Evidence map›Paper›PMID 40965027›Full record

ArticleEndocrine, metabolic & immune disorders drug targets2026

Analysis of Necroptosis-Related Genes and Immune Cell Infiltration in Infantile Hemangioma.

Yunpeng Xu, Jiajie Chen, Zhihao Huang, Rui Zhao, Can Jin, Zichen Li, Shengxiu Liu

Abstract read
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Article in Endocrine, metabolic & immune disorders drug targets, 2026. 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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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

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Yunpeng XuDepartment of Dermatovenerology, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China.
Jiajie ChenDepartment of Dermatovenerology, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China.
Zhihao HuangDepartment of Dermatovenerology, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China.
Rui ZhaoDepartment of Dermatovenerology, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China.
Can JinDepartment of Dermatovenerology, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China.
Zichen LiDepartment of Dermatovenerology, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China.
Shengxiu LiuDepartment of Dermatovenerology, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionInfantile hemangioma (IH) is the most prevalent benign tumor in neonates, yet the role of necroptosis in its pathogenesis remains underexplored. This study aimed to investigate necroptosis-related gene expression patterns in IH, identify critical biomarkers, and develop a diagnostic model to enhance precision medicine approaches.

methodsGene expression data from GSE127487 and GSE100682 datasets were analyzed to screen necroptosis-related differentially expressed genes (NRDEGs). A diagnostic model was constructed using a support vector machine (SVM) and least absolute shrinkage and selection operator (LASSO) regression. Model validity was assessed via receiver operating characteristic (ROC) curve analysis. mRNA interaction networks and immune cell associations were explored using MCPCounter algorithms and single-sample gene set enrichment analysis.

resultsSeventy-four NRDEGs were identified, with six key genes highlighted. Network analysis revealed six miRNAs and 47 transcription factors associated with these genes. Additionally, six key genes showed associations with eight distinct immune cell types, suggesting potential roles in regulating the immune microenvironment. DISCUSSION: In this study, bioinformatics was employed to analyze data, construct diagnostic models, and identify key genes.

conclusionThe six key genes may serve as reliable biomarkers for IH, offering insights into precise diagnosis and personalized therapeutic strategies. This study advances understanding of necroptosis mechanisms in IH and their interplay with immune cells.

Indexed as

Biomarkers, TumorHemangiomaNecroptosisComputational BiologyDatabases, GeneticGene Expression ProfilingGene Expression Regulation, NeoplasticGene Regulatory NetworksHumansInfantInfant, NewbornBiomarkers, Tumorbioinformatics analysisdiagnostic modelimmune cell filtrationInfantile hemangiomainteraction networknecroptosis-related genes (NRGs)

Identifiers

PMID40965027
PMCPMC13635964

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