Expression of lineage markers using real-time quantitative polymerase chain reaction (RT-qPCR) in normal and in leukemia bone marrow
Bibliographic record
Abstract
BACKGROUND: The study of lineage markers by real-time quantitative polymerase chain reaction (RT-qPCR) at diagnosis enables differentiation between acute myeloblastic leukemia, B- or T-lineage acute lymphoblastic leukemia, without cell sorting. Our objective was to assess the relationship between protein expression and the amount of lineage marker mRNA in acute leukemia samples and to determine whether four lineage markers could be used to differentiate between normal and acute leukemia bone marrow (BM) without cell sorting. METHODS: Quantification of the mRNA of CD19, CD79a, CD3e, and myeloperoxidase was performed by RT-qPCR on 130 acute leukemia BM samples at diagnosis and on 20 BM samples from healthy donors, without cell sorting. Immunophenotyping of leukemia samples was performed after manual gating around the blastic population. RESULTS: Reference values for the four lineage markers were established by RT-qPCR for normal BM. The mRNA expression levels of these four lineage markers allowed the distinction between normal samples and 100% of acute leukemia samples. CONCLUSIONS: With 92% congruence for protein expression and amount of mRNA in acute leukemias, these four lineage markers, essential for diagnosis and subclassification of acute leukemias by flow cytometry, also represent excellent candidate genes when using RT-qPCR technology as a diagnostic tool for molecular cancer class prediction.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".