Chen Qingsong, Li Tao, Zhang Tao, Zhou Yue, Huang Weifeng, Li Hui, Shi Li, Li Jianxiao, Zhang Qi, Ma Man, Wang Pan, Hu Hui, Wei Gongbin, Xiang Jiangxia, Cheng Yuan, Yang Jun, Huang Guangbin, Li Yongming, Du Dingyuan
School of Microelectronics and Communication Engineering of Chongqing University, Chongqing University Central Hospital (Chongqing Emergency Medical Center), Chongqing, China.
Department of the Traumatology, Chongqing Emergency Medical Center, National Regional Tramadol Center, Chongqing University Central Hospital, Chongqing, China.
Sci Rep. 2025 Jul 8;15(1):24578. doi: 10.1038/s41598-025-10323-4.
Despite advancements in trauma care, uncontrolled hemorrhage and trauma-induced coagulopathy (TIC) remain the leading causes of preventable deaths after trauma. Understanding the genetic underpinnings and molecular mechanisms of TIC is crucial for developing effective diagnostic and therapeutic strategies. This study employed a comprehensive bioinformatics approach to elucidate the genetic landscape associated with TIC. Gene expression data from 20 samples, comprising 10 controls and 10 severe trauma patients with TIC, were analyzed. This approach included principal component analysis, differential gene expression analysis using DESeq2, Gene Set Enrichment Analysis (GSEA), weighted gene co-expression network analysis (WGCNA), and machine learning (ML) algorithms (support vector machine-recursive feature elimination, least absolute shrinkage and selection operator, and random forest) for feature gene identification. Functional analysis of genes and immunoinfiltration analysis were also conducted. A total of 1014 differentially expressed genes (DEGs) were identified, indicating significant genetic alterations in TIC. GSEA confirmed the involvement of critical pathways, and WGCNA identified 35 relevant gene modules. The integration of ML algorithms highlighted nine key feature genes (TFPI, MMP9, ABCG5, TPSAB1, TK1, IGKV3D.11, SAMSN1, TIMP3, and GZMB). Immunoinfiltration analysis revealed distinct immune cell compositions in TIC samples. The multifactor regulation network provided insights into complex gene regulatory mechanisms. This study presents a detailed genetic and molecular profile of TIC. Integrating various bioinformatics tools and ML algorithms has enabled the identification of potential biomarkers and therapeutic targets. These findings could significantly contribute to improving the diagnostic accuracy and treatment efficacy for patients with TIC, potentially reducing the mortality rates associated with trauma.
尽管创伤护理取得了进展，但无法控制的出血和创伤性凝血病（TIC）仍然是创伤后可预防死亡的主要原因。了解TIC的遗传基础和分子机制对于制定有效的诊断和治疗策略至关重要。本研究采用了全面的生物信息学方法来阐明与TIC相关的遗传图谱。分析了来自20个样本的基因表达数据，其中包括10名对照和10名患有TIC的严重创伤患者。该方法包括主成分分析、使用DESeq2进行差异基因表达分析、基因集富集分析（GSEA）、加权基因共表达网络分析（WGCNA）以及用于特征基因识别的机器学习（ML）算法（支持向量机-递归特征消除、最小绝对收缩和选择算子以及随机森林）。还进行了基因功能分析和免疫浸润分析。共鉴定出1014个差异表达基因（DEG），表明TIC中存在显著的基因改变。GSEA证实了关键通路的参与，WGCNA识别出35个相关基因模块。ML算法的整合突出了9个关键特征基因（TFPI、MMP9、ABCG5、TPSAB1、TK1、IGKV3D.11、SAMSN1、TIMP3和GZMB）。免疫浸润分析揭示了TIC样本中不同的免疫细胞组成。多因素调控网络提供了对复杂基因调控机制的见解。本研究展示了TIC详细的遗传和分子概况。整合各种生物信息学工具和ML算法能够识别潜在的生物标志物和治疗靶点。这些发现可能对提高TIC患者的诊断准确性和治疗效果做出重大贡献，有可能降低与创伤相关的死亡率。
