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<p>As of now, current studies have confirmed that circadian rhythm genes (CRGs) can modulate the onset and development of metabolic dysfunction-associated fatty liver disease (MAFLD). Leveraging computational biology methodologies, systematic and thorough investigations into MAFLD and CRGs enhance our capacity to gain deeper insights into the disease’s pathogenic mechanisms. MAFLD-associated datasets (GSE89632 and GSE126848) and 248 circadian rhythm genes genes (CRGs) were analyzed. Principal component analysis (PCA) was performed on the GSE89632 dataset, and samples were divided into training and test sets. Differentially expressed genes (DEGs) from the training set were intersected with key module genes from WGCNA to identify intersection genes. These were then analyzed for expression consistency across datasets, followed by machine learning to identify key genes. Further analyses, including nomogram development, gene set enrichment analysis (GSEA), immune infiltration, and drug prediction, were also conducted. Four outliers were excluded, leaving 17 MAFLD and 22 control samples for analysis. 141 intersection genes were identified, with 36 showing consistent expression trends. Machine learning identified SOCS2, FBXO27, and HMMR as key genes. A nomogram based on these genes predicted MAFLD patient survival. GSEA revealed significant enrichment in pathways like olfactory transduction and peroxisome. Mast cell activation correlated strongly with SOCS2 and FBXO27. SOCS2, FBXO27, and HMMR are identified as key genes in MAFLD, providing new insights into the disease mechanisms.</p>

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Identification of circadian rhythm-associated key genes in metabolic dysfunction-associated fatty liver disease

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月22日

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  • Jinbo Luo,
  • Xiuying Ma,
  • Quanyu Wang,
  • Ruyi Zhang,
  • Ling Zhu,
  • Xue Feng,
  • Wenxue Wang,
  • Jiawei Geng

摘要

As of now, current studies have confirmed that circadian rhythm genes (CRGs) can modulate the onset and development of metabolic dysfunction-associated fatty liver disease (MAFLD). Leveraging computational biology methodologies, systematic and thorough investigations into MAFLD and CRGs enhance our capacity to gain deeper insights into the disease’s pathogenic mechanisms. MAFLD-associated datasets (GSE89632 and GSE126848) and 248 circadian rhythm genes genes (CRGs) were analyzed. Principal component analysis (PCA) was performed on the GSE89632 dataset, and samples were divided into training and test sets. Differentially expressed genes (DEGs) from the training set were intersected with key module genes from WGCNA to identify intersection genes. These were then analyzed for expression consistency across datasets, followed by machine learning to identify key genes. Further analyses, including nomogram development, gene set enrichment analysis (GSEA), immune infiltration, and drug prediction, were also conducted. Four outliers were excluded, leaving 17 MAFLD and 22 control samples for analysis. 141 intersection genes were identified, with 36 showing consistent expression trends. Machine learning identified SOCS2, FBXO27, and HMMR as key genes. A nomogram based on these genes predicted MAFLD patient survival. GSEA revealed significant enrichment in pathways like olfactory transduction and peroxisome. Mast cell activation correlated strongly with SOCS2 and FBXO27. SOCS2, FBXO27, and HMMR are identified as key genes in MAFLD, providing new insights into the disease mechanisms.