Abstract 4087: Epithelial to mesenchymal transition in the metastatic progression of gastroenteropancreatic neuroendocrine tumors
Notice bibliographique
Résumé
Abstract Gastroenteropancreatic neuroendocrine tumors (GEP-NETs) are malignant epithelial cancer arising from the diffuse neuroendocrine system. Diagnosis is usually made late in the disease course, with 60-80% of diagnosed cases presenting with, or developing metastatic disease. The lack of biomarkers indicative of aggressive behavior, in particular metastasis, hinders prognosis and proper treatment of GEP-NETs. A cellular process underlying the aggressive behavior of cancer is epithelial to mesenchymal transition (EMT). In this study, we aimed to determine whether EMT is involved in the pathogenesis of NETs. Using tissue samples from NETs arising from small intestine (SI-NET), pancreas (P-NETs), and colorectum (C-NETs), we have examined EMT and profiled the signaling mechanisms involved. Initial studies utilizing targeted real-time PCR-based gene profiling comparing primary (n = 9) and metastatic (n = 4) SI-NETs relative to control (normal small bowel epithelium, n = 3) revealed gene expression profiles suggestive of EMT and cell guidance in both primary and metastatic tumors, including elevation of vascular endothelial growth factor signaling, changes in matrix remodeling genes, and abundant transforming growth factor-β (TGF-β) receptor 1. A more comprehensive examination of the EMT phenotype was then undertaken based on primary site of origin. RNA samples from P-NETs (n = 9), SI-NETs (n = 8), and C-NETs (n = 8) were used for gene expression studies utilizing an EMT-focused PCR array. Changes in gene expression profiles consistent with an EMT phenotype were seen across all primary sites, including elevated expression of transcription factors SMAD2, ZEB1/2, HIF-1α. Differential expression of TGF-β family receptor ligands, including TGF-β1 and BMP2, was observed when comparing results from NETs of different primary sites, suggesting alternate signaling pathways leading to EMT. Confirmatory immunohistochemistry studies were then carried out, demonstrating an EMT phenotype as evidenced by loss of E-cadherin/β-catenin expression and/or vimentin induction in 42% of cases (n = 52). Well-differentiated GEP-NETs, while morphologically similar, are extremely heterogeneous in both clinical presentation and outcome. We have examined a series of 52 GEP-NETs, of which 77% either presented with, or eventually developed metastatic disease. EMT is likely a key process by which this occurs, as we observe expression changes in EMT-associated genes across all subtypes of GEP-NETs, and an EMT phenotype by immunohistochemistry in 42% of the tumors. Our gene expression studies suggest that differential TGF-β family signaling is a key mediator in driving EMT, however, the specific pathway may involve different factors depending on the site of NET origin. Future studies are needed to elucidate the exact pathways involved in this process. Citation Format: Stephanie Mok, Zia A. Khan, Douglas Quan, Christopher J. Howlett. Epithelial to mesenchymal transition in the metastatic progression of gastroenteropancreatic neuroendocrine tumors. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 4087. doi:10.1158/1538-7445.AM2015-4087
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».