Abstract 278: Deciphering circular RNA axes in early-onset breast cancer using a breast epithelial risk-progression 3D culture model
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".