Transcriptomics of interstitial lung disease: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: Gene expression (transcriptomics) studies have revealed potential mechanisms of interstitial lung disease, yet sample sizes of studies are often limited and between-subtype comparisons are scarce. The aim of this study was to identify and validate consensus transcriptomic signatures of interstitial lung disease subtypes. METHODS: We performed a systematic review and meta-analysis of fibrotic interstitial lung disease transcriptomics studies using an individual participant data approach. We included studies examining bulk transcriptomics of human adult interstitial lung disease samples and excluded those focusing on individual cell populations. Patient-level data and expression matrices were extracted from 43 studies and integrated using a multivariable integrative algorithm to develop interstitial lung disease classification models. RESULTS: Using 1459 samples from 24 studies, we identified transcriptomic signatures for idiopathic pulmonary fibrosis, hypersensitivity pneumonitis, idiopathic nonspecific interstitial pneumonia and systemic sclerosis-associated interstitial lung disease against control samples, which were validated on 308 samples from eight studies (idiopathic pulmonary fibrosis area under receiver operating curve (AUC) 0.99, 95% CI 0.99-1.00; hypersensitivity pneumonitis AUC 0.91, 95% CI 0.84-0.99; nonspecific interstitial pneumonia AUC 0.94, 95% CI 0.88-0.99; systemic sclerosis-associated interstitial lung disease AUC 0.98, 95% CI 0.93-1.00). Significantly, meta-analysis allowed us to identify, for the first time, robust lung transcriptomics signatures to discriminate idiopathic pulmonary fibrosis (AUC 0.71, 95% CI 0.63-0.79) and hypersensitivity pneumonitis (AUC 0.76, 95% CI 0.63-0.89) from other fibrotic interstitial lung disease, and unsupervised learning algorithms identified putative molecular endotypes of interstitial lung disease associated with decreased forced vital capacity and diffusing capacity of the lungs for carbon monoxide % predicted. Transcriptomics signatures were reflective of both cell-specific and disease-specific changes in gene expression. CONCLUSION: We present the first systematic review and largest meta-analysis of fibrotic interstitial lung disease transcriptomics to date, identifying reproducible transcriptomic signatures with clinical relevance.
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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 | 0.016 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.028 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".