Integrative analysis explores potential trigeminal neuralgia-associated biomarkers in major depressive disorder via bioinformatics and machine learning

Trigeminal neuralgia (TN) is a severe neuropathic pain disorder that frequently co-occurs with major depressive disorder (MDD), a common and disabling mental illness. However, the role of TN-associated genes (TRGs) in MDD remains unclear. In this study, we used transcriptomic data from MDD and TN patients in the GEO database and employed machine-learning approaches to prioritize 25 TN-associated genes with potential diagnostic relevance in MDD. We explored their potential molecular relevance to MDD and evaluated their discriminatory performance using receiver operating characteristic (ROC) analysis, providing candidate molecular features for further validation. Using clustering algorithms, we defined two distinct TN-associated phenotypes and combined them with the immunomodulatory characteristics of MDD to preliminarily explore the biological functions of these phenotypes in MDD. Simultaneously, we provided a new perspective on the different roles of trigeminal neuralgia in MDD cellular subtypes through single-cell analysis. Furthermore, the protein expression of key TN–MDD–related targets was confirmed in HT22 cells, demonstrating their responsiveness under neuronal stress conditions. Our findings provide exploratory evidence of shared molecular features between TN and MDD and identify candidate genes for further mechanistic and clinical investigation.