ArticleDisease markers2023
The Identification and Clinical Value Evaluation of CYCS Related to Asthma through Bioinformatics Analysis and Functional Experiments.
Article in Disease markers, 2023. 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 It has been retracted, and should not be counted.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 3 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
3 citing papers in PubMed, 3 citations in OpenAlex.
- CDC167 exhibits potential as a biomarker for airway inflammation in asthma.Mammalian genome : official journal of the International Mammalian Genome Society · 2024Article
- M6A-related bioinformatics analysis indicates that LRPPRC is an immune marker for ischemic stroke.Scientific reports · 2024Article
- Retracted: The Identification and Clinical Value Evaluation of CYCS Related to Asthma through Bioinformatics Analysis and Functional Experiments.Disease markers · 2023Article
Corrections and comments
- Retraction · 2023-12-13Compromised Peer Review · Investigation by Journal/Publisher · Investigation by Third Party · Paper Mill · Unreliable Results and/or Conclusions · · See also: https://pubpeer.com/publications/5F1E8BA9F3824A3AC21ACD4763ED4B
- Retracted
Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Asthma is one of the most common respiratory diseases and one of the largest burdens of health care resources across the world. This study is aimed at using bioinformatics methods to find effective clinical indicators for asthma and conducting experimental validation. Methods: We downloaded GSE64913 data and performed differentially expressed gene (DEG) screening. Weighted gene coexpression network analysis (WGCNA) on DEGs was applied to identify key module most associated with asthma for protein-protein interaction (PPI) analysis. According to the degree value, ten genes were obtained and subjected to expression analysis and receiver operating characteristic (ROC) analysis. Next, key genes were screened for expression analysis and immunological analysis. Finally, cell counting kit-8 (CCK-8) and qRT-PCR were also conducted to observe the influence of hub gene on cell proliferation and inflammatory cytokines. Results: From the GSE64913 dataset, 711 upregulated and 684 downregulated DEGs were found. In WGCNA, the top 10 genes in the key module were examined by expression analysis in asthma, and CYCS was determined as an asthma-related oncogene with a good predictive ability for the prognosis of asthmatic patients. CYCS is significantly associated with immune cells, such as HHLA2, IDO1, TGFBR1, and CCL18 and promoted the proliferation of asthmatic cells in vitro. Conclusion: CYCS plays an oncogenic role in the pathophysiology of asthma, indicating that this gene may become a novel diagnostic biomarker and promising target of asthma treatment.
Indexed as
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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.