Analyze the biomarkers of trauma-induced coagulopathy based on machine learning and transcriptomics
- JOURNAL OF REGIONAL ANATOMY AND OPERATIVE SURGERY Vol. 34, Issue 10, Pages: 846-854(2025)
- 作者机构：
陆军军医大学大坪医院麻醉科，重庆400042
- 作者简介：
- 基金信息：
DOI：10.11659/jjssx.04E025099
CLC： R641Received：18 April 2025，
Published：25 October 2025
- 稿件说明：
移动端阅览
幸熙尧,佘汉,吴银雨等.基于机器学习和转录组学分析创伤性凝血功能障碍生物标志物[J].局解手术学杂志,2025,34(10):846-854.
XING Xi-yao,SHE Han,WU Yin-yu,et al.Analyze the biomarkers of trauma-induced coagulopathy based on machine learning and transcriptomics[J].JOURNAL OF REGIONAL ANATOMY AND OPERATIVE SURGERY,2025,34(10):846-854.
幸熙尧,佘汉,吴银雨等.基于机器学习和转录组学分析创伤性凝血功能障碍生物标志物[J].局解手术学杂志,2025,34(10):846-854. DOI： 10.11659/jjssx.04E025099.
XING Xi-yao,SHE Han,WU Yin-yu,et al.Analyze the biomarkers of trauma-induced coagulopathy based on machine learning and transcriptomics[J].JOURNAL OF REGIONAL ANATOMY AND OPERATIVE SURGERY,2025,34(10):846-854. DOI： 10.11659/jjssx.04E025099.
摘要
目的
2
深入探究创伤性凝血功能障碍（TIC）的发病机制，明确其具体的致病因素和病理生理过程，以寻找有效的诊断指标和治疗靶点。
方法
2
从基因表达综合数据库（GEO）获取创伤失血性休克患者的转录组数据，筛选差异表达基因（DEGs）。从京都基因与基因组百科全书（KEGG）获取凝血相关基因（CRGs），并与DEGs进行交集分析。采用最小绝对收缩和选择算子（LASSO）及随机森林（RF）两种机器学习算法识别关键基因。采用CIBERSORT算法分析关键基因与免疫细胞浸润的相关性。通过共识聚类，将创伤患者进行亚型分析，以比较免疫细胞浸润情况。构建创伤失血性休克大鼠模型，验证凝血功能及关键基因的表达。
结果
2
数据集包含17例健康对照者和478例创伤失血性休克患者的样本。在校正
P
<
0.05的筛选标准下，共鉴定出6 315个DEGs。基因集富集分析（GSEA）显示，上调的DEGs显著富集于葡萄糖代谢通路，而下调的DEGs则富集于免疫反应相关通路。通过交叉分析DEGs与CRGs，共筛选出65个差异表达的凝血相关基因（DE-CRGs），GO功能富集显示，这些基因主要定位于分泌颗粒膜和血小板α颗粒，并参与血液凝固、体液水平调节及伤口愈合等生理过程。KEGG通路分析显示，这些基因显著富集于血小板激活、补体和凝血级联反应、Rap1信号通路及人巨细胞病毒感染等途径。通过机器学习鉴定出6个关键DE-CRGs，受试者工作特征（
ROC
）曲线分析表明这些基因具有良好的诊断效能。CIBERSORT分析显示，关键基因与免疫细胞浸润之间存在显著相关性。基于6个关键基因将患者分为两类：亚型A富含CD8
+
T细胞与活化NK细胞，表现为免疫活跃状态；亚型B以单核细胞和静息NK细胞为主，免疫通路激活不足。大鼠动物实验表明，失血性休克可导致凝血功能障碍，qRT-PCR结果进一步证实关键基因的表达趋势与生物信息学分析结果一致。
结论
2
本研究通过转录组学与机器学习方法，系统性筛选出了6个与TIC密切相关的关键基因，即GNA13、PIK3R3、ITGAM、MAPK14、PPP1CC和LYN，并揭示了其与凝血功能、免疫浸润的紧密联系。动物实验验证进一步支持了这些基因作为潜在诊断与治疗靶点的价值。
Abstract
Objective
2
To elucidate the mechanisms of trauma-induced coagulopathy （TIC），clarify the specific pathogenic factors and pathophysiological processes，and discover the effective diagnostic indicators and therapeutic targets.
Methods
2
Transcriptomic data of traumatic hemorrhagic shock patients were obtained from the Gene Expression Omnibus （GEO） to identify differentially expressed genes （DEGs）.Coagulation-related genes （CRGs） from the Kyoto Encyclopedia of Genes and Genomes （KEGG） were intersected with DEGs.Machine learning algorithms，including least absolute shrinkage and selection operator （LASSO） and random forest （RF），were applied to identify key genes.The CIBERSORT algorithm was used to analyze the correlation between key genes and immune cell infiltration.Through consensus clustering，subtype analysis was conducted on trauma patients to compare the infiltration of immune cells.A rat model of traumatic hemorrhagic shock was established to validate coagulation function and the expression of key genes.
Results
2
The dataset included samples from 17 healthy controls and 478 patients with traumatic hemorrhagic shock.A total of 6 315 DEGs were identified under the screening criterion of corrected
P
<
0.05.Gene set enrichment analysis （GSEA） showed that the up-regulated DEGs were significantly enriched in the glucose metabolism pathway，while the down-regulated DEGs were enriched in the immune reaction-related pathways.Through cross-analysis of DEGs and CRGs，a total of 65 differentially expressed coagulation-related genes （DE-CRGs） were screened out.GO functional enrichment showed that these genes were mainly located in secreting granular membranes and platelet α-granules，and were involved in physiological processes such as blood coagulation，regulation of body fluid levels，and wound healing.KEGG pathway analysis revealed that these genes were significantly enriched in pathways such as platelet ac
tivation，complement and coagulation cascade reactions，Rap1 signaling pathway，and human cytomegalovirus infection.Six key DE-CRGs were identified through machine learning.
Receiver operating characteristic
（
ROC
） curve analysis indicated that these genes had good diagnostic efficacy.CIBERSORT analysis revealed a significant correlation between key genes and immune cell infiltration.Patients were classified into two subtypes based on the six key genes： subtype A was rich in CD8
+
T cells and activated NK cells，presented an immune-active state； subtype B was mainly composed of monocytes and resting NK cells，with insufficient activation of immune pathways.Animal experiments on rats showed that hemorrhagic shock can lead to coagulation dysfunction. The results of qRT-PCR further confirmed that the expression trend of key genes was consistent with the results of bioinformatics analysis.
Conclusion
2
In this study，through transcriptomics and machine learning methods，six key genes closely related to TIC were systematically screened out，namely GNA13，PIK3R3，ITGAM，MAPK14，PPP1CC and LYN，and their close connections with coagulation function and immune infiltration were revealed. Animal experiments have further verified the value of these genes as potential diagnostic and therapeutic targets.
关键词
Keywords
references
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