Cuproptosis-related genes prediction feature and immune microenvironment in major depressive disorder

Abstract

Background: 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. Aim: This investigation intends to shed light on the predictive value of cuproptosis-related genes for MDD and the immunological microenvironment. Methods: 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. Results: 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). Conclusion: Our study constructed a prediction model to predict MDD risk and clarified the potential connection between cuproptosis and MDD.

Keywords

• Cuproptosis
• Major depressive disorder
• Gene
• Predictive model
• RF model