Elucidation of the <i>Pax‐5/</i> miRNA interactome and its oncogenic effects in cancer cells
Bibliographic record
Abstract
The Pax‐5 oncogene has consistently been associated to B cell cancer lesions and more recently solid tumors including breast carcinoma. Although Pax‐5 downstream activity is relatively well characterized, aberrant Pax‐5 expression in a cancer specific context is poorly understood. To investigate the regulation of Pax‐5 expression, we studied micro‐RNAs (miRNAs) and mRNA polymorphism in cancer cells. Using bioinformatics and next‐generation sequencing, we found that miRNAs 484 and 210 are aberrantly expressed in breast cancer and predicted to target Pax‐5 mRNA. Through conditional modulation of these miRNAs, we demonstrate that miRNA‐484 and 210 inhibit Pax‐5 expression and regulate Pax‐5‐associated cancer processes. In validation, we show that these effects are reversible by either Pax‐5 recombinant overexpression; or, selective miRNA inhibition. On the other hand, upon our analysis of the Pax‐5 transcript, we found that not only is the 3′UTR submitted to alternative polyadenylation; but also, alternative splicing of the Pax‐5 mRNA 3′UTR. These transcript polymorphism events lead to the shortening of the Pax‐5 3′UTR which resulted in greater oncogene translation frequency. More importantly, we also found that Pax‐5 transcripts characterized with shorter 3′UTRs were associated with advanced staging of cancer lesions. Our findings identify novel molecular mechanisms, which account for Pax‐5 aberrant expression and function in cancer cells. These findings will further elucidate Pax‐5 ‐mediated cancer processes and may provide new avenues for therapeutic intervention. Support or Funding Information This work was supported by grants from the New Brunswick (NB) Innovation Foundation, the Canadian Breast Cancer Foundation, the Canadian Breast Cancer Society/QEII Foundation, the NB Health Research Foundation and by the Beatrice Hunter Cancer Research Institute This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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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".