Metabolomics strategy assisted by transcriptomics analysis to identify biomarkers associated with schizophrenia

代谢组学 精神分裂症(面向对象编程) 计算生物学 生物标志物 代谢组 生物标志物发现 随机森林 化学 心理学 生物化学 计算机科学 机器学习 生物 色谱法 蛋白质组学 基因 精神科
作者
Liyan Liu,Jiao Zhao,Yang Chen,Rennan Feng
出处
期刊:Analytica Chimica Acta [Elsevier BV]
卷期号:1140: 18-29 被引量:19
标识
DOI:10.1016/j.aca.2020.09.054
摘要

Metabolomics strategy was perform to identify the novel serum biomarkers linked to schizophrenia with the assistance of transcriptomics analysis. Two analytical platforms, UPLC-Q-TOF MS/MS and 1H NMR, were used to acquire the serum fingerprinting profiles from a total of 112 participants (57 healthy controls and 55 schizophrenia patients). The differential metabolites were primarily selected after statistical analyses. Meanwhile, GSE17612 dataset downloaded from GEO database was implemented WGCNA analysis to discover crucial genes and corresponding biological processes. Based on metabolomics analysis, the metabolic distinctions were explored under the aid of transcriptomics. Then using Boruta algorithm identified the biomarkers, and LASSO regression analysis and Random Forest algorithm were used to evaluate the performance of the diagnostic model constructed by biomarkers selected. A total of four metabolites (α-CEHC, neuraminic acid, glyceraldehyde and asparagine) were selected as the biomarkers to establish diagnosis model. The performance of this model showed a higher accuracy rate to distinguish schizophrenia patients from healthy controls (area under the receive operating characteristic curve, 0.992; precision recall curve, 1.000, the mean accuracy of random forest algorithm, 95.00%). A four–biomarker model (α-CEHC, neuraminic acid, glyceraldehyde and asparagine) seems to be a good model for diagnosing schizophrenia patients. It might be helpful to guide the future studies on permitting early intervention designed to prevent disease progression.
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