lncRNA deregulation in childhood acute lymphoblastic leukemia: A systematic review
Bibliographic record
Abstract
Childhood acute lymphoblastic leukemia (ALL), the most common pediatric cancer, is a heterogeneous disease comprised of multiple molecular subtypes with distinct somatic genetic alterations, which results in different outcomes for the patients. Accurate patient risk stratification through genetic markers could increase survival rates, but the identification of reliable biomarkers is needed, as 20‑30% of B‑ALL patients cannot be classified in the clinic with routine techniques and some patients classified as low‑risk and good‑responders to treatment will eventually relapse. Long non‑coding RNAs (lncRNAs) can represent novel candidates with diagnostic, classification, prognosis, and treatment response potential. However, regarding childhood ALL, there is inconsistency in the data reported due to the lack of a consensus nomenclature for lncRNA naming and the methodology and designing applied for their study. Therefore, the aim of the article is to clarify the potential of lncRNAs as biomarkers in childhood ALL through a systematic review. From a revision of 23 manuscripts, it was found that AWPPH overexpression could represent a novel marker for ALL diagnosis, including both B and T immunophenotypes, and 18 lncRNAs were specifically associated with B‑cell ALL (B‑ALL) patients. We identified subtype‑specific signatures for ETV6‑RUNX1, hyperdiploidy and KMT2A subtypes. These signatures hold promise as novel diagnostic markers and could refine the classification of patients.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".