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Integrative multi-omics and machine learning analysis identifies candidate biomarkers associated with mitochondrial quality control in major depressive disorder
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DOI：10.3389/fpsyt.2026.1828806.png)
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Abstract
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BackgroundMajor depressive disorder (MDD) possesses a complex pathogenesis; with abnormal mitochondrial quality control (MQC) proposed as a potential mechanism involved in the pathological process.MethodsThis study integrated two microarray expression profiling datasets with a single-nucleus RNA sequencing (snRNA-seq) dataset from the human prefrontal cortex (PFC). Candidate genes were identified by intersecting differentially expressed genes (DEGs) from the training set with MQC-associated module genes identified through WGCNA. Ten machine learning algorithms ranked MQC-associated candidate biomarkers; followed by preliminary mRNA-level verification using PFC tissues from chronic restraint stress (CRS) rats. Additionally; MQC-related gene set activity was computationally inferred at the single-cell level to examine cell-type-specific transcriptional alterations associated with MDD.ResultsThe application of ten machine learning algorithms highlighted DCHS1 and HS3ST2 as candidate biomarkers linked to MQC-related transcriptional alterations. Gene set enrichment analysis (GSEA) indicated associations of these genes with oxidative phosphorylation and cytokine-cytokine receptor interaction pathways. In CRS rats; DCHS1 mRNA expression decreased; while HS3ST2 mRNA expression increased; aligning with bioinformatic findings. Among the 18 annotated cell types in the snRNA-seq dataset; computationally inferred MQRG activity significantly decreased in eight cell types; including several excitatory and inhibitory neuronal subtypes. Inhib_GRIK1 neurons exhibited cell-type-specific expression differences in DCHS1 and HS3ST2.ConclusionDCHS1 and HS3ST2 may serve as candidate biomarkers associated with MQC-related transcriptional alterations in the PFC of patients with MDD. MQRG activity demonstrated marked cell-type heterogeneity and reduction across multiple PFC cell populations. These findings provide preliminary; hypothesis-generating evidence for the link between MQC-related transcriptional dysregulation and MDD; however; further functional experiments are necessary to ascertain whether DCHS1 and HS3ST2 directly regulate mitochondrial quality control.
Keywords:
machine learning
multi-omics
major depressive disorder
mitochondrial quality control
DCHS1
HS3ST2
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