Serum levels of <i>hsa‐miR‐16‐5p</i>, <i>hsa‐miR‐29a‐3p</i>, <i>hsa‐miR‐150‐5p</i>, <i>hsa‐miR‐155‐5p</i> and <i>hsa‐miR</i>‐<i>223‐3p</i> and subsequent risk of chronic lymphocytic leukemia in the EPIC study
Bibliographic record
Abstract
Chronic lymphocytic leukemia (CLL) is an incurable disease accounting for almost one‐third of leukemias in the Western world. Aberrant expression of microRNAs (miRNAs) is a well‐established characteristic of CLL, and the robust nature of miRNAs makes them eminently suitable liquid biopsy biomarkers. Using a nested case–control study within the European Prospective Investigation into Cancer and Nutrition (EPIC), the predictive values of five promising human miRNAs (hsa‐miR‐16‐5p, hsa‐miR‐29a‐3p, hsa‐miR‐150‐5p, hsa‐miR‐155‐5p and hsa‐miR‐223‐3p), identified in a pilot study, were examined in serum of 224 CLL cases (diagnosed 3 months to 18 years after enrollment) and 224 matched controls using Taqman based assays. Conditional logistic regressions were applied to adjust for potential confounders. The median time from blood collection to CLL diagnosis was 10 years (p25–p75: 7–13 years). Overall, the upregulation of hsa‐miR‐150‐5p, hsa‐miR‐155‐5p and hsa‐miR‐29a‐3p was associated with subsequent risk of CLL [OR1∆Ct‐unit increase (95%CI) = 1.42 (1.18–1.72), 1.64 (1.31–2.04) and 1.75 (1.31–2.34) for hsa‐miR‐150‐5p, hsa‐miR‐155‐5p and hsa‐miR‐29a‐3p, respectively] and the strongest associations were observed within 10 years of diagnosis. However, the predictive performance of these miRNAs was modest (area under the curve <0.62). hsa‐miR‐16‐5p and hsa‐miR‐223‐3p levels were unrelated to CLL risk. The findings of this first prospective study suggest that hsa‐miR‐29a, hsa‐miR‐150‐5p and hsa‐miR‐155‐5p were upregulated in early stages of CLL but were modest predictive biomarkers of CLL risk.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".