Protein Acetylation as an Integral Part of Metabolism in Cancer Development and Progression
Bibliographic record
Abstract
Acetylation of lysine is one of the major post-translational modifications of histone and non-histone proteins of eukaryotic cells. Acetylation has been indicated as an avenue for cellular response to environmental, nutritional and behavioral factors. At the same time, aberrant protein acetylation has been related to cancer as well as many other diseases. Abnormal expression of some classes of histone deacetylases and histone acetyl transferases has been documented for the majority of cancers. These observations have led to extensive efforts in the development of inhibitors for these enzymes for the treatment of cancer as well as other diseases as well as pathogen control. Regulation of protein activities and gene expression by acetylation influences many processes relevant for cancer development, including metabolism. At the same time acetylation depends on a number of metabolic co-factors and a variety of metabolites act as inhibitors of acetylation proteins making acetylation enzymes an integral part of metabolism. Cancer metabolic phenotype is generally understood as one of the major hallmarks of cancer and thus the interplay between acetylation, anabolism and catabolism provides a very interesting forum for exploration of cancer development and for novel treatments. An ever increasing pool of publications shows relationships between the acetylation process and related enzymes with metabolites in cancerous and non-cancerous systems. In this review we are presenting previously established relationships between acetylation/deacetylation, metabolites and enzyme regulation particularly in relation to cancer development, progression and treatment.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".