Integrative multi-omics and machine learning analysis identifies candidate biomarkers associated with mitochondrial quality control in major depressive disorder.
Overview
- Department of Neurology, Dongfang Hospital, Beijing University of Chinese Medicine, Beijing, China
- Department of Psychiatry and Psychology, The Third Affiliated Hospital of Southern Medical University, Guangzhou, Guangdong, China
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 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.
Conclusion: DCHS1 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.
Reproduced under the paper's license (CC BY), from the paper cited above.
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Data
Datasets cited
- geo:GSE92538, at NCBI GEO; found in “Data availability statement”
- zenodo:21447176, at Zenodo; found in “Data availability statement”
Other data links
- ncbi.nlm.nih.gov/
geo , NCBI; found in the text, “Source of data”
Data availability statement
Publicly available datasets were analyzed in this study. This data can be found here: https://
Reproduced under the paper's license (CC BY), from the paper cited above.
Versions
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Version 1, 27 September 2026: the first record
Recorded: type, language, journal, volume, pages, dates, 5 authors, 6 keywords, 77 references.
Cite
This paper
Li, J., Wang, M., Liu, H., Dong, K., & Guo, R. (2026). Integrative multi-omics and machine learning analysis identifies candidate biomarkers associated with mitochondrial quality control in major depressive disorder. Frontiers in psychiatry, 17, 1828806. https://
BibTeX
@article{li2026integrati
author = {Li, Jingchun and Wang, Min and Liu, Haoqi and Dong, Kaiqiang and Guo, Rongjuan},
title = {{Integrative multi-omics and machine learning analysis identifies candidate biomarkers associated with mitochondrial quality control in major depressive disorder}},
journal = {Frontiers in psychiatry},
year = {2026},
month = aug,
volume = {17},
pages = {1828806},
publisher = {Frontiers Media SA},
issn = {1664-0640},
doi = {10.3389/
url = {https://
pmid = {42656552},
pmcid = {PMC13506908}
}
RIS
TY - JOUR
AU - Li, Jingchun
AU - Wang, Min
AU - Liu, Haoqi
AU - Dong, Kaiqiang
AU - Guo, Rongjuan
TI - Integrative multi-omics and machine learning analysis identifies candidate biomarkers associated with mitochondrial quality control in major depressive disorder
T2 - Frontiers in psychiatry
J2 - Front Psychiatry
PY - 2026
DA - 2026/
VL - 17
SP - 1828806
SN - 1664-0640
PB - Frontiers Media SA
DO - 10.3389/
UR - https://
LA - en
ER -
CSL-JSON
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