First pan-endocrine meta-analysis identifies MIR-221 as a cross-tumour transcriptomic biomarker: a machine learning framework for precision surgical oncology
Bibliographic record
Abstract
Abstract Background Surgical decision-making in endocrine malignancies is often confounded by heterogeneous biology, multifocality and unpredictable recurrence. In this context, microRNAs (miRNAs) have emerged as non-invasive biomarkers with translational potential. Their integration could refine diagnostic and prognostic pathways across thyroid, adrenal, parathyroid and neuroendocrine tumours. Although tumour-specific miRNA signatures have been reported, no unifying biomarker has been validated across endocrine cancers. This study presents the first transcriptomic, machine learning-integrated meta-analysis with in-silico pathway mapping to identify dominant miRNA classifiers, building on prior findings in a 30-patient indeterminate thyroid nodule cohort. Method A systematic review and meta-analysis of 34 studies (n = 3412) was performed. Pooled diagnostic metrics (Area Under Curve [AUC], sensitivity) and prognostic estimates (hazard ratios) were derived using DerSimonian–Laird and Bayesian hierarchical models. SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) assessed feature attribution. Logistic regression evaluated additive effects. Validated targets were mapped to canonical pathways using support vector machine (SVM)-ranked miRDB, miRWalk and TarBase databases. Risk of bias was evaluated using Newcastle-Ottawa. Results miR-221 emerged as the highest-ranking biomarker (AUC 0.841;HR 10.10;P < 0.001), improving multi-miRNA classifiers by +0.07 AUC. SHAP and LIME confirmed dominance over miR-146b (AUC 0.60) and miR-375 (AUC 0.66). In thyroid cancer alone, miR-221 achieved 88% sensitivity, outperforming cytology (60–75%) and ultrasound (52–85%). Logistic regression validated it as the top individual classifier (AUC 0.77). Validated targets were enriched in MAPK and PI3K–Akt. Conclusion We propose miR-221 as the first cross-endocrine molecular marker with both diagnostic and prognostic value. A translational pipeline is planned, including prospective validation in a 30-patient indeterminate thyroid cohort.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.037 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.029 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".