Purpose <p>Increasing evidence has indicated a role of oxidative stress in the pathogenesis of biliary atresia (BA). This study aimed to identify key oxidative stress-related biomarkers in BA and explore their therapeutic potential.</p> Methods <p>BA datasets were obtained from the GEO database. Differentially expressed genes (DEGs) were identified using the GSE46960 dataset. These DEGs were intersected with oxidative stress-related genes from GeneCards to obtain the oxidative stress-related DEGs (ORDEGs). Functional enrichment (GO, KEGG) and gene set enrichment analyses were performed. Hub genes were identified using machine learning and protein-protein interaction (PPI) network analysis. Their expression was validated via qRT-PCR in BA patient liver samples and rhesus rotavirus (RRV)-induced mouse models. Potential transcription factors (TFs), microRNAs, and drugs targeting hub ORDEGs were predicted using TRRUST, TarBase, and DSigDB, respectively.</p> Results <p>Six hub ORDEGs (<i>CCL2</i>, <i>CXCL8</i>, <i>SPP1</i>, <i>EDN1</i>, <i>TGFB3</i>, and <i>F3</i>) were ultimately identified. Their mRNA levels were significantly upregulated in both BA patients and mice. A total of 52 TFs and 13 microRNAs were identified. Among the predicted drugs, simvastatin may hold therapeutic potential.</p> Conclusion <p>This study identified six key ORDEGs (<i>CCL2</i>, <i>CXCL8</i>, <i>SPP1</i>, <i>EDN1</i>, <i>TGFB3</i>, and <i>F3</i>) to be important pathogenic factors and potential therapeutic targets in BA.</p>

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Identification and validation of oxidative stress-related genes in biliary atresia

  • Liying Rong,
  • Mengxin Zhang,
  • Junni Ma,
  • Xiaoyu Wang,
  • Jiayu Zheng,
  • Xi Zhang,
  • Shao-tao Tang

摘要

Purpose

Increasing evidence has indicated a role of oxidative stress in the pathogenesis of biliary atresia (BA). This study aimed to identify key oxidative stress-related biomarkers in BA and explore their therapeutic potential.

Methods

BA datasets were obtained from the GEO database. Differentially expressed genes (DEGs) were identified using the GSE46960 dataset. These DEGs were intersected with oxidative stress-related genes from GeneCards to obtain the oxidative stress-related DEGs (ORDEGs). Functional enrichment (GO, KEGG) and gene set enrichment analyses were performed. Hub genes were identified using machine learning and protein-protein interaction (PPI) network analysis. Their expression was validated via qRT-PCR in BA patient liver samples and rhesus rotavirus (RRV)-induced mouse models. Potential transcription factors (TFs), microRNAs, and drugs targeting hub ORDEGs were predicted using TRRUST, TarBase, and DSigDB, respectively.

Results

Six hub ORDEGs (CCL2, CXCL8, SPP1, EDN1, TGFB3, and F3) were ultimately identified. Their mRNA levels were significantly upregulated in both BA patients and mice. A total of 52 TFs and 13 microRNAs were identified. Among the predicted drugs, simvastatin may hold therapeutic potential.

Conclusion

This study identified six key ORDEGs (CCL2, CXCL8, SPP1, EDN1, TGFB3, and F3) to be important pathogenic factors and potential therapeutic targets in BA.