ArticleFrontiers in psychiatry2026
Integrative multi-omics and machine learning analysis identifies candidate biomarkers associated with mitochondrial quality control in major depressive disorder.
Article in Frontiers in psychiatry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Major depressive disorder (MDD) possesses a complex pathogenesis, with abnormal mitochondrial quality control (MQC) proposed as a potential mechanism involved in the pathological process. Methods: This 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. Results: The application of ten machine learning algorithms highlighted Conclusion:
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