Abstract LB-391: Hsa_circ_001783 regulates breast cancer progression via sponging miR-200c
Bibliographic record
Abstract
Abstract Introduction: Increasing evidence suggests circRNAs exert vital functions in tumor progression via sponging miRNAs. However, the role of circular RNAs in breast cancer remains mostly unclear. Here we reported the molecular mechanisms of a novel circRNA, hsa_circ_001783 in regulating breast cancer progression and its ability in predicting clinical outcomes by integrating high throughput computation, experimental technologies in vitro and clinical investigation. Methods: We extracted sequences and annotations of circRNAs and miRNAs from circbase and miRbase respectively. Four algorithms were used to predict the potential bindings of miRNAs to the conserved sequences of individual circRNAs. Computationally screening Ingenuity knowledge database (Qiagen), PubMed and Embase identified breast cancer associated miRNAs and their related functions in cancer. Five essential functional features were used to score the strength associations between miRNAs and breast cancer, which was subsequently employed to rank the network branches across circRNA-miRNA-breast cancer. Expression levels of hsa_circ_001783 in human breast tissue and cancer cells were quantified by FISH and qPCR. The effects of knocking down hsa_circ_001783 on breast cancer cells were examined by Edu, CCK-8, colony formation and transwell assays. K-M model was used to define the predictor performance of hsa_circ_001783 in a Chinese breast cancer cohort of 128 patients. Results: Our computational pipeline identified hsa_circ_001783 as the one with highest score out of 594 breast cancer-associated circRNA candidates. We found the circRNA was enriched in cytoplasma and overexpressed in breast tumor as compared to paired non-cancerous tissue. High expression of hsa_circ_001783 correlated with higher tumor burden (p=0.047) and poor overall survival (p=0.025) in 128 patients. Knock-down of hsa_circ_001783 remarkably inhibited the proliferation and invasion of MDA-MB-231 and MDA-MB-468 cells. We found hsa_circ_001783 increased significantly by 1.5-2 folds while 7 miRNAs, predicted targets of hsa_circ_001783, were remarkably reduced (fold change>1.5) in mRNA expression levels in BT549, MDA-MB-468 and MDA-MB-231 as compared with MCF-7 breast cancer cell lines. Among all the targets, miR-200c was the one in the strongest correlation with hsa_circ_001783 in expression levels. Knockdown of hsa_circ_00178 in MDA-MB-231 breast cancer cells suppressed expression of miR-200c-targeted genes ZEB1, ZEB2 and CCNA2. The expression level of hsa_circ_001783 in human breast cancer tissues negatively correlated with expression of miR-200c (p=0.0286), but positively correlated with that of ZEB1 (p=0.002), ZEB2 (p=0.0001) and CCNA2 (p=0.005). Conclusion: Hsa_circ_001783 regulates breast cancer progression via sponging miR-200c. The circRNA may serve as a novel predictor of clinical outcomes for breast cancer. Citation Format: Zihao Liu#, You Zhou#, Gehao Liang, Yun Ling, Luyuan Tan, Yan Wang, Wenjing Zhong, Chang Gong*. Hsa_circ_001783 regulates breast cancer progression via sponging miR-200c [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr LB-391.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".