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骨关节炎的生物标志物与免疫微环境评估:来自组学数据和机器学习的证据

Evaluation of Biomarkers and Immune Microenvironment of Osteoarthritis: Evidence From Omics Data and Machine Learning.

作者信息

Liu Zhixin, Liu Heng, Li Deqiang, Ma Liang, Lu Tongxin, Sun Hao, Zhang Yuankai, Yang Hui

机构信息

Department of Orthopedics, Qilu Hospital of Shandong University, Jinan, China.

NHC Key Laboratory of Otorhinolaryngology, Qilu Hospital of Shandong University, Jinan, China.

出版信息

Front Genet. 2022 May 16;13:905027. doi: 10.3389/fgene.2022.905027. eCollection 2022.

Abstract

This study aimed to identify novel biomarkers for osteoarthritis (OA) and explore potential pathological immune cell infiltration. We identified differentially expressed genes (DEGs) between OA and normal synovial tissues using the package in R, and performed enrichment analyses to understand the functions and enriched pathways of DEGs. Weighted gene co-expression network analysis (WGCNA) and distinct machine-learning algorithms were then used to identify hub modules and candidate biomarkers. We assessed the diagnostic value of the candidate biomarkers using receiver operating characteristic (ROC) analysis. We then used the CIBERSORT algorithm to analyze immune cell infiltration patterns, and the Wilcoxon test to screen out hub immune cells that might affect OA occurrence. Finally, the expression levels of hub biomarkers were confirmed by quantitative reverse transcription-polymerase chain reaction (qRT-PCR). We identified 102 up-regulated genes and 110 down-regulated genes. The functional enrichment analysis results showed that DEGs are enriched mainly in immune response pathways. Combining the results of the algorithms and ROC analysis, we identified GUCA1A and NELL1 as potential diagnostic biomarkers for OA, and validated their diagnosibility using an external dataset. Construction of a TF-mRNA-miRNA network enabled prediction of potential candidate compounds targeting hub biomarkers. Immune cell infiltration analyses revealed the expression of hub biomarkers to be correlated with CD8 T cells, memory B cells, M0/M2 macrophages, resting mast cells and resting dendritic cells. qRT-PCR results showed both GUCA1A and NELL1 were significantly increased in OA samples ( < 0.01). All validations are consistent with the microarray hybridization, indicating that GUCA1A and NELL1 may be involved in the pathogenesis of OA. The findings suggest that GUCA1A and NELL1, closely related to OA occurrence and progression, represent new OA candidate markers, and that immune cell infiltration plays a significant role in the progression of OA.

摘要

本研究旨在鉴定骨关节炎(OA)的新型生物标志物,并探索潜在的病理性免疫细胞浸润情况。我们使用R语言中的软件包来鉴定OA与正常滑膜组织之间的差异表达基因(DEGs),并进行富集分析以了解DEGs的功能和富集通路。然后使用加权基因共表达网络分析(WGCNA)和不同的机器学习算法来鉴定枢纽模块和候选生物标志物。我们使用受试者工作特征(ROC)分析评估候选生物标志物的诊断价值。接着,我们使用CIBERSORT算法分析免疫细胞浸润模式,并使用Wilcoxon检验筛选出可能影响OA发生的枢纽免疫细胞。最后,通过定量逆转录-聚合酶链反应(qRT-PCR)确认枢纽生物标志物的表达水平。我们鉴定出102个上调基因和110个下调基因。功能富集分析结果表明,DEGs主要富集于免疫反应通路。结合算法结果和ROC分析,我们鉴定出GUCA1A和NELL1作为OA的潜在诊断生物标志物,并使用外部数据集验证了它们的诊断能力。构建转录因子-信使核糖核酸-微小核糖核酸网络能够预测靶向枢纽生物标志物的潜在候选化合物。免疫细胞浸润分析显示,枢纽生物标志物的表达与CD8 T细胞、记忆B细胞、M0/M2巨噬细胞、静息肥大细胞和静息树突状细胞相关。qRT-PCR结果显示,OA样本中GUCA1A和NELL1均显著升高(<0.01)。所有验证结果均与微阵列杂交一致,表明GUCA1A和NELL1可能参与OA的发病机制。研究结果表明,与OA发生和进展密切相关的GUCA1A和NELL1代表了新的OA候选标志物,并且免疫细胞浸润在OA进展中起重要作用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a834/9149375/cbf2842de246/fgene-13-905027-g001.jpg

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