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<p>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 (<i>TFPI</i>, <i>MMP9</i>, <i>ABCG5</i>, <i>TPSAB1</i>, <i>TK1</i>, <i>IGKV3D.11</i>, <i>SAMSN1</i>, <i>TIMP3</i>, and <i>GZMB</i>). 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.</p>

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Integrated analysis of WGCNA and machine learning identified diagnostic biomarkers in trauma-induced coagulopathy

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月8日

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  • Qingsong Chen,
  • Tao Li,
  • Tao Zhang,
  • Yue Zhou,
  • Weifeng Huang,
  • Hui Li,
  • Li Shi,
  • Jianxiao Li,
  • Qi Zhang,
  • Man Ma,
  • Pan Wang,
  • Hui Hu,
  • Gongbin Wei,
  • Jiangxia Xiang,
  • Yuan Cheng,
  • Jun Yang,
  • Guangbin Huang,
  • Yongming Li,
  • Dingyuan Du

摘要

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.