Discovery of Novel Non‐Coding Products of the Pax‐5 Gene and Their Clinical Significance in Lymphoid Cancers
Bibliographic record
Abstract
Circular RNAs (circRNAs) are a newly identified class of RNA which are highly expressed and conserved in mammalian cells. With the intrinsic property of being resistant to exonucleolytic activity due to their circular configuration, circRNAs are remarkably stable compared to their linear RNA species counterpart. CircRNAs regulate many biological processes and their aberrant expression is often associated with disease progression. Recently, we have characterized the expression of circRNAs of the potent Pax‐5 oncogene in B cell cancers. In this present study, we set out to identify and establish the expression profiles of Pax‐5 circRNAs in B cell cancers and also, to evaluate the potential of these non‐coding products to act as disease biomarkers. Using RT‐PCR in B cell lines, Pax‐5 circRNAs were amplified with divergent primers. PCR products were then sequenced and aligned for identification and validation. Pax‐5 circRNAs were also evaluated by qPCR from peripheral blood mononuclear cells (PBMCs) from healthy donors and clinical samples. Four circRNA isoforms of the Pax‐5 gene were isolated, sequenced and identified from various B cell models. These latter Pax‐5 circRNA variants consisted of a circular single RNA strand characterized with either exons 2, 3, 4; exons 2, 3, 4, 5; exons 2, 3, 4, 5, 6; or exons 2, 3, 4, 5, 6, 7, 8 of the human Pax‐5 gene. Profiling of Pax‐5 circRNA expression levels in clinical samples reveal an overexpression in chronic lymphocytic leukemia in comparison to other B cell cancer lesions and healthy donors. Altogether, we describe 4 novel gene products (i.e. circRNAs) of the Pax‐5 oncogene in B cell cancers. Preliminary results also support a potential role for Pax‐5 circRNAs as prognostic or diagnostic biomarkers for specific B cell cancers. Further studies are ongoing to validate the correlation between Pax‐5 circRNA levels and disease progression. In addition, the mechanistic elucidation of Pax‐5 circRNAs in cancer processes will also potentially identify new avenues for B cell cancer therapeutic interventions. 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 2018 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 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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".