing the need for reliable biomarkers. We chose the RNA-Seq-based comparative transcriptomics approach to evaluate the systemic effect of metformin and highlight potential predictive biomarkers of metformin response in drug-naïve type 2 diabetes patient volunteers in vivo. Longitudinal blood-derived transcriptome analysis revealed metformin-induced differential expression of novel and previously described genes involved in cholesterol homeostasis (SLC46A1 and LRP1), cancer development (CYP1B1, STAB1, CCR2, TMEM176B) and immune responses (CD14, CD163) after administration of metformin for three months. We demonstrate for the first time transcriptome-based molecular discrimination between metformin responders (delta HbA1c ≥ 1% or 12.6 mmol/mol) and non-responders (delta HbA1c < 1% or 12.6 mmol/mol) determined by expression levels of 56 genes, explaining 13.9% of the variance in the therapeutic efficacy of the drug. Moreover, we found a significant upregulation of IRS2 gene (log2FC 0.89) in responders compared to non-responders before the use of metformin. Finally, we provide evidence for the mitochondrial respiratory complex I as one of the factors related to the high variability of the therapeutic response to metforminin patients with type 2 diabetes mellitus.

2020-07-05 | GSE153792 | GEO

Synovial inflammatory pathways characterize anti-TNF-responsive rheumatoid arthritis patients

Project description:Objectives: This study was undertaken to understand the mechanistic basis of response to anti-TNF therapies and determine if transcriptomic changes in the synovium are reflected in peripheral protein markers. Methods: Synovial tissue from 46 RA patients was profiled with RNA sequencing before and 12 weeks after treatment with anti-TNF therapies. Pathway and gene signature analyses were performed on RNA expression profiles of synovial biopsies to identify mechanisms that could discriminate among EULAR good, moderate and non-responders. Serum proteins encoded by synovial genes differentially expressed between EULAR response groups were measured in the same patients. Results: The gene signatures were able to predict good responder patients and pathway analysis identified elevations in immune pathways including chemokine signaling, Th1 and Th2 cell differentiation, and Toll-like receptor signaling uniquely in good responders. These inflammatory pathways were correspondingly down-modulated by anti-TNF therapy only in good responders. Based on cell signature analysis, lymphocyte, myeloid and fibroblast cell populations were elevated in good responders relative to non-responders, consistent with the increased inflammatory pathways. Cell signatures which decreased following anti-TNF treatment were predominately associated with lymphocytes and fewer were associated with myeloid and fibroblast populations. Following anti-TNF treatment and only in good responders, several peripheral inflammatory proteins decreased consistent with corresponding synovial gene changes. Conclusions: Collectively, these data suggest that RA patients with robust responses to anti-TNF therapies are characterized at baseline by immune pathway activation, which decreases following anti-TNF treatment. Understanding mechanisms that define patient responsiveness to anti-TNF may assist in development of predictive markers of patient response and earlier treatment options.

2022-06-06 | GSE198520 | GEO

DNA methylation of estrogen regulated enhancers defines endocrine sensitivity in breast cancer

Project description:Expression of estrogen receptor (ESR1) determines whether a breast cancer patient receives endocrine therapy as part of their adjuvant care, but does not guarantee patient response. However, the molecular factors that define endocrine response in ESR1-positive breast cancer patients remain poorly understood. Here, we characterize the DNA methylome of endocrine sensitivity and demonstrate the potential impact of differential DNA methylation on endocrine response in breast cancer. We show that DNA hypermethylation occurs predominantly at estrogen-responsive enhancers and is associated with reduced ESR1 binding and decreased gene expression of key regulators of ESR1-activity; thus providing a novel mechanism by which endocrine response is abated in ESR1-positive breast cancers. Conversely, we delineate that ESR1-responsive enhancer hypomethylation is critical in transition from normal mammary epithelial cells to endocrine responsive ESR1-positive cancer. Cumulatively these novel insights highlight the potential of ESR1-responsive enhancer methylation to both predict ESR1-positive disease and stratify ESR1-positive breast cancer patients as responders to endocrine therapy. Methylation profiling with Illumina's HumanMethylation450K array was performed on ESR1-positive hormone sensitive MCF7 cells, and three different well characterised endocrine resistant MCF7-derived cell lines; tamoxifen-resistant (TAMR), fulvestrant-resistant (FASR) and estrogen deprivation resistant (MCF7X) cells. For each cell line two biological replicates were profiled bringing the number of samples to eight.

