The Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv, Israel.
Department of Physics of Complex Systems, Weizmann Institute of Science, 76100, Rehovot, Israel.
Neuromolecular Med. 2024 Nov 28;26(1):50. doi: 10.1007/s12017-024-08817-x.
Schizophrenia is a chronic illness that imposes a significant burden on patients, their families, and the health care system. While it has a substantial genetic component, its heterogeneous nature-both genetic and clinical-limits the ability to identify causal genes and mechanisms. In this study, we analyzed the blood transcriptomes of 398 samples (212 patients with schizophrenia and 186 controls) obtained from five public datasets. We demonstrated this heterogeneity by clustering patients with schizophrenia into two molecular subtypes using an unsupervised machine-learning algorithm. We found that the genes most influential in clustering were enriched in pathways related to the ribosome and ubiquitin-proteasomes system, which are known to be associated with schizophrenia. Based on the expression levels of these genes, we developed a logistic regression model capable of predicting schizophrenia samples in unrelated datasets with a positive predictive value of 64% (p value = 0.039). In the future, integrating blood transcriptomics with clinical characteristics may enable the definition of distinct molecular subtypes, leading to a better understanding of schizophrenia pathophysiology and aiding in the development of personalized drugs and treatment options.
精神分裂症是一种慢性病，给患者、他们的家庭和医疗保健系统带来了巨大的负担。尽管它有很大的遗传成分，但它的异质性——遗传和临床——限制了识别因果基因和机制的能力。在这项研究中，我们分析了来自五个公共数据集的 398 个样本（212 名精神分裂症患者和 186 名对照者）的血液转录组。我们使用无监督机器学习算法将精神分裂症患者聚类为两个分子亚型，证明了这种异质性。我们发现，聚类中最有影响的基因在与核糖体和泛素-蛋白酶体系统相关的途径中富集，这些途径已知与精神分裂症有关。基于这些基因的表达水平，我们开发了一个逻辑回归模型，能够在无关联的数据集预测精神分裂症样本，其阳性预测值为 64%（p 值=0.039）。在未来，将血液转录组学与临床特征相结合，可能会定义出不同的分子亚型，从而更好地理解精神分裂症的病理生理学，并有助于开发个性化药物和治疗方案。
