Department of Nephrology, The Affiliated Taian City Centeral Hospital of Qingdao University, Tai'an 271000, Shandong Province, China.
Intensive Care Unit, The Affiliated Taian City Centeral Hospital of Qingdao University, Tai'an, Shandong Province, China.
Dis Markers. 2022 Jul 12;2022:8911321. doi: 10.1155/2022/8911321. eCollection 2022.
The present investigation is aimed at identifying key immune-related genes linked with SLE and their roles using integrative analysis of publically available gene expression datasets.
Four gene expression datasets pertaining to SLE, 2 from whole blood and 2 experimental PMBC, were sourced from GEO. Shared differentially expressed genes (DEG) were determined as SLE-related genes. Immune cell infiltration analysis was performed using CIBERSORT, and case samples were subjected to -means cluster analysis using high-abundance immune cells. Key immune-related SLE genes were identified using correlation analysis with high-abundance immune cells and subjected to functional enrichment analysis for enriched Gene Ontology Biological Processes and KEGG pathways. A PPI network of genes interacting with the key immune-related SLE genes was constructed. LASSO regression analysis was performed to identify the most significant key immune-related SLE genes, and correlation with clinicopathological features was examined.
309 SLE-related genes were identified and found functionally enriched in several pathways related to regulation of viral defenses and T cell functions. -means cluster analysis identified 2 sample clusters which significantly differed in monocytes, dendritic cell resting, and neutrophil abundance. 65 immune-related SLE genes were identified, functionally enriched in immune response-related signaling, antigen receptor-mediated signaling, and T cell receptor signaling, along with Th17, Th1, and Th2 cell differentiation, IL-17, NF-kappa B, and VEGF signaling pathways. LASSO regression identified 9 key immune-related genes: DUSP7, DYSF, KCNA3, P2RY10, S100A12, SLC38A1, TLR2, TSR2, and TXN. Imputed neutrophil percentage was consistent with their expression pattern, whereas anti-Ro showed the inverse pattern as gene expression.
Comprehensive bioinformatics analyses revealed 9 key immune-related genes and their associated functions highly pertinent to SLE pathogenesis, subtypes, and identified valuable candidates for experimental research.
本研究旨在通过整合分析公共基因表达数据集，鉴定与系统性红斑狼疮（SLE）相关的关键免疫相关基因及其作用。
从 GEO 中获取了 4 个与 SLE 相关的基因表达数据集，其中 2 个来自全血，2 个来自实验性 PMBC。确定共享的差异表达基因（DEG）为与 SLE 相关的基因。使用 CIBERSORT 进行免疫细胞浸润分析，并用高丰度免疫细胞对病例样本进行均值聚类分析。使用与高丰度免疫细胞的相关性分析和功能富集分析来鉴定关键免疫相关的 SLE 基因，对富集的基因本体生物学过程和 KEGG 途径进行功能分析。构建与关键免疫相关的 SLE 基因相互作用的蛋白质-蛋白质相互作用网络。通过 LASSO 回归分析鉴定最显著的关键免疫相关 SLE 基因，并检测其与临床病理特征的相关性。
鉴定出 309 个与 SLE 相关的基因，并发现其在与病毒防御和 T 细胞功能调节相关的几个途径中具有功能富集。均值聚类分析鉴定出 2 个样本聚类，在单核细胞、树突状细胞静止和中性粒细胞丰度方面存在显著差异。鉴定出 65 个与免疫相关的 SLE 基因，在免疫反应相关信号、抗原受体介导的信号和 T 细胞受体信号以及 Th17、Th1 和 Th2 细胞分化、IL-17、NF-κB 和 VEGF 信号通路中具有功能富集。LASSO 回归鉴定出 9 个关键免疫相关基因：DUSP7、DYSF、KCNA3、P2RY10、S100A12、SLC38A1、TLR2、TSR2 和 TXN。推断的中性粒细胞百分比与其表达模式一致，而抗 Ro 与基因表达呈相反模式。
全面的生物信息学分析揭示了 9 个关键免疫相关基因及其与 SLE 发病机制、亚型相关的功能，为实验研究提供了有价值的候选基因。
