Evidence map›Paper›PMID 41311024›Full record

ArticleIET systems biology

Identification of Ferroptosis-Related Hub Genes as Diagnosis Biomarkers and Therapeutic Monitoring for Major Depressive Disorder Diagnosis.

Shenghui Huang, Shoupin Xie, Fei Feng, Yanyan Wan, Yanping Ma, Yafeng Wang, Fan Zhang, Xinhong Chen, Ping Tang, Hailong Li

Abstract read
In one paragraph

Article in IET systems biology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Bioinformatics Analysis of the Diagnostic Value of Copper and Zinc Metabolism-Related Genes in Major Depressive Disorder: An In Silico Multi-Cohort Study.FASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2026
    Article
  2. Review
  3. 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

10 authors.

Shenghui HuangDepartment of Mental Health and Sleep Center, Gansu Provincial Hospital of Traditional Chinese Medicine, Lanzhou, Gansu, China.
Shoupin XieDepartment of Neurology, Lanzhou First People's Hospital, Lanzhou, Gansu, China.
Fei FengDepartment of Clinical Psychology (Psychosomatic Medicine), Shenzhen People's Hospital, Shenzhen, China.
Yanyan WanDepartment of Science and Education, Luohu District People's Hospital, Shenzhen, China.
Yanping MaDepartment of Geriatrics, Affiliated Hospital of Gansu University of Traditional Chinese Medicine, Lanzhou, China.
Yafeng WangDepartment of Geriatrics, Affiliated Hospital of Gansu University of Traditional Chinese Medicine, Lanzhou, China.
Fan ZhangDepartment of Geriatrics, Affiliated Hospital of Gansu University of Traditional Chinese Medicine, Lanzhou, China.
Xinhong ChenDepartment of General Practice, Luohu Clinical College, School of Medicine, Shantou University, Shenzhen, China.
Ping TangDepartment of General Practice, Luohu Clinical College, School of Medicine, Shantou University, Shenzhen, China.
Hailong LiDepartment of Geriatrics, Shenzhen Integrated Traditional Chinese and Western Medicine Hospital, Shenzhen, Guangdong, China.ORCID 0000-0002-5972-907X

Funding

Gansu Province Traditional Chinese Medicine Research Project GZKP-2023-2Shenzhen Key Medical Discipline Construction Fund SZXK062The Joint Research Fund of Provincial Science and Technology Program of Gansu Province 23JRRA1536
6 · The paper itself

Abstract

Major Depressive Disorder (MDD) is linked to increased neurodegenerative risk. Emerging evidence implicates ferroptosis in neuropsychiatric disorders, prompting investigation of its role in MDD through key gene identification. Three microarray datasets from the GEO database were analysed. Weighted gene co-expression network analysis (WGCNA) identified MDD-related module genes (MRGs) while ferroptosis-related genes (FRGs) were extracted from the FerrDb database. Overlapping genes between MRGs and FRGs were prioritised for mechanistic exploration. Functional enrichment (GO/KEGG) and protein-protein interaction (PPI) network analyses (via Cytoscape and CytoHubba) highlighted hub genes. Machine learning algorithms were applied to develop a diagnostic model, validated through nomogram analysis, calibration curves, decision curve analysis (DCA), ROC curves (AUC evaluation), gene set enrichment analysis (GSEA), and DGIdb-based drug prediction. Differential expression analysis identified 1878 MDD-associated genes (715 downregulated, 1163 upregulated). Four FRGs-MAPK14, WIPI1, DUSP1, and ULK1-emerged as diagnostic biomarkers, showing significant immune cell infiltration correlations (e.g., neutrophils, dendritic cells) and enrichment in pathways like MAPK signalling. The study highlights ferroptosis-related genes (ULK1, MAPK14, WIPI1, DUSP1) as potential diagnostic and therapeutic targets in MDD, linked to neuroimmune interactions and cellular stress responses. These findings underscore MDD's pathophysiological complexity and may guide strategies for managing MDD and neurodegenerative comorbidities.

Indexed as

FerroptosisMajor Depressive DisorderBiomarkersComputational BiologyGene Expression ProfilingGene Regulatory NetworksHumansMachine LearningProtein Interaction MapsBiomarkersbiocomputersbioinformaticsdata analysisdata mining

Identifiers

PMID41311024
PMCPMC12660494

What OpenQuestion holds

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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.