Cellular signaling pathways affect the function of ribonucleotide reductase mRNA binding proteins: mRNA stabilization, drug resistance, and malignancy (Review)
Bibliographic record
Abstract
Ribonucleotide reductase is an enzyme that is essential for DNA synthesis and repair. It is composed of 2 dimeric proteins called R1 and R2 that are both necessary for enzymatic activity that reduces ribonucleotides to deoxyribonucleotides. This is the rate-limiting reaction that provides a supply of precursors for DNA synthesis therefore it is essential for cell proliferation. The importance of understanding the complex regulation of ribonucleotide reductase is emphasized by observations that mechanisms controlling its expression and activity may be altered during malignant cell proliferation which leads to drug resistance, making it a useful target to develop chemotherapeutic compounds in the treatment of cancer. Expression studies with the R1 and R2 genes have provided evidence for a direct role for the components of ribonucleotide reductase in determining malignant potential. Ribonucleotide reductase is regulated by transcriptional activation of gene expression and post-transcriptional mechanisms that alter mRNA message stability. Post-transcriptional regulation of mRNA turnover plays an important role in modulating mRNA steady state levels and therefore directly influences gene expression. The 3'-untranslated region (UTR) of R1 and R2 messages contain sequences that are important in regulating gene expression through changes in message stability. Studies have found that mRNA message stability is mediated by growth factors, cytokines and tumor promoters. Several studies have elucidated signal transduction pathways of tumor promoters, TGF-beta and oxidation/reduction agents. This report reviews how knowledge of these signaling pathways is revealing new insights into how ribonucleotide reductase mRNA binding proteins are important in regulating cellular proliferation, drug resistance and malignancy.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".