Abstract 2848: Identifying and targeting competing endogenous RNA (ceRNAs) networks to inhibit lung metastasis in triple negative breast cancer
Bibliographic record
Abstract
Abstract Introduction: Triple negative breast cancer (TNBC), the most aggressive breast cancer subtype, has high incidence rate of lung metastasis. Not only protein coding transcripts, but also non-coding transcriptome, such as microRNAs (miRNAs) and long non-coding RNAs (lncRNAs), have active roles in cancer progression and metastasis. Additionally, lncRNAs can act as sponges for miRNAs. Here, we aimed i) to construct the first mRNA-miRNA-lncRNA competing endogenous RNA (ceRNA) network controlling metastasis in TNBC, and ii) to prevent lung metastasis by targeting identified central candidate genes. Material/Method: We established primary tumor and human-in-mouse (HIM) and mouse-in-mouse (MIM) lung metastasis models using TNBC cell lines in nude and Balb/c mice, respectively. We visualized both primary and metastatic tumors using in vivo imaging system, harvested tumors and performed both RNA and small RNA sequencing. We obtained differentially expressed miRNAs, mRNAs and lncRNAs between primary and metastatic tumors. Using several bioinformatics tools, we did enrichment analyses, miRNA target predictions, and network construction. Identified central lncRNAs were overexpressed or knocked down and will be tested in in vitro and vivo metastasis-related assays. Results and Conclusions: We obtained 45 and 91 miRNAs which were differentially expressed between primary and metastatic tumors in HIM and MIM models, respectively. A miRNA family with an established role in metastasis as well as several other miRNAs was identified as highly differentially expressed in the same direction in both models. Moreover, 1127 and 3350 mRNAs, and 85 and 111 lncRNAs were differentially expressed in HIM and MIM models, respectively. Metastasis-related processes based on differentially expressed mRNAs were enriched in the data. We then integrated these three layers of data, functional enrichments, pathway maps and target predictions to construct the first ceRNA network controlling lung metastasis in TNBCs. Currently, we are testing the functional roles of candidate lncRNAs in in vitro and in vivo metastasis assays. Ultimately, our study will uncover novel lncRNAs that can be used as potential targets and/or biomarkers in breast-to-lung metastasis. Funding: This study is supported by TUBITAK-CNRS Bilateral Grant with project number 214S364. Citation Format: Pelin Ersan, Unal Tokat, Erol Eyupoglu, Umar Raza, Yasser Riazalhosseini, Can Alkan, Denis Thieffry, Daniel Gautheret, Ozgur Sahin. Identifying and targeting competing endogenous RNA (ceRNAs) networks to inhibit lung metastasis in triple negative breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 2848. doi:10.1158/1538-7445.AM2017-2848
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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 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".