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利用加权基因共表达网络分析(WGCNA)和机器学习方法鉴定特发性肺纤维化细胞外基质中的核心基因及免疫微环境的调控机制

Identification of core genes in the extracellular matrix and the regulatory mechanisms of the immune microenvironment in idiopathic pulmonary fibrosis using WGCNA and machine learning methods.

作者信息

Wang Man, Liu Lu, Liu Yang, Yu Shihuan

机构信息

Department of Respiratory Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang Province, China.

出版信息

PLoS One. 2025 Aug 26;20(8):e0330725. doi: 10.1371/journal.pone.0330725. eCollection 2025.

Abstract

OBJECTIVE

This research aims to detect genes associated with the extracellular matrix (ECM) in idiopathic pulmonary fibrosis (IPF) using bioinformatics techniques and investigate their relationships with immune infiltration, with the goal of identifying new diagnostic and therapeutic targets for IPF.

METHODS

The study employed a combination of differential expression analysis, weighted gene co-expression network analysis (WGCNA), and various machine learning algorithms to screen for characteristic genes. Gene set enrichment analysis (GSEA), gene ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) were utilized to evaluate relevant biological functions and pathways. Additionally, the analysis of immune cell infiltration was conducted to assess the disease's immune status and the correlations between genes and immunity.

RESULTS

IPF is strongly linked to pathways such as ECM organization and immune response, with differentially expressed genes primarily involving signal pathways related to collagen deposition in the extracellular matrix. A total of 1,193 ECM-related genes associated with IPF were identified, and 94 differentially expressed ECM-related genes were further screened compared to the normal control group. Through machine learning approaches, three key genes-BAAT, COMP, and CXCL13-were pinpointed. These genes are closely tied to the onset, progression, and immune processes of IPF, and clustering analysis based on them can reveal distinct disease states and changes in immune cell infiltration patterns.

CONCLUSION

BAAT, COMP, and CXCL13 may serve as potential therapeutic targets for slowing the progression and preventing the exacerbation of IPF. Moreover, monocytes demonstrate consistent infiltration patterns across the disease group, control group, and various subgroups, indicating their potential significance in the development of IPF.

摘要

目的

本研究旨在利用生物信息学技术检测特发性肺纤维化(IPF)中与细胞外基质(ECM)相关的基因,并研究它们与免疫浸润的关系,以确定IPF新的诊断和治疗靶点。

方法

该研究采用差异表达分析、加权基因共表达网络分析(WGCNA)和各种机器学习算法相结合的方法来筛选特征基因。利用基因集富集分析(GSEA)、基因本体论(GO)和京都基因与基因组百科全书(KEGG)来评估相关的生物学功能和通路。此外,进行免疫细胞浸润分析以评估疾病的免疫状态以及基因与免疫之间的相关性。

结果

IPF与ECM组织和免疫反应等通路密切相关,差异表达基因主要涉及细胞外基质中胶原沉积相关的信号通路。共鉴定出1193个与IPF相关的ECM相关基因,与正常对照组相比,进一步筛选出94个差异表达的ECM相关基因。通过机器学习方法,确定了三个关键基因——BAAT、COMP和CXCL13。这些基因与IPF的发病、进展和免疫过程密切相关,基于它们的聚类分析可以揭示不同的疾病状态和免疫细胞浸润模式的变化。

结论

BAAT、COMP和CXCL13可能作为减缓IPF进展和预防其恶化的潜在治疗靶点。此外,单核细胞在疾病组、对照组和各个亚组中表现出一致的浸润模式,表明它们在IPF发生发展中的潜在重要性。

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