Evidence map›Paper›PMID 40214882›Full record

ArticleBlood research2025

Analysis of hemolysis-associated acute myeloid leukemia genes obtained using weighted gene co-expression network analysis and a Mendelian randomization study.

Rui Zhang, Yan Zang, Linguo Wan, Hui Yu, Zhanshan Cha, Haihui Gu

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Article in Blood research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

6 authors.

Rui Zhang *Department of Transfusion Medicine, the, First Affiliated Hospitalaq , Naval Military Medical University, Shanghai, 200433, China.
Yan Zang *Department of Transfusion Medicine, the, First Affiliated Hospitalaq , Naval Military Medical University, Shanghai, 200433, China.
Linguo Wan *Department of Transfusion Medicine, the, First Affiliated Hospitalaq , Naval Military Medical University, Shanghai, 200433, China.
Hui YuDepartment of Transfusion Medicine, the, First Affiliated Hospitalaq , Naval Military Medical University, Shanghai, 200433, China.
Zhanshan ChaDepartment of Transfusion Medicine, the, First Affiliated Hospitalaq , Naval Military Medical University, Shanghai, 200433, China. chazhanshan@163.com.
Haihui GuDepartment of Transfusion Medicine, the, First Affiliated Hospitalaq , Naval Military Medical University, Shanghai, 200433, China. haihuigu@126.com.

Funding

the 14th Five Year Plan Discipline Construction Project of The First Affiliated Hospital of Naval Military Medical University GH-145-37the National Natural Science Foundation of China 81970165
6 · The paper itself

Abstract

purposeWe used bioinformatics methods and Mendelian randomization (MR) analysis to investigate the hub genes involved in acute myeloid leukemia (AML) and their causal relationship with hemolysis, to explore a new direction for molecular biology research of AML.

methodsWe first differentially analyzed peripheral blood samples from 62 healthy volunteers and 65 patients with AML from the Gene Expression Omnibus database to obtain differentially expressed genes (DEGs), and intersected them with genes sourced from weighted gene co-expression network analysis (WGCNA) and the GeneCards database to obtain target genes. Target genes were screened using protein-protein interaction (PPI) network analysis and ROC curves to identify genes associated with AML. Finally, we analyzed the correlation between genes and immune cells and the relationship between toll-like receptor 4 (TLR4) and AML using MR.

resultsWe compared peripheral blood expression profiles using an array of 62 healthy volunteers (GSE164191) and 65 patients with AML (GSE89565) (M0:25; M1:11; M2:10; M3:1; M4:7; M4 eo t [16;16] ou inv [16]:4; M5:6; M6:1) and obtained 7,339 DEGs (3,733 upregulated and 3,606 downregulated). We intersected these DEGs with 4,724 genes from WGCNA and 1,330 genes related to hemolysis that were identified in the GeneCards database to obtain 190 target genes. After further screening these genes using the PPI network, we identified TLR4, PTPRC, FCGR3B, STAT1, and APOE, which are closely associated with hemolysis in patients with AML. Finally, we found a causal relationship between TLR4 and AML occurrence using MR analysis (p < 0.05).

conclusionWe constructed a WGCNA-based co-expression network and identified hemolysis-associated AML genes.

Indexed as

AMLHemolysisMendelian randomizationToll-like receptor 4 (TLR4)Weighted gene co-expression network analysis

Identifiers

PMID40214882
PMCPMC11992295

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