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已发表论文
通过机器学习与孟德尔随机化方法鉴别出六个与细胞死亡相关、对溃疡性结肠炎的病情进展及治疗反应起到推动作用的基因
Authors Dai L, Zhou W, Li A, Xu X, Yuan B, Zhang Z
Received 23 April 2025
Accepted for publication 5 September 2025
Published 19 September 2025 Volume 2025:18 Pages 13073—13088
DOI https://doi.org/10.2147/JIR.S536145
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Dr Fatih Türker
Longfei Dai,1 Weiguo Zhou,1 Along Li,1 Xinjian Xu,1 Bin Yuan,2,3 Zhen Zhang1
1Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, People’s Republic of China; 2Department of Pharmacology, School of Pharmaceutical Sciences, Anhui Medical University, Hefei, Anhui, People’s Republic of China; 3The First Department of Critical Care Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui, People’s Republic of China
Correspondence: Zhen Zhang, Email zhz36@sina.com Bin Yuan, Email yuanb_ahmu@163.com
Background: The pathogenesis of ulcerative colitis (UC) is thought to involve abnormal regulation of cell death. However, key cell death-related genes (CDGs) that drive disease progression have not been fully characterized. The identification of these CDGs is thought to potentially reveal new therapeutic targets.
Methods: Machine learning (ML) and Mendelian randomization (MR) methods were integrated to identify CDGs with causal effects in UC progression. The validation included immune-related analysis, drug response assessment (infliximab/vedolizumab/golimumab), patient stratification based on consensus clustering, and functional validation.
Results: Six key CDG genes (VNN1, PTGDS, MMP9, IL13RA2, S100A8, and IL1B) were identified by ML. VNN1 and MMP9 were confirmed by MR to be pathogenic risk factors for UC progression. All six genes were significantly associated with immune cell infiltration, pro-inflammatory cytokines, and intestinal barrier dysfunction. Compared with non-responders, the expression of these six CDGs was significantly downregulated in biologic therapy responders. Based on these genes, patients with UC were classified into two groups: the C1 group with severe disease activity and the C2 group with reduced Mayo scores and enhanced treatment sensitivity. Additionally, knocking down VNN1 functionally alleviated intestinal inflammation.
Conclusion: These six genes can be used to assess the severity of UC and predict treatment outcomes.
Keywords: machine learning, Mendelian randomization, cell death, VNN1, ulcerative colitis
1Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, People’s Republic of China; 2Department of Pharmacology, School of Pharmaceutical Sciences, Anhui Medical University, Hefei, Anhui, People’s Republic of China; 3The First Department of Critical Care Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui, People’s Republic of China
Correspondence: Zhen Zhang, Email zhz36@sina.com Bin Yuan, Email yuanb_ahmu@163.com
Background: The pathogenesis of ulcerative colitis (UC) is thought to involve abnormal regulation of cell death. However, key cell death-related genes (CDGs) that drive disease progression have not been fully characterized. The identification of these CDGs is thought to potentially reveal new therapeutic targets.
Methods: Machine learning (ML) and Mendelian randomization (MR) methods were integrated to identify CDGs with causal effects in UC progression. The validation included immune-related analysis, drug response assessment (infliximab/vedolizumab/golimumab), patient stratification based on consensus clustering, and functional validation.
Results: Six key CDG genes (VNN1, PTGDS, MMP9, IL13RA2, S100A8, and IL1B) were identified by ML. VNN1 and MMP9 were confirmed by MR to be pathogenic risk factors for UC progression. All six genes were significantly associated with immune cell infiltration, pro-inflammatory cytokines, and intestinal barrier dysfunction. Compared with non-responders, the expression of these six CDGs was significantly downregulated in biologic therapy responders. Based on these genes, patients with UC were classified into two groups: the C1 group with severe disease activity and the C2 group with reduced Mayo scores and enhanced treatment sensitivity. Additionally, knocking down VNN1 functionally alleviated intestinal inflammation.
Conclusion: These six genes can be used to assess the severity of UC and predict treatment outcomes.
Keywords: machine learning, Mendelian randomization, cell death, VNN1, ulcerative colitis