2015-07-01 | E-GEOD-69118 | biostudies-arrayexpress

Microarray analysis of differences in circulating miRNA expression between responders and non-responders to treatment of rheumatoid arthritis with allogeneic adipose-derived mesenchymal stem cells

Project description:This experiment investigates differences in expression of circulating miRNAs between responders and non-responders to treatment of rheumatoid arthritis with allogeneic adipose-derived mesenchymal stem cells. The ability to stratify patients based on their response to treatment has been transformational in a number of disease areas, and it is anticipated that this will also be the case for cell-based therapies. In line with this, the goal of this study was to identify miRNA biomarkers that could be used to predict response to treatment of rheumatoid arthritis with allogeneic adipose-derived mesenchymal stem cells. Promising candidates from the microarrays were validated with quantitative reverse transcriptase PCR.

2017-01-09 | E-MTAB-4988 | biostudies-arrayexpress

qPCR panels for plasma microRNAs of responders versus non responders metastatic castration resistant prostate cancer patients to abiraterone acetate

Project description:Abiraterone acetate (AA) serves as a medication for managing persistent testosterone production in patients with metastatic castration-resistant prostate cancer (mCRPC). However, its efficacy varies among individuals, thus the identification of biomarkers to predict and follow treatment response is required. In this pilot study, we explored the potential of circulating miRNAs (c-miRNAs) to stratify patients based on their responsiveness to AA. We conducted a miRNA candidate analysis on 9 responders versus 9 non-responders mCRPC patients via Exiqon qPCR panels The patients were considered responders to AA if they either achieved a radiological response without PSA progression and performance status deterioration, or in the presence of PSA reduction ≥ 50% compared to the baseline values without radiological progression and performance status deterioration.

2024-06-20 | GSE262550 | GEO

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

DNA methylation of estrogen regulated enhancers defines endocrine sensitivity in breast cancer

2015-07-01 | GSE69118 | GEO

Multi-omics identifies the link between intratumor steatosis and exhausted tumor immune microenvironment in hepatocellular carcinoma

Project description:Immunotherapy becomes standard-of-care treatment of hepatocellular carcinoma (HCC) but still limited number of patients benefit from immunotherapy due to the high heterogeneity of tumor immune-microenvironment (TIME) of HCC patients. Therefore, it is important to stratify patients who benefit from immunotherapy by analyzing the molecular and immunological diversity of HCC. Since non-viral HCC is rapidly increasing all over the world, in this study, we focused on non-viral HCC and aimed to identify immunotherapy-susceptible patients through multi-omics. Global RNA sequencing of surgically-resected tumor tissues in 113 non-viral HCC patients was performed. Unsupervised hierarchical clustering classified non-viral HCCs into 3 molecular classes associated with patient prognosis and corresponding driver gene abnormality. We further identified the immune-enriched but -exhausted subclass with intratumor steatosis in non-viral HCCs, characterized by T-cell exhaustion, infiltration of M2 macrophage and cancer-associated fibroblast (CAF), high PD-L1 expression, and TGF-β signaling activation..

2022-05-22 | GSE190967 | GEO

Molecular profiling of pre-and post-5-azacytidine myelodysplastic syndrome samples identifies predictors of response

Project description:Treatment with the hypomethylating agent 5-azacytidine (AZA) increases survival in high-risk (HR) myelodysplastic syndrome (MDS) patients, but predicting patient response and overall survival remains challenging. To address these issues, we analyzed mutational and transcriptional profiles in CD34+ hematopoietic stem/progenitor cells (HSPCs) before and following AZA therapy in MDS patients. AZA treatment led to a greater reduction in the mutational burden in both blast and hematological responders than non-responders. Blast and hematological responders showed transcriptional evidence of pre-treatment enrichment for pathways such as oxidative phosphorylation, MYC targets, and mTORC1 signaling. While blast non-response was associated with TNFa signaling and leukemia stem cell signature, hematological non-response was associated with cell-cycle related pathways. AZA induced similar transcriptional responses in MDS patients regardless of response type. Comparison of blast responders and non-responders to normal controls, allowed us to generate a transcriptional classifier that could predict AZA response and survival. This classifier outperformed a previously developed gene signature in a second MDS patient cohort, but signatures of hematological responses were unable to predict survival. Overall, these studies characterize the molecular consequences of AZA treatment in MDS HSPCs and identify a potential tool for predicting AZA therapy responses and overall survival prior to initiation of therapy.

2024-07-21 | GSE272173 | 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