Biomarkers prediction and immune landscape in ulcerative colitis: Findings based on bioinformatics and machine learning

作者：Yuanming Yang, Yiwei Hua, Huan Zheng, Jia Rui, Zhining Ye, Guifang Su, Yueming Gu, Kai Zhan, Kairui Tang, Shuhao Qi, Haomeng Wu, Shumin Qin, Shaogang Huang · 发表于：Computers in Biology and Medicine · 年份：2023 · DOI：10.1016/j.compbiomed.2023.107778 · 被引用次数：49 · 研究领域：Inflammatory Bowel Disease、Ferroptosis and cancer prognosis、Biomarkers in Disease Mechanisms

BACKGROUND: Ulcerative colitis (UC) presents diagnostic and therapeutic difficulties. The primary objective of this study is to identify efficacious biomarkers for diagnosis and treatment, as well as acquire a deeper understanding of the immuneological characteristics associated with the disease. METHODS: Datasets relating to UC were obtained from GEO database. Among these, three datasets were merged to create a metadata for bioinformatics analysis and machine learning. Additionally, one dataset specifically utilized for external validation. Least absolute shrinkage and selection operator (LASSO) and random forest (RF) were employed to screen signature genes. The artificial neural network (ANN) model and receiver operating characteristic (ROC) curve were used to assess the diagnostic performance of signature genes. The single sample gene set enrichment analysis (ssGSEA) was applied to reveal the immune landscape. Finally, the relationship between the signature genes, immune infiltration, and clinical characteristics was investigated through correlation analysis. RESULT: By intersecting the result of LASSO, RF and WGCNA, 8 signature genes were identified, including S100A8, IL-1B, CXCL1, TCN1, MMP10, GREM1, DUOX2 and SLC6A14. The biological progress of this gene mostly encompasses acute inflammatory response, aggregation and chemotaxis of leukocyte, and response to lipopolysaccharide by mediating IL-17 signaling pathway, NF-kappa B signaling pathway, TNF signaling pathway, NOD-...