Data_Sheet_1_Potential biomarkers for active renal involvement in systemic lupus erythematosus patients.docx

ObjectiveThis study aimed to identify the key genes related to active renal involvement in patients with systemic lupus erythematosus (SLE).
MethodsMicroarray datasets were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between SLE patients with active renal involvement and those who did not have active renal involvement were identified by R software. Hub genes were identified using protein–protein interaction networks. The relationships between the expression levels of identified hub genes and SLEDAI were subjected to linear correlation analysis. The diagnostic accuracy of the hub genes was evaluated with the area under the curve of the receiver operating characteristic curve (ROC-AUC). Transcription factors (TFs) were predicted. The expression levels of different hub genes and histopathological patterns were also examined.
ResultsA total of 182 DEGs were identified. Enrichment analysis indicated that DEGs were primarily enriched in neutrophil degranulation, neutrophil activation involved in immune response and neutrophil activation. The expression levels of 12 identified hub genes were verified. Ten of the 12 hub genes were positively associated with SLEDAI. The combination model of DEFA4, CTSG, RETN, CEACAM8, TOP2A, LTF, MPO, ELANE, BIRC5, and LCN2 had a certain diagnostic accuracy in detecting renal involvement with high disease activity in SLE patients. The expressions of five predicted TFs were validated by GSE65391 dataset.
ConclusionThis work explored the pathogenesis of renal involvement in SLE. These results may guide future experimental research and clinical transformation.

研究目的：本研究旨在明确与系统性红斑狼疮（Systemic Lupus Erythematosus, SLE）患者活动性肾脏受累相关的关键基因。
研究方法：从基因表达数据库（Gene Expression Omnibus, GEO）下载微阵列数据集；通过R软件筛选伴活动性肾脏受累与无活动性肾脏受累的SLE患者之间的差异表达基因（Differentially Expressed Genes, DEGs）；利用蛋白质相互作用网络筛选核心基因，对核心基因的表达水平与SLE疾病活动指数（SLEDAI）进行线性相关分析；采用受试者工作特征曲线下面积（area under the curve of the receiver operating characteristic curve, ROC-AUC）评估核心基因的诊断效能；预测潜在转录因子（Transcription Factors, TFs），并分析不同核心基因的表达水平与肾脏组织病理分型的关联。
研究结果：共筛选得到182个差异表达基因；富集分析显示，差异表达基因主要富集于中性粒细胞脱颗粒、免疫应答相关中性粒细胞活化及中性粒细胞活化通路；验证了12个核心基因的表达水平，其中10个核心基因的表达水平与SLEDAI呈正相关；联合DEFA4、CTSG、RETN、CEACAM8、TOP2A、LTF、MPO、ELANE、BIRC5及LCN2构建的诊断模型，在检测SLE患者高疾病活动度肾脏受累时具有一定诊断效能；通过GSE65391数据集验证了5个预测转录因子的表达水平。
研究结论：本研究揭示了SLE患者肾脏受累的发病机制，研究结果可为后续实验研究与临床转化提供参考依据。