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<p>Ulcerative colitis (UC) is a chronic inflammatory bowel disease of unknown etiology characterized by abnormal mucosal immune responses and persistent intestinal inflammation. UC significantly reduces quality of life and imposes a substantial economic burden. Early diagnosis and personalized treatment are crucial for improving outcomes; however, significant challenges remain. This study aimed to identify novel molecular targets for UC treatment. Gene expression data related to UC were obtained from the Gene Expression Omnibus, and differentially expressed genes (DEGs) were identified based on the criteria <i>P</i> &lt; 0.05 and |logFC| &gt; 0.5. The DEGs were intersected with autophagy-related genes (ARGs) to explore the relationship between UC pathogenesis and autophagy. LASSO regression, random forest regression, and support vector machine-recursive feature elimination were employed to screen for differentially expressed ARGs. The robustness and generalizability of the intersected genes were validated in external datasets and mouse models, and immune infiltration was analyzed to assess the interaction between UC and the immune system. <i>PEA15</i> was identified as a potential UC diagnostic marker, and the diagnostic value of <i>SERPINA1</i>,<i> CASP4</i>, and <i>CASP1</i> was reconfirmed. Our findings provide new insights into the pathogenesis of UC, facilitating early diagnosis and the development of personalized therapeutic strategies.</p>

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Autophagy related biomarkers in ulcerative colitis revealed by bioinformatics analysis and immune correlation

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月20日

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  • Xinyi Xue Chen,
  • Shichen Min,
  • Zhaofeng Shen,
  • Mengyuan Zhang,
  • Lejuan Huang,
  • Mengran Su,
  • Hong Shen,
  • Lei Zhu

摘要

Ulcerative colitis (UC) is a chronic inflammatory bowel disease of unknown etiology characterized by abnormal mucosal immune responses and persistent intestinal inflammation. UC significantly reduces quality of life and imposes a substantial economic burden. Early diagnosis and personalized treatment are crucial for improving outcomes; however, significant challenges remain. This study aimed to identify novel molecular targets for UC treatment. Gene expression data related to UC were obtained from the Gene Expression Omnibus, and differentially expressed genes (DEGs) were identified based on the criteria P < 0.05 and |logFC| > 0.5. The DEGs were intersected with autophagy-related genes (ARGs) to explore the relationship between UC pathogenesis and autophagy. LASSO regression, random forest regression, and support vector machine-recursive feature elimination were employed to screen for differentially expressed ARGs. The robustness and generalizability of the intersected genes were validated in external datasets and mouse models, and immune infiltration was analyzed to assess the interaction between UC and the immune system. PEA15 was identified as a potential UC diagnostic marker, and the diagnostic value of SERPINA1, CASP4, and CASP1 was reconfirmed. Our findings provide new insights into the pathogenesis of UC, facilitating early diagnosis and the development of personalized therapeutic strategies.