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Identifying Immunological Biomarkers for Major Depressive Disorder: Insights From Machine Learning, Single-Nucleus
Long Kangsheng1, Yang Xiaohui2, Pei Xin2
1The First Affiliated Hospital of Hunan University of Chinese Medicine, Changsha, Hunan, China, hnctcm.edu.cn.
Biomed Research International
|April 11, 2026
Summary
This study identifies three key genes (DACH1, FZD7, GULP1) as potential biomarkers for major depressive disorder (MDD) by analyzing immune cell infiltration and gene expression. These findings offer new therapeutic targets for MDD.
Area of Science:
- Neuroscience
- Genomics
- Immunology
Background:
- Major Depressive Disorder (MDD) is a growing global health concern.
- Understanding the molecular mechanisms, particularly immune involvement, is crucial for effective treatment.
Purpose of the Study:
- To identify novel biomarkers for MDD.
- To comprehensively analyze immune cell infiltration in MDD using bioinformatics.
Main Methods:
- Differential gene expression analysis and Weighted Gene Coexpression Network Analysis (WGCNA).
- Single-nucleus RNA sequencing (snRNA-seq) and machine learning algorithms.
- Chronic unpredictable mild stress (CUMS) rat model for MDD, followed by transcriptional sequencing, RT-qPCR, and Western blot.
Main Results:
- 132 differentially expressed genes (DEGs) linked to immune functions were identified.
- WGCNA and machine learning identified three hub genes: DACH1, FZD7, and GULP1.
- snRNA-seq showed distinct immune cell expression patterns in MDD patients; CUMS rat model confirmed hub gene presence and differential expression (DACH1/GULP1 upregulated, FZD7 downregulated).
Conclusions:
- DACH1, FZD7, and GULP1 are implicated in MDD pathogenesis.
- These genes represent potential diagnostic biomarkers and therapeutic targets for major depressive disorder.
