Gene correlation network analysis to identify regulatory factors in idiopathic pulmonary fibrosis
Bibliographic record
Abstract
Background Idiopathic pulmonary fibrosis (IPF) is a severe lung disease characterised by extensive pathological changes. The objective for this study was to identify the gene network and regulators underlying disease pathology in IPF and its association with lung function. Methods Lung Tissue Research Consortium dataset with 262 IPF and control subjects (GSE47460) was randomly divided into two non-overlapping groups for cross-validated differential gene expression analysis. Consensus weighted gene coexpression network analysis identified overlapping coexpressed gene modules between both IPF groups. Modules were correlated with lung function (diffusion capacity, DL CO ; forced expiratory volume in 1 s, FEV 1 ; forced vital capacity, FVC) and enrichment analyses used to identify biological function and transcription factors. Module correlation with miRNA data (GSE72967) identified associated regulators. Clinical relevance in IPF was assessed in a peripheral blood gene expression dataset (GSE93606) to identify modules related to survival. Results Correlation network analysis identified 16 modules in IPF. Upregulated modules were associated with cilia, DNA replication and repair, contractile fibres, B-cell and unfolded protein response, and extracellular matrix. Downregulated modules were associated with blood vessels, T-cell and interferon responses, leucocyte activation and degranulation, surfactant metabolism, and cellular metabolic and catabolic processes. Lung function correlated with nine modules (eight with DL CO , five with FVC). Intermodular network of transcription factors and miRNA showed clustering of fibrosis, immune response and contractile modules. The cilia-associated module was able to predict survival (p=0.0097) in an independent peripheral blood IPF cohort. Conclusions We identified a correlation gene expression network with associated regulators in IPF that provides novel insight into the pathological process of this disease.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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".