The Impact of Post-transcriptional Control: Better Living Through RNA Regulons
Bibliographic record
Abstract
Traditionally, cancer is viewed as a disease driven by genetic mutations and/or epigenetic and transcriptional dysregulation. While these are undoubtedly important drivers, many recent studies highlight the disconnect between the proteome and the genome or transcriptome. At least in part, this disconnect arises as a result of dysregulated RNA metabolism which underpins the altered proteomic landscape observed. Thus, it is important to understand the basic mechanisms governing post-transcriptional control and how these processes can be co-opted to drive cancer cell phenotypes. In some cases, groups of mRNAs that encode protein involved in specific oncogenic processes can be co-regulated at multiple processing levels in order to turn on entire biochemical pathways. Indeed, the RNA regulon model was postulated as a means to understand how cells coordinately regulate transcripts encoding proteins in the same biochemical pathways. In this review, we describe some of the basic mRNA processes that are dysregulated in cancer and the biological impact this has on the cell. This dysregulation can affect networks of RNAs simultaneously thereby underpinning the oncogenic phenotypes observed.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".