Abstract 117: The role of microRNAs in mammary tumorigenesis
Notice bibliographique
Résumé
Abstract MicroRNAs (miRNAs) are small non-coding regulatory RNA molecules that act post-transcriptionally on mRNA to regulate the expression of most cellular genes. Many miRNAs are aberrantly expressed in tumor tissue, acting either as tumor suppressors or oncogenes, and their expression has been implicated in the development of breast cancer. Using a transgenic mouse model of mammary tumorigenesis where transgenic overexpression of IGF-IR drives mammary tumor development (MTB-IGFIR transgenic mice), a miRNA array was performed to study the expression level of various miRNAs in mammary tumors compared to wild-type mammary tissue. The miRNA array revealed that 43 miRNAs were significantly elevated by a minimum of 2-fold in tumor tissue, while 45 miRNAs were significantly downregulated by at least 2-fold in tumor tissue when compared to normal mammary tissue. The expression differences of miRNAs (miR-24, miR-31, miR-183, miR-200a*, miR-200b, miR-200c, miR-210, miR-378 and miR-574-3p) were confirmed in tumor and normal mammary tissues using qRT-PCR. These miRNAs were chosen as they displayed some of the largest expression differences between the mammary tumors and normal mammary tissue, and several have been implicated in tumor progression and metastasis. In particular, miR-200c and miR-31 levels are inversely related to metastasis, whereas miR-183 may be involved in reducing cellular migratory properties. Next, the expression of these miRNAs was examined in a tumor cell line (RM11A) generated from a MTB-IGFIR mammary tumor. We found that miR-31, miR-183, miR-200c, miR-210, and miR-378 were expressed in the RM11A cells. We then used synthetic miRNA precursors that mimic the mature form of miRNAs and inhibitors to overexpress and knockdown, respectively, the levels of these miRNAs in RM11A cells. Overexpression of these five miRNAs was optimized using qRT-PCR with TaqMan primers to increase expression levels between 30 and 400 fold, as this magnitude of change was observed in the miRNA array. To determine the impact of miRNAs on mammary tumorigenesis, assays for proliferation (Ki-67 immunofluorescence), migration (scratch wound and Boyden chamber), and cell survival (JC-1 fluorescence) will be performed. To date we have observed that when compared to negative controls, overexpression of miR-210 significantly increased proliferation by approximately 1.5- fold, while the overexpression of miR-31, miR-183, and miR-378 did not significantly affect proliferation. Functional assays performed in the RM11A cell line will be repeated in a second mammary tumour cell line (4T1) to compare and contrast the importance of these miRNAs in another cell line. Discovering the differential expression of miRNAs and their effects on proliferation, migration, and survival in the RM11A and 4T1 cells will enhance our understanding of the function of miRNAs in human breast cancer. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 117. doi:10.1158/1538-7445.AM2011-117
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 | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,002 |
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 ».