Abstract 2718: Genomic and microRNAome subtraction identifies pathogenic viral sequences in pancreatic ductal adenocarcinoma
Notice bibliographique
Résumé
Abstract Introduction: Pancreatic ductal adenocarcinoma (PDAC) is by far the most common type of pancreatic cancer. It constitutes about 90% of tumors of the exocrine pancreas. The aggressive nature of PDAC along with its poor diagnostic markers contributes to a high lethality of this disease. Studies from other cancer entities implicate the role of viruses in tumor development. However, there have been no established reports about virus(es) associated with pancreatic cancer. The present study identified a virus by genomic and digital microRNAome subtraction between healthy and PDAC patients, suggesting that this virus may play a role in carcinogenesis. Experimental procedures: Representational difference analysis (RDA) was utilized to perform an experimental genome-wide search for DNA sequences that occur in PDAC but not in normal tissues. Ten healthy and ten PDAC tissue DNA samples were used for the generation of representations and subtractive hybridization. The difference products (DPs) so obtained were paired end sequenced on the Illumina MiSeq platform. The sequencing data were aligned against the viral RefSeq database. We also did in silico subtraction of microRNA content (miRNA-seq) of PDAC samples. Eight PDAC samples were analyzed for viral sequences using relevant RefSeq databases. The two approaches (RDA and miRNA-seq) were pursued in parallel. RT-qPCR analysis and the in vitro functional characterization of viral sequences in PDAC cell lines were respectively performed using hydrolysis probes and second generation microRNA mimic systems. Stable cell lines expressing viral microRNA and luciferase were produced using lentivirus for mice in vivo studies. Results and Summary: We found DNA sequences of a virus in the difference products of RDA whose signature was also implicated in the microRNA analysis. Further, using RT-qPCR, we identified that a particular microRNA from this virus is expressed at significantly higher levels in PDAC samples than in normal tissue. This observation was also verified by droplet digital PCR analysis. Non-metastatic PDAC cell lines overexpressing viral microRNA showed significant invasion against relevant controls, interestingly with no significant change in proliferation. We are currently analyzing the functional consequences of these viral sequences and their involvement in carcinogenesis. Conclusion: From two different robust approaches, these findings strongly indicate viral sequences associated with PDAC that could affect pathways relevant to tumor development. Citation Format: Mohanachary Amaravadi, Agnes Hotz-Wagenblatt, Sandeep Kumar Botla, Pouria Jandaghi, Mehdi Manoochehri, Nathalia Giese, Markus W. Büchler, Andrea S. Bauer, Jörg D. Hoheisel. Genomic and microRNAome subtraction identifies pathogenic viral sequences in pancreatic ductal adenocarcinoma. [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 2718. doi:10.1158/1538-7445.AM2015-2718
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 | 0,001 |
| 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) | 0,002 | 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 ».