Wan Y, Wang Y, Xu S, Du H, Liu Z. Identification of the role of pyroptosis-related genes in chronic rhinosinusitis based on WGCNA.
Heliyon 2024;
10:e22944. [PMID:
38173477 PMCID:
PMC10761365 DOI:
10.1016/j.heliyon.2023.e22944]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 11/20/2023] [Accepted: 11/22/2023] [Indexed: 01/05/2024] Open
Abstract
Background
Chronic rhinosinusitis (CRS) is a complex chronic inflammatory disease of the nose, paranasal sinus, and upper respiratory tract. Its treatment methods mainly include antibiotic treatment and surgical treatment. However, the molecular mechanism of its inflammation is still unclear. Pyroptosis is a programmed cell death. As an important natural immune response, pyroptosis plays an essential role in fighting infection.
Methods
In this paper, a weighted co-expression network (WGCNA) was used to screen gene modules significantly related to CRS. Then it intersects with the genes related to scorching death (PRGs). We evaluated the immune landscape of CRS by the expression of intersecting genes. In addition, in the enrichment analysis of intersection genes and PPI network analysis, we verified the pathways closely related to CRS and hub genes. Finally, the interaction network between the hub gene, miRNA, and TF was constructed. In this paper, qRT-qPCR technology was also used to detect the hub gene related to CRS.
Results
Hub genes (CASP3, IL18, NAIP, NLRC4, and TP53) found in this paper are directly or indirectly related to CRS, and these genes were proved to be of diagnostic significance to CRS by ROC curve and qRT-qPCR vertification. In the infiltration abundance of CRS and its control group, the infiltration abundance of Plasma cells, T cells follicular helper, Macrophages M2, Dendritic cells activated, and Neutrophils cells in the two groups were significantly different. We also constructed the interaction network between the hub genes and miRNAs and the interaction network between hub genes and TFs. Most of these miRNAs and TFs were also related to CRS.
Conclusions
With the help of the WGCNA and PPI analysis, our results provide a better understanding of the role of biomarkers CASP3, IL18, NAIP, NLRC4, and TP53 in the development of CRS and provide a research basis for the mining of biomarkers related to the diagnosis and treatment of CRS.
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