The Identification and Clinical Value Evaluation of CYCS Related to Asthma through Bioinformatics Analysis and Functional Experiments

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.

M6A-related bioinformatics analysis indicates that LRPPRC is an immune marker for ischemic stroke

“…Mutations in ERBB2 promote PD-L1-mediated immune escape in gallbladder cancer 34 . CYCS is significantly associated with immune cells and promotes the proliferation of asthma cells in vitro 35 . Meanwhile, we also identified immune-related molecules by molecular prediction.…”

CDC167 exhibits potential as a biomarker for airway inflammation in asthma

“…In their study, Yan Li et al conducted an analysis of DEGs in the GSE64913 dataset, and characterized the asthma-related traits within the same dataset. Their findings demonstrated that ANXA8, ATF4, CD44, CYCS, DDIT3, FKBP5, LDHA, PMAIP1, S100A2, and SFN exhibited potential as hub genes associated with asthma (Li et al 2023 ). Although the majority of their predicted genes demonstrated limited prognostic value when referencing the same dataset, this finding underscores the limited efficacy of hub gene characterization solely through the use of WGCNA.…”

Retracted: The Identification and Clinical Value Evaluation of CYCS Related to Asthma through Bioinformatics Analysis and Functional Experiments

“…This article has been retracted by Hindawi, as publisher, following an investigation undertaken by the publisher [ 1 ]. This investigation has uncovered evidence of systematic manipulation of the publication and peer-review process.…”