利用生物信息学分析和机器学习策略识别 CRSwNP 中的潜在特征基因。

IF 4.2 2区 医学 Q2 IMMUNOLOGY Journal of Inflammation Research Pub Date : 2024-10-22 eCollection Date: 2024-01-01 DOI:10.2147/JIR.S484914
Huikang Wang, Xinjun Xu, Haoran Lu, Yang Zheng, Liting Shao, Zhaoyang Lu, Yu Zhang, Xicheng Song
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引用次数: 0

摘要

目的:CRSwNP的发病机制复杂且尚未完全探明,因此我们旨在确定CRSwNP的关键枢纽基因和相关通路,以促进新型诊断或治疗靶点的发现:利用GEO的两个CRSwNP测序数据集,应用差异表达基因分析、WGCNA和三种机器学习方法(LASSO、RF和SVM-RFE)筛选枢纽基因。然后利用枢纽基因建立诊断模型,并生成 AUC 以评估预后模型和候选基因的性能。通过对正常小鼠和 CRSwNP 小鼠模型进行验证集和 qPCR 验证了枢纽基因。最后,采用ssGSEA算法评估免疫浸润水平的差异:结果:共鉴定出 239 个 DEGs,其中 170 个在 CRSwNP 中上调,69 个下调。富集分析显示,这些 DEGs 主要富集在与核细胞质转运和 HIF-1 信号通路相关的通路中。WGCNA 分析得出的数据包含 183 个 DEGs。应用三种机器学习算法确定了 11 个枢纽基因。在建立小鼠模型后,利用验证集和 qPCR 进行的并行验证分析证实了 BTBD10、ERAP1、GIPC1 和 PEX6 在 CRSwNP 中的过表达。对免疫细胞浸润的检测表明,在 CRSwNP 组中,2 型 T 辅助细胞和记忆 B 细胞的浸润率有所下降。相反,未成熟树突状细胞和效应记忆 CD8 T 细胞的浸润率呈正相关:本研究成功鉴定并验证了 BTBD10、ERAP1、GIPC1 和 PEX6 是 CRSwNP 潜在的新型诊断或治疗靶点,为 CRSwNP 的诊断预测和治疗方法提供了新的视角和理论基础。
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Identification of Potential Feature Genes in CRSwNP Using Bioinformatics Analysis and Machine Learning Strategies.

Purpose: The pathogenesis of CRSwNP is complex and not yet fully explored, so we aimed to identify the pivotal hub genes and associated pathways of CRSwNP, to facilitate the detection of novel diagnostic or therapeutic targets.

Methods: Utilizing two CRSwNP sequencing datasets from GEO, differential expression gene analysis, WGCNA, and three machine learning methods (LASSO, RF and SVM-RFE) were applied to screen for hub genes. A diagnostic model was then formulated utilizing hub genes, and the AUC was generated to evaluate the performance of the prognostic model and candidate genes. Hub genes were validated through the validation set and qPCR performed on normal mice and CRSwNP mouse model. Lastly, the ssGSEA algorithm was employed to assess the differences in immune infiltration levels.

Results: A total of 239 DEGs were identified, with 170 upregulated and 69 downregulated in CRSwNP. Enrichment analysis revealed that these DEGs were primarily enriched in pathways related to nucleocytoplasmic transport and HIF-1 signaling pathway. Data yielded by WGCNA analysis contained 183 DEGs. The application of three machine learning algorithms identified 11 hub genes. Following concurrent validation analysis with the validation set and qPCR performed after establishing the mouse model confirmed the overexpression of BTBD10, ERAP1, GIPC1, and PEX6 in CRSwNP. The examination of immune cell infiltration suggested that the infiltration rate of type 2 T helper cell and memory B cell experienced a decline in the CRSwNP group. Conversely, the infiltration rates of Immature dendritic cell and Effector memory CD8 T cell were positive correlation.

Conclusion: This study successfully identified and validated BTBD10, ERAP1, GIPC1, and PEX6 as potential novel diagnostic or therapeutic targets for CRSwNP, which offers a fresh perspective and a theoretical foundation for the diagnostic prediction and therapeutic approach to CRSwNP.

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来源期刊
Journal of Inflammation Research
Journal of Inflammation Research Immunology and Microbiology-Immunology
CiteScore
6.10
自引率
2.20%
发文量
658
审稿时长
16 weeks
期刊介绍: An international, peer-reviewed, open access, online journal that welcomes laboratory and clinical findings on the molecular basis, cell biology and pharmacology of inflammation.
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