Abstract 3037: Integrated Genomic, MicroRNA (miRNA) and Proteomic Profiling of Ovarian Carcinoma for Biomarker Discovery
Notice bibliographique
Résumé
Abstract Ovarian cancer (OCa) is the fifth leading cause of cancer-related deaths in North American women and the first due to a gynecologic malignancy. The long-term effectiveness of standard therapy is generally poor and is accompanied by serious side effects. Thus there is a need for developing markers not only for diagnosis and prognosis, but also for predicting therapeutic response. Tumour progression and resistance to therapy is a consequence of the complexity of DNA, RNA and proteins. The search for effective and specific biomarkers should integrate aspects of all these factors. We have previously demonstrated that KLK6 is a promising biomarker for OCa and its observed over-expression is linked to copy-number gains of the 19q13.3/4 locus. Here, we demonstrate by multi-colour FISH analyses that the KLK locus in 81 OCas is subject to high-level of genomic instability (p<0.001); and such instability is significantly co-related to grade (p<0.001). KLK6-specific immunohistochemistry (IHC) showed no strong corelation with KLK6 copy-number, suggesting that other mechanisms, together with copy-number, drive its over-expression. Because 19q contains the highest number of annotated microRNAs (miRNAs) and copy-number instability may affect expression of these miRNAs, we investigated the role of miRNAs in OCa, not only for regulating KLK6, but as biomarkers for OCa. miRNA profiling of OCa cell lines and primary tumours by RT2-PCR showed the differential expression of miRNAs, consistent with other published studies in OCa. Since miRNAs can potentially affect the protein expression of hundreds of genes, the identification of such differentially expressed proteins not only provides putative biomarkers, but may also elucidate pathways for therapeutic intervention. Using Stable Isotope Labelling with Amino Acids in Cell Culture (SILAC) coupled to mass spectrometry for the OVCAR-3 cell line, cultures were labeled separately in light-Arg/Lys and heavy-Arg/Lys. In this control experiment, over 2,800 proteins were identified with 2,465 quantified. Over 94% of these quantified proteins showed a heavy:light ratio between 0.8 and 1.2, making this a robust system for quantitatively distinguishing differentially expressed proteins in the presence of miRNA precursors or inhibitors. Our profiling, as well as others, has shown the loss of expression of let-7 family members and hsa-125a-5p in OCas, and both miRNAs show decreased expression in OVCAR-3. Interestingly, these miRNAs are also predicted to target KLK6. Thus, we hypothesize that these miRNAs will not only affect the expression of KLK6, but also the expression of other target genes. In the future, using SILAC, we will identify the differentially expressed proteins affected upon re-introduction of these miRNAs through the differential labeling of miRNA-transfected OVCAR-3 vs. non-transfected OVCAR-3; thus revealing novel biomarkers for OCa. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 3037.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 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 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».