Sputum MicroRNA Profiling: A Novel Approach for the Early Detection of Non-Small Cell Lung Cancer
Bibliographic record
Abstract
PURPOSE: MicroRNAs (miRNAs) post-transcriptionally regulate hundreds of gene targets involved in tumorigenesis thereby controlling vital biological processes, including cellular proliferation, differentiation and apoptosis. MiRNA profiling is an emerging tool for the potential early detection of a variety of malignancies. This study was conducyed to assess the feasibility and methodological robustness of quantifying sputum miRNAs, employing quantitative real-time polymerase chain reaction (RT-qPCR) and cluster analysis on an optimized miRNA profile as a novel approach for the early detection of non-small cell lung cancer (NSCLC). METHODS: The relative expressions of 11 miRNAs in sputum (miR-21, miR-145, miR-155, miR-205, miR-210, miR-92, miR-17-5p, miR-143, miR-182, miR-372, and let-7a) in addition to U6 were retrospectively assessed in four NSCLC-positive and four negative controls. Subsequently, a set of five miRNAs (miR-21, miR-143, miR-155, miR-210, miR-372) was selected because of degree of relatedness observed in the cluster analysis and tested in the same sputum sample set. The five optimized miRNAs accurately clustered these eight retrospective patients into NSCLC positive cases and negative controls. The five miRNA panel was then prospectively quantified in the sputum of 30 study patients (24 NSCLC cases and six negative controls) in a double-blind fashion to validate a five miRNA panel using hierarchical cluster analysis. RESULTS: The optimized five miRNA panel detected NSCLC (83.3% sensitivity and 100% specificity) in 30 prospectively accrued study patients. CONCLUSION: Sputum miRNA profiling using cluster analysis is a promising approach for the early detection of non-small cell lung cancer. Further investigation using this approach is warranted.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".