Evidence map›Paper›PMID 41964403›Full record

ArticleBioMed research international2026

Identifying Immunological Biomarkers for Major Depressive Disorder: Insights From Machine Learning, Single-Nucleus Bioinformatics, and Experimental Validation.

Long Kangsheng, Yang Xiaohui, Pei Xin, Ye Yong, Li Hongliang, Deng Yihui

Abstract read
In one paragraph

Article in BioMed research international, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Long KangshengThe First Affiliated Hospital of Hunan University of Chinese Medicine, Changsha, Hunan, China, hnctcm.edu.cn.ORCID https://orcid.org/0009-0009-7486-8295
Yang XiaohuiHunan University of Chinese Medicine, Changsha, China, hnctcm.edu.cn.ORCID https://orcid.org/0009-0001-7417-2943
Pei XinHunan University of Chinese Medicine, Changsha, China, hnctcm.edu.cn.ORCID https://orcid.org/0009-0003-4092-4179
Ye YongThe First Affiliated Hospital of Hunan University of Chinese Medicine, Changsha, Hunan, China, hnctcm.edu.cn.ORCID https://orcid.org/0009-0002-4901-1037
Li HongliangThe First Affiliated Hospital of Hunan University of Chinese Medicine, Changsha, Hunan, China, hnctcm.edu.cn.ORCID https://orcid.org/0009-0000-6596-3244
Deng YihuiHunan University of Chinese Medicine, Changsha, China, hnctcm.edu.cn.ORCID https://orcid.org/0009-0003-2756-3831

Funding

Hunan Provincial Health and Wellness Research Project 20257417Natural Science Foundation of Hunan Province 2026JJ82467Science and Technology Innovation Team Project of Hunan Province 2020RC4050
6 · The paper itself

Abstract

backgroundMajor depressive disorder (MDD) is a chronic mental illness rapidly approaching the status of a significant global burden of disease. This study is aimed at investigating novel biomarkers in MDD and performing a comprehensive analysis of immune infiltration through an integrated bioinformatics approach.

methodsThe study involved differential gene expression analysis, weighted gene coexpression network analysis (WGCNA), single-nucleus RNA sequencing (snRNA-seq), and the application of three machine learning methods. Additionally, we constructed the chronic unpredictable mild stress (CUMS) rat model of MDD, and their brain tissues were analyzed by transcriptional sequencing to explore the differentially expressed genes (DEGs). Finally, quantitative reverse transcription-polymerase chain reaction (RT-qPCR) and western blot experiments were conducted to verify the expression level of hub gene in brain tissues.

resultsA total of 132 DEGs were discovered, with enrichment analysis revealing their significant involvement in immune-related functions and pathways. WGCNA analysis yielded three hub genes (DACH1, FZD7, and GULP1). These hub genes were identified by intersecting candidate signature genes obtained from three machine learning analyses with DEGs. SnRNA-seq analysis revealed significant differences in immune cell-related expression patterns between MDD patients and healthy controls. The presence of three hub genes was found by DEGs in brain tissues of CUMS rats. Further RT-qPCR and western blot experiments demonstrated that DACH1 and GULP1 were upregulated, and FZD7 was downregulated in brain tissues of CUMS rats.

conclusionOur findings contribute to the understanding of the relationship between MDD and immune infiltration. DACH1, FZD7, and GULP1 may be key biomarkers and potential therapeutic targets for MDD.

Indexed as

Computational BiologyMachine LearningMajor Depressive DisorderAnimalsBiomarkersDisease Models, AnimalGene Expression ProfilingGene Regulatory NetworksHumansMaleRatsRats, Sprague-DawleyBiomarkersbioinformaticsbiomarkersmachine learningmajor depressive disordersingle-nucleus RNA sequencing

Identifiers

PMID41964403
PMCPMC13069472

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.