Identification of mitophagy-related genes and analysis of immune infiltration in the astrocytes based on machine learning in the pathogenesis of major depressive disorder.

BACKGROUNDS

Major depressive disorder (MDD) is a pervasive mental and mood disorder with complicated and heterogeneous etiology. Mitophagy, a selective autophagy of cells, specifically eliminates dysfunctional mitochondria. The mitochondria dysfunction in the astrocytes is regarded as a critical pathogenetic mechanism of MDD. However, studies on the mitophagy of astrocytes in MDD are scarce. To explore the impact of mitophagy on the astrocytes, we used bioinformatic methods to analyze the correlation between astrocyte-related genes and mitophagy-related genes in MDD.

METHODS

The microarray dataset of MDD was downloaded from the Gene Expression Omnibus database and identified astrocyte- and mitophagy-related differentially expressed genes (AMRDEGs). We used three machine learning algorithms to identify hub AMRDEGs and constructed a diagnostic prediction model. Also, we analyzed transcription factor-gene and gene-microRNA interaction networks, and the immune infiltration in MDD and healthy controls. Besides, we performed consensus clustering analysis, immune infiltration analysis, and gene set variation analysis of MDD samples.

RESULTS

The present research identified 3 hub AMRDEGs (GRN, NDUFAF4, and SNCA), and a good diagnostic model with potential clinical applications was constructed and validated. Besides, we identified 6 transcription factors and 14 microRNAs. The immune infiltration analysis showed that MDD was closely related to immune cells. Gene set variant analysis showed that MDD was related to immune and mitochondrial metabolism and inflammatory signaling pathways.

CONCLUSIONS

We identified 3 hub AMRDEGs, new biomarkers for treating and diagnosing MDD and associated with immuno-inflammation. Our research provides new insights into the early diagnosis and treatment of MDD.