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Relevance of pyroptosis‐associated genes in nasopharyngeal carcinoma diagnosis and subtype classification

鼻咽癌 上睑下垂 小桶 基因 基因表达 医学 坏死性下垂 计算生物学 生物信息学 生物 遗传学 内科学 程序性细胞死亡 细胞凋亡 转录组 放射治疗
作者
Li Wang,Yuxia Zou,Xianghui Chen,Xiaoyan Wang,Hao Zheng,Qing Ye
出处
期刊:Journal of Gene Medicine [Wiley]
卷期号:26 (1)
标识
DOI:10.1002/jgm.3653
摘要

Abstract Background Nasopharyngeal carcinoma (NPC) is a highly aggressive and metastatic malignancy originating in the nasopharyngeal tissue. Pyroptosis is a relatively newly discovered, regulated form of necrotic cell death induced by inflammatory caspases that is associated with a variety of diseases. However, the role and mechanism of pyroptosis in NPC are not fully understood. Methods We analyzed the differential expression of pyroptosis‐related genes (PRGs) between patients with and without NPC from the GSE53819 and GSE64634 datasets of the Gene Expression Omnibus (GEO) database. We mapped receptor operating characteristic profiles for these key PRGs to assess the accuracy of the genes for disease diagnosis and prediction of patient prognosis. In addition, we constructed a nomogram based on these key PRGs and carried out a decision curve analysis. The NPC patients were classified into different pyroptosis gene clusters by the consensus clustering method based on key PRGs, whereas the expression profiles of the key PRGs were analyzed by applying principal component analysis. We also analyzed the differences in key PRGs, immune cell infiltration and NPC‐related genes between the clusters. Finally, we performed differential expression analysis for pyroptosis clusters and obtained differentially expressed genes (DEGs) and performed Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses. Results We obtained 14 differentially expressed PRGs from GEO database. Based on these 14 differentially expressed PRGs, we applied least absolute shrinkage and selection operator analysis and the random forest algorithm to obtain four key PRGs (CHMP7, IL1A, TP63 and GSDMB). We completely distinguished the NPC patients into two pyroptosis gene clusters (pyroptosis clusters A and B) based on four key PRGs. Furthermore, we determined the immune cell abundance of each NPC sample, estimated the association between the four PRGs and immune cells, and determined the difference in immune cell infiltration between the two pyroptosis gene clusters. Finally, we obtained and functional enrichment analyses 259 DEGs by differential expression analysis for both pyroptosis clusters. Conclusions PRGs are critical in the development of NPC, and our research on the pyroptosis gene cluster may help direct future NPC therapeutic approaches.
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