Parkinson's disease (PD) is the second-most common neurodegenerative disorder, and the actual cause of this disease is still unknown. Identifying the target genes that are associated with disease plays an essential role in the treatment of PD. Various genetic studies have determined the significant target genes for disease progression, although this continues to be challenging in the field of drug designing. In this study, we proposed a network-based approach to identify target genes for PD using gene mutation, gene expression, and gene deletion analysis. The subnetwork of PD genes was constructed from human protein–protein interaction network, and the potential genes were identified using network centrality measures. Two genes, PARK1 and PARK2, were identified as target genes by integrating gene mutation and expression data into the subnetwork. Gene deletion analysis was carried out to determine the significant target, and results revealed that VDAC1 and ATP5C1 genes were crucial for the Parkinson's subnetwork. Thus, findings from the network-based approach will provide additional insight for understanding the disease mechanism of PD. Future enhancement of this study may help in predicting disease biomarkers as well as designing novel compounds in rational drug designing.

中文翻译：

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使用人类蛋白质-蛋白质相互作用网络对帕金森病进行基因优先排序。

帕金森病 (PD) 是第二常见的神经退行性疾病，这种疾病的真正原因尚不清楚。识别与疾病相关的靶基因在 PD 的治疗中起着至关重要的作用。各种遗传研究已经确定了疾病进展的重要靶基因，尽管这在药物设计领域仍然具有挑战性。在这项研究中，我们提出了一种基于网络的方法，使用基因突变、基因表达和基因缺失分析来识别 PD 的靶基因。PD 基因的子网络由人类蛋白质-蛋白质相互作用网络构建，并使用网络中心性度量确定潜在基因。两个基因，PARK1和PARK2, 通过将基因突变和表达数据整合到子网络中，被确定为目标基因。进行基因缺失分析以确定重要目标，结果显示VDAC1和ATP5C1基因对帕金森子网络至关重要。因此，基于网络的方法的发现将为理解 PD 的疾病机制提供额外的见解。这项研究的未来增强可能有助于预测疾病生物标志物以及在合理的药物设计中设计新化合物。