慢性阻塞性肺疾病相关铁死亡的关键基因筛选及免疫浸润分析

doi:10.5246/jcps.2025.07.046

摘要/Abstract

摘要：

本研究旨在探讨慢性阻塞性肺疾病(COPD)相关铁死亡的关键基因, 并分析其免疫浸润特性, 为COPD的诊疗提供理论基础。研究采用生物信息学方法, 依据GEO数据库筛选出COPD相关铁死亡差异基因(FeDEGs), 并进行GO和KEGG富集分析, 以及机器学习筛选特征基因。此外, 利用CIBERSORT算法分析了FeDEGs核心基因与免疫细胞的相关性。研究共筛选出175个FeDEGs, GO富集分析显示其涉及细胞对化学压力和氧化应激的反应等生物学过程, KEGG富集分析显示其与铁死亡、坏死性凋亡等信号通路相关。机器学习分析并且进行诊断效能验证确定DUOX2、CDO1、POM121L12、IFNA17、MICU1、NQO1、NFS1和PARP4为核心基因, 这些基因与COPD发生的多种免疫细胞密切相关。本研究揭示了COPD相关铁死亡的关键基因和免疫浸润特性, 为COPD的发病机制和诊疗提供了新的线索, 也为开发针对这些基因和通路的治疗策略提供了理论基础。

关键词: 慢性阻塞性肺疾病, 铁死亡, 生物信息学, 免疫浸润分析

Abstract:

This study investigated key genes linked to ferroptosis in chronic obstructive pulmonary disease (COPD) and examined their immune infiltration profiles, establishing a theoretical foundation for COPD diagnosis and treatment. Employing bioinformatics methodologies, ferroptosis-related differentially expressed genes (FeDEGs) in COPD were identified using data from the Gene Expression Omnibus (GEO) database. These FeDEGs underwent comprehensive Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses, alongside machine learning algorithms, to pinpoint characteristic genes. The CIBERSORT algorithm further evaluated the association between core FeDEGs and immune cell infiltration patterns. In total, 175 FeDEGs were identified. GO enrichment analysis highlighted their roles in biological processes such as cellular responses to chemical and oxidative stress, while KEGG pathways indicated significant associations with ferroptosis and necroptosis signaling. Machine learning and diagnostic validation pinpointed DUOX2, CDO1, POM121L12, IFNA17, MICU1, NQO1, NFS1, and PARP4 as core genes, revealing their strong correlation with diverse immune cell types involved in COPD pathogenesis. This study elucidated critical genes connected to ferroptosis and immune infiltration in COPD, providing fresh insights into its pathogenesis and diagnostic approaches, while establishing a theoretical basis for therapeutic strategies targeting these pathways and genes.

Key words: Chronic obstructive pulmonary disease, Ferroptosis, Bioinformatics, Immunoinfiltration analysis

Supporting:

聂敏, 刘八一, 林冰, 王华敏, 陈华琼, 黄莹. 慢性阻塞性肺疾病相关铁死亡的关键基因筛选及免疫浸润分析[J]. 中国药学(英文版), 2025, 34(7): 622-632.

Min Nie, Bayi Liu, Bing Lin, Huamin Wang, Huaqiong Chen, Ying Huang. Screening of key genes and immune infiltration analysis of chronic obstructive pulmonary disease-related ferroptosis[J]. Journal of Chinese Pharmaceutical Sciences, 2025, 34(7): 622-632.

图/表 7

Figure 1. Flowchart of the research process.

Table 1. Information on GEO chips included in the study.

Figure 2. Expression profiles before and after correction of COPD gene chips and heatmap of FeDEGs expression. (A) Statistics of gene expression levels and principal component analysis before and after batch processing; (B) Volcano plot of DEGs expression; (C) Heatmap of FDEGs expression. Herein, blue indicates low expression, red indicates high expression, *P < 0.05, **P < 0.01, ***P < 0.001.

Figure 3. GO enrichment analysis and KEGG pathway enrichment. (A) GO enrichment bubble chart; (B) In the circular diagram, the outer blue circle represents biological processes, the yellow one represents cell components, the green one represents molecular processes, and the inner circle represents the magnitude of the P value. The darker the color, the smaller the P value; (C) KEGG enrichment bar chart; (D) KEGG network analysis.

Figure 4. Screening of characteristic genes of FeDEGs. (A) Identification of characteristic genes by LASSO analysis; (B) Biomarker screening by SVM-RFE algorithm; (C) Venn diagram of the intersection genes from the two machine learning methods; (D) Expression of the 10 core FeDEGs genes obtained. Green represents the control group, and yellow represents the disease group. *P < 0.05; **P < 0.01; ***P < 0.001.

Figure 5. ROC curve of characteristic genes of FeDEGs.

Figure 6. (A) Violin plot of the proportion differences of immune cells; (B) Correlation between core genes of FeDEGs and immune cells.

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