Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Integrating methylome and transcriptome signatures expands the molecular classification of the pituitary tumors [RNA-seq]

ABSTRACT: Integrating methylome and transcriptome signatures expands the molecular classification of the pituitary tumors [RNA-seq]

PROVIDER: PRJNA862905 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Similar Datasets

Integrating methylome and transcriptome signatures expands the molecular classification of the pituitary tumors

Project description:Integrating methylome and transcriptome signatures expands the molecular classification of the pituitary tumors

| PRJNA857710 | ENA

Integrating methylome and transcriptome signatures expands the molecular classification of the pituitary tumors

Project description:Abnormal DNA methylation contributes to tumor progression and is emerging as a prognostic marker in several types of cancers. Pituitary tumors (PitNETs) tumorigenesis is still not completely understood. Few studies have integrated exome, methylome, and transcriptome analyses. Taking advantage of a comprehensive phenotypic characterized Brazilian cohort, which has heterogeneous ethnic origin, we combined methylome and transcriptome analysis of the three major subtypes of surgically resectable PitNETs (n = 77): GH-secreting (acromegaly, n = 18), ACTH-secreting (Cushing disease, n = 13), and non-functioning (n = 46) PitNETs. Here, we included methylome and basal patient / tumor data.

2023-01-26 | GSE207937 | GEO

Integrating methylome and transcriptome signatures expands the molecular classification of the pituitary tumors [RNA-seq]

Project description:Pituitary tumors (PitNETs) tumorigenesis is still not completely understood. Few studies have integrated exome, methylome, and transcriptome analyses. Taking advantage of a comprehensive phenotypic characterized Brazilian cohort, which has heterogeneous ethnic origin, we combined methylome and transcriptome analysis of the three major subtypes of surgically resectable PitNETs. Here, we included RNA-seq and basal patient / tumor data of 65 PitNETs: GH-secreting (acromegaly,n = 15), ACTH-secreting (Cushing disease, n = 7), and Non-Functioning (n = 43 PitNETs).

2023-01-26 | GSE209903 | GEO

Methylome analysis in pituitary neuroendocrine tumors

Project description:Methylome analysis in pituitary neuroendocrine tumors

| PRJEB50807 | ENA

Transcriptome and methylome analysis reveal three cellular origins of pituitary tumors

Project description:The pituitary tumors (PA) arise in adenohypophyseal cells and are the second most common tumor in central nervous system. Reflective of their monoclonal cell of origin these tumors could be classified according to the hormone that they produce. The mutational and copy number variation burden in these tumors are scarce, indicating other molecular events are involved in pituitary tumorigenesis. Here we show throughout transcriptome and methylome analysis that there are three readily distinctive molecular signatures. The first group is comprised by the gonadotropes, null cell and silent corticotroph PA, the second group comprised by ACTH PA and the group cluster together the TSH-, PRL- and GH- PA. These groups showed CACNA2D4, EPHA4 and SLIT1 gene up-regulation, respectively. Pathway enrichment analysis support the previous observations. The calcium signaling pathway is characteristic for gonadotropes null cell and silent corticotroph, the Renin-Angiotensin system for the ACTH PA and the Fatty acid metabolisms for the TSH-, PRL-, GH- cluster. The analysis of scRNA-seq from non-tumoral pituitary tissue revealed that these three groups originate since the pituitary development/embryogenesis. The immune cell infiltration landscape revealed that PA could be potentially infiltrated by NK and mast cells. Taken together these results correlate with the expression of the NR5A1, TBX19 and POU1F1 transcription factors, which drive pituitary embryogenesis and theoretically tumorigenesis and potentially indicates three divergent cell precursors cells. We used microarrays to detail the molecular alteration in PA compared to non-tumoral gland.

2020-11-16 | GSE147786 | GEO

Pangenomic classification of pituitary adenomas

Project description:Pituitary neuroendocrine tumors (PitNETs) are common. They are classified in five main histological types: PitNETs of POU1F1 (Pit1) lineage -lactotroph, somatotroph and thyrotroph -, gonadotroph, and corticotroph PitNETs. Pituitary tumorigenesis is poorly understood. We report here a pangenomic characterization of 134 PitNETs from all types, based on exome, genome, transcriptome, miRnome and methylome. Pit1 lineage was associated with a specific epigenetic signature of diffuse DNA hypomethylation, and correlated with expression of Pit1 markers and chromosomal instability. 45% of somatotroph PitNETs showed GNAS mutations. GNAS-wild type somatotroph showed a distinct transcriptome profile, expressing gonadotroph transcription factor NR5A1 (SF1). Sparsely granulated somatotroph PitNETs was classified apart from somatotroph, owing to a thyrotroph transcriptome signature. 43% of corticotroph PitNETs showed USP8 mutations, sharing a low epithelial-mesenchimal transition (EMT) transcriptome signature. Silent corticotroph PitNETs showed a gonadotroph transcriptome signature evoking transdifferentiation, and lost somatostatin target SSTR5 expression.

| EGAS00001003642 | EGA

Transcriptome and methylome analysis reveal three cellular origins of pituitary tumors

Project description:Transcriptome and methylome analysis reveal three cellular origins of pituitary tumors

| PRJNA616365 | ENA

Integrating Next Generation Sequencing with Morphology Improves Prognostic and Biologic Classification of Spitz Neoplasms [RNA-Seq]

Project description:The newest WHO classification suggests eliminating cases with BRAF and NRAS mutations from the categories of Spitz tumors (ST) and Spitz melanoma (SM). We aimed to better characterize the genomics of Spitz neoplasms and assess whether integrating genomic data with morphologic diagnosis improves classification and prognostication. We performed DNA and RNA sequencing on 80 STs, 26 SMs, and 22 melanomas with Spitzoid features (MSF). NGS data was used to reclassify tumors by moving BRAF/NRAS-mutated cases to MSF. Eighty-one percent of STs harbored kinase fusions/truncations. Of SMs, 77% had fusions/truncations, 8 involving MAP3K8. Novel fusions identified were MYO5A-FGFR1, MYO5A-ERBB4, and PRKDC-CTNNB1. The majority of MSFs (84%) had BRAF, NRAS, or NF1 mutations, and 62% had TERT promoter mutations. Only after reclassification, the following was observed: 1) mRNA expression showed distinct clustering of MSF; 2) 6/7 cases with recurrence and all distant metastases were MSFs; 3) RFS was worse in MSF than ST and SM groups (p=0.0073); 4) classification incorporating genomic data was highly predictive of recurrence (OR 13.20, p=0.0197). The majority of STs and SMs have kinase fusions as primary initiating genomic events. Eliminating BRAF/NRAS-mutated neoplasms from these categories results in improved classification and prognostication of melanocytic neoplasms with Spitzoid cytomorphology.

2020-02-26 | GSE142441 | GEO

Integrating Next Generation Sequencing with Morphology Improves Prognostic and Biologic Classification of Spitz Neoplasms [DNA-Seq]

2020-08-27 | GSE142442 | GEO

Integrating Next Generation Sequencing with Morphology Improves Prognostic and Biologic Classification of Spitz Neoplasms

Project description:Integrating Next Generation Sequencing with Morphology Improves Prognostic and Biologic Classification of Spitz Neoplasms

| PRJNA608792 | ENA