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基于整合生物信息学、机器学习和实验技术对克罗恩病中 PANoptosis 相关基因的特征分析。

Characterization of PANoptosis-related genes in Crohn's disease by integrated bioinformatics, machine learning and experiments.

机构信息

Department of Gastroenterology, Xiangya Hospital, Central South University, Changsha, Hunan, China.

Hunan International Scientific and Technological Cooperation Base of Artificial Intelligence Computer Aided Diagnosis and Treatment for Digestive Disease, Changsha, Hunan, China.

出版信息

Sci Rep. 2024 May 22;14(1):11731. doi: 10.1038/s41598-024-62259-w.

Abstract

Currently, the biological understanding of Crohn's disease (CD) remains limited. PANoptosis is a revolutionary form of cell death reported to participate in numerous diseases, including CD. In our study, we aimed to uncover the roles of PANoptosis in CD. Differentially expressed PANoptosis-related genes (DE-PRGs) were identified by overlapping PANoptosis-related genes and differentially expressed genes between CD and normal samples in a combined microarray dataset. Three machine learning algorithms were adopted to detect hub DE-PRGs. To stratify the heterogeneity within CD patients, nonnegative matrix factorization clustering was conducted. In terms of immune landscape analysis, the "ssGSEA" method was applied. qRT-PCR was performed to examine the expression levels of the hub DE-PRGs in CD patients and colitis model mice. Ten hub DE-PRGs with satisfactory diagnostic performance were identified and validated: CD44, CIDEC, NDRG1, NUMA1, PEA15, RAG1, S100A8, S100A9, TIMP1 and XBP1. These genes displayed significant associations with certain immune cell types and CD-related genes. We also constructed gene‒microRNA, gene‒transcription factor and drug‒gene interaction networks. CD samples were classified into two PANoptosis patterns according to the expression levels of the hub DE-PRGs. Our results suggest that PANoptosis plays a nonnegligible role in CD by modulating the immune system and interacting with CD-related genes.

摘要

目前,对克罗恩病(CD)的生物学认识仍然有限。PANoptosis 是一种革命性的细胞死亡形式,据报道它参与了许多疾病,包括 CD。在我们的研究中,我们旨在揭示 PANoptosis 在 CD 中的作用。通过重叠 CD 和正常样本之间的 PANoptosis 相关基因和差异表达基因,在一个联合微阵列数据集确定差异表达的 PANoptosis 相关基因(DE-PRGs)。采用三种机器学习算法来检测枢纽 DE-PRGs。为了分层 CD 患者的异质性,进行了非负矩阵分解聚类。在免疫景观分析方面,采用了“ssGSEA”方法。qRT-PCR 用于检测 CD 患者和结肠炎模型小鼠中枢纽 DE-PRGs 的表达水平。确定并验证了具有满意诊断性能的 10 个枢纽 DE-PRGs:CD44、CIDEC、NDRG1、NUMA1、PEA15、RAG1、S100A8、S100A9、TIMP1 和 XBP1。这些基因与某些免疫细胞类型和 CD 相关基因显著相关。我们还构建了基因- microRNA、基因-转录因子和药物-基因相互作用网络。根据枢纽 DE-PRGs 的表达水平,将 CD 样本分为两种 PANoptosis 模式。我们的结果表明,PANoptosis 通过调节免疫系统并与 CD 相关基因相互作用,在 CD 中发挥不可忽视的作用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/15a0/11111690/b687b6042948/41598_2024_62259_Fig1_HTML.jpg

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