Abstract P4-07-10: MiRNAs are important regulators of Pax-5 expression and function during breast cancer progression
Bibliographic record
Abstract
Abstract Recent studies have enabled the identification of important factors regulating cancer progression, one of these being the Pax-5 gene. Pax-5, an essential developmental factor of B cells, is aberrantly expressed in various B cell cancer lesions and solid tumors such as breast carcinoma. Although Pax-5 downstream activity is relatively well characterized, the regulation of aberrant Pax-5 expression in a cancer specific context is poorly understood. To investigate the regulation of Pax-5 expression, we turned our attention to micro-RNAs (miRNAs). MiRNAs are highly conserved, small non-coding RNA molecules that regulate key biological processes. Extensive studies also show their deregulation in multiple cancer lesions. In this study, we aim to elucidate a causal link between differentially expressed miRNAs in cancer cells and their putative targeting of Pax-5-dependent cancer processes. With the help of biobank data and bioinformatics analyses, we observe that miRNAs 484 and 210 are aberrantly expressed in breast cancer cells and cross-reference with their predicted capacity to target the Pax-5 mRNA 3’ untranslated region (3’UTR). Using anti- or pre-miRNAs transfected into Pax-5 expressing breast cancer cell lines (MCF-7 and MB231), we demonstrate that miRNAs 484 and 210 are capable of regulating Pax-5 expression. In addition, miRNA-regulated Pax-5 expression resulted in a concomitant alteration in Pax-5-mediated phenotype and cancer processes. This is the first study demonstrating the regulation of Pax-5 expression and function by non-coding RNAs in cancer cells. We believe that the aberrant expression of Pax-5 in cancer cells is in part due to deregulated miRNA expression profiles. This study will bring insight in regards to cancer regulating processes associated with miRNA and Pax-5 deregulations and help us better understand aberrant Pax-5 expression levels within cancerous states. This study can therefore provide the eventual possibility of earlier, more efficient diagnostics as well as more targeted treatments for cancer patients. Citation Format: Jason M Harquail, Gilles A Robichaud. MiRNAs are important regulators of Pax-5 expression and function during breast cancer progression [abstract]. In: Proceedings of the Thirty-Seventh Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2014 Dec 9-13; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2015;75(9 Suppl):Abstract nr P4-07-10.
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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 | 0.001 | 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) | 0.007 | 0.003 |
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".