Abstract
BACKGROUND: The endocannabinoid system (ES) plays a pivotal role in modulating central nervous system activity in response to emotional stimuli. This study aimed to identify and validate biomarkers associated with ES-related genes (ES-RGs) in major depressive disorder (MDD), providing insights into potential therapeutic targets. METHODS: Datasets GSE52790 and GSE38206 were analyzed in this study. Overlapping differential expression analysis and weighted gene co-expression network analysis (WGCNA) were integrated to identify intersecting genes. Candidate genes were selected through protein-protein interaction (PPI) analysis. Biomarker identification involved the integration of machine learning techniques, gene expression data, and receiver operating characteristic (ROC) analysis. A nomogram was developed and evaluated using these biomarkers as key indicators. Comprehensive analyses, including functional exploration, immune infiltration assessment, regulatory network construction, and reverse transcription-quantitative polymerase chain reaction (RT-qPCR) validation, were conducted. RESULTS: Mitochondrial ribosome protein S11 (MRPS11) and mitochondrial serine hydroxymethyltransferase2 (SHMT2) were identified as significant biomarkers for MDD, with markedly reduced expression in patient samples. These findings were validated by RT-qPCR analysis. The development of a biomarker-based nomogram successfully predicted MDD risk. Enrichment analysis highlighted the co-enrichment of both biomarkers in the "ribosome" pathway. Differential immune cell analysis revealed four immune cell types distinguishing MDD from control samples. Moreover, five key miRNAs targeting these biomarkers were predicted, along with 31 lncRNAs targeting the miRNAs, establishing an lncRNA-miRNA-mRNA network. Ten transcription factors (TFs) targeting the biomarkers were also identified, leading to the construction of a TF-mRNA network. Furthermore, 15 drugs targeting MRPS11 and 56 drugs targeting SHMT2 were identified, resulting in the formation of a biomarker-drug network. These findings may inform more precise and personalized therapeutic strategies for MDD. CONCLUSION: MRPS11 and SHMT2 were identified as biomarkers for MDD through the validation of their expression patterns in clinical samples. This study provides a theoretical foundation for the development of targeted therapies for MDD.