Identifying biomarkers associated with the diagnosis of ulcerative colitis via bioinformatics and machine learning.
Identifying biomarkers associated with the diagnosis of ulcerative colitis via bioinformatics and machine learning.
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DOI：
10.3934/mbe.2023476
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发表时间：
2023-04
期刊：
影响因子：
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通讯作者：
Yuedan Wang;Jinke Huang;Jiaqi Zhang;Fengyun Wang;Xudong Tang
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文献类型：
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作者：
Yuedan Wang;Jinke Huang;Jiaqi Zhang;Fengyun Wang;Xudong Tang
背景溃疡性结肠炎（UC）是一种发病率不断上升的特发性炎症性疾病。本研究旨在确定潜在的UC生物标志物和相关的免疫浸润特征。方法采用GSE 87473和GSE 92415两个数据集进行数据融合，获得193例UC样本和42例正常人样本。利用R筛选UC与正常人之间的差异表达基因，并利用基因本体论和京都基因百科全书和基因组分析研究其生物学功能。使用最小绝对收缩选择算子回归和支持向量机递归特征消除识别有希望的生物标志物，并通过受试者工作特征（ROC）曲线评估其诊断效力。最后，使用CIBERSORT研究UC中的免疫浸润特征，并检查所鉴定的生物标志物与各种免疫细胞之间的关系。结果我们发现102个DEG，其中64个显著上调，38个显著下调。DEG富集在与白细胞介素-17相关的途径中，其中包括精氨酸-细胞因子受体相互作用和病毒蛋白与细胞因子和细胞因子受体的相互作用。利用机器学习方法和ROC检验，我们确认DUOX 2、DMBT 1、CYP 2B 7 P、PITX 2和DEFB 1是UC的必需诊断基因。免疫细胞浸润分析显示，所有5个诊断基因与调节性T细胞、CD 8 T细胞、活化和静息记忆性CD 4 T细胞、活化自然杀伤细胞、中性粒细胞、活化和静息肥大细胞、活化和静息树突状细胞以及M0、M1和M2巨噬细胞相关。结论DUOX 2、DMBT 1、CYP 2B 7 P、PITX 2和DEFB 1是UC的潜在生物标志物。这些生物标志物及其与免疫细胞浸润的关系可能为理解UC的进展提供新的视角。
BACKGROUND Ulcerative colitis (UC) is an idiopathic inflammatory disease with an increasing incidence. This study aimed to identify potential UC biomarkers and associated immune infiltration characteristics. METHODS Two datasets (GSE87473 and GSE92415) were merged to obtain 193 UC samples and 42 normal samples. Using R, differentially expressed genes (DEGs) between UC and normal samples were filtered out, and their biological functions were investigated using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses. Promising biomarkers were identified using least absolute shrinkage selector operator regression and support vector machine recursive feature elimination, and their diagnostic efficacy was evaluated through receiver operating characteristic (ROC) curves. Finally, CIBERSORT was used to investigate the immune infiltration characteristics in UC, and the relationship between the identified biomarkers and various immune cells was examined. RESULTS We found 102 DEGs, of which 64 were significantly upregulated, and 38 were significantly downregulated. The DEGs were enriched in pathways associated with interleukin-17, cytokine-cytokine receptor interaction and viral protein interactions with cytokines and cytokine receptors, among others. Using machine learning methods and ROC tests, we confirmed DUOX2, DMBT1, CYP2B7P, PITX2 and DEFB1 to be essential diagnostic genes for UC. Immune cell infiltration analysis revealed that all five diagnostic genes were correlated with regulatory T cells, CD8 T cells, activated and resting memory CD4 T cells, activated natural killer cells, neutrophils, activated and resting mast cells, activated and resting dendritic cells and M0, M1 and M2 macrophages. CONCLUSIONS DUOX2, DMBT1, CYP2B7P, PITX2 and DEFB1 were identified as prospective biomarkers for UC. A new perspective on understanding the progression of UC may be provided by these biomarkers and their relationship with immune cell infiltration.