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<p>Metabolic dysfunction-associated steatotic liver disease (MASLD) is the leading cause of chronic liver disease worldwide. The mechanisms of liver injury in MASLD patients remain incompletely elucidated. This study aims to identify novel critical genes associated with MASLD using bioinformatics approaches.&#xa0;Microarray datasets were downloaded from the GEO database, and differentially expressed genes (DEGs) were identified. We performed enrichment analyses, constructed protein–protein interaction (PPI) networks, and visualized co-expression networks between mRNAs and miRNAs. Hub gene expression was validated using an independent dataset, and ROC curves were generated. Subsequent analyses included clinical correlation assessments and ceRNA network construction.&#xa0;Enrichment analyses revealed that the cell cycle, cytokine–cytokine receptor interactions, cellular senescence, oocyte meiosis, chemokine signaling pathways, viral protein–cytokine interactions, luteinizing hormone-mediated oocyte maturation, PPAR signaling, and p53 signaling pathways were significantly enriched. Three key genes (IL1β, TLR2, and TLR4) were validated using GEO datasets. The constructed ceRNA network, comprising miRNAs, circRNAs, mRNAs, and lncRNAs, identified the hsa-miR-21-5p-TLR2and hsa-miR-34a-IL1Bpathways as critical regulators of MASLD pathogenesis.&#xa0;IL1B, TLR2, TLR4, CCL2, and ICAM1were identified as central genes in MASLD. The hsa-miR-21-5p-TLR2and hsa-miR-34a-IL1Bpathways are potentially associated with MASLD development, offering insights for therapeutic targeting.</p>

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Integrated bioinformatics decoding of the MASLD ceRNA network reveals novel therapeutic targets

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月6日

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  • Shian Yu,
  • Xinhua Jiang,
  • Long Peng,
  • Xianwen Zeng,
  • Suqi Wen,
  • Hui Xiong,
  • Sumei Guo,
  • Fufeng Huang,
  • Hanwen Liu,
  • Qiulan Luo

摘要

Metabolic dysfunction-associated steatotic liver disease (MASLD) is the leading cause of chronic liver disease worldwide. The mechanisms of liver injury in MASLD patients remain incompletely elucidated. This study aims to identify novel critical genes associated with MASLD using bioinformatics approaches. Microarray datasets were downloaded from the GEO database, and differentially expressed genes (DEGs) were identified. We performed enrichment analyses, constructed protein–protein interaction (PPI) networks, and visualized co-expression networks between mRNAs and miRNAs. Hub gene expression was validated using an independent dataset, and ROC curves were generated. Subsequent analyses included clinical correlation assessments and ceRNA network construction. Enrichment analyses revealed that the cell cycle, cytokine–cytokine receptor interactions, cellular senescence, oocyte meiosis, chemokine signaling pathways, viral protein–cytokine interactions, luteinizing hormone-mediated oocyte maturation, PPAR signaling, and p53 signaling pathways were significantly enriched. Three key genes (IL1β, TLR2, and TLR4) were validated using GEO datasets. The constructed ceRNA network, comprising miRNAs, circRNAs, mRNAs, and lncRNAs, identified the hsa-miR-21-5p-TLR2and hsa-miR-34a-IL1Bpathways as critical regulators of MASLD pathogenesis. IL1B, TLR2, TLR4, CCL2, and ICAM1were identified as central genes in MASLD. The hsa-miR-21-5p-TLR2and hsa-miR-34a-IL1Bpathways are potentially associated with MASLD development, offering insights for therapeutic targeting.