法医学杂志

法医学杂志 ›› 2026, Vol. 42 ›› Issue (2): 94-101.DOI: 10.12116/j.issn.1004-5619.2026.460406

• 论著 • 上一篇    下一篇

基于生物信息学的白血病猝死生物标志物筛选与验证

刘博文1(), 张忠2,3,4, 张峰2,3,4, 范琰琰2,3,4(), 赖沛龙1()   

  1. 1.南方医科大学附属广东省人民医院血液内科 广东省医学科学院，广东 广州 510080
    2.温州医科大学法医学系，浙江 温州 325035
    3.温州医科大学司法鉴定中心，浙江 温州 325035
    4.温州医科大学司法鉴定科学技术研究所，浙江 温州 325035
  • 收稿日期:2025-08-06 发布日期:2026-07-08 出版日期:2026-04-25
  • 通讯作者: 范琰琰,赖沛龙
  • 作者简介:刘博文（1997—），男，硕士研究生，主要从事血液病学研究；E-mail：liubowen1445@163.com
  • 基金资助:
    国家自然科学基金资助项目(82270161);广东省基础与应用基础研究基金资助项目(2024B1515020054);广东省科技计划项目(2023B1111050004)

Bioinformatics-Based Screening and Validation of Biomarkers for Sudden Death in Leukemia

Bowen LIU1(), Zhong ZHANG2,3,4, Feng ZHANG2,3,4, Yanyan FAN2,3,4(), Peilong LAI1()   

  1. 1.Department of Hematology, Guangdong Provincial People’s Hospital Affiliated to Southern Medical University, Guangdong Academy of Medical Sciences, Guangzhou 510080, China
    2.Department of Forensic Medicine, Wenzhou Medical University, Wenzhou 325035, Zhejiang Province, China
    3.Forensic Center of Wenzhou Medical University, Wenzhou 325035, Zhejiang Province, China
    4.Institute of Forensic Science and Technology, Wenzhou Medical University, Wenzhou 325035, Zhejiang Province, China
  • Received:2025-08-06 Online:2026-07-08 Published:2026-04-25
  • Contact: Yanyan FAN, Peilong LAI

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摘要：

目的 利用生物信息学技术从跨亚型共有基因筛选白血病猝死生物标志物，并在特定白血病猝死病例库中进行验证，为白血病猝死死因鉴定、医疗损害死因分析提供参考。 方法 基于GEO数据库（GSE13159数据集）筛选不同类型白血病共同的差异表达基因，使用基因本体论（Gene Ontology，GO）及京都基因和基因组数据库（Kyoto Encyclopedia of Genes and Genomes，KEGG）进行富集分析，并将富集通路映射至临床常规检验指标。收集涵盖脑卒中、呼吸衰竭、心源性猝死、感染性休克等各类猝死患者的病例资料，分为病例组（白血病猝死者）与对照组（非白血病猝死者）进行验证。采用Wilcoxon秩和检验及单因素logistic回归筛选生物标志物，基于单因素logistic回归分析结果构建多因素logistic回归模型预测猝死患者是否为白血病患者，通过受试者操作特征（receiver operator characteristic，ROC）曲线及曲线下面积（area under the curve，AUC）评价模型效能。 结果 4种白血病亚型共有的540个差异表达基因显著富集于细胞周期、炎症免疫以及凝血等通路。通路富集结果与临床常规检验指标存在明确的映射关系，与对照组相比，病例组整体呈现低白细胞（均值为2.42×109/L）、低血小板（均值为23×109/L）、低白细胞介素-6（interleukin-6，IL-6；均值为386.0 pg/mL）的特征。基于单因素logistic回归分析结果，纳入IL-6和血小板建立多因素logistic回归模型，模型的AUC为0.828。 结论 白血病猝死生物标志物差异表达基因富集模式与白血病细胞增殖失控、免疫逃逸及高凝状态的共同分子机制相吻合，联合血常规及IL-6有助于白血病猝死的鉴别，可为法医学实践中白血病猝死死因的辅助判断提供客观依据，也为医疗损害鉴定死因分析提供了客观量化参考。

关键词: 法医病理学, 生物信息学, 白血病, 猝死, 差异基因表达, 基因集富集分析, 生物标志物

Abstract:

Objective To identify biomarkers for sudden death in leukemia through common genes across leukemia subtypes using bioinformatics technology, and to validate them in a specific case repository of sudden death in leukemia, thereby providing reference for cause of death determination in leukemia-related sudden death and cause of death analysis in medical malpractice cases. Methods Differentially expressed genes （DEGs） common across different types of leukemia were identified using the GEO database （GSE13159 dataset）. Enrichment analyses were performed using the Gene Ontology （GO） and the Kyoto Encyclopedia of Genes and Genomes （KEGG） databases, and enriched pathways were mapped to routine clinical laboratory parameters. Case data from patients who died suddenly due to stroke, respiratory failure, sudden cardiac death, and infectious shock, were collected and divided into the case group（leukemia sudden death group） and the control group（non-leukemia sudden death group） for validation. The Wilcoxon rank sum test and univariate logistic regression were used to screen biomarkers. A multivariate logistic regression model was constructed based on univariate logistic regression results to predict whether a sudden death patient had leukemia. Model performance was evaluated using receiver operator characteristic （ROC） curves and area under the curve （AUC）. Results A total of 540 DEGs shared across four leukemia subtypes were significantly enriched in pathways related to cell cycle, inflammatory immunity, and coagulation. The pathway enrichment results showed clear correspondence with routine clinical laboratory parameters. Compared with the control group, the case group exhibited overall characteristics of low white blood cell counts （mean: 2.42×109/L）, low platelet counts （mean: 23×109/L）, and low interleukin-6 （IL-6） levels （mean: 386.0 pg/mL）. Based on the univariate logistic regression results, IL-6 and platelet count were included in a multivariate logistic regression model. The model yielded an AUC of 0.828. Conclusion The enrichment pattern of DEGs in leukemia sudden death biomarkers is consistent with the shared molecular mechanisms of uncontrolled leukemia cell proliferation, immune evasion, and hypercoagulable state. The combination of blood routine test parameters and IL-6 can be used for the differential diagnosis of sudden death in leukemia, providing objective evidence for auxiliary determination of cause of death in leukemia sudden death cases and offering a quantitative reference for cause of death analysis in medical malpractice identification.

Key words: forensic pathology, bioinformatics, leukemia, sudden death, differential gene expression, gene set enrichment analysis, biomarker

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