背景:越来越多的证据表明铁性凋亡参与了帕金森病(PD)的进展。本研究旨在探讨铁凋亡相关基因(FRGs)的作用,免疫浸润和免疫检查点基因(ICGs)在PD的发病和发展中的作用。
方法:下载来自基因表达综合(GEO)数据库的PD患者和健康对照(HC)的微阵列数据。处理加权基因共表达网络分析(WGCNA)以鉴定与GSE18838数据集中的PD相关的重要模块。机器学习算法用于基于WGCNA之间的相交筛选候选生物标志物,FRGs和差异表达基因。GSVA的富集分析,GSEA,GO,KEGG,和免疫浸润,还进行了ICGs的分组比较。接下来,通过ELISA在临床样本中验证候选生物标志物,并使用受试者工作特征曲线(ROC)评估诊断能力。
结果:在这项研究中,FRGs与ICGs有相关性,免疫浸润。然后,PD中LPIN1的血浆水平明显低于健康对照组,与HC相比,PD中TNFAIP3的表达更高。ROC曲线显示LPIN1和TNFAIP3组合的曲线下面积(AUC)为0.833(95%CI:0.750-0.916)。此外,每种生物标志物单独可以区分PD和HC(LPIN1:AUC=0.754,95%CI:0.659-0.849;TNFAIP3:AUC=0.754,95%CI:0.660-0.849).为了检测来自HC的早期PD,组合模型保持了诊断准确性,AUC为0.831(95%CI:0.734-0.927),LPIN1在区分早期PD和HC方面也表现良好(AUC=0.817,95%CI:0.717-0.917)。然而,在区分早期和中晚期PD患者方面,诊断效能相对较差.
结论:由LPIN1和TNFAIP3以及每种生物标志物组成的组合模型可能是区分PD和HC的有效工具。
Increasing evidence has indicated that ferroptosis engages in the progression of Parkinson\'s disease (PD). This study aimed to explore the role of ferroptosis-related genes (FRGs), immune infiltration and immune checkpoint genes (ICGs) in the pathogenesis and development of PD.
The microarray data of PD patients and healthy controls (HC) from the Gene Expression Omnibus (GEO) database was downloaded. Weighted gene co-expression network analysis (WGCNA) was processed to identify the significant modules related to PD in the GSE18838 dataset. Machine learning algorithms were used to screen the candidate biomarkers based on the intersect between WGCNA, FRGs and differentially expressed genes. Enrichment analysis of GSVA, GSEA, GO, KEGG, and immune infiltration, group comparison of ICGs were also performed. Next, candidate biomarkers were validated in clinical samples by ELISA and receiver operating characteristic curve (ROC) was used to assess diagnose ability.
In this study, FRGs had correlations with ICGs, immune infiltration. Then, plasma levels of LPIN1 in PD was significantly lower than that in healthy controls, while the expression of TNFAIP3 was higher in PD in comparison with HC. ROC curves showed that the area under curve (AUC) of the LPIN1 and TNFAIP3 combination was 0.833 (95% CI: 0.750-0.916). Moreover, each biomarker alone could discriminate the PD from HC (LPIN1: AUC = 0.754, 95% CI: 0.659-0.849; TNFAIP3: AUC = 0.754, 95% CI: 0.660-0.849). For detection of early PD from HC, the model of combination maintained diagnostic accuracy with an AUC of 0.831 (95% CI: 0.734-0.927), LPIN1 also performed well in distinguishing the early PD from HC (AUC = 0.817, 95% CI: 0.717-0.917). However, the diagnostic efficacy was relatively poor in distinguishing the early from middle-advanced PD patients.
The combination model composed of LPIN1 and TNFAIP3, and each biomarker may serve as an efficient tool for distinguishing PD from HC.