Transcriptome of GH-producing pituitary neuroendocrine tumours and models are significantly affected by somatostatin analogues.
Cancer Cell Int
; 23(1): 25, 2023 Feb 11.
Article
en En
| MEDLINE
| ID: mdl-36774501
ABSTRACT
Pituitary neuroendocrine tumours (PitNETs) are neoplasms of the pituitary that overproduce hormones or cause unspecific symptoms due to mass effect. Growth hormone overproducing GH-producing PitNETs cause acromegaly leading to connective tissue, metabolic or oncologic disorders. The medical treatment of acromegaly is somatostatin analogues (SSA) in specific cases combined with dopamine agonists (DA), but almost half of patients display partial or full SSA resistance and potential causes of this are unknown. In this study we investigated transcriptomic landscape of GH-producing PitNETs on several levels and functional models-tumour tissue of patients with and without SSA preoperative treatment, tumour derived pituispheres and GH3 cell line incubated with SSA to study effect of medication on gene expression. MGI sequencing platform was used to sequence total RNA from PitNET tissue, pituispheres, mesenchymal stromal stem-like cells (MSC), and GH3 cell cultures, and data were analysed with Salmon-DeSeq2 pipeline. We observed that the GH-producing PitNETs have distinct changes in growth hormone related pathways related to its functional status alongside inner cell signalling, ion transport, cell adhesion and extracellular matrix characteristic patterns. In pituispheres model, treatment regimens (octreotide and cabergoline) affect specific cell proliferation (MKI67) and core functionality pathways (RYR2, COL8A2, HLA-G, ARFGAP1, TGFBR2). In GH3 cells we observed that medication did not have transcriptomic effects similar to preoperative treatment in PitNET tissue or pituisphere model. This study highlights the importance of correct model system selection for cell transcriptomic profiling and data interpretation that could be achieved in future by incorporating NGS methods and detailed cell omics profiling in PitNET model research.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Tipo de estudio:
Prognostic_studies
Idioma:
En
Revista:
Cancer Cell Int
Año:
2023
Tipo del documento:
Article
País de afiliación:
Letonia