Nephrology Department, First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China.
Graduate School, Tianjin University of Traditional Chinese Medicine, Tianjin, China.
Front Immunol. 2022 Dec 2;13:839197. doi: 10.3389/fimmu.2022.839197. eCollection 2022.
To identify potential diagnostic markers of lupus nephritis (LN) based on bioinformatics and machine learning and to explore the significance of immune cell infiltration in this pathology.
Seven LN gene expression datasets were downloaded from the GEO database, and the larger sample size was used as the training group to obtain differential genes (DEGs) between LN and healthy controls, and to perform gene function, disease ontology (DO), and gene set enrichment analyses (GSEA). Two machine learning algorithms, least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE), were applied to identify candidate biomarkers. The diagnostic value of LN diagnostic gene biomarkers was further evaluated in the area under the ROC curve observed in the validation dataset. CIBERSORT was used to analyze 22 immune cell fractions from LN patients and to analyze their correlation with diagnostic markers.
Thirty and twenty-one DEGs were screened in kidney tissue and peripheral blood, respectively. Both of which covered macrophages and interferons. The disease enrichment analysis of DEGs in kidney tissues showed that they were mainly involved in immune and renal diseases, and in peripheral blood it was mainly enriched in cardiovascular system, bone marrow, and oral cavity. The machine learning algorithm combined with external dataset validation revealed that C1QA(AUC = 0.741), C1QB(AUC = 0.758), MX1(AUC = 0.865), RORC(AUC = 0.911), CD177(AUC = 0.855), DEFA4(AUC= 0.843)and HERC5(AUC = 0.880) had high diagnostic value and could be used as diagnostic biomarkers of LN. Compared to controls, pathways such as cell adhesion molecule cam, and systemic lupus erythematosus were activated in kidney tissues; cell cycle, cytoplasmic DNA sensing pathways, NOD-like receptor signaling pathways, proteasome, and RIG-1-like receptors were activated in peripheral blood. Immune cell infiltration analysis showed that diagnostic markers in kidney tissue were associated with T cells CD8 and Dendritic cells resting, and in blood were associated with T cells CD4 memory resting, suggesting that CD4 T cells, CD8 T cells and dendritic cells are closely related to the development and progression of LN.
C1QA, C1QB, MX1, RORC, CD177, DEFA4 and HERC5 could be used as new candidate molecular markers for LN. It may provide new insights into the diagnosis and molecular treatment of LN in the future.
基于生物信息学和机器学习，鉴定狼疮肾炎（LN）的潜在诊断标志物，并探讨免疫细胞浸润在该疾病中的意义。
从 GEO 数据库中下载了 7 个 LN 基因表达数据集，使用较大的样本量作为训练组，以获得 LN 与健康对照组之间的差异基因（DEGs），并进行基因功能、疾病本体论（DO）和基因集富集分析（GSEA）。应用最小绝对收缩和选择算子（LASSO）和支持向量机递归特征消除（SVM-RFE）两种机器学习算法，识别候选生物标志物。在验证数据集中观察到的 ROC 曲线下面积，进一步评估 LN 诊断基因生物标志物的诊断价值。使用 CIBERSORT 分析 LN 患者的 22 种免疫细胞亚群，并分析其与诊断标志物的相关性。
在肾脏组织和外周血中分别筛选出 30 个和 21 个 DEGs。它们都包含巨噬细胞和干扰素。肾脏组织中 DEGs 的疾病富集分析表明，它们主要涉及免疫和肾脏疾病，而在外周血中主要富集于心血管系统、骨髓和口腔。机器学习算法结合外部数据集验证表明，C1QA（AUC = 0.741）、C1QB（AUC = 0.758）、MX1（AUC = 0.865）、RORC（AUC = 0.911）、CD177（AUC = 0.855）、DEFA4（AUC = 0.843）和 HERC5（AUC = 0.880）具有较高的诊断价值，可作为 LN 的诊断生物标志物。与对照组相比，肾脏组织中细胞黏附分子 cam 和系统性红斑狼疮等途径被激活；细胞周期、细胞质 DNA 感应途径、NOD 样受体信号通路、蛋白酶体和 RIG-1 样受体在外周血中被激活。免疫细胞浸润分析表明，肾脏组织中的诊断标志物与 T 细胞 CD8 和树突状细胞静止有关，而血液中的诊断标志物与 T 细胞 CD4 记忆静止有关，提示 CD4 T 细胞、CD8 T 细胞和树突状细胞与 LN 的发生和发展密切相关。
C1QA、C1QB、MX1、RORC、CD177、DEFA4 和 HERC5 可作为 LN 的新候选分子标志物。这可能为未来 LN 的诊断和分子治疗提供新的思路。
