Abstract 88: Comparative tumorigenesis of progranulin and fibroblast growth factor 4 and in adrenocortical carcinoma cells.
Notice bibliographique
Résumé
Abstract Adrenal carcinomas are almost always fatal, but the mechanisms that influence transition from low malignancy adenomas to metastatic carcinomas are ill defined. SW-13 adrenocortical carcinoma cells are not tumorigenic in nude mice and do not support anchorage-independent growth at low cell density unless stimulated by secreted forms of fibroblast growth factors (FGF) family, such as FGF4, or the growth factor-like proteins progranulin (PGRN) which is over produced by many cancers. This growth factor-dependent acquisition of tumorigenesis may model the transition from benign to malignant adrenocortical cancer. Both PGRN and FGF4 were shown to stimulate phosphorylation of MEK1/2 in SW-13 cells. The aim of this study is to investigate the tumorigenic mechanisms of PGRN and FGF, in particular to identify “core” malignancy responses shared by both tumorigenic pathways. We employed Affymetrix Human Genome U133A microarrays to examine the transcriptome profiles in SW-13 cells with elevated PGRN or FGF4. We employed Ingenuity Pathway Analysis (IPA®) to visualize differentially expressed genes in the context of biological pathways. In SW-13 cells with elevated PGRN, those transcripts that were significantly up-regulated were related to cell morphology, cellular assembly and organization, nervous system development and function, carbohydrate metabolism, molecular transport. In cells with elevated FGF4, significantly up-regulated molecules were associated with lipid metabolism, molecular transport, small molecule biochemistry, cell death. 80 transcripts that were up-regulated in common by FGF and PGRN were identified, and were significantly associated with gene expression, cell death, cellular development. 28 of those genes were related to cancer, including nuclear factor of kappa light polypeptide gene enhancer in B-cells inhibitor, alpha, v-rel reticuloendotheliosis viral oncogene homolog (avian), BCL2-associated X protein, early growth response 4, nuclear factor of kappa light polypeptide gene enhancer in B-cells 2 (p49/p100), etc. 65 FGF and PGRN common down-regulated genes were identified, and were significantly associated with reproductive system development and function, cellular development, cellular growth and proliferation. We found, however, that the majority of genes were regulated differentially between PGRN and FGF. Our results suggest that PGRN and FGF4 share common signals which contribute to tumorgenesis, but also reveal that the common malignant phenotype attributed to both PGRN and FGF4 differs significantly at the transcriptional level suggesting unique pathways linking PGRN and FGF to tumor progression in these cells. Citation Format: Yonghua Zhang, Amin Ismail, Andrew Bateman. Comparative tumorigenesis of progranulin and fibroblast growth factor 4 and in adrenocortical carcinoma cells. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 88. doi:10.1158/1538-7445.AM2013-88
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 | 0,001 |
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 ».