中国药物警戒 ›› 2025, Vol. 22 ›› Issue (11): 1271-1275.
DOI: 10.19803/j.1672-8629.20241039

• 安全与合理用药 • 上一篇    下一篇

基于生物信息学方法探讨人参、丹参、视黄酸等与重型流感关键差异基因关系及免疫浸润机制

刘莲莲1, 王成祥2, 郭姗姗3, 于会勇4, 乜炜成2, 陈天韵2, 李磊4*   

  1. 1北京市第六医院呼吸科,北京 100007;
    2北京中医药大学第三附属医院呼吸科,北京 100029;
    3中国中医科学院中药研究所,道地药材品质保障与资源持续利用全国重点实验室,北京100700;
    4北京中医药大学第三附属医院感染科,北京 100029
  • 收稿日期:2024-12-25 出版日期:2025-11-15 发布日期:2025-11-14
  • 通讯作者: *李磊,女,主治医师,中医药防治呼吸系统疾病的临床研究。E-mail：qiangxiaoshizi@163.com
  • 作者简介:刘莲莲,女,博士,中医药防治呼吸系统疾病。
  • 基金资助:
    国家自然科学基金资助项目（82505558、82074389）

Relationships between Ginseng,Salvia Miltiorrhiza and Retinoic Acid and Key Differential Genes of Severe Influenza and Immune Infiltration Mechanisms Using Bioinformatics

LIU Lianlian1, WANG Chengxiang2, GUO Shanshan3, YU Huiyong4, NIE Weicheng2, CHHEN Tianyun2, LI Lei4*   

  1. 1Respiratory Department, Beijing Sixth Hopital , Beijing 100007;
    2Respiratory Department, the Third Affiliated Hospital of Beijing University of Chinese Medicine, Beijing 100029, China;
    3State Key Laboratory for Quality Ensurance and Sustainable Use of Dao-Di Herbs, Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China;
    4Department of Infectious Diseases, the Third Affiliated Hospital of Beijing University of Chinese Medicine, Beijing 100029, China
  • Received:2024-12-25 Online:2025-11-15 Published:2025-11-14

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摘要： 目的 基于生物信息学方法探讨重型流感关键基因及其免疫浸润机制,分析预测相关作用的药物。方法 基于GSE101702数据集筛选重型流感差异表达基因,进行GO和KEGG富集分析后,采用LASSO、SVM-RFE和随机森林算法鉴定关键基因,并分析其与免疫浸润的相关性及诊断价值,最后通过Coremine Medical与DSigDB数据库预测潜在治疗药物。结果 在GSE101702数据集中共筛选出82个差异表达基因（Differentially Expressed Genes,DEGs）,包括68个表达上调的DEGs和14个表达下调的DEGs,从中筛选得到3个关键基因,分别为IL18R1、CSF1R、MPO,其中IL18R1、MPO表达水平上调,CSF1R表达水平下调,ROC曲线验证结果符合预期。免疫细胞浸润分析显示重型流感组相对健康对照组B细胞、CD8+T细胞、自然杀伤细胞、HLA等较多免疫细胞及免疫功能相对抑制。关键基因与较多免疫浸润细胞及免疫功能亦具有显著相关性。通过数据库预测发现,人参、丹参、视黄酸等药物与重型流感关键基因关系密切,中药功效多与扶正化瘀相关。结论 本研究筛选了重型流感的差异表达基因,明确了在重型流感中的关键基因、免疫浸润机制及潜在的治疗药物,为重型流感发生发展的潜在机制、临床预测及治疗提供参考,为后续研发“免疫调节+病毒清除”双效药物（如丹参活性成分、人参皂苷等）提供基础。

关键词: 重型流感, 人参, 丹参, 视黄酸, 免疫浸润, 药物预测, 生物信息学, 关键基因

Abstract: Objective To explore the key genes and immune infiltration mechanisms of severe influenza and predicts related drugs using bioinformatics. Methods Differential expression genes (DEGs) of severe influenza were screened from the GSE101702 dataset. Following GO and KEGG enrichment analyses, the key genes were identified using LASSO, SVM-RFE, and random forest algorithms. The correlations of these genes with immune infiltration and diagnostic value were evaluated. Finally, potential drugs were predicted via the Coremine Medical and DSigDB databases. Results A total of 82 DEGs were identified from the GSE101702 dataset, including 68 up-regulated and 14 down-regulated ones. Three key genes (IL18R1, CSF1R, and MPO) were selected, with IL18R1 and MPO up- regulated and CSF1R down-regulated. ROC curve analysis confirmed their diagnostic value. Immune cell infiltration analysis revealed significant suppression of multiple immune cells and functions (B cells, CD8+T cells, natural killer cells, and HLA) in the severe influenza group compared to the healthy control group. The key genes showed significant correlations with a range of immune infiltrates. Drug prediction suggested that ginseng, salvia miltiorrhiza and retinoic acid were closely related to the key genes, often corresponding to such effects traditional Chinese medicine as “nourishing healthy qi and removing blood stasis”. Conclusion This study has identified DEGs and key genes associated with severe influenza, elucidated the immune infiltration mechanisms, and predicted potential therapeutic drugs, thus providing insights into the underlying mechanisms, clinical prediction, and treatment of severe influenza.

Key words: Severe Influenza, Ginseng, Salvia Miltiorrhiza, Retinoic Acid, Immune Infiltration, Herbal Prediction, Bioinformatics, Machine Learning, Key Genes

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