Laboratory of Complex Biological Systems and Bioinformatics (CBB), Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran.
School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.
Sci Rep. 2024 Oct 12;14(1):23899. doi: 10.1038/s41598-024-74728-3.
Common Variable Immunodeficiency (CVID) is a primary immunodeficiency characterized by reduced levels of specific immunoglobulins, resulting in frequent infections, autoimmune disorders, increased cancer risk, and diminished antibody production despite an adequate B cell count. With its clinical manifestations being highly variable, the classification of CVID, including the widely recognized Freiburg classification, is primarily based on clinical symptoms and genetic variations. Our study aims to refine the classification of CVID by analyzing transcriptomics data to identify distinct disease subtypes. We utilized the GSE51405 dataset, examining transcriptomic profiles from 30 CVID patients without complications. Employing a combination of clustering techniques-KMeans, hierarchical agglomerative clustering, spectral clustering, and Gaussian Mixture models-and differential gene expression analysis with R's limma package, we integrated molecular findings with demographic data (age and gender) through correlation analysis and identified common genes among clusters. Three distinct clusters of CVID patients were identified using KMeans, Agglomerative Clustering, and Gaussian Mixture Models, highlighting the disease's heterogeneity. Differential expression analysis unveiled 31 genes with variable expression levels across these clusters. Notably, nine genes (EIF5A, RPL21, ANP32A, DTX3L, NCF2, CDC42EP3, CHP1, FOLR3, and DEFA4) exhibited consistent differential expression across all clusters, independent of demographic factors. The study recommends categorizing patients based on the four genes, NCF2, CHP1, FOLR3, and DEFA4-as they may assist in prognostic prediction. Transcriptomic analysis of common variable immunodeficiency (CVID) patients identified three distinct clusters based on gene expression, independent of age and gender. Nine differentially expressed genes were identified across these clusters, suggesting potential biomarkers for CVID subtype classification. These findings highlight the genetic heterogeneity of CVID and provide novel insights into disease classification and potential personalized treatment approaches.
普通变异性免疫缺陷症（CVID）是一种以特定免疫球蛋白水平降低为特征的原发性免疫缺陷症，导致频繁感染、自身免疫性疾病、癌症风险增加和抗体产生减少，尽管 B 细胞计数正常。由于其临床表现高度可变，CVID 的分类，包括广泛认可的弗莱堡分类，主要基于临床症状和遗传变异。我们的研究旨在通过分析转录组数据来识别不同的疾病亚型，从而完善 CVID 的分类。我们使用了 GSE51405 数据集，该数据集检查了 30 名没有并发症的 CVID 患者的转录组谱。我们使用聚类技术（KMeans、层次聚类、谱聚类和高斯混合模型）和 R 的 limma 包进行差异基因表达分析，将分子发现与人口统计学数据（年龄和性别）相结合，通过相关性分析识别聚类之间的常见基因，并使用 KMeans、Agglomerative Clustering 和高斯混合模型识别了三种不同的 CVID 患者聚类，突出了该疾病的异质性。差异表达分析揭示了这些聚类中 31 个基因的表达水平存在差异。值得注意的是，有九个基因（EIF5A、RPL21、ANP32A、DTX3L、NCF2、CDC42EP3、CHP1、FOLR3 和 DEFA4）在所有聚类中表现出一致的差异表达，不受人口统计学因素的影响。该研究建议根据 NCF2、CHP1、FOLR3 和 DEFA4 这四个基因对患者进行分类，因为它们可能有助于预测预后。对普通变异性免疫缺陷症（CVID）患者的转录组分析基于基因表达将患者分为三个不同的聚类，与年龄和性别无关。在这些聚类中发现了九个差异表达基因，表明 CVID 亚型分类的潜在生物标志物。这些发现突出了 CVID 的遗传异质性，并为疾病分类和潜在的个性化治疗方法提供了新的见解。
