整合转录组学和蛋白质组学,分析巨细胞病毒相关性溃疡性结肠炎的免疫微环境并确定相关生物标记物。

IF 4 3区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Yang Chen, Qingqing Zheng, Hui Wang, Peiren Tang, Li Deng, Pu Li, Huan Li, Jianhong Hou, Jie Li, Li Wang, Jun Peng
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引用次数: 0

摘要

背景:近年来,严重炎症性肠病(IBD)和巨细胞病毒(CMV)患者的发病率和死亡率显著上升,这引起了人们对IBD患者肠粘膜CMV感染状况及其在疾病进展中所起作用的极大关注。然而,目前还没有CMV感染的溃疡性结肠炎患者(CMV + UC)的高通量测序数据,CMV + UC患者的免疫微环境也有待探索:方法:采用 xCell 算法评估 CMV + UC 患者的免疫微环境。方法:采用 xCell 算法评估 CMV + UC 患者的免疫微环境,然后通过 WGCNA 分析获得异常免疫细胞与基因水平或蛋白质水平的共表达模块。接着,使用随机森林、SVM-rfe 和 Lasso 三种机器学习方法筛选候选生物标记物。最后,采用最佳子集选择算法构建诊断模型:在这项研究中,我们对 CMV + UC 患者进行了转录组学和蛋白质组学测序,以建立全面的免疫微环境谱,并在 CMV + UC 组中发现了 11 种特异性异常免疫细胞。在使用多组学整合算法后,我们确定了 7 个共表达基因模块和 5 个共表达蛋白质模块。随后,我们利用各种机器学习算法确定了具有诊断功效的关键生物标志物,并构建了早期诊断模型。我们共发现了8个生物标志物(PPP1R12B、CIRBP、CSNK2A2、DNAJB11、PIK3R4、RRBP1、STX5、TMEM214),它们在CMV + UC的免疫微环境中发挥着关键作用,并对CMV + UC表现出卓越的诊断性能:结论:这 8 个生物标志物模型为 CMV 感染后 IBD 患者的诊断和治疗提供了新的范例。对该模型的进一步研究将对了解 CMV 感染后宿主免疫微环境的变化具有重要意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Integrating transcriptomics and proteomics to analyze the immune microenvironment of cytomegalovirus associated ulcerative colitis and identify relevant biomarkers.

Background: In recent years, significant morbidity and mortality in patients with severe inflammatory bowel disease (IBD) and cytomegalovirus (CMV) have drawn considerable attention to the status of CMV infection in the intestinal mucosa of IBD patients and its role in disease progression. However, there is currently no high-throughput sequencing data for ulcerative colitis patients with CMV infection (CMV + UC), and the immune microenvironment in CMV + UC patients have yet to be explored.

Method: The xCell algorithm was used for evaluate the immune microenvironment of CMV + UC patients. Then, WGCNA analysis was explored to obtain the co-expression modules between abnormal immune cells and gene level or protein level. Next, three machine learning approach include Random Forest, SVM-rfe, and Lasso were used to filter candidate biomarkers. Finally, Best Subset Selection algorithms was performed to construct the diagnostic model.

Results: In this study, we performed transcriptomic and proteomic sequencing on CMV + UC patients to establish a comprehensive immune microenvironment profile and found 11 specific abnormal immune cells in CMV + UC group. After using multi-omics integration algorithms, we identified seven co-expression gene modules and five co-expression protein modules. Subsequently, we utilized various machine learning algorithms to identify key biomarkers with diagnostic efficacy and constructed an early diagnostic model. We identified a total of eight biomarkers (PPP1R12B, CIRBP, CSNK2A2, DNAJB11, PIK3R4, RRBP1, STX5, TMEM214) that play crucial roles in the immune microenvironment of CMV + UC and exhibit superior diagnostic performance for CMV + UC.

Conclusion: This 8 biomarkers model offers a new paradigm for the diagnosis and treatment of IBD patients post-CMV infection. Further research into this model will be significant for understanding the changes in the host immune microenvironment following CMV infection.

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来源期刊
Biodata Mining
Biodata Mining MATHEMATICAL & COMPUTATIONAL BIOLOGY-
CiteScore
7.90
自引率
0.00%
发文量
28
审稿时长
23 weeks
期刊介绍: BioData Mining is an open access, open peer-reviewed journal encompassing research on all aspects of data mining applied to high-dimensional biological and biomedical data, focusing on computational aspects of knowledge discovery from large-scale genetic, transcriptomic, genomic, proteomic, and metabolomic data. Topical areas include, but are not limited to: -Development, evaluation, and application of novel data mining and machine learning algorithms. -Adaptation, evaluation, and application of traditional data mining and machine learning algorithms. -Open-source software for the application of data mining and machine learning algorithms. -Design, development and integration of databases, software and web services for the storage, management, retrieval, and analysis of data from large scale studies. -Pre-processing, post-processing, modeling, and interpretation of data mining and machine learning results for biological interpretation and knowledge discovery.
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