Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Effect of FTO-KD on m6A modification in GH3 cell line [MeRIP-seq]

ABSTRACT: Growth hormone-secreting pituitary adenomas account for approximately 10% of endocrine pituitary tumors and are pathologically classified into densely granulated adenomas (DGGH) and sparsely granulated adenomas (SGGH). m6A has been reported to play an important role in tumorigenesis and development, but its potential role in the pathogenesis of these two subtypes has not yet been thoroughly investigated. Consequently, we performed MeRIP-seq analysis of pituitary adenomas and the GH3 cell line. We observed that the expression of FTO was significantly increased in SGGH, leading to a reduction in m6A modification and a disruption of desmosome organization.

ORGANISM(S): Rattus norvegicus

PROVIDER: GSE269321 | GEO | 2024/09/25

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Effect of FTO-KD or FTO-OE on gene expression in GH3 cell line

Project description:Growth hormone-secreting pituitary adenomas account for approximately 10% of endocrine pituitary tumors and are pathologically classified into densely granulated adenomas (DGGH) and sparsely granulated adenomas (SGGH). m6A has been reported to play an important role in tumorigenesis and development, but its potential role in the pathogenesis of these two subtypes has not yet been thoroughly investigated. Consequently, we performed MeRIP-seq analysis of pituitary adenomas and the GH3 cell line. We observed that the expression of FTO was significantly increased in SGGH, leading to a reduction in m6A modification and a disruption of desmosome organization.

2024-09-25 | GSE269044 | GEO

Multilineage pituitary neuroendocrine tumors with PIT1/SF1 co-expression

Project description:Pituitary neuroendocrine tumors (PitNET)/adenomas are classified according to cell lineage, which requires immunohistochemistry for the transcription factors (TFs) PIT1, SF1, and TPIT. Co-expression of PIT1/SF1 was previously reported in PitNETs, which otherwise correspond to the somatotroph lineage. However, little is known about the clinicopathological features of these tumors. We compiled an in-house case series of 100 tumors, previously diagnosed as densely or sparsely granulated somatotroph PitNETs. Following TF staining, histopathological features associated with PIT1/SF1-coexpression were assessed. Global DNA methylation profiling was conducted on 31 of 100 in-house samples and integrated with publicly available sample data. The majority (74%, 52/70) of our densely granulated somatotroph PitNETs (DGST) unequivocally co-expressed PIT1 and SF1 (DGST-PIT1/SF1). None of our SGST (0%, 0/30) stained positive for SF1 (SGST-PIT1). Integrated molecular analyses including publicly available sample data confirmed that DGST-PIT1/SF1, DGST-PIT1 and SGST-PIT1 represent distinct tumour subtypes. In summary, we spotlight that a substantial proportion of previously diagnosed densely granulated somatotroph PitNET co-express PIT1 and SF1 and exhibit clinical, histopathological, and molecular distinctness from other pure PIT1-lineage somatotroph PitNET.

2024-01-24 | GSE246645 | GEO

Differential Gene Expression and Pathway Analysis in Growth Hormone-Secreting Pituitary Tumors According to Granulation Pattern

Project description:This study investigated differential gene expression between granulation patterns in growth hormone (GH)-secreting pituitary tumors, aiming to elucidate novel transcriptomes that explain clinical variances in patients with acromegaly. Transcriptome analysis was conducted on 6 normal pituitary tissues and 15 GH-secreting pituitary tumors, including 9 densely granulated somatotroph tumors (DGSTs) and 6 sparsely granulated somatotroph tumors (SGSTs). We identified 3111 differentially expressed genes (DEGs) in tumors compared to normal pituitaries, with 1117 DEGs unique to a specific granulation within tumors. SGST showed enrichment of neuronal development and acute inflammatory response pathways, along with a significant enhancement of JAK–STAT, phosphatidylinositol 3-kinase, and MAPK signaling. The results suggest that granulation-specific gene expression may underpin diverse clinical presentations in acromegaly, highlighting the potential for further investigation into these transcriptomic variations and their roles in disease pathology, particularly the involvement of genes linked to neuronal development, inflammatory response, and JAK–STAT signaling in SGST.

