Evidence map›Paper›PMID 40744973›Full record

ArticleScientific reports2025

Integrated bioinformatics and experimental validation identify lysosome and immune infiltration-related genes as therapeutic targets in late-onset major depressive disorder.

Jian-Zhen Hu, Yao Gao, Xiao-Na Song, Dan Wang, Xin-Zhe Du, Xiao Wang, Xiao-Dong Hu, Sha Liu

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In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Jian-Zhen Hu *Department of Psychiatry, First Hospital/First Clinical Medical College of Shanxi Medical University, Taiyuan, China.
Yao Gao *Department of Psychiatry, First Hospital/First Clinical Medical College of Shanxi Medical University, Taiyuan, China.
Xiao-Na Song *Department of Basic Medical Sciences, Shanxi Medical University, 56 Xinjian South Road, Taiyuan, 030001, China.
Dan WangShanxi Provincial Integrated Traditional Chinese Medicine (TCM) and Western Medicine (WM) Hospital, 13 Fudong Street, Xinghualing District, Taiyuan City, Shanxi, China.
Xin-Zhe DuDepartment of Psychiatry, First Hospital/First Clinical Medical College of Shanxi Medical University, Taiyuan, China.
Xiao WangDepartment of Psychiatry, First Hospital/First Clinical Medical College of Shanxi Medical University, Taiyuan, China.
Xiao-Dong HuDepartment of Psychiatry, First Hospital/First Clinical Medical College of Shanxi Medical University, Taiyuan, China.
Sha LiuDepartment of Psychiatry, First Hospital/First Clinical Medical College of Shanxi Medical University, Taiyuan, China. liusha1984114@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study leverages bioinformatics to identify differential genes linked to lysosomal alterations in late-onset major depressivedisorder (LOD) patients and explores potential therapeutic drugs. We analyzed differential genes in the GSE76826 dataset usingWGCNA to identify modules associated with LOD. After intersecting with lysosomal genes, we utilized ROC and Lasso regression toassess diagnostic significance. Pathway enrichment analysis was conducted on key modules, followed by CIBESORT, MCPcounter,and quanTIseq to analyze immune infiltration in LOD patients. The changes in the expression of selected genes were confirmedthrough a chronic unpredictable mild stress model. The ITCM database predicted small molecule drugs targeting lysosome-relatedgenes. We selected ANK3, BIN1, CKAP4, GPRASP1, MYO7A, and RAB20 from the Green module, which showed diagnostic value.GO biological processes revealed a link to T cell differentiation and its regulation. Immune infiltration analysis indicated a relationshipbetween LOD patients and CD8 + T cells and neutrophils, with BIN1 positively correlating with CD8 + T cells. RT-qPCR verification inanimal models confirmed our bioinformatics findings. The ITCM database suggested that 17-beta-estradiol and nickel compoundscould be potential treatments for LOD. LOD's etiology involves multiple genes and pathways, with CD8 + T cells and Neutrophils cellspotentially advancing the disorder. 17-beta-estradiol and nickel may offer targeted therapeutic options for LOD.

Indexed as

Computational BiologyLysosomesMajor Depressive DisorderAnimalsDatabases, GeneticGene Expression ProfilingGene Regulatory NetworksHumansBiological informationCD8 + T cellsImmune infiltrationLate-onset major depressive disorderRegression analysis

Identifiers

PMID40744973
PMCPMC12313857

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