Abstract 278: Deciphering circular RNA axes in early-onset breast cancer using a breast epithelial risk-progression 3D culture model
Notice bibliographique
Résumé
Abstract One of the known circular RNAs (circRNAs) functions is to sponge microRNAs (miRNAs). Studies reported dysregulated mRNA-circRNA-miRNAs axes as biomarker signatures in breast cancer (BC). Signatures in early-onset BC have not been characterized, while its incidence increased drastically worldwide. To address this gap, we performed circRNAs microarrays and miRNA-Sequencing on 3D breast epithelial risk-progression HMT-3522 S1 (S1) culture model to identify dysregulated post-transcriptional axes. S1 (non-neoplastic) cells form fully-polarized epithelium and closely mimic normal in vivo mammary epithelial morphology, while (pre-tumorigenic) Cx43KO-S1 (S1 silenced with shRNA for gap junction Cx43) lose epithelial polarity, multi-layer and mimic tumor-initiated in vivo mammary epithelial morphology. We detected through circRNA microarray (Arraystar Human circRNA Array V2) 75 significantly upregulated and 46 significantly downregulated circRNAs (Fold Change > 2, p-value < 0.05) in 6 samples of Cx43KO-S1 versus S1 cells in 3D. We then selected 10 upregulated and 8 downregulated circRNAs for validation. Of these, hsa_circ_0007961 and hsa_circ_0081481 were most significantly upregulated while hsa_circ_0060055, hsa_circ_0005185, hsa_circ_0083442 and hsa_circ_0077755 were most significantly downregulated by RT-qPCR. We focus on “possible” sponging activity of validated circRNAs to their predicted (in silico) target miRNAs, and cross-compared their dysregulation to our validated miRNA datasets (from young patients and 3D cell line). When circRNAs are upregulated, they downregulate the function of their sponged miRNAs, hence preventing them from deregulating their target mRNAs. For upregulated hsa_circ_0007961 (originating from SPRED2 mRNA), its predicted miR-99a-3p was indeed downregulated in young Lebanese and matched U.S. BC patient tissues (and in Cx43KO-S1 versus S1 microarrays) and predicted miR-653 possessed treatment potential in BC cells. For the second upregulated hsa_circ_0081481 (originating from FBXO24 mRNA), its predicted miR-3960 was exclusively dysregulated in young Lebanese BC patients. For downregulated hsa_circ_0077755 (originating from Cx43mRNA), its predicted miR-182 was commonly upregulated in young Lebanese and matched US patients and predicted miR-203a-3p was reported as promising prognostic biomarker in triple negative BC. Additionally, all predicted miRNAs targeted by all detected circRNAs were predominantly enriched in pathways in cancer. Thus, we suggest that our 3D risk-progression culture model might help delineate post-transcriptional axes to better understand early-onset BC initiation. We reveal three validated axes: i) SPRED2/hsa_circ_0007961/miR-99a-3p or miR-653-5p, ii) FBXO24/hsa_circ_0081481/miR-3960 and iii) Cx43/hsa_circ_0077755/miR-182 or miR-203, which could serve as potential biomarker signatures in early-onset BC. Citation Format: Nataly Naser Al Deen, Nadia Atallah Lanman, Shirisha Chittiboyina, Sophie Lelièvre, Heinrich Zu Dohna, Mounir AbouHaidar, Rabih Talhouk. Deciphering circular RNA axes in early-onset breast cancer using a breast epithelial risk-progression 3D culture model [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 278.
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 | 0,000 | 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 | 0,000 | 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 ».