2024-04-28 | GSE264621 | GEO

DNA methylation pattern in somatotroph pituitary neuroendocrine tumors

Project description:Secretion of growth hormone by sporadic somatotroph neuroendocrine pituitary tumors (PitNETs) is the most common cause of acromegaly.Genome-wide DNA methylation was investigated in 48 somatotroph PitNETs with EPIC microarrays. Three subtypes of the tumors were identified. Subtype 1 tumors are densely granulated tumors without GNAS mutation characterized by high expression of NR5A1 (SF-1) and GIPR. The expression of both genes is correlated with specific methylation pattern at gene body and promoter methylation. Subtype 1 has generally lower methylation level at 5’ gene regulatory regions and CpG islands as compared to other tumor clusters. Subtype 2 are densely granulated PiNETs with common GNAS-mutations while Subtype 3 are mainly sparsely granulated tumors. Methylation/expression analysis indicate that the levels of ~50% genes differentially expressed genes between tumor subtypes that are located at in differentially methylated regions are DNA methylation dependent. These DNA methylation-controlled genes include CDKN1B, CCND2, EBF3, CDH4, CDH12 MGMT, STAT5A, PLXND1, PTPRE and MMP16 as wells as genes encoding ion channels and semaphorins. Results of DNA methylation profiling confirms three molecular subtypes of somatotroph PitNETs that differ in both gene expression and methylation pattern. High expression of NR5A1 and GIPR in subtype 1 tumors is correlated to specific methylation at both genes.

2023-09-25 | GSE226764 | GEO

Aspirin antitumoral effect on pituitary adenoma

Project description:Aspirin, is a commonly used non-steroidal anti-inflammatory drug with antipyretic, analgesic, anti-inflammatory, and anti-thrombotic effect. Its antitumoral effect was also demonstrated in several cancer types. In this study our aim was to investigate the genomic effects of ASA in pituitary adenomas using high-throughput profiling approaches and validate our findings by targeted methodologies, using in vitro functional assays. Whole transcriptome profile of RC4-B/C and GH3 pituitary cell lines upon Aspirin treatment was investigated.

2022-06-19 | GSE202934 | GEO

Response of mammary tissue to high-LET HZE particle (Silicon ions) radiation or low-LET gamma-rays

Project description:Transcriptional profiling of mammary tissue irradiated at 10 weeks of age with either 100 cGy sparsely ionizing gamma-rays, or 10 cGy or 30 cGy densely ionizing radiation (350 MeV/amu Si). Mammary tissue was collected 1 weeks, 4 weeks, and 12 weeks post-irradiation. Four radiation treatment groups: sham, 100 cGy sparsely ionizing gamma-rays, 10 cGy or 30 cGy densely ionizing radiation (350 MeV/amu Si). Three time points post-irradiation (1, 4, and 12 weeks). Three or four replicates per time point.

2013-11-30 | E-GEOD-48392 | biostudies-arrayexpress

The differential gene screening for invasive pituitary adenoma and non-invasive pituitary adenoma

Project description:Pituitary adenomas are benign tumors originating from the endocrine cells of the pituitary gland, but some pathological subtypes are highly invasive, known as invasive pituitary adenomas. Invasive pituitary adenomas are relatively rare, progress rapidly, easily invade surrounding tissues, have a high risk of recurrence, and have poor response to standard treatments. This study collected tumor specimens from 17 patients with non-invasive pituitary adenomas (FSH type) and 15 patients with invasive pituitary adenomas (ACTH-silent type), and performed transcriptome sequencing, aiming to explore the genetic differences between invasive and non-invasive pituitary adenomas.

2024-03-04 | GSE260487 | GEO

UPLC-MS/MS of YAP co-IP during activating and inactivating plating conditions

Project description:To identify YAP binding partners potentially involved in mechanotransduction, we performed a YAP co-IP/MS experiment using sparsely or densely plated MCF10A cells.

2019-11-21 | PXD007287 | Pride

Natural and molecular history of prolactinoma: insights from a Prlr -/- mouse model

Project description:Mouse models of prolactinoma can be useful to better understand molecular mechanisms involved in abnormal lactotroph cell proliferation and secretion. We have previously developed a prolactin receptor deficient (Prlr -/-) mouse, in which Prlr -/- females develop large secreting prolactinomas from 12 months of age with a penetrance of 100%, mimicking human aggressive densely granulated macroprolactinoma, a highly secreting subtype.

2018-02-21 | GSE100334 | GEO

The Obesity-associated Fto gene regulates activity of the dopaminergic midbrain circuitry [FTO KO]

Project description:Here we use MeRIP-Seq to analyze global adenosine methylation (m6A) in mRNAs in the midbrain and striatum of Fto-deficient mice. We find that Fto deficiency leads to increased methylation within a subset of mRNAs important for neuronal signaling, including many within the dopaminergic signaling pathway. Collectively, our results show that Fto regulates demethylation of specific mRNAs in vivo, and this activity relates to control of dopaminergic transmission. Profiling of m6A in midbrain and striatum from FTO knockout mice

2013-06-15 | E-GEOD-47216 | 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