A glimpse into novel acylations and their emerging role in regulating cancer metastasis
Bibliographic record
Abstract
Metastatic cancer is a major cause of cancer-related mortality; however, the complex regulation process remains to be further elucidated. A large amount of preliminary investigations focus on the role of epigenetic mechanisms in cancer metastasis. Notably, the posttranslational modifications were found to be critically involved in malignancy, thus attracting considerable attention. Beyond acetylation, novel forms of acylation have been recently identified following advances in mass spectrometry, proteomics technologies, and bioinformatics, such as propionylation, butyrylation, malonylation, succinylation, crotonylation, 2-hydroxyisobutyrylation, lactylation, among others. These novel acylations play pivotal roles in regulating different aspects of energy mechanism and mediating signal transduction by covalently modifying histone or nonhistone proteins. Furthermore, these acylations and their modifying enzymes show promise regarding the diagnosis and treatment of tumors, especially tumor metastasis. Here, we comprehensively review the identification and characterization of 11 novel acylations, and the corresponding modifying enzymes, highlighting their significance for tumor metastasis. We also focus on their potential application as clinical therapeutic targets and diagnostic predictors, discussing the current obstacles and future research prospects.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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".