Evidence map›Paper›PMID 41020919›Full record

ArticleDiscover oncology2025

Integrative bioinformatics analysis and elastic network modeling elucidate the role of cellular senescence in meningioma recurrence.

Jian-Huang Huang, Yao Chen, Yuan-Bao Kang, Cai-Hou Lin

Abstract read
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Article in Discover oncology, 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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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Jian-Huang Huang *Department of Neurosurgery, Affiliated Hospital of Putian University, Putian, Fujian Province, China. teamhuang@sina.com.
Yao Chen *Department of Neurosurgery, Affiliated Hospital of Putian University, Putian, Fujian Province, China.
Yuan-Bao KangDepartment of Neurosurgery, Affiliated Hospital of Putian University, Putian, Fujian Province, China.
Cai-Hou LinDepartment of Neurosurgery, Fujian Medical University Union Hospital, Fuzhou, Fujian Province, China. grouplin@fjmu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCellular senescence is intimately tied to tumorigenesis and progression, yet its exploration in meningiomas remains inadequate. In this study, we aim to unravel the role of cellular senescence-associated genes (CSA-genes) in meningioma recurrence and identify potential diagnostic markers and therapeutic targets.

methodsWe analyzed GSE136661 and GSE173825 datasets to identify CSA-signature genes through differential expression analysis, weighted gene co-expression network analysis, protein-protein interaction network construction, and elastic net regression modeling. Functional enrichment, immune cell infiltration using CIBERSORT, and transcription factor prediction were performed. Potential drugs were screened using Enrichr database.

resultsA total of 1827 differentially expressed genes (DEGs) were identified, among which 48 were cell senescence-associated differentially expressed genes (CSA-DEGs). Four key CSA-signature genes (CDK1, FOXM1, MYBL2, and BIRC5) were discovered by integrating elastic net regression and network algorithms. The elastic net model demonstrated strong classification performance with an area under the curve (AUC) of 0.816 in distinguishing recurrent meningiomas. Recurrent tumors exhibited significant immune heterogeneity, including increased neutrophils and M0 macrophages (p = 0.007), and CSA-genes were significantly correlated with immune infiltration and checkpoint molecules such as VSIR (p < 0.05). Transcription factor E2F1 was identified as a potential regulator of CSA-signature genes. Drug screening highlighted Dasatinib and Rapamycin as promising candidates with notable anti-meningioma potential.

conclusionOur findings highlight crucial genes and pathways in meningioma recurrence, introducing novel therapeutic candidates. These findings pave new avenues for further elucidating meningioma recurrence mechanisms and developing innovative treatments.

Indexed as

Cellular senescenceElastic netImmune infiltrationMeningioma recurrence

Identifiers

PMID41020919
PMCPMC12480273

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