Zhang Haofuzi, Nagai Jun, Hao Lu, Jiang Xiaofan
Department of Neurosurgery, Xijing Hospital, Fourth Military Medical University, Xi'an, 710032, China.
Laboratory for Glia-Neuron Circuit Dynamics, RIKEN Center for Brain Science, Wako, 351-0198, Japan.
Mol Neurobiol. 2024 Feb;61(2):799-811. doi: 10.1007/s12035-023-03565-8. Epub 2023 Sep 2.
To explore diagnostic genes associated with cuproptosis in Parkinson's disease (PD) and to characterize immune cell infiltration by comprehensive bioinformatics analysis, three PD datasets were downloaded from the GEO database, two of which were merged and preprocessed as the internal training set and the remaining one as the external validation set. Based on the internal training set, differential analysis was performed to obtain differentially expressed genes (DEGs), and weighted gene co-expression network analysis (WGCNA) was conducted to obtain significant module genes. The genes obtained here were intersected to form the intersecting genes. The intersecting genes obtained from DEGs and WGCNA were intersected with cuproptosis-related genes (CRGs) to generate cuproptosis-related disease signature genes, and functional enrichment analysis was performed on Disease Ontology (DO), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG). Subsequently, LASSO analysis of the cuproptosis-related disease signature genes was performed to identify key genes and construct a diagnostic and predictive model. Then, single sample gene set enrichment analysis (ssGSEA) was performed on the internal training set to further analyze the correlation between key genes and immune cells. Lastly, the results were validated using an external validation set. A total of 405 DEGs were obtained by differential analysis, and 6 gene modules were identified by WGCNA analysis. The genes in the most significant modules were intersected with the DEGs to obtain 21 intersecting genes. The functions of the intersecting genes were mainly enriched in neurotransmitter transport, GABA-ergic synapse, synaptic vesicle cycle, serotonergic synapse, phenylalanine metabolism, tyrosine metabolism, tryptophan metabolism, etc. Subsequently, the intersecting genes were intersected with CRGs, and LASSO regression analysis was performed to screen 3 key cuproptosis-related disease signature genes, namely, SLC18A2, SLC6A3, and SV2C. The calibration curve of the nomogram model constructed based on these 3 key genes to predict PD showed good agreement, with a C-index of 0.944 and an area under the ROC (AUC) of 0.944 (0.833-1.000). It was also validated by the external dataset that the model constructed with these 3 key genes had good diagnostic and predictive power for PD. The ssGSEA analysis revealed that neutrophils might be the potential core immune cells and that SLC18A2, SLC6A3, and SV2C were significantly negatively correlated with neutrophils, which was also verified in the validation set. PD diagnosis and prediction model based on CRGs (SLC18A2, SLC6A3, and SV2C) has good diagnostic and predictive performance and could be a useful tool in the diagnosis of PD.
为了探索与帕金森病（PD）铜死亡相关的诊断基因，并通过全面的生物信息学分析来表征免疫细胞浸润情况，从基因表达综合数据库（GEO）下载了三个PD数据集，其中两个合并并预处理作为内部训练集，另一个作为外部验证集。基于内部训练集进行差异分析以获得差异表达基因（DEGs），并进行加权基因共表达网络分析（WGCNA）以获得显著模块基因。将此处获得的基因进行交集运算以形成交集基因。将从DEGs和WGCNA获得的交集基因与铜死亡相关基因（CRGs）进行交集运算，以生成铜死亡相关疾病特征基因，并对疾病本体（DO）、基因本体（GO）和京都基因与基因组百科全书（KEGG）进行功能富集分析。随后，对铜死亡相关疾病特征基因进行LASSO分析以鉴定关键基因并构建诊断和预测模型。然后，对内部训练集进行单样本基因集富集分析（ssGSEA），以进一步分析关键基因与免疫细胞之间的相关性。最后，使用外部验证集对结果进行验证。差异分析共获得405个DEGs，WGCNA分析鉴定出6个基因模块。将最显著模块中的基因与DEGs进行交集运算，获得21个交集基因。交集基因的功能主要富集在神经递质转运、γ-氨基丁酸能突触、突触小泡循环、5-羟色胺能突触、苯丙氨酸代谢、酪氨酸代谢、色氨酸代谢等方面。随后，将交集基因与CRGs进行交集运算，并进行LASSO回归分析，筛选出3个关键的铜死亡相关疾病特征基因，即SLC18A2、SLC6A3和SV2C。基于这3个关键基因构建的预测PD的列线图模型的校准曲线显示出良好的一致性，C指数为0.944，ROC曲线下面积（AUC）为0.944（0.833 - 1.000）。外部数据集也验证了用这3个关键基因构建的模型对PD具有良好的诊断和预测能力。ssGSEA分析表明中性粒细胞可能是潜在的核心免疫细胞，且SLC18A2、SLC6A3和SV2C与中性粒细胞显著负相关，这在验证集中也得到了验证。基于CRGs（SLC18A2、SLC6A3和SV2C）的PD诊断和预测模型具有良好的诊断和预测性能，可能是PD诊断中的一个有用工具。
