Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Bari, Via A. Orabona 4, 70125 Bari, Italy.
Dipartimento di Scienze Mediche di Base, Neuroscienze e Organi di Senso, Università degli Studi di Bari Aldo Moro, Piazza G. Cesare 11, 70124 Bari, Italy.
Genes (Basel). 2022 Apr 21;13(5):727. doi: 10.3390/genes13050727.
The increased incidence and the significant health burden associated with Parkinson's disease (PD) have stimulated substantial research efforts towards the identification of effective treatments and diagnostic procedures. Despite technological advancements, a cure is still not available and PD is often diagnosed a long time after onset when irreversible damage has already occurred. Blood transcriptomics represents a potentially disruptive technology for the early diagnosis of PD. We used transcriptome data from the PPMI study, a large cohort study with early PD subjects and age matched controls (HC), to perform the classification of PD vs. HC in around 550 samples. Using a nested feature selection procedure based on Random Forests and XGBoost we reached an AUC of 72% and found 493 candidate genes. We further discussed the importance of the selected genes through a functional analysis based on GOs and KEGG pathways.
帕金森病（PD）发病率的增加及其带来的巨大健康负担，促使人们进行了大量研究，以期找到有效的治疗方法和诊断程序。尽管技术在不断进步，但仍没有治愈方法，而且 PD 通常在发病很久后才被诊断出来，此时已经发生了不可逆转的损伤。血液转录组学代表了一种具有潜在颠覆性的 PD 早期诊断技术。我们使用 PPMI 研究中的转录组数据，这是一项针对早期 PD 患者和年龄匹配的对照组（HC）的大型队列研究，对大约 550 个样本进行 PD 与 HC 的分类。通过基于随机森林和 XGBoost 的嵌套特征选择过程，我们达到了 72%的 AUC，并发现了 493 个候选基因。我们进一步通过基于 GOs 和 KEGG 途径的功能分析讨论了所选基因的重要性。
