ArticleScientific reports2021
Novel computational analysis of large transcriptome datasets identifies sets of genes distinguishing chronic obstructive pulmonary disease from healthy lung samples.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed, 9 citations in OpenAlex.
- From COPD and asthma to lung cancer: potential candidate genes and their associated biological pathways for the incidence risk of lung cancer.Journal, genetic engineering & biotechnology · 2026Article
- Epitranscriptomics, immunometabolism, and NETosis: unlocking novel mechanisms of traditional Chinese medicine in COPD.Frontiers in immunology · 2026Review
- Secretoglobin 3A2 peptides have therapeutic potential for allergic airway inflammation.Life sciences · 2024Article
- Inefficient antiviral response in reconstituted small-airway epithelium from chronic obstructive pulmonary disease patients following human parainfluenza virus type 3 infection.Virology journal · 2024Article
- Secretory Cells Are the Primary Source of pIgR in Small Airways.American journal of respiratory cell and molecular biology · 2022Article
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Authors and funding
4 authors at 2 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Chronic obstructive pulmonary disease (COPD) kills over three million people worldwide every year. Despite its high global impact, the knowledge about the underlying molecular mechanisms is still limited. In this study, we aimed to extend the available knowledge by identifying a small set of COPD-associated genes. We analysed different publicly available gene expression datasets containing whole lung tissue (WLT) and airway epithelium (AE) samples from over 400 human subjects for differentially expressed genes (DEGs). We reduced the resulting sets of 436 and 663 DEGs using a novel computational approach that utilises a random depth-first search to identify genes which improve the distinction between COPD patients and controls along the first principle component of the data. Our method identified small sets of 10 and 15 genes in the WLT and AE, respectively. These sets of genes significantly (p < 10
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