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Background <p>Thrombus formation is a severe complication in orthopedic surgery, significantly increasing mortality in patients with fractures. Therefore, identifying feature genes to determine thrombus presence in fracture surgeries is critical.</p> Methods <p>Whole blood samples were collected from 18 patients with fractures with thrombosis (YES_thrombus) and 18 patients with fractures without thrombosis (NO_thrombus) from the Second Hospital of Shanxi Medical University, China, and used for transcriptome sequencing and quality control to generate the YES_thrombus dataset. Candidate genes were identified by overlapping differentially expressed genes (DEGs) with key module genes from weighted gene co-expression network analysis (WGCNA). Functional enrichment analysis was then performed to explore the roles of the candidate genes. Feature genes were further refined by intersecting results from three machine learning algorithms and constructing an artificial neural network (ANN). Diagnostic performance was assessed using receiver operating characteristic (ROC) curves. Additionally, single-gene gene set enrichment analysis (GSEA) was conducted, and correlations between feature genes and differential immune cells were analyzed. The competing endogenouse RNA (ceRNA) regulatory network for feature genes was also constructed. Finally, quantitative reverse transcriptase PCR (qRT-PCR) was used to validate gene expression.</p> Results <p>Seven candidate genes were selected, with functional enrichment analysis linking them to the autophagosome and PPAR signaling pathways. Five feature genes with excellent diagnostic performance were identified. Single-gene GSEA enrichment showed that the feature genes were primarily associated with the cytosolic ribosome and oxidative phosphorylation. The correlation analysis revealed that aDC exhibited the strongest negative correlation with <i>WDR81</i> and the strongest positive correlation with <i>RGS1</i>. The ceRNA regulatory network encompassed three feature genes, five miRNAs, and 236 lncRNAs. Expression analysis indicated that, with the exception of WDR81, other genes were significantly upregulated in the NO_thrombus group. qRT-PCR validation confirmed that the expression of <i>AAED1</i>, <i>ARL4A</i>, and <i>WDR81</i> matched sequencing results.</p> Conclusions <p>In conclusion, five feature genes (<i>RGS1</i>, <i>HSF2</i>, <i>ARL4A</i>, <i>AAED1</i>, and <i>WDR81</i>) were identified, and functional enrichment analyses were conducted, providing a foundation for predicting the diagnosis of fractures associated with thrombosis.</p>

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The underlying molecular mechanisms and biomarkers of Hip fracture combined with deep vein thrombosis based on self sequencing bioinformatics analysis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年5月16日

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  • Guanghua Shi,
  • Xiaocui Shi,
  • Meng Zhang,
  • Rui Cheng,
  • Mengqing Hu,
  • Yu Zhao,
  • Shimei Li,
  • Xiuxiu Li,
  • Haiyun Ma,
  • Pengcui Li

摘要

Background

Thrombus formation is a severe complication in orthopedic surgery, significantly increasing mortality in patients with fractures. Therefore, identifying feature genes to determine thrombus presence in fracture surgeries is critical.

Methods

Whole blood samples were collected from 18 patients with fractures with thrombosis (YES_thrombus) and 18 patients with fractures without thrombosis (NO_thrombus) from the Second Hospital of Shanxi Medical University, China, and used for transcriptome sequencing and quality control to generate the YES_thrombus dataset. Candidate genes were identified by overlapping differentially expressed genes (DEGs) with key module genes from weighted gene co-expression network analysis (WGCNA). Functional enrichment analysis was then performed to explore the roles of the candidate genes. Feature genes were further refined by intersecting results from three machine learning algorithms and constructing an artificial neural network (ANN). Diagnostic performance was assessed using receiver operating characteristic (ROC) curves. Additionally, single-gene gene set enrichment analysis (GSEA) was conducted, and correlations between feature genes and differential immune cells were analyzed. The competing endogenouse RNA (ceRNA) regulatory network for feature genes was also constructed. Finally, quantitative reverse transcriptase PCR (qRT-PCR) was used to validate gene expression.

Results

Seven candidate genes were selected, with functional enrichment analysis linking them to the autophagosome and PPAR signaling pathways. Five feature genes with excellent diagnostic performance were identified. Single-gene GSEA enrichment showed that the feature genes were primarily associated with the cytosolic ribosome and oxidative phosphorylation. The correlation analysis revealed that aDC exhibited the strongest negative correlation with WDR81 and the strongest positive correlation with RGS1. The ceRNA regulatory network encompassed three feature genes, five miRNAs, and 236 lncRNAs. Expression analysis indicated that, with the exception of WDR81, other genes were significantly upregulated in the NO_thrombus group. qRT-PCR validation confirmed that the expression of AAED1, ARL4A, and WDR81 matched sequencing results.

Conclusions

In conclusion, five feature genes (RGS1, HSF2, ARL4A, AAED1, and WDR81) were identified, and functional enrichment analyses were conducted, providing a foundation for predicting the diagnosis of fractures associated with thrombosis.