鼻咽癌诊断和亚型分类中的热解相关基因的相关性

IF 4.3 3区 材料科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC ACS Applied Electronic Materials Pub Date : 2024-01-08 DOI:10.1002/jgm.3653
Yan Wang, Yuxia Zou, Xianghui Chen, Xiaoyan Wang, Hao Zheng, Qing Ye
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引用次数: 0

摘要

背景 鼻咽癌(NPC)是一种起源于鼻咽组织的高度侵袭性和转移性恶性肿瘤。炭疽是一种相对较新发现的、由炎性卡巴酶诱导的细胞坏死调控形式,与多种疾病相关。然而,鼻咽癌中热凋亡的作用和机制尚不完全清楚。 方法 我们从基因表达总库(Gene Expression Omnibus,GEO)数据库的 GSE53819 和 GSE64634 数据集中分析了鼻咽癌患者和非鼻咽癌患者中热解相关基因(PRGs)的差异表达。我们绘制了这些关键 PRGs 的受体操作特征图谱,以评估这些基因在疾病诊断和预测患者预后方面的准确性。此外,我们还根据这些关键 PRGs 构建了一个提名图,并进行了决策曲线分析。通过基于关键 PRGs 的共识聚类方法,我们将鼻咽癌患者分为不同的热病基因群,并应用主成分分析法对关键 PRGs 的表达谱进行了分析。我们还分析了群组间关键 PRGs、免疫细胞浸润和 NPC 相关基因的差异。最后,我们对热病群进行了差异表达分析,获得了差异表达基因(DEGs),并进行了基因本体和京都基因与基因组百科全书的富集分析。 结果 我们从 GEO 数据库中获得了 14 个差异表达的 PRGs。在这 14 个差异表达 PRGs 的基础上,我们应用最小绝对收缩和选择算子分析以及随机森林算法得到了四个关键 PRGs(CHMP7、IL1A、TP63 和 GSDMB)。根据四个关键 PRGs,我们将鼻咽癌患者完全区分为两个化脓基因群(化脓群 A 和 B)。此外,我们还测定了每个鼻咽癌样本的免疫细胞丰度,估算了四个 PRGs 与免疫细胞之间的关联,并确定了两个化脓基因簇之间免疫细胞浸润的差异。最后,我们通过差异表达分析获得了 259 个 DEGs,并对两个热蛋白沉积基因簇进行了功能富集分析。 结论 PRGs 在鼻咽癌的发展过程中至关重要,我们对热蛋白沉积基因簇的研究可能有助于指导未来的鼻咽癌治疗方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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

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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