Scholarlink

Background <p>The rising mortality rates in sepsis highlight the current lack of reliable therapeutic biomarkers. This study aims to identify markers associated with biological functions to offer new strategies for sepsis diagnosis.</p> Methods <p>We conducted differential expression analysis to identify differentially expressed messenger RNAs (DEmRs), long non-coding RNA (DElncRs), and microRNAs (DEmiRs) in sepsis compared to healthy controls. Enrichment analysis was performed using DEmRs, and a lncRNA-miRNA-mRNA competing endogenous RNA network was constructed. Least absolute shrinkage and selection operator (LASSO) and random forest models were applied to identify diagnostic mRNAs. The optimal diagnostic model was determined through decision curve analysis, resulting in the identification of seven hub genes. The key gene, determined by its highest importance and the largest area under the receiver operating characteristics curve (AUC) value, was further validated. Additionally, we analyzed the correlation of the key gene with microenvironment cell infiltration and immune genes.</p> Results <p>A total of 4,450 intersected DEmRs (GSE66099, GSE13904, GSE154918, GSE8121) that were significantly involved in the cell cycle. We obtained 13 mRNAs, and further screened seven hub genes, including PPARD, ZSCAN2, ABI2, ENPP5, FMNL3, CD3E, and CAMK4. Subsequently, ENPP5 was as the key gene based on importance and AUC value. Moreover, Neutrophil cells and macrophages had a high abundance in sepsis patients. ENPP5 was positively associated with T cells but negatively associated with mast cells.</p> Conclusion <p>ENPP5, identified as a key gene, exhibits significant associations with immune cell infiltration and immune-related genes. This suggests its potential role as a biomarker for novel therapeutic strategies in sepsis.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Exploring ENPP5 as a diagnostic biomarker for sepsis: a comprehensive bioinformatics analysis

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月1日

查看全文
  • Jiamin Gao,
  • Yanjun Li,
  • Jinping Huang,
  • Cailing Wei,
  • Jieling Chen,
  • Aichun Huang,
  • Ningmei Liu,
  • Yibo Lu,
  • Shixiong Yang

摘要

Background

The rising mortality rates in sepsis highlight the current lack of reliable therapeutic biomarkers. This study aims to identify markers associated with biological functions to offer new strategies for sepsis diagnosis.

Methods

We conducted differential expression analysis to identify differentially expressed messenger RNAs (DEmRs), long non-coding RNA (DElncRs), and microRNAs (DEmiRs) in sepsis compared to healthy controls. Enrichment analysis was performed using DEmRs, and a lncRNA-miRNA-mRNA competing endogenous RNA network was constructed. Least absolute shrinkage and selection operator (LASSO) and random forest models were applied to identify diagnostic mRNAs. The optimal diagnostic model was determined through decision curve analysis, resulting in the identification of seven hub genes. The key gene, determined by its highest importance and the largest area under the receiver operating characteristics curve (AUC) value, was further validated. Additionally, we analyzed the correlation of the key gene with microenvironment cell infiltration and immune genes.

Results

A total of 4,450 intersected DEmRs (GSE66099, GSE13904, GSE154918, GSE8121) that were significantly involved in the cell cycle. We obtained 13 mRNAs, and further screened seven hub genes, including PPARD, ZSCAN2, ABI2, ENPP5, FMNL3, CD3E, and CAMK4. Subsequently, ENPP5 was as the key gene based on importance and AUC value. Moreover, Neutrophil cells and macrophages had a high abundance in sepsis patients. ENPP5 was positively associated with T cells but negatively associated with mast cells.

Conclusion

ENPP5, identified as a key gene, exhibits significant associations with immune cell infiltration and immune-related genes. This suggests its potential role as a biomarker for novel therapeutic strategies in sepsis.