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Abstract：
UNASSIGNED: Interstitial cystitis/bladder pain syndrome (IC/BPS) is a chronic, severely distressing clinical syndrome characterized by bladder pain and pressure perceptions. The origin and pathophysiology of IC/BPS are currently unclear, making it difficult to diagnose and formulate successful treatments. Our study is aimed at investigating the role of immune-related genes in the diagnosis, progression, and therapy of IC/BPS.
UNASSIGNED: The gene expression datasets GSE11783, GSE11839, GSE28242, and GSE57560 were retrieved from the GEO database for further analysis. Immune-related IC/BPS differentially expressed genes (DEGs) were identified by limma. Three distinct machine learning approaches, least absolute shrinkage and selection operator (LASSO), support vector machine-recursive feature elimination (SVM-RFE), and random forest (RF), were used to find the immune-related IC characteristic genes. Nomogram and receiving operator curves (ROC) were plotted to measure characteristic effectiveness. Using the CMap database and the molecular docking approach, potential small-molecule medicines were found and verified. Consensus cluster analysis was also performed to separate the IC/BPS samples into immunological subtypes.
UNASSIGNED: A total of 24 immune-related IC/BPS-DEGs were identified. When compared to the normal control group, the IC/BPS cohort had significantly more immune cell infiltration. Integrative machine learning methods discovered 5 IC/BPS characteristic genes (RASGRP1, PPBP, RBP4, CR2, and PROS2) that may predict IC/BPS diagnosis and immune cell infiltration. Furthermore, two immunological subgroups with substantial variations in immune cell infiltration across IC/BPS samples were identified, which were named cluster1 and cluster2, with the hallmark genes having greater expression in cluster2. Finally, bumetanide was shown to have the potential to be a medication for the treatment of IC/BPS, and it performed well in terms of its molecular binding with RASGRP1.
UNASSIGNED: We found and validated 5 immune-related IC/BPS genes (RASGRP1, PPBP, RBP4, CR2, and PROS2) and 2 IC/BPS immune subtypes. In addition, bumetanide was discovered to be a potential drug for treating IC/BPS, which may provide new insight into the diagnosis and immune therapy of IC/BPS patients.
UNASSIGNED: The gene expression datasets GSE11783, GSE11839, GSE28242, and GSE57560 were retrieved from the GEO database for further analysis. Immune-related IC/BPS differentially expressed genes (DEGs) were identified by limma. Three distinct machine learning approaches, least absolute shrinkage and selection operator (LASSO), support vector machine-recursive feature elimination (SVM-RFE), and random forest (RF), were used to find the immune-related IC characteristic genes. Nomogram and receiving operator curves (ROC) were plotted to measure characteristic effectiveness. Using the CMap database and the molecular docking approach, potential small-molecule medicines were found and verified. Consensus cluster analysis was also performed to separate the IC/BPS samples into immunological subtypes.
UNASSIGNED: A total of 24 immune-related IC/BPS-DEGs were identified. When compared to the normal control group, the IC/BPS cohort had significantly more immune cell infiltration. Integrative machine learning methods discovered 5 IC/BPS characteristic genes (RASGRP1, PPBP, RBP4, CR2, and PROS2) that may predict IC/BPS diagnosis and immune cell infiltration. Furthermore, two immunological subgroups with substantial variations in immune cell infiltration across IC/BPS samples were identified, which were named cluster1 and cluster2, with the hallmark genes having greater expression in cluster2. Finally, bumetanide was shown to have the potential to be a medication for the treatment of IC/BPS, and it performed well in terms of its molecular binding with RASGRP1.
UNASSIGNED: We found and validated 5 immune-related IC/BPS genes (RASGRP1, PPBP, RBP4, CR2, and PROS2) and 2 IC/BPS immune subtypes. In addition, bumetanide was discovered to be a potential drug for treating IC/BPS, which may provide new insight into the diagnosis and immune therapy of IC/BPS patients.
摘要：
未经证实：间质性膀胱炎/膀胱疼痛综合征（IC/BPS）是一种慢性，以膀胱疼痛和压力感知为特征的严重痛苦临床综合征。IC/BPS的起源和病理生理学目前尚不清楚，很难诊断和制定成功的治疗方法。我们的研究旨在研究免疫相关基因在诊断中的作用，programming,和IC/BPS的治疗。
UNASSIGNED：从GEO数据库检索基因表达数据集GSE11783、GSE11839、GSE28242和GSE57560用于进一步分析。免疫相关的IC/BPS差异表达基因（DEGs）通过limma鉴定。三种不同的机器学习方法，最小绝对收缩和选择运算符(LASSO)，支持向量机-递归特征消除(SVM-RFE)，和随机森林(RF)，用于寻找与免疫相关的IC特征基因。绘制列线图和接收操作曲线(ROC)以测量特征有效性。使用CMap数据库和分子对接方法，发现并验证了潜在的小分子药物。还进行了共有聚类分析以将IC/BPS样品分离为免疫亚型。
未经证实：共鉴定出24个免疫相关IC/BPS-DEGs。与正常对照组相比，IC/BPS组有明显更多的免疫细胞浸润。综合机器学习方法发现了5个IC/BPS特征基因(RASGRP1，PPBP，RBP4，CR2和PROS2）可以预测IC/BPS诊断和免疫细胞浸润。此外，在IC/BPS样品中免疫细胞浸润有显著差异的两个免疫亚组被鉴定,分别命名为cluster1和cluster2，标志基因在cluster2中表达较高。最后,布美他尼被证明有可能成为治疗IC/BPS的药物，在与RASGRP1的分子结合方面表现良好。
未经证实：我们发现并验证了5个免疫相关的IC/BPS基因（RASGRP1，PPBP，RBP4、CR2和PROS2)和2种IC/BPS免疫亚型。此外,发现布美他尼是治疗IC/BPS的潜在药物，这可能为IC/BPS患者的诊断和免疫治疗提供新的见解。
UNASSIGNED：从GEO数据库检索基因表达数据集GSE11783、GSE11839、GSE28242和GSE57560用于进一步分析。免疫相关的IC/BPS差异表达基因（DEGs）通过limma鉴定。三种不同的机器学习方法，最小绝对收缩和选择运算符(LASSO)，支持向量机-递归特征消除(SVM-RFE)，和随机森林(RF)，用于寻找与免疫相关的IC特征基因。绘制列线图和接收操作曲线(ROC)以测量特征有效性。使用CMap数据库和分子对接方法，发现并验证了潜在的小分子药物。还进行了共有聚类分析以将IC/BPS样品分离为免疫亚型。
未经证实：共鉴定出24个免疫相关IC/BPS-DEGs。与正常对照组相比，IC/BPS组有明显更多的免疫细胞浸润。综合机器学习方法发现了5个IC/BPS特征基因(RASGRP1，PPBP，RBP4，CR2和PROS2）可以预测IC/BPS诊断和免疫细胞浸润。此外，在IC/BPS样品中免疫细胞浸润有显著差异的两个免疫亚组被鉴定,分别命名为cluster1和cluster2，标志基因在cluster2中表达较高。最后,布美他尼被证明有可能成为治疗IC/BPS的药物，在与RASGRP1的分子结合方面表现良好。
未经证实：我们发现并验证了5个免疫相关的IC/BPS基因（RASGRP1，PPBP，RBP4、CR2和PROS2)和2种IC/BPS免疫亚型。此外,发现布美他尼是治疗IC/BPS的潜在药物，这可能为IC/BPS患者的诊断和免疫治疗提供新的见解。
