Lei Daoyun, Sun Jie, Xia Jiangyan
Department of Anesthesiology, Zhongda Hospital Southeast University (Jiangbei), Nanjing, 210048 Jiangsu, China.
Department of Anesthesiology, Zhongda Hospital Southeast University, Nanjing, 210009 Jiangsu, China.
Heliyon. 2023 Jul 26;9(8):e18497. doi: 10.1016/j.heliyon.2023.e18497. eCollection 2023 Aug.
Major depressive disorder (MDD) is a severe, unpredictable, ill-cured, relapsing neuropsychiatric disorder. A recently identified type of death called cuproptosis has been linked to a number of illnesses. However, the influence of cuproptosis-related genes in MDD has not been comprehensively assessed in prior study.
This investigation intends to shed light on the predictive value of cuproptosis-related genes for MDD and the immunological microenvironment.
GSE38206, GSE76826, GSE9653 databases were used to analyze cuproptosis regulators and immune characteristics. To find the genes that were differently expressed, weighted gene co-expression network analysis was employed. We calculated the effectiveness of the random forest model, generalized linear model, and limit gradient lifting to arrive at the best machine prediction model. Nomogram, calibration curve, and decision curve analysis were used to show the anticipated MDD's accuracy.
This study found that there were activated immune responses and cuproptosis-related genes that were dysregulated in people with MDD compared to healthy controls. Considering the test performance of the learned model and validation on subsequent datasets, the RF model (including OSBPL8, VBP1, MTM1, ELK3, and SLC39A6) was considered to have the best discriminative performance. (AUC = 0.875).
Our study constructed a prediction model to predict MDD risk and clarified the potential connection between cuproptosis and MDD.
重度抑郁症（MDD）是一种严重、不可预测、难以治愈且易复发的神经精神疾病。最近发现的一种名为铜死亡的死亡类型与多种疾病有关。然而，先前的研究尚未全面评估铜死亡相关基因在MDD中的影响。
本研究旨在阐明铜死亡相关基因对MDD的预测价值以及免疫微环境。
使用GSE38206、GSE76826、GSE9653数据库分析铜死亡调节因子和免疫特征。采用加权基因共表达网络分析来寻找差异表达的基因。我们计算了随机森林模型、广义线性模型和极限梯度提升的有效性，以得出最佳的机器预测模型。使用列线图、校准曲线和决策曲线分析来展示预测MDD的准确性。
本研究发现，与健康对照相比，MDD患者存在激活的免疫反应和失调的铜死亡相关基因。考虑到学习模型的测试性能以及在后续数据集上的验证，RF模型（包括OSBPL8、VBP1、MTM1、ELK3和SLC39A6）被认为具有最佳的判别性能（AUC = 0.875）。
我们的研究构建了一个预测模型来预测MDD风险，并阐明了铜死亡与MDD之间的潜在联系。
