The oncogenic potential of Rab-like protein 1A (RBEL1A) GTPase: The first review of RBEL1A research with future research directions and challenges
Bibliographic record
Abstract
Research on Rab-like protein 1A (RBEL1A) in the past two decades highlighted the oncogenic properties of this gene. Despite the emerging evidence, its importance in cancer biology was underrated. This is the first RBEL1A critical review covering its discovery, biochemistry, physiological functions, and clinical insights. RBEL1A expression at the appropriate levels appears essential in normal cells and tissues to maintain chromosomal stability; however, its overexpression is linked to tumorigenesis. Furthermore, the upstream and downstream targets of the RBEL1A signaling pathways will be discussed. Mechanistically, RBEL1A promotes cell proliferation signals by enhancing the Erk1/2, Akt, c-Myc, and CDK pathways while blunting the apoptotic signals via inhibitions on p53, Rb, and caspase pathways. More importantly, this review covers the clinical relevance of RBEL1A in the cancer field, such as drug resistance and poor overall survival rate. Also, this review points out the bottle-necks of the RBEL1A research and its future research directions. It is becoming clear that RBEL1A could potentially serve as a valuable target of anticancer therapy. Genetic and pharmacological researches are expected to facilitate the identification and development of RBEL1A inhibitors as cancer therapeutics in the future, which could undoubtedly improve the management of human 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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".