Abstract
Major depressive disorder (MDD) is a common mental disorder and is amongst the most prevalent psychiatric disorders. MDD remains challenging to diagnose and predict its onset due to its heterogeneous phenotype and complex etiology. Hence, early detection using diagnostic biomarkers is critical for rapid intervention. In this study, a mixture of AI and bioinformatics were used to mine transcriptomic data from publicly available datasets including 170 MDD patients and 121 healthy controls. Bioinformatics analysis using gene set enrichment analysis (GSEA) and machine learning (ML) algorithms were applied. The GSEA revealed that differentially expressed genes in MDD patients are mainly enriched in pathways related to immune response, inflammatory response, neurodegeneration pathways and cerebellar atrophy pathways. Feature selection methods and ML provided predicted models based on MDD-altered genes with ≥75% of accuracy. The integrative analysis between the bioinformatics and ML approaches identified ten key MDD-related biomarkers including NRG1, CEACAM8, CLEC12B, DEFA4, HP, LCN2, OLFM4, SERPING1, TCN1 and THBS1. Among them, NRG1, active in synaptic plasticity and neurotransmission, was the most robust and reliable to distinguish between MDD patients and healthy controls amongst independent external datasets consisting of a mixture of populations. Further evaluation using saliva samples from an independent cohort of MDD and healthy individuals confirmed the upregulation of NRG1 in patients with MDD compared to healthy controls. Functional mapping to the human brain regions showed NRG1 to have high expression in the main subcortical limbic brain regions implicated in depression. In conclusion, integrative bioinformatics and ML approaches identified putative non-invasive diagnostic MDD-related biomarkers panel for the onset of depression.