Clinical College of Neurology, Neurosurgery and Neurorehabilitation, Tianjin Medical University, Tianjin, China.
Tianjin Key Laboratory of Cerebral Vascular and Neurodegenerative Diseases, Tianjin Neurosurgical Institute, Tianjin Huanhu Hospital, Tianjin, China.
BMC Med Genomics. 2023 Mar 14;16(1):55. doi: 10.1186/s12920-023-01481-3.
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.
越来越多的证据表明铁死亡参与了帕金森病（PD）的进展。本研究旨在探讨铁死亡相关基因（FRGs）、免疫浸润和免疫检查点基因（ICGs）在 PD 发病机制和发展中的作用。
从基因表达综合数据库（GEO）中下载 PD 患者和健康对照（HC）的微阵列数据。对 GSE18838 数据集进行加权基因共表达网络分析（WGCNA），以鉴定与 PD 相关的显著模块。基于 WGCNA、FRGs 和差异表达基因的交集，使用机器学习算法筛选候选生物标志物。还进行了 GSVA、GSEA、GO、KEGG 的富集分析和 ICGs 的组间比较。接下来，通过 ELISA 法在临床样本中验证候选生物标志物，并使用接收者操作特征曲线（ROC）评估诊断能力。
在这项研究中，FRGs 与 ICGs 和免疫浸润有关。然后，LPIN1 在 PD 患者的血浆水平明显低于健康对照者，而 TNFAIP3 在 PD 患者中的表达高于 HC。ROC 曲线显示，LPIN1 和 TNFAIP3 联合的曲线下面积（AUC）为 0.833（95%CI：0.750-0.916）。此外，每个生物标志物单独都可以区分 PD 与 HC（LPIN1：AUC=0.754，95%CI：0.659-0.849；TNFAIP3：AUC=0.754，95%CI：0.660-0.849）。对于从 HC 中检测早期 PD，组合模型以 AUC 为 0.831（95%CI：0.734-0.927）保持诊断准确性，LPIN1 也能很好地区分早期 PD 与 HC（AUC=0.817，95%CI：0.717-0.917）。然而，在区分早期与中晚期 PD 患者方面，诊断效果相对较差。
由 LPIN1 和 TNFAIP3 组成的组合模型以及每个生物标志物都可能成为区分 PD 与 HC 的有效工具。
