ArticleScientific reports2020
Integrated transcriptomic correlation network analysis identifies COPD molecular determinants.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
32 citing papers in PubMed, 47 citations in OpenAlex.
- A Bioinformatics Analysis Based on Omics and Clinical Data for Graph-Based Patient Stratification in Hepatocellular Carcinoma.Biology · 2026Article
- The role of miR-335-5p in the redifferentiation of BRAF p.V600E thyroid cancers.Molecular oncology · 2026Article
- Network-based analysis and multi-omics approaches for biology and medicine.Biotechnology reports (Amsterdam, Netherlands) · 2026Article
- Article
- Identification of Novel Lactylation-Related Biomarkers for COPD Diagnosis Through Machine Learning and Experimental Validation.Biomedicines · 2025Article
- HHIP protein interactions in lung cells provide insight into COPD pathogenesis.Human molecular genetics · 2025Article
- Partial correlation network analysis identifies coordinated gene expression within a regional cluster of COPD genome-wide association signals.PLoS computational biology · 2024Article
- Article
- Blood-based Transcriptomic and Proteomic Biomarkers of Emphysema.American journal of respiratory and critical care medicine · 2024Article
- Serpin peptidase inhibitor, clade E, member 2 in physiology and pathology: recent advancements.Frontiers in molecular biosciences · 2024Review
- The immune response to arterial damage in a mouse model of intermittent hypoxia: a transcriptomics analysis.Sleep & breathing = Schlaf & Atmung · 2023Article
- Three genes expressed in relation to lipid metabolism considered as potential biomarkers for the diagnosis and treatment of diabetic peripheral neuropathy.Scientific reports · 2023Article
- Co-Expression Network Analysis Identifies Molecular Determinants of Loneliness Associated with Neuropsychiatric and Neurodegenerative Diseases.International journal of molecular sciences · 2023Article
- Applying Next-Generation Sequencing and Multi-Omics in Chronic Obstructive Pulmonary Disease.International journal of molecular sciences · 2023Review
- Article
- Airway and parenchyma transcriptomics in a house dust mite model of experimental asthma.Respiratory research · 2023Article
- Markers of immune dysregulation in response to the ageing gut: insights from aged murine gut microbiota transplants.BMC gastroenterology · 2022Article
- Comparative Analysis of Gene Correlation Networks of Breast Cancer Patients Based on Mutations in TP53.Biomolecules · 2022Article
- Recent evidence from omic analysis for redox signalling and mitochondrial oxidative stress in COPD.Journal of inflammation (London, England) · 2022Review
- A Comparison of Network-Based Methods for Drug Repurposing along with an Application to Human Complex Diseases.International journal of molecular sciences · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors at 4 institutions in 3 countries.
Funding
Abstract
Chronic obstructive pulmonary disease (COPD) is a complex and heterogeneous syndrome. Network-based analysis implemented by SWIM software can be exploited to identify key molecular switches - called "switch genes" - for the disease. Genes contributing to common biological processes or defining given cell types are usually co-regulated and co-expressed, forming expression network modules. Consistently, we found that the COPD correlation network built by SWIM consists of three well-characterized modules: one populated by switch genes, all up-regulated in COPD cases and related to the regulation of immune response, inflammatory response, and hypoxia (like TIMP1, HIF1A, SYK, LY96, BLNK and PRDX4); one populated by well-recognized immune signature genes, all up-regulated in COPD cases; one where the GWAS genes AGER and CAVIN1 are the most representative module genes, both down-regulated in COPD cases. Interestingly, 70% of AGER negative interactors are switch genes including PRDX4, whose activation strongly correlates with the activation of known COPD GWAS interactors SERPINE2, CD79A, and POUF2AF1. These results suggest that SWIM analysis can identify key network modules related to complex diseases like COPD.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.