MicroRNAs as prognostic and predictive biomarkers among chronic myeloid leukemia patients in Addis Ababa, Ethiopia
Bibliographic record
Abstract
Approximately 1.5 million people worldwide suffer from chronic myeloid leukemia (CML). MicroRNAs (miRs) are important regulators of gene expression and offer an attractive option as biomarkers for cancer detection, diagnosis, and prognosis assessment in solid and liquid tumors. To assess miRs as prognostic and predictive biomarkers among CML patients at the Tikur Anbessa Specialized Hospital (TASH), Addis Ababa, Ethiopia from April 2021 to May 2023. Blood samples were collected from newly diagnosed CML patients before initiation of tyrosine kinase inhibitor (TKI), imatinib treatment, and while on therapy. The expression level of miRs were determined using the NanoString platform. LIMMA analysis was used to identify differentially expressed miR between TKI response groups and disease phases. Fifty-two study participants were enrolled in the study. From each sample, 798 hsa-miRs included on the Nanostring assay were measured. Comparing TKI naive new CML patients (n = 14) with those progressed or had blast crisis (BC) on TKI therapy (n = 12), 97 miRs were differentially expressed (|log2FC|, FDR, and P-value at > 1, < 0.001, and < 0.0001, respectively). Most miRs showed upregulation in BC CML patients compared to new CML cases except miR-223-3p, miR-4454, miR-7975, and miR-630 which were downregulated in patients with BC. In addition, eight miRs were differentially expressed comparing poor molecular responder (n = 12) with good molecular responder (n = 28) patients (P < 0.05). MiR-223-3p, miR-4454, miR-7975, and miR-630 were commonly deregulated in BC and poor molecular response groups. MiRs have significant potential as prognostic and predictive biomarkers for CML patients. MiR-223-3p, miR-4454, miR-7975 and miR-630 could be considered as prognostic and predictive biomarkers for disease progression and treatment response if validated by other large studies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".