A diagnostic model based on bioinformatics and machine learning to differentiate bipolar disorder from schizophrenia and major depressive disorder.

IF 3 Q2 PSYCHIATRY Schizophrenia (Heidelberg, Germany) Pub Date : 2024-02-14 DOI:10.1038/s41537-023-00417-1
Jing Shen, Chenxu Xiao, Xiwen Qiao, Qichen Zhu, Hanfei Yan, Julong Pan, Yu Feng
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Abstract

Bipolar disorder (BD) showed the highest suicide rate of all psychiatric disorders, and its underlying causative genes and effective treatments remain unclear. During diagnosis, BD is often confused with schizophrenia (SC) and major depressive disorder (MDD), due to which patients may receive inadequate or inappropriate treatment, which is detrimental to their prognosis. This study aims to establish a diagnostic model to distinguish BD from SC and MDD in multiple public datasets through bioinformatics and machine learning and to provide new ideas for diagnosing BD in the future. Three brain tissue datasets containing BD, SC, and MDD were chosen from the Gene Expression Omnibus database (GEO), and two peripheral blood datasets were selected for validation. Linear Models for Microarray Data (Limma) analysis was carried out to identify differentially expressed genes (DEGs). Functional enrichment analysis and machine learning were utilized to identify. Least absolute shrinkage and selection operator (LASSO) regression was employed for identifying candidate immune-associated central genes, constructing protein-protein interaction networks (PPI), building artificial neural networks (ANN) for validation, and plotting receiver operating characteristic curve (ROC curve) for differentiating BD from SC and MDD and creating immune cell infiltration to study immune cell dysregulation in the three diseases. RBM10 was obtained as a candidate gene to distinguish BD from SC. Five candidate genes (LYPD1, HMBS, HEBP2, SETD3, and ECM2) were obtained to distinguish BD from MDD. The validation was performed by ANN, and ROC curves were plotted for diagnostic value assessment. The outcomes exhibited the prediction model to have a promising diagnostic value. In the immune infiltration analysis, Naive B, Resting NK, and Activated Mast Cells were found to be substantially different between BD and SC. Naive B and Memory B cells were prominently variant between BD and MDD. In this study, RBM10 was found as a candidate gene to distinguish BD from SC; LYPD1, HMBS, HEBP2, SETD3, and ECM2 serve as five candidate genes to distinguish BD from MDD. The results obtained from the ANN network showed that these candidate genes could perfectly distinguish BD from SC and MDD (76.923% and 81.538%, respectively).

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基于生物信息学和机器学习的诊断模型,用于区分双相情感障碍与精神分裂症和重度抑郁症。
躁郁症(BD)是所有精神疾病中自杀率最高的一种,其潜在的致病基因和有效的治疗方法仍不明确。在诊断过程中,双相情感障碍常常与精神分裂症(SC)和重度抑郁症(MDD)混淆,因此患者可能会接受不充分或不适当的治疗,这对他们的预后不利。本研究旨在通过生物信息学和机器学习建立一个诊断模型,在多个公共数据集中区分BD与SC和MDD,为未来诊断BD提供新思路。研究人员从基因表达总库(GEO)中选取了包含BD、SC和MDD的三个脑组织数据集,并选取了两个外周血数据集进行验证。对微阵列数据进行线性模型(Limma)分析,以确定差异表达基因(DEGs)。利用功能富集分析和机器学习进行识别。利用最小绝对收缩和选择算子(LASSO)回归确定候选免疫相关中心基因,构建蛋白质-蛋白质相互作用网络(PPI),建立人工神经网络(ANN)进行验证,绘制接收者操作特征曲线(ROC曲线)以区分BD与SC和MDD,并创建免疫细胞浸润以研究这三种疾病中的免疫细胞失调。RBM10 被认为是区分 BD 和 SC 的候选基因。五个候选基因(LYPD1、HMBS、HEBP2、SETD3 和 ECM2)用于区分 BD 和 MDD。利用 ANN 进行了验证,并绘制了 ROC 曲线以评估诊断价值。结果显示预测模型具有良好的诊断价值。在免疫浸润分析中,发现 BD 和 SC 之间的 Naive B 细胞、静息 NK 细胞和活化肥大细胞有很大差异。在 BD 和 MDD 之间,Naive B 细胞和记忆 B 细胞有显著差异。本研究发现,RBM10 是区分 BD 和 SC 的候选基因;LYPD1、HMBS、HEBP2、SETD3 和 ECM2 是区分 BD 和 MDD 的五个候选基因。ANN 网络得出的结果表明,这些候选基因可以完美地区分 BD 与 SC 和 MDD(分别为 76.923% 和 81.538%)。
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