ArticleLung2014
Protein-protein interaction network analysis in chronic obstructive pulmonary disease.
Article in Lung, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 4 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
4 citing papers in PubMed, 6 citations in OpenAlex.
- Knockdown of lncRNA PCAT6 suppresses the growth of non-small cell lung cancer cells by inhibiting macrophages M2 polarization via miR-326/KLF1 axis.Bioengineered · 2022Article
- A review of connectivity map and computational approaches in pharmacogenomics.Briefings in bioinformatics · 2018Review
- Systems Medicine: from molecular features and models to the clinic in COPD.Journal of translational medicine · 2014Article
- Synergy-COPD: a systems approach for understanding and managing chronic diseases.Journal of translational medicine · 2014Article
Corrections and comments
- Retraction · 2015-10-01Compromised Peer Review · Investigation by Journal/Publisher ·
- Retracted
Authors and funding
10 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe aim of this study was to investigate the gene expression profile of chronic obstructive pulmonary disease (COPD) patients and non-COPD patients.
methodsMicroarray raw data (GSE29133) was downloaded from Gene Expression Omnibus, including three COPD samples and three normal controls. Gene expression profiling was performed using Affymetrix human genome u133 plus 2.0 GeneChip. Differentially expressed genes were identified by Student's t test and genes with p < 0.05 were considered significantly changed. Up- and downregulated genes were submitted to the molecular signatures database (MSigDB) to search for a possible association with other previously published gene expression signatures. Furthermore, we constructed a COPD protein-protein interaction (PPI) network and used the connectivity map (cMap) to query for potential drugs for COPD.
resultsA total of 680 upregulated genes and 530 downregulated genes in COPD were identified. The MSigDB investigation found that upregulated genes were highly similar to gene signatures that respond to interferon and downregulated genes were similar to erythroid progenitor cells from fetal livers of E13.5 embryos with KLF1 knocked out. A PPI network consisting of 814 gene/proteins and 2,613 interactions was identified by Search Tool for the Retrieval of Interacting Genes. The cMap predicted helveticoside, disulfiram, and lanatoside C as the top three possible drugs that could perhaps treat COPD.
conclusionComprehensive analysis of the gene expression profile for COPD versus control reveals helveticoside, disulfiram, and lanatoside C as potential molecular targets in COPD. This evidence provides a new breakthrough in the medical treatment of patients with COPD.
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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.