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

整合生物信息学分析和弹性网络建模阐明了细胞衰老在脑膜瘤复发中的作用

阅读:3

Abstract

BACKGROUND: Cellular 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. METHODS: We 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. RESULTS: A 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. CONCLUSION: Our 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.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。