Integrative Genomics of Emphysema-Associated Genes Reveals Potential Disease Biomarkers
Bibliographic record
Abstract
Chronic obstructive pulmonary disease is the third leading cause of death worldwide. Gene expression profiling across multiple regions of the same lung identified genes significantly related to emphysema. We sought to determine whether the lung and epithelial expression of 127 emphysema-related genes was also related to lung function in independent cohorts, and whether any of these genes could be used as biomarkers in the peripheral blood of patients with chronic obstructive pulmonary disease. To that end, we examined whether the expression levels of these genes were under genetic control in lung tissue (n = 1,111). We then determined whether the mRNA levels of these genes in lung tissue (n = 727), small airway epithelial cells (n = 238), and peripheral blood (n = 620) were significantly related to lung function measurements. The expression of 63 of the 127 genes (50%) was under genetic control in lung tissue. The lung and epithelial mRNA expression of a subset of the emphysema-associated genes, including ASRGL1, LPHN2, and EDNRB, was strongly associated with lung function. In peripheral blood, the expression of 40 genes was significantly associated with lung function. Twenty-nine of these genes (73%) were also associated with lung function in lung tissue, but with the opposite direction of effect for 24 of the 29 genes, including those involved in hypoxia and B cell-related responses. The integrative genomics approach uncovered a significant overlap of emphysema genes associations with lung function between lung and blood with opposite directions between the two. These results support the use of peripheral blood to detect disease biomarkers.
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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) | 0.001 | 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) | 0.001 | 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".