Gene expression analysis identifies potential therapeutic targets in idiopathic pulmonary fibrosis
Bibliographic record
Abstract
Background: Idiopathic pulmonary fibrosis (IPF) is a common interstitial lung disease resulting in lung scarring and progressive respiratory failure with a 3-5-year median survival and limited treatment options. Aim: Determine the transcriptomic drivers of fibrosis in IPF lungs to identify potential therapeutic targets. Methods: Eight control and eight IPF lungs were inflated, frozen, and sampled using systematic uniform random sampling to obtain 128 samples for micro-CT (Micro-computed tomography) imaging to assess fibrosis (volume fraction of tissue). Samples were then used for RNA sequencing. Linear mixed-effects models (FDR<0.05, |Effect size|>1) and gene set enrichment analysis (GSEA) identified differentially expressed genes (DEG) and pathways linked to fibrosis progression. Master regulator and Connectivity Maps (C-Map) analysis identified potential therapeutic targets. Results: DEG analysis identified 1511 upregulated and 963 downregulated genes linked to lung fibrosis. These genes led to the upregulation of 65 pathways, mainly affecting cilia function and adaptive immunity, and 316 downregulated pathways, mainly affecting lipid metabolism and innate immunity. We identified 136 master regulator genes regulating the 2474 fibrosis genes. C-Map analysis identified DNA synthesis and vascular endothelial growth factor inhibitors as potential targets to modify the expression of master regulator genes driving tissue fibrosis in IPF lungs. Conclusion: Upregulated cilia-related genes may promote mucus clearance and fibrosis, while downregulated lipid metabolism may contribute to epithelial injury and fibrosis. Future work will assess master regulator modifications in in vitro IPF models.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".