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Enregistrement W2403162184 · doi:10.1158/1538-7445.nonrna15-b36

Abstract B36: Developing circRNA signatures as a biomarker for the early diagnosis of pancreatic carcinoma

2016· article· en· W2403162184 sur OpenAlexaff
Jessica Kalra, Keith R. Laderoute, Daniel J. Renouf, David Shaeffer, Marcel B. Bally

Notice bibliographique

RevueCancer Research · 2016
Typearticle
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueExtracellular vesicles in disease
Établissements canadiensUniversity of British ColumbiaBC Cancer Agency
Organismes subventionnairesnon disponible
Mots-clésMicrovesiclesBiologyCancer researchPancreatic cancerBiomarkerCD81CD63microRNATranscriptomeGeneComputational biologyCancerGene expressionImmunologyGenetics

Résumé

récupéré en direct d'OpenAlex

Abstract Background: At the time of diagnosis, pancreatic ductal adenocarcinoma (PDAC) is typically already advanced and incurable. Current research has concentrated on finding tumor markers for early detection while the cancer is still localized and amenable to therapy, however, these markers remain elusive. The studies described focus on developing exonic circular RNAs (circRNA) as a novel set of diagnostic/prognostic biomarkers for PDAC. CircRNAs found in mammalian cells, are backsplice variants of transcripts that are derived from approximately 15% of actively transcribed genes. The prevalence, stability and cell-specific expression patterns of circRNAs suggest that they could be exploited as an indirect or surrogate readout of transcriptional activity in normal and diseased states. Both coding and non-coding RNAs are encapsulated within cytoplasmic endosomes, which are subsequently released as extracellular or circulating microvesicles called exosomes. Exosomes have become a promising research focus as a source for biomarkers. Objective: We are interested in elucidating whether aberrantly expressed genes in PDAC produce different types of circRNAs that become enriched in tumor-secreted exosomes. Hypothesis: Exosomal circRNA (exo-circRNA) expression patterns are potentially specific to different stages/types of PDAC and therefore can be used in disease sub-typing and prognosis. Methods: Exosomes were isolated from a normal pancreatic exocrine cell line (htert-HPNE) as well as three PDAC cell lines ranging from well to poorly differentiated, including PANC-1, BxPC3and MIAPaCa-2. The size and relative abundance of exosomes was quantified by transmission electron microscopy (TEM). The expression of common exosomal markers (CD63, CD9, CD81, and HSP70) and the PDAC exosomal marker glypican-1 (GPC-1) was evaluated by flow cytometry. RNA was purified from exosomes (exo-RNA) and the rRNA depleted samples were subject to circular RNA isolation. Exo-circRNA was used to construct RNA-Seq libraries. Sequencing of the generated libraries was performed on the Illumina Nextseq platform using 2x100 reads V1 chemistry at a targeted depth of 25 million paired end reads per library. The four read libraries were mapped to the human reference genome GRCh38.p3 using BWA-MEM, and analyzed using two bioinformatics platforms, “CIRI” and “find_circ”. Comparison of membership and expression levels was made between a normal cell line and well-, moderately- and poorly differentiated, PDAC cell lines. Results: Exosome size ranged from 20nm to 80nm. These structures demonstrated some diversity in size and marker expression when comparing cell lines. The smallest structures were observed from BxPC3 cell. Here, we show for the first time the presence of circRNAs in exosomes collected from PDAC cell lines. RNA-seq analyses revealed a number of interesting circRNA species that show cell line specificity. Preliminary examination of PANC-1 RNA-seq libraries from the ENCODE database identified over 800 circRNA isoforms from total cellular transcriptome. The number of circRNA isoforms for PANC-1 cells decreased when using an enriched exo-circRNA library for alignment to approximately 19 putative circRNA markers. Furthermore, circRNA isoforms for each of the cell lines examined were distinct. Interestingly, no circRNAs of genes known to be overexpressed in PDAC (such as K-RAS) were found in the fraction of exo-cricRNA for any of the cell lines tested. Impact: The studies described demonstrate that specific circRNAs can be readily extracted from the exosomes of conditioned media. We hope that this novel tool can be further developed to help to diagnose pancreatic carcinoma when it is amenable to surgical resection and/or chemotherapy, thereby reducing the mortality associated with this disease. Citation Format: Jessica Kalra, Keith Laderoute, Daniel Renouf, David Shaeffer, Marcel Bally. Developing circRNA signatures as a biomarker for the early diagnosis of pancreatic carcinoma. [abstract]. In: Proceedings of the AACR Special Conference on Noncoding RNAs and Cancer: Mechanisms to Medicines ; 2015 Dec 4-7; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2016;76(6 Suppl):Abstract nr B36.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,002
Score d'incertitude au seuil0,007

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0010,001
Science ouverte0,0000,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0020,001

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.

Tête enseignante Opus0,062
Tête enseignante GPT0,380
Écart entre enseignants0,318 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2016
Routes d'admission1
Résumé présentoui

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