<i>IKZF3</i> polymorphisms contribute to the increased risk of acute lymphoblastic leukemia in children
Bibliographic record
Abstract
BACKGROUND: Acute lymphoblastic leukemia (ALL) is the most common cancer in children. IKZF3 (IKAROS family zinc finger 3) is a hematopoietic-specific transcription factor, and it has been validated that it is involved in leukemia. However, the role of IKZF3 single-nucleotide polymorphisms (SNPs) remains unclear. In this case-control study, the authors investigated the association of IKZF3 SNPs with ALL in children. METHODS: Six IKZF3 reference SNPs (rs9635726, rs2060941, rs907092, rs12946510, rs1453559, and rs62066988) were genotyped in 692 patients who had ALL (cases) and in 926 controls. The associations between IKZF3 polymorphisms and ALL risk were determined using odds ratios (ORs) and 95% confidence intervals (CIs). The associations of rs9635726 and rs2060941 with the risk of ALL were further estimated by using false-positive report probability (FPRP) analysis. Functional analysis in silico was performed to evaluate the probability that rs9635726 and rs2060941 might influence the regulation of IKZF3. RESULTS: The authors observed that rs9635726C>T (adjusted OR, 1.49; 95% CI, 1.06-2.11; p = .023) and rs2060941G>T (adjusted OR, 1.51; 95% CI, 1.24-1.84; p = .001) were related to and increased risk of ALL in the recessive and dominant models, respectively. Furthermore, the associations of both rs9635726 (FPRP = .177) and rs2060941 (FPRP < .001) with ALL were noteworthy in the FPRP analysis. Functional analysis indicated that rs9635726 and rs2060941 might repress the transcription of IKZF3 by disrupting its binding to MLLT1, TAF1, POLR2A, and/or RAD21. CONCLUSIONS: This study revealed that IKZF3 polymorphisms were associated with increased ALL susceptibility in children and might influence the expression of IKZF3 by disrupting its binding to MLLT1, TAF1, POLR2A, and/or RAD21. IKZF3 polymorphisms were suggested as a biomarker for childhood ALL.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".