Abstract A28: Differentially expressed microRNA profiles in pancreatic ductal and ampullary adenocarcinomas
Notice bibliographique
Résumé
Abstract Background: Pancreatic cancer is associated with 6.9% and 4% of all cancer-related deaths in the United States and Brazil, respectively. Pancreatic ductal carcinoma comprises 90% of cases, the majority being of adenocarcinoma subtype. Approximately 12% of periampullary tumors are adenocarcinomas of Vater papilla (ampullary adenocarcinomas); ampullary tumors are often associated with a better prognosis than ductal adenocarcinomas. Although genetic alterations were previously identified in pancreatic carcinomas, there is still a lack of effective treatment strategies. Therefore, the identification of new biomarkers, such as alterations in non-coding RNAs, is urgently needed for the development of novel molecularly targeted therapies for these cancers. microRNAs (miRNAs) are frequently deregulated and contribute to cancer development and progression and have potential prognostic and predictive value. Global miRNA expression profiling analysis in pancreatic cancer, followed by the identification of miRNA target genes may lead to the identification of clinically applicable biomarkers. The novel aspect of our work is the investigation of pancreatic tumors from Brazilian patients, with the inclusion of ampullary adenocarcinomas, a rare subtype. Objectives: To identify global miRNA expression profiles and miRNA target genes in pancreatic ductal and ampullary adenocarcinomas compared to paired histologically normal pancreatic tissue. Patients and Methods: 30 formalin fixed, paraffin embedded (FFPE) pancreatic carcinoma samples were used, including 24 pancreatic ductal adenocarcinomas (PDAC) and 6 ampullary adenocarcinomas (AMP). Paired histologically normal pancreatic tissues were used as controls. All tumor and normal tissues were needle microdissected (Leica EZ4 stereomicroscope). Global miRNA expression profiles were determined using the TaqMan Array Human MicroRNA Cards (TLDA) (card A, v3.0) (Life Technologies) platform. Data analysis was performed using the ExpressionSuite Software v1.0.3. Statistical analysis was performed to correlate miRNA expression with relevant clinical data, using SAS 9.3 software. Computational bioinformatics analysis was performed to identify miRNA target genes, as well as to construct protein-protein interaction and miRNA-gene targets networks. Results and Discussion: We identified 63 significantly deregulated (FC≥2 and p<0.05) miRNAs in PDAC (33 over- and 30 under-expressed) compared to paired histologically normal pancreatic tissue. In AMP, a group of 7 miRNAs was significantly deregulated (4 over- and 3 under-expressed) compared to normal pancreas. Our results showed differentially expressed miRNAs and a complexity of miRNA changes potentially associated to PDAC and AMP tumorigenesis. 3/7 miRNAs (miR-222, 148a and 375) were commonly deregulated in PDAC and AMP tumors. Furthermore, miRNA-gene targets networks were distinct in these different histological subtypes of pancreatic carcinomas. Global miRNA expression profiles showed that PDAC have a significantly higher number of altered miRNAs and a higher number of predicted miRNA target genes than AMP tumors, which could be potentially associated to disease progression and tumor aggressiveness in PDAC compared to AMP. Although these tumors have biological differences, commonly deregulated miRNAs in PDAC and AMP suggest that PDAC and AMP tumorigenesis may share commonly deregulated pathways. Conclusion: miRNAs identified herein may be associated to the biology of PDAC and AMP. Among the miRNAs exclusively deregulated in PDAC, we identified known and not previously reported (novel) miRNAs. In addition, we identified several miRNA target genes associated with tumor invasion, metastasis and poor patient prognosis. Functional in vitro and in vivo validation studies may elucidate the role of identified miRNAs as modulators of oncogenesis mechanisms in PDAC and AMP. T. Felix was funded through São Paulo Research Foundation (FAPESP), MSc. fellowship (2014/00367-4) Citation Format: Tainara F. Felix, Tomas Tokar, Maria A. M. Rodrigues, Rogerio A. Oliveira, Claudia N. Hasimoto, Juan C. Llanos, Robson F. Carvalho, Silvia R. Rogatto, Wan Lam, Igor Jurisica, Sandra A. Drigo, Patricia P. Reis.{Authors}. Differentially expressed microRNA profiles in pancreatic ductal and ampullary adenocarcinomas. [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer: Advances in Science and Clinical Care; 2016 May 12-15; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2016;76(24 Suppl):Abstract nr A28.
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 | 0,001 | 0,001 |
| 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 ».