Abstract：
BACKGROUND: The pathophysiology of ulcerative colitis (UC) is believed to be heavily influenced by immunology, which presents challenges for both diagnosis and treatment. The main aims of this study are to deepen our understanding of the immunological characteristics associated with the disease and to identify valuable biomarkers for diagnosis and treatment.
METHODS: The UC datasets were sourced from the GEO database and were analyzed using unsupervised clustering to identify different subtypes of UC. Twelve machine learning algorithms and Deep learning model DNN were developed to identify potential UC biomarkers, with the LIME and SHAP methods used to explain the models\' findings. PPI network is used to verify the identified key biomarkers, and then a network connecting super enhancers, transcription factors and genes is constructed. Single-cell sequencing technology was utilized to investigate the role of Peroxisome Proliferator Activated Receptor Gamma (PPARG) in UC and its correlation with macrophage infiltration. Furthermore, alterations in PPARG expression were validated through Western blot (WB) and immunohistochemistry (IHC) in both in vitro and in vivo experiments.
RESULTS: By utilizing bioinformatics techniques, we were able to pinpoint PPARG as a key biomarker for UC. The expression of PPARG was significantly reduced in cell models, UC animal models, and colitis models induced by dextran sodium sulfate (DSS). Interestingly, overexpression of PPARG was able to restore intestinal barrier function in H2O2-induced IEC-6 cells. Additionally, immune-related differentially expressed genes (DEGs) allowed for efficient classification of UC samples into neutrophil and mitochondrial metabolic subtypes. A diagnostic model incorporating the three disease-specific genes PPARG, PLA2G2A, and IDO1 demonstrated high accuracy in distinguishing between the UC group and the control group. Furthermore, single-cell analysis revealed that decreased PPARG expression in colon tissue may contribute to the polarization of M1 macrophages through activation of inflammatory pathways.
CONCLUSIONS: In conclusion, PPARG, a gene related to immunity, has been established as a reliable potential biomarker for the diagnosis and treatment of UC. The immune response it controls plays a key role in the progression and development of UC by enabling interaction between characteristic biomarkers and immune infiltrating cells.
METHODS: The UC datasets were sourced from the GEO database and were analyzed using unsupervised clustering to identify different subtypes of UC. Twelve machine learning algorithms and Deep learning model DNN were developed to identify potential UC biomarkers, with the LIME and SHAP methods used to explain the models\' findings. PPI network is used to verify the identified key biomarkers, and then a network connecting super enhancers, transcription factors and genes is constructed. Single-cell sequencing technology was utilized to investigate the role of Peroxisome Proliferator Activated Receptor Gamma (PPARG) in UC and its correlation with macrophage infiltration. Furthermore, alterations in PPARG expression were validated through Western blot (WB) and immunohistochemistry (IHC) in both in vitro and in vivo experiments.
RESULTS: By utilizing bioinformatics techniques, we were able to pinpoint PPARG as a key biomarker for UC. The expression of PPARG was significantly reduced in cell models, UC animal models, and colitis models induced by dextran sodium sulfate (DSS). Interestingly, overexpression of PPARG was able to restore intestinal barrier function in H2O2-induced IEC-6 cells. Additionally, immune-related differentially expressed genes (DEGs) allowed for efficient classification of UC samples into neutrophil and mitochondrial metabolic subtypes. A diagnostic model incorporating the three disease-specific genes PPARG, PLA2G2A, and IDO1 demonstrated high accuracy in distinguishing between the UC group and the control group. Furthermore, single-cell analysis revealed that decreased PPARG expression in colon tissue may contribute to the polarization of M1 macrophages through activation of inflammatory pathways.
CONCLUSIONS: In conclusion, PPARG, a gene related to immunity, has been established as a reliable potential biomarker for the diagnosis and treatment of UC. The immune response it controls plays a key role in the progression and development of UC by enabling interaction between characteristic biomarkers and immune infiltrating cells.
摘要：
背景：溃疡性结肠炎（UC）的病理生理学被认为受到免疫学的严重影响，这对诊断和治疗都提出了挑战。这项研究的主要目的是加深我们对与疾病相关的免疫学特征的理解，并确定诊断和治疗有价值的生物标志物。
方法：UC数据集来自GEO数据库，并使用无监督聚类进行分析以识别UC的不同亚型。开发了12种机器学习算法和深度学习模型DNN来识别潜在的UC生物标志物。使用LIME和SHAP方法解释模型的发现。PPI网络用于验证确定的关键生物标志物，然后是连接超级增强器的网络，转录因子和基因构建。利用单细胞测序技术研究过氧化物酶体增殖物激活受体γ（PPARG）在UC中的作用及其与巨噬细胞浸润的相关性。此外，在体外和体内实验中,通过Westernblot(WB)和免疫组织化学(IHC)验证了PPARG表达的改变.
结果：利用生物信息学技术，我们能够将PPARG确定为UC的关键生物标志物.细胞模型中PPARG的表达显著降低,UC动物模型,和葡聚糖硫酸钠（DSS）诱导的结肠炎模型。有趣的是,PPARG的过表达能够恢复H2O2诱导的IEC-6细胞的肠屏障功能。此外,免疫相关差异表达基因(DEGs)可将UC样本有效分类为中性粒细胞和线粒体代谢亚型.结合了三个疾病特异性基因PPARG的诊断模型，PLA2G2A,和IDO1在区分UC组和对照组方面表现出很高的准确性。此外，单细胞分析显示,结肠组织中PPARG表达的降低可能通过激活炎症途径促进M1巨噬细胞的极化.
结论：结论：PPARG,与免疫有关的基因,已被确立为诊断和治疗UC的可靠潜在生物标志物。它控制的免疫反应通过使特征性生物标志物与免疫浸润细胞之间相互作用，在UC的进展和发展中起关键作用。
方法：UC数据集来自GEO数据库，并使用无监督聚类进行分析以识别UC的不同亚型。开发了12种机器学习算法和深度学习模型DNN来识别潜在的UC生物标志物。使用LIME和SHAP方法解释模型的发现。PPI网络用于验证确定的关键生物标志物，然后是连接超级增强器的网络，转录因子和基因构建。利用单细胞测序技术研究过氧化物酶体增殖物激活受体γ（PPARG）在UC中的作用及其与巨噬细胞浸润的相关性。此外，在体外和体内实验中,通过Westernblot(WB)和免疫组织化学(IHC)验证了PPARG表达的改变.
结果：利用生物信息学技术，我们能够将PPARG确定为UC的关键生物标志物.细胞模型中PPARG的表达显著降低,UC动物模型,和葡聚糖硫酸钠（DSS）诱导的结肠炎模型。有趣的是,PPARG的过表达能够恢复H2O2诱导的IEC-6细胞的肠屏障功能。此外,免疫相关差异表达基因(DEGs)可将UC样本有效分类为中性粒细胞和线粒体代谢亚型.结合了三个疾病特异性基因PPARG的诊断模型，PLA2G2A,和IDO1在区分UC组和对照组方面表现出很高的准确性。此外，单细胞分析显示,结肠组织中PPARG表达的降低可能通过激活炎症途径促进M1巨噬细胞的极化.
结论：结论：PPARG,与免疫有关的基因,已被确立为诊断和治疗UC的可靠潜在生物标志物。它控制的免疫反应通过使特征性生物标志物与免疫浸润细胞之间相互作用，在UC的进展和发展中起关键作用。
