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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".