Identification of Diagnostic Markers for Major Depressive Disorder Using Machine Learning Methods

Abstract

BackgroundMajor depressive disorder (MDD) is a global health challenge that impacts the quality of patients’ lives severely. The disorder can manifest in many forms with different combinations of symptoms, which makes its clinical diagnosis difficult. Robust biomarkers are greatly needed to improve diagnosis and to understand the etiology of the disease. The main purpose of this study was to create a predictive model for MDD diagnosis based on peripheral blood transcriptomes.Materials and MethodsWe collected nine RNA expression datasets for MDD patients and healthy samples from the Gene Expression Omnibus database. After a series of quality control and heterogeneity tests, 302 samples from six studies were deemed suitable for the study. R package “MetaOmics” was applied for systematic meta-analysis of genome-wide expression data. Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic effectiveness of individual genes. To obtain a better diagnostic model, we also adopted the support vector machine (SVM), random forest (RF), k-nearest neighbors (kNN), and naive Bayesian (NB) tools for modeling, with the RF method being used for feature selection.ResultsOur analysis revealed six differentially expressed genes (AKR1C3, ARG1, KLRB1, MAFG, TPST1, and WWC3) with a false discovery rate (FDR) < 0.05 between MDD patients and control subjects. We then evaluated the diagnostic ability of these genes individually. With single gene prediction, we achieved a corresponding area under the curve (AUC) value of 0.63 ± 0.04, 0.67 ± 0.07, 0.70 ± 0.11, 0.64 ± 0.08, 0.68 ± 0.07, and 0.62 ± 0.09, respectively, for these genes. Next, we constructed the classifiers of SVM, RF, kNN, and NB with an AUC of 0.84 ± 0.09, 0.81 ± 0.10, 0.73 ± 0.11, and 0.83 ± 0.09, respectively, in validation datasets, suggesting that the SVM classifier might be superior for constructing an MDD diagnostic model. The final SVM classifier including 70 feature genes was capable of distinguishing MDD samples from healthy controls and yielded an AUC of 0.78 in an independent dataset.ConclusionThis study provides new insights into potential biomarkers through meta-analysis of GEO data. Constructing different machine learning models based on these biomarkers could be a valuable approach for diagnosing MDD in clinical practice.

A novel 4 immune-related genes as diagnostic markers and correlated with immune infiltrates in major depressive disorder

“…He et al found that base on 4 autophagy-related signature have a good diagnostic performance in MDD (AUC = 0.779) [ 26 ]. By using machine learning methods, Zhao et al constructed the classifiers of SVM, RF, kNN, and NB, and AUC for SVM, RF, kNN, and NB was 0.84, 0.81, 0.73, and 0.83, suggesting that they have a good diagnostic performance [ 27 ]. In this study, we found the AUC for IRGs model was 0.861, suggesting that IRGs model is better than the prediction performan of two recently published model.…”

Novel feature selection methods for construction of accurate epigenetic clocks

“…Given the vast feature space of -omics datasets, creating an accurate model through penalised regression is often not difficult, however finding the right features for further study to infer biological understanding is harder. In recent years, feature selection has become a popular method for novel biomarker discovery [ 35 , 36 , 37 , 38 , 39 , 40 ] and the application of the novel feature selection methods in this paper could accelerate the discovery of biomarkers in many fields.…”

A machine learning model for predicting patients with major depressive disorder: A study based on transcriptomic data

“…In particular, Zhao et al compared different machine learning approaches using the same data set which also used by us. It was found that compared with other methods, such as SVM, RF, kNN, NB, SVM could distinguish MDD from healthy controls more accurately ( Zhao et al, 2021 ). Overall, compared with previous studies, our model evaluation provides new ideas for the application of peripheral blood in aiding diagnostic machine learning.…”