Modulators of Inhibitor of Growth (ING) Family Expression in Development and Disease
Bibliographic record
Abstract
The inhibitor of growth (ING) gene family proteins regulate many critical cellular processes such as cell proliferation and growth, apoptosis, DNA repair, senescence, angiogenesis, and drug resistance. Their transcripts and proteins are differentially expressed in health and disease and there is evidence for developmental regulation. The vast majority of studies have characterized ING levels in the context of cancer. However, relatively little attention has been paid to the expression of ING family members in other contexts. This review summarizes the findings from human and animal model systems that provide insight into the factors influencing the expression of these important proteins. We examine the influence of cell cycle and aging as well as genotoxic stress on ING expression levels and evaluate several emerging areas of inquiry demonstrating that ING gene activity may be modulated by factors such as the p53 tumor suppressor, DNA methylation, and ING proteins themselves with external factors such as hormones, reactive oxygen species, TGFbeta signalling, and other proteins of pathological significance also influencing ING levels. We then briefly discuss the influence of post-translational modification and changes in subcellular localization as it pertains to modulation of ING expression. Understanding how ING expression is modulated represents a vital aspect of effective drug targeting strategies.
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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 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 0.003 |
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".