Construction of an Osteoarthritis Diagnostic Model Based on Hub Immune Cells and Genes by Machine Learning Method.

IF 2.7 4区 生物学 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY
Rong Jiang, Xiaoyu Peng, Kai Zhao
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引用次数: 0

Abstract

The objective of this investigation was to develop a diagnostic model for osteoarthritis (OA) by integrating immune cell profiling with transcriptomic signatures. Four gene expression datasets related to OA were downloaded from the GEO database. CIBERSORT was employed to evaluate the proportion of different immune cell types. Hub immune cells were selected using three distinct optimization algorithms (LASSO, RFE, and RF). Differentially expressed genes (DEGs) between OA and control samples were screened using the limma package. Subsequently, function analysis and protein-protein interaction (PPI) analysis were conducted. Topology analysis based on four algorithms was performed, and hub genes were identified by overlapping the results of these four algorithms. A diagnostic model was constructed and validated using the ROC curve method. Pearson correlation coefficients between hub immune cell populations and candidate genes were computed using the cor() function in R. Seven types of differentially abundant immune cells were identified between the two groups. After analysis with the RF, LASSO, and RFE algorithms, five overlapping immune cells, namely, T cell CD4 memory resting, NK cell activated, T cell CD4 naive, mast cell resting, and mast cell activated, were selected as hub immune cells. A total of 578 DEGs were selected, which were implicated in the MAPK signaling pathway, focal adhesion, and osteoclast differentiation. Following PPI analysis, five hub genes (CXCL8, EEF1A1, IL1B, EEF2, and IL6) were obtained. The diagnostic model demonstrated excellent performance. Significant correlations were observed between the hub genes and immune cell populations. Through systematic analysis, we identified five key immune cell types and five hub genes associated with immune infiltration in OA. These biomarkers were subsequently utilized to construct a diagnostic prediction model.

基于中枢免疫细胞和基因的骨关节炎诊断模型的机器学习构建。
本研究的目的是通过整合免疫细胞谱和转录组特征来开发骨关节炎(OA)的诊断模型。从GEO数据库下载OA相关的4个基因表达数据集。采用CIBERSORT法评价不同免疫细胞类型的比例。使用三种不同的优化算法(LASSO、RFE和RF)选择中枢免疫细胞。使用limma包筛选OA与对照样品之间的差异表达基因(DEGs)。随后进行功能分析和蛋白相互作用(PPI)分析。基于四种算法进行拓扑分析,并将四种算法的结果进行重叠,从而识别出轮毂基因。采用ROC曲线法建立诊断模型并进行验证。利用r中的cor()函数计算中心免疫细胞群与候选基因之间的Pearson相关系数。两组之间鉴定出7种差异丰富的免疫细胞。通过RF、LASSO和RFE算法分析,选择T细胞CD4记忆静止、NK细胞激活、T细胞CD4未激活、肥大细胞静止和肥大细胞激活5个重叠的免疫细胞作为中心免疫细胞。共选择了578个基因,这些基因与MAPK信号通路、局灶黏附和破骨细胞分化有关。通过PPI分析,获得了5个枢纽基因(CXCL8、EEF1A1、IL1B、EEF2和IL6)。该诊断模型表现出良好的性能。中心基因与免疫细胞群之间存在显著相关性。通过系统分析,我们确定了OA中与免疫浸润相关的5种关键免疫细胞类型和5个枢纽基因。这些生物标志物随后被用于构建诊断预测模型。
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来源期刊
Biotechnology and applied biochemistry
Biotechnology and applied biochemistry 工程技术-生化与分子生物学
CiteScore
6.00
自引率
7.10%
发文量
117
审稿时长
3 months
期刊介绍: Published since 1979, Biotechnology and Applied Biochemistry is dedicated to the rapid publication of high quality, significant research at the interface between life sciences and their technological exploitation. The Editors will consider papers for publication based on their novelty and impact as well as their contribution to the advancement of medical biotechnology and industrial biotechnology, covering cutting-edge research in synthetic biology, systems biology, metabolic engineering, bioengineering, biomaterials, biosensing, and nano-biotechnology.
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