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Peer-reviewedCC BYTranslational Psychiatry

From brain networks to peripheral signatures: candidate biomarkers for major depressive disorder

  • Yuan Liu1
  • Meijuan Li1
  • Bo Yan2
  • Ying Gao1
  • Yifan Jing1
  • Chengfeng Chen3
  • Xue Tian1
  • Yan Zhou1
  • Mengxin Xie1
  • Changlin Zhang1
  • Bin Zhang1
  • Jie Li1
  1. 1Institute of Mental Health, Tianjin Anding Hospital, Mental Health Center of Tianjin Medical University
  2. 2Department of Gastroenterology, The Central Hospital of Enshi Tujia and Miao Autonomous Prefecture
  3. 3Department of Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou
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Abstract

Major depressive disorder (MDD) is a leading cause of disability worldwide, yet its neural basis and biomarkers remain elusive. In this study, we combined neuroimaging, transcriptomics, proteomics, and machine learning to bridge large-scale brain network dysfunction with peripheral molecular signatures. Resting-state functional magnetic resonance imaging (fMRI) from 544 MDD patients and 569 healthy controls (HC) revealed altered network topology involving thalamic, fronto-striatal, and parietal regions. Connectome–transcriptomic association analysis of these network alterations, in conjunction with differential gene expression analysis, identified 24 candidate genes, with FKBP5, PTX3, and APCDD1 prioritized by machine learning. Plasma proteomics further validated altered protein expression of the three prioritized genes in two independent cohorts (adults: 162 MDD patients and 103 HC; adolescents: 71 MDD patients and 39 HC). Protein-based diagnostic models achieved accuracies of 69.6% in adults and 83.1% in adolescents, while protein levels were significantly higher in adolescents with MDD than in HC (all P < 0.05). These findings suggest that PTX3, APCDD1, and FKBP5 may serve as candidate peripheral biomarkers associated with MDD-related brain network alterations.

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Cite

BibTeXRIS
BibTeX
@article{liu2026from,
  title = {{From brain networks to peripheral signatures: candidate biomarkers for major depressive disorder}},
  author = {Yuan Liu and Meijuan Li and Bo Yan and Ying Gao and Yifan Jing and Chengfeng Chen and Xue Tian and Yan Zhou and Mengxin Xie and Changlin Zhang and Bin Zhang and Jie Li},
  journal = {Translational Psychiatry},
  year = {2026},
  month = jul,
  doi = {10.1038/s41398-026-04234-9},
  url = {https://doi.org/10.1038/s41398-026-04234-9},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - From brain networks to peripheral signatures: candidate biomarkers for major depressive disorder
AU  - Yuan Liu
AU  - Meijuan Li
AU  - Bo Yan
AU  - Ying Gao
AU  - Yifan Jing
AU  - Chengfeng Chen
AU  - Xue Tian
AU  - Yan Zhou
AU  - Mengxin Xie
AU  - Changlin Zhang
AU  - Bin Zhang
AU  - Jie Li
PY  - 2026
DA  - 2026/07/01
JO  - Translational Psychiatry
DO  - 10.1038/s41398-026-04234-9
UR  - https://doi.org/10.1038/s41398-026-04234-9
AB  - Major depressive disorder (MDD) is a leading cause of disability worldwide, yet its neural basis and biomarkers remain elusive. In this study, we combined neuroimaging, transcriptomics, proteomics, and machine learning to bridge large-scale brain network dysfunction with peripheral molecular signatures. Resting-state functional magnetic resonance imaging (fMRI) from 544 MDD patients and 569 healthy controls (HC) revealed altered network topology involving thalamic, fronto-striatal, and parietal regions. Connectome–transcriptomic association analysis of these network alterations, in conjunction with differential gene expression analysis, identified 24 candidate genes, with FKBP5, PTX3, and APCDD1 prioritized by machine learning. Plasma proteomics further validated altered protein expression of the three prioritized genes in two independent cohorts (adults: 162 MDD patients and 103 HC; adolescents: 71 MDD patients and 39 HC). Protein-based diagnostic models achieved accuracies of 69.6% in adults and 83.1% in adolescents, while protein levels were significantly higher in adolescents with MDD than in HC (all P < 0.05). These findings suggest that PTX3, APCDD1, and FKBP5 may serve as candidate peripheral biomarkers associated with MDD-related brain network alterations.
SN  - 2158-3188
ER  -