108139
论文已发表
注册即可获取德孚的最新动态
IF 收录期刊
- 3.4 Breast Cancer (Dove Med Press)
- 3.2 Clin Epidemiol
- 2.6 Cancer Manag Res
- 2.9 Infect Drug Resist
- 3.7 Clin Interv Aging
- 5.1 Drug Des Dev Ther
- 3.1 Int J Chronic Obstr
- 6.6 Int J Nanomed
- 2.6 Int J Women's Health
- 2.9 Neuropsych Dis Treat
- 2.8 OncoTargets Ther
- 2.0 Patient Prefer Adher
- 2.2 Ther Clin Risk Manag
- 2.5 J Pain Res
- 3.0 Diabet Metab Synd Ob
- 3.2 Psychol Res Behav Ma
- 3.4 Nat Sci Sleep
- 1.8 Pharmgenomics Pers Med
- 2.0 Risk Manag Healthc Policy
- 4.1 J Inflamm Res
- 2.0 Int J Gen Med
- 3.4 J Hepatocell Carcinoma
- 3.0 J Asthma Allergy
- 2.2 Clin Cosmet Investig Dermatol
- 2.4 J Multidiscip Healthc
已发表论文
溃疡性结肠炎关键生物标志物及免疫微环境特征的识别：基于 WGCNA 和多种机器学习算法的综合分析
Authors Qi Y , Wang Y , Zhang S, Pan X, Li W, Cheng M, Ma W, Li J, Pei Y, Liu Y , Yu Y
Received 10 June 2025
Accepted for publication 1 October 2025
Published 7 October 2025 Volume 2025:18 Pages 13879—13896
DOI https://doi.org/10.2147/JIR.S545790
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 3
Editor who approved publication: Dr Fatih Türker
Yingchao Qi,1,* Yichen Wang,2,* Siyao Zhang,1,3,* Xinxin Pan,4 Wenkai Li,2 Meijia Cheng,2 Wenjing Ma,2 Jiajia Li,2 Yue Pei,2 Yunen Liu,1,2 Yongduo Yu5
1First Clinical School, Liaoning University of Traditional Chinese Medicine, Shenyang, People’s Republic of China; 2School of Shuren International, Shenyang Medical College, Shenyang, People’s Republic of China; 3Department of Anorectal Surgery, The Third Affiliated Hospital of Liaoning University of Traditional Chinese Medicine, Shenyang, People’s Republic of China; 4Department of Anorectal Surgery, Wuxi Huishan District Hospital of Traditional Chinese Medicine, Wuxi, People’s Republic of China; 5Department of Anorectal Surgery, The Second Affiliated Hospital of Liaoning University of Traditional Chinese Medicine, Shenyang, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Yongduo Yu, Department of Anorectal Surgery, The Second Affiliated Hospital of Liaoning University of Traditional Chinese Medicine, Shenyang, People’s Republic of China, Email yuyongduo@163.com
Background: Ulcerative colitis (UC) is an inflammatory bowel disease (IBD) characterized by a dysregulated mucosal immune response in the intestine. The disease poses significant challenges for both diagnosis and treatment. This study aims to identify reliable biomarkers for UC and investigate its immunological characteristics, with the goal of improving diagnostic accuracy and informing treatment strategies.
Methods: This study integrated multiple GEO datasets and identified UC-associated hub genes through differential expression and Weighted Gene Co-expression Network Analysis (WGCNA), with subsequent refinement using least absolute shrinkage and selection operator (LASSO), randomForest (RF), and support vector machine-recursive feature elimination (SVM-RFE) algorithms. These genes were used to construct a feedforward neural network (FNN) diagnostic model, whose performance was assessed using receiver operating characteristic (ROC) curve analysis. Immune profiling based on CIBERSORT, ssGSEA, Gene set variation analysis (GSVA), and ESTIMATE revealed associations between hub gene expression, immune cell infiltration, and inflammatory activity. Immunohistochemistry was performed to validate the protein expression of hub genes.
Results: Ten hub genes (ACOX2, MMP3, CPT2, CTSK, CHP2, VCAM1, SLC25A34, BASP1, NCF2, and GLB1L2) were identified, and the FNN model showed strong diagnostic accuracy. Notably, NCF2 expression correlated with immune cell infiltration and immune/inflammation scores, and was confirmed to be elevated in UC tissues, suggesting a key role in disease pathogenesis.
Conclusion: This study identified ten hub genes with potential as biomarkers for the diagnosis and treatment of UC. NCF2 may contribute to UC pathogenesis by modulating inflammatory and immune responses, serving as a potential immune-related biomarker for improved UC diagnosis and treatment.
Keywords: ulcerative colitis, biomarkers, immune microenvironment, machine learning
1First Clinical School, Liaoning University of Traditional Chinese Medicine, Shenyang, People’s Republic of China; 2School of Shuren International, Shenyang Medical College, Shenyang, People’s Republic of China; 3Department of Anorectal Surgery, The Third Affiliated Hospital of Liaoning University of Traditional Chinese Medicine, Shenyang, People’s Republic of China; 4Department of Anorectal Surgery, Wuxi Huishan District Hospital of Traditional Chinese Medicine, Wuxi, People’s Republic of China; 5Department of Anorectal Surgery, The Second Affiliated Hospital of Liaoning University of Traditional Chinese Medicine, Shenyang, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Yongduo Yu, Department of Anorectal Surgery, The Second Affiliated Hospital of Liaoning University of Traditional Chinese Medicine, Shenyang, People’s Republic of China, Email yuyongduo@163.com
Background: Ulcerative colitis (UC) is an inflammatory bowel disease (IBD) characterized by a dysregulated mucosal immune response in the intestine. The disease poses significant challenges for both diagnosis and treatment. This study aims to identify reliable biomarkers for UC and investigate its immunological characteristics, with the goal of improving diagnostic accuracy and informing treatment strategies.
Methods: This study integrated multiple GEO datasets and identified UC-associated hub genes through differential expression and Weighted Gene Co-expression Network Analysis (WGCNA), with subsequent refinement using least absolute shrinkage and selection operator (LASSO), randomForest (RF), and support vector machine-recursive feature elimination (SVM-RFE) algorithms. These genes were used to construct a feedforward neural network (FNN) diagnostic model, whose performance was assessed using receiver operating characteristic (ROC) curve analysis. Immune profiling based on CIBERSORT, ssGSEA, Gene set variation analysis (GSVA), and ESTIMATE revealed associations between hub gene expression, immune cell infiltration, and inflammatory activity. Immunohistochemistry was performed to validate the protein expression of hub genes.
Results: Ten hub genes (ACOX2, MMP3, CPT2, CTSK, CHP2, VCAM1, SLC25A34, BASP1, NCF2, and GLB1L2) were identified, and the FNN model showed strong diagnostic accuracy. Notably, NCF2 expression correlated with immune cell infiltration and immune/inflammation scores, and was confirmed to be elevated in UC tissues, suggesting a key role in disease pathogenesis.
Conclusion: This study identified ten hub genes with potential as biomarkers for the diagnosis and treatment of UC. NCF2 may contribute to UC pathogenesis by modulating inflammatory and immune responses, serving as a potential immune-related biomarker for improved UC diagnosis and treatment.
Keywords: ulcerative colitis, biomarkers, immune microenvironment, machine learning