m<sup>6</sup>A RNA methylation regulators predict prognosis and indicate characteristics of tumour microenvironment infiltration in acute myeloid leukaemia
Bibliographic record
Abstract
Patients with acute myeloid leukaemia (AML) have poor prognoses and low overall survival (OS) rates owing to its heterogeneity and the complexity of its tumour microenvironment (TME). N6-methyladenosine (m6A) modification plays a key role in the initiation and progression of haematopoietic malignancies. However, the underlying function of m6A regulators in AML remains elusive. This study thoroughly analysed the m6A modification features of 177 AML patients based on 22 m6A regulators. Utilizing unsupervised clustering, we determined three distinct m6A modification patterns related to different biological functions, TME cell-infiltrating characteristics and clinical outcomes. Additionally, a risk score was constructed based on six m6A regulators-associated prognostic signatures and was validated as an independent and valuable prognostic factor for AML. Patients with a low-risk score exhibited better survival than those with a high-risk score. Many m6A regulators were aberrantly expressed in AML, among which METTL14, YTHDC2, ZC3H13 and RBM15 were observed to be associated with the OS of AML. In addition, these four m6A regulators were found to be noticeably related to the immune checkpoint inhibitor (ICI) treatments. Finally, we verified the expression levels of these four m6A regulators in AML and healthy samples and three groups of AML patients with different risk categories. Collectively, our study indicates that the m6A modification pattern is involved in TME immune-infiltrating characteristics and prognosis in AML. A better understanding of the m6A modification pattern will help enhance our knowledge of the molecular mechanisms of AML and develop potential prognosis prediction indicators and more effective immunotherapeutic 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".