Revealing and validating the biomarkers associated with demethylation in major depressive disorder: comprehensive insights based on bulk RNA sequencing data, single-nucleus RNA sequencing data, and clinical experiments.

BACKGROUND: The demethylation is suspected to play a role in the development of major depressive disorder (MDD), but the precise biological mechanisms remain unclear. Therefore, this study aimed to investigate biomarkers linked to demethylation in MDD by integrating bulk RNA sequencing (bulk RNA-seq) data and single-nucleus RNA sequencing (snRNA-seq) data. METHODS: The bulk RNA-seq data and snRNA-seq data for MDD were sourced from public databases, and the demethylation-related genes (DRGs) were extracted from the literature. Employing differential expression analysis, machine learning algorithms, and expression profiling, this study identified biomarkers. Afterward, biomarkers were incorporated into a nomogram. The study explored underlying biological mechanisms through enrichment and immune infiltration analyses, while deciphering the regulatory networks of biomarkers. Subsequent analyses included the prediction of targeted drugs, molecular docking, and the examination of the expression of these biomarkers at the single-nucleus level, followed by the identification of key cells and pseudo-time analysis of these cells. Eventually, the expression of biomarkers was clinically validated using reverse transcription-quantitative polymerase chain reaction (RT-qPCR). RESULTS: AREG and NR4A1 were identified as biomarkers, all exhibiting down-regulated expression in MDD samples. Furthermore, the nomogram demonstrated satisfactory clinical utility for assessing MDD probability. Enrichment analysis indicated that biomarkers might affect the occurrence of MDD through "oxidative phosphorylation". Moreover, 6 types of immune infiltrating cells showed significant differences between MDD and control samples. Also, the regulatory networks were constructed, identifying the potential regulators of the biomarkers. The drugs were predicted, and the molecular docking illustrated that a robust binding interaction was found between AREG and dinoprost, with a binding energy of -84.0 kcal/mol. Additionally, snRNA-seq analysis identified 6 cell types, with excitatory neuron (EX) cells were identified as the key cells, and the expression levels of biomarkers exhibited dynamic and nonlinear changes during the differentiation of EX cells. CONCLUSION: AREG and NR4A1, which were related to demethylation, were identified as biomarkers in MDD, providing a new perspective to understand the molecular mechanisms underlying MDD.