MicroRNA Expression Profiling of Sputum for the Detection of Early and Locally Advanced Non-Small-Cell Lung Cancer: A Prospective Case–Control Study
Bibliographic record
Abstract
BACKGROUND: Non-small-cell lung cancer (nsclc) is associated with very poor overall survival because 70% of patients present with locally advanced or metastatic disease at the time of diagnosis. Micrornas (mirnas) are a class of short, noncoding rna molecules whose presence in samples of biologic fluids such as sputum has demonstrated promise as a potential means of detecting nsclc. We investigated the stage-specific nsclc detection potential of an efficient panel of 3 mirnas (mir-21, mir-210, mir-372) using a single sputum sample. METHODS: A single spontaneously expectorated sputum sample was prospectively collected from 21 early nsclc (≤stage ii) patients, 22 advanced nsclc (≥stage iii) patients, and 10 control subjects. Mirna expression profiles were determined by quantitative real-time polymerase chain reaction and were analyzed by unsupervised hierarchical cluster analysis. RESULTS: Mean tumour size (±95% confidence interval) in the early and advanced nsclc patients was 3.3 cm ± 0.9 cm and 4.8 cm ± 0.7 cm respectively. Adenocarcinoma constituted 61.9% of the early and 45.5% of the advanced nsclc cases respectively. In comparing the early nsclc group with the control group, the mirna panel yielded a diagnostic sensitivity of 67% and a specificity of 90.0%. For the advanced nsclc group, the mirna panel detected nsclc with a sensitivity and specificity of 64% and 100% respectively. CONCLUSIONS: A sputum mir-21, mir-210, and mir-372 expression profile might provide a sensitive and highly specific means for detecting nsclc. Sputum mirna analysis demonstrates promise as a potential complementary screening tool.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".