Multiple Machine Learning Models, Molecular Subtyping and Singlecell Analysis Identify PANoptosis-related Core Genes and their Association with Subtypes in Crohn's Disease.

IF 3.5 4区 医学 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY Current medicinal chemistry Pub Date : 2024-10-21 DOI:10.2174/0109298673330894241008060309
Yi Chen, Lu Zhang, Wan-Ying Huang, Rong-Quan He, Zhi-Guang Huang, Hui Li, Rui Song, Jia-Wei Zhang, Juan He, Gang Chen
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Abstract

Background: PANoptosis plays an important role in many inflammatory diseases. However, there are no reports on the association between PANoptosis and CD.

Materials and methods: This study used five machine learning algorithms - least absolute shrinkage and selection operator, support vector machine, random forest, decision tree and Gaussian mixture models - to construct CD's PANoptosis signature. Unsupervised hierarchical clustering analysis was used to identify PANoptosis-associated subgroups of CD. Gene Ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) were conducted to compare the PANoptosis-associated subgroups of CD among the potential biological mechanisms. Single sample GSEA was used to assess immune microenvironmental differences among the subgroups. The potential role of PANoptosis in CD was further explored using single-cell RNA-Seq (scRNA-Seq) for PANoptosis scoring, differential analysis, pseudotime analysis, cellular communication analysis and weighted gene co-expression network analysis (WGCNA) analysis.

Results: CD's PANoptosis signature consisted of seven genes: CEACAM6, CHP2, PIK3R1, CASP10, PSMB1, PSMB8 and UBC. The PANoptosis signature in multiple cohorts had a strong ability to recognise CD. The levels of immune cell infiltration and the vigour of the immune responses significantly varied between the two subpopulations of CD associated with PANoptosis. Multiple lines of evidence from the GO, KEGG, GSEA, GSVA, scRNA-Seq and WGCNA analyses suggested that I-kappaB kinase/NF- kappaB signalling, mitogen-activated protein kinase (MAPK), leukocyte activation and leukocyte migration were mechanisms closely associated with PANoptosis in CD.

Conclusion: This study is the first to construct a PANoptosis signature with excellent efficacy in recognising CD. PANoptosis may mediate the process of CD through inflammatory and immune mechanisms, such as NF- kappaB, MAPK and leukocyte migration.

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多种机器学习模型、分子亚型分析和单细胞分析确定了克罗恩病的 PANoptosis 相关核心基因及其与亚型的关系。
背景:PAN凋亡在许多炎症性疾病中发挥着重要作用。材料与方法:本研究使用了五种机器学习算法--最小绝对收缩和选择算子、支持向量机、随机森林、决策树和高斯混合模型:本研究使用了五种机器学习算法--最小绝对收缩和选择算子、支持向量机、随机森林、决策树和高斯混合模型--来构建 CD 的 PANoptosis 特征。无监督分层聚类分析用于识别 CD 的 PANoptosis 相关亚组。通过基因本体(GO)富集分析、京都基因和基因组百科全书(KEGG)通路分析、基因组富集分析(GSEA)和基因组变异分析(GSVA)来比较潜在生物学机制中与 CD PANoptosis 相关的亚组。单样本GSEA用于评估亚组间免疫微环境的差异。利用单细胞RNA-Seq(scRNA-Seq)进行PAN凋亡评分、差异分析、伪时间分析、细胞通讯分析和加权基因共表达网络分析(WGCNA),进一步探讨了PAN凋亡在CD中的潜在作用:结果:CD的PAN凋亡特征由7个基因组成:结果:CD 的 PANoptosis 特征包括七个基因:CEACAM6、CHP2、PIK3R1、CASP10、PSMB1、PSMB8 和 UBC。多个队列中的 PANoptosis 特征对 CD 有很强的识别能力。与 PANoptosis 相关的两个 CD 亚群的免疫细胞浸润水平和免疫反应强度存在显著差异。来自GO、KEGG、GSEA、GSVA、scRNA-Seq和WGCNA分析的多种证据表明,I-kappaB激酶/NF- kappaB信号、丝裂原活化蛋白激酶(MAPK)、白细胞活化和白细胞迁移是与CD患者PAN凋亡密切相关的机制:本研究首次构建了 PAN 细胞凋亡特征,该特征在识别 CD 方面具有极佳的效果。PAN凋亡可能通过NF- kappaB、MAPK和白细胞迁移等炎症和免疫机制介导了CD的发病过程。
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来源期刊
Current medicinal chemistry
Current medicinal chemistry 医学-生化与分子生物学
CiteScore
8.60
自引率
2.40%
发文量
468
审稿时长
3 months
期刊介绍: Aims & Scope Current Medicinal Chemistry covers all the latest and outstanding developments in medicinal chemistry and rational drug design. Each issue contains a series of timely in-depth reviews and guest edited thematic issues written by leaders in the field covering a range of the current topics in medicinal chemistry. The journal also publishes reviews on recent patents. Current Medicinal Chemistry is an essential journal for every medicinal chemist who wishes to be kept informed and up-to-date with the latest and most important developments.
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