NAT10 mediated mRNA acetylation modification patterns associated with colon cancer progression and microsatellite status
Bibliographic record
Abstract
N4-acetylcytidine (ac4C) is one type of RNA modification found in eukaryotes. RNA acetylation modifications are gradually expanding in oncology. However, the role of RNA acetylation modifications in colorectal cancer and its association with colorectal cancer microsatellite status remain unclear. Using public databases and in vitro experiments, we verified the expression and biological function of NAT10, as the key RNA acetylation modification enzyme, in colorectal cancer. The results showed that NAT10 was highly expressed in colorectal cancer, and significantly promoted colorectal cancer cell proliferation. NAT10 was also involved in several aspects of cell homoeostasis such as ion transport, calcium-dependent phospholipid binding, and RNA stability. NAT10 expression positively correlated with immune infiltration in colorectal cancer. We further constructed a risk regression model for mRNA acetylation in colorectal cancer using acetylation-related differential genes. We found that tumour immune infiltration, microsatellite instability (MSI) proportion, tumour immune mutation burden, and patient response to immunotherapy were positively correlated with risk scores. For the first time, our study showed that the level of mRNA acetylation modification level is elevated in colorectal cancer and positively correlates with immune infiltration and microsatellite status of patients. Based on our findings, NAT10 may be a new target for colorectal cancer treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.010 |
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".