Dissecting the Role of Cellular Senescence in Meningioma Recurrence: Integrative Bioinformatics and Elastic Network Modeling

Dissecting the Role of Cellular Senescence in Meningioma Recurrence: Integrative Bioinformatics and Elastic Network Modeling | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Dissecting the Role of Cellular Senescence in Meningioma Recurrence: Integrative Bioinformatics and Elastic Network Modeling Jian-huang Huang, Yao Chen, Yuan-bao Kang, Cai-hou Lin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5126255/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version 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 CDK1, FOXM1, MYBL2, and BIRC5 emerged as key CSA-genes related to cell cycle and DNA damage. Recurrent meningiomas showed immune heterogeneity, with CSA-genes correlating with immune infiltration and checkpoint molecules. E2F1 was predicted as a regulator. Dasatinib and Rapamycin showed promising 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. Biological sciences/Cancer Biological sciences/Cell biology Biological sciences/Genetics Biological sciences/Neuroscience Health sciences/Biomarkers Meningioma recurrence Immune Infiltration Elastic Net Cellular Senescence Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Revised for the Style of Nature: Meningiomas, the most prevalent primary tumors of the central nervous system, constitute 53% of non-malignant CNS neoplasms, with an incidence rate of 7.86 cases per 100,000 individuals annually [ 1 , 2 ]. Current standard-of-care treatments for meningiomas encompass surgical resection and radiotherapy. Notably, WHO grading and the extent of surgical resection significantly impact recurrence rates and survival outcomes [ 3 ]. Although most meningiomas are benign, tumors in surgically challenging locations (e.g., skull base meningiomas) or those of higher grades often exhibit brain invasion and a high propensity for recurrence, even after multiple rounds of surgery, chemotherapy, and radiotherapy. Studies report recurrence rates of approximately 90% for these subtypes [ 4 ]. Recurrence of meningiomas poses significant therapeutic challenges and adversely affects patient survival, with 10-year overall survival rates of 81.4% for non-malignant meningiomas and 57.1% for malignant ones, particularly dismal at 0% for grade III meningiomas [ 2 ]. Furthermore, a striking 20% of WHO grade I meningiomas recur following complete resection [ 5 ], underscoring the existence of underlying biological mechanisms yet to be elucidated. The surgical resection or radiotherapy of recurrent meningiomas remains a formidable challenge, prompting the frequent consideration of systemic therapies. However, meningiomas have historically been understudied diseases, with evidence for systemic treatments generally scarce [ 6 , 7 ]. Several compounds have been explored in small prospective studies, but while preliminary evidence suggests antitumor activity in patients with recurrent meningiomas, subsequent trials have failed to confirm significant clinical benefits [ 7 , 8 ]. Thus, unraveling the underlying biological mechanisms and investigating novel targeted therapeutic strategies hold paramount importance. Delving deeper into these avenues has the potential to yield transformative treatments and improve outcomes for patients with recurrent meningiomas. In recent years, cellular senescence strategies have garnered considerable attention, yet their investigation in the context of meningiomas remains inadequate. Cellular senescence, a process that not only contributes to aging but also underlies numerous age-related diseases such as atherosclerosis, neuropsychiatric disorders, chronic nephritis, and cancer [ 9 ], has emerged as a key player in meningioma pathogenesis. The incidence of meningiomas markedly increases with age [ 10 , 11 ], highlighting a strong association between meningiomas and cellular senescence [ 12 ]. Defined as an irreversible state of cell cycle arrest, cellular senescence serves as a critical tumor-suppressive mechanism [ 13 , 14 ]. Some scholars even posit that escape from senescence is a prerequisite for tumors to progress towards overt malignancy [ 15 ]. Moreover, senescence in tumor cells can foster the emergence of more aggressive variants, particularly when induced by chemotherapy, which can reprogram cancer cells with stem-like properties, enhancing their invasiveness, therapy resistance, and recurrence potential [ 16 ]. Notably, the elimination of senescent cells delays tumorigenesis [ 17 ]. Therefore, elucidating the role of cellular senescence in meningioma recurrence mechanisms warrants further investigation. In this study, we employ a combined approach utilizing bioinformatics and elastic net models to interrogate recurrent meningioma data from public repositories, aiming to clarify the contribution of cellular senescence in meningioma recurrence and provide insights for targeted meningioma therapies. Materials and Methods Bulk-RNA Sequencing Data Sources We commence by outlining the study's workflow in Fig. 1 . The bulk-RNA sequencing transcriptome data for meningiomas were sourced from the Gene Expression Omnibus (GEO) database ( https://www.ncbi.nlm.nih.gov/geo ). Our search strategy encompassed the following criteria: (1) a keyword search for "Meningioma"; (2) selection of "Expression profiling by array" under the Study type option; (3) samples derived from Homo sapiens; and (4) datasets encompassing both primary and recurrent meningioma samples. Specifically, the mRNA sequencing for dataset GSE136661 was performed on the GPL20301 platform, comprising 15 recurrent meningioma samples and 145 primary meningioma samples. Meanwhile, dataset GSE173825, based on the GPL16791 platform, contained 5 recurrent meningioma samples and 3 primary meningioma samples. For this study, GSE136661 served as the discovery set, while GSE173825 functioned as the validation set. The Cellular Senescence Gene Database ( http://csgene.bioinfo-minzhao.org ) provided a list of 503 genes associated with cellular senescence. Differential Expression Analysis Our initial analysis of the bulk-RNA sequencing datasets was conducted using R software (version 4.3.2). Prior to data analysis, rigorous data cleaning procedures were implemented, including normalization using the "NormalizeBetweenArrays" function and subsequent log2 transformation. Differentially expressed genes (DEGs) were identified utilizing the "Limma" package [ 18 ]. Initially, a design matrix was constructed to reflect the relationships between experimental and control groups. Subsequently, the lmFit function was employed to fit a linear model, and the eBayes function was applied for empirical Bayes moderation to calculate statistical significance. We established selection criteria of |log2 fold change| > 0.5 and adjusted P-value < 0.05 to identify DEGs. Finally, the results were visually represented through volcano plots and heatmaps. Identification of Cell Senescence-Associated Differentially Expressed Genes To pinpoint differentially expressed genes that are intimately linked to cellular senescence, we employed a Venn diagram approach. By comparing the identified DEGs with a known set of genes associated with cellular senescence, we filtered out those DEGs that overlapped with the senescence-related gene list. This process allowed us to precisely identify genes that play pivotal roles in the cellular senescence process. Gene Enrichment Analysis To gain deeper insights into the differential genes, we utilized the "clusterProfiler" package (version 4.10) [ 19 ] for ID conversion and subsequent enrichment analysis. This encompassed Gene Ontology Biological Process (GO_BP) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis [ 20 ]. Entries with an FDR < 0.05 were considered significantly enriched, providing valuable insights into the biological processes and pathways associated with the differential genes. Construction of Protein-Protein Interaction (PPI) Network To construct a protein-protein interaction (PPI) network encompassing the cell senescence-associated differentially expressed genes (CSA-DEGs), we leveraged the STRING database version 12 ( http://string-db.org ). During network construction, we set the "minimum required interaction score" to 0.4 to ensure the reliability of interactions and opted to hide disconnected nodes for a simplified network structure. This approach yielded interaction scores among the CSA-DEGs. Subsequently, these data were imported into Cytoscape software version 3.9.1 [ 21 ] for further visualization. To identify key genes within the network, we employed the "cytoHubba" plugin [ 22 ] in Cytoscape to score all genes. Specifically, we ranked genes using the Betweenness and Degree algorithms, ultimately selecting the top 10 genes ranked by both methods as our key genes. Weighted Gene Co-expression Network Analysis (WGCNA) WGCNA is a systems biology approach used to identify gene modules closely related to specific phenotypes and explore the interrelationships among these genes. In this study, we employed the R package "WGCNA" [ 23 ] for our analysis. Initially, the GSE136661 expression data was cleaned and processed, with missing values handled and the top 15,000 genes with the highest median absolute deviation selected as the data source for analysis. Next, hierarchical clustering of samples was performed to assess similarity among samples and identify outliers for exclusion. Selecting an optimal soft-thresholding power, a crucial step in constructing a scale-free network, was done to ensure the network's stability. We set the networkType parameter to "unsigned" and RsquaredCut to 0.9. Subsequently, a co-expression network was constructed using the one-step method, with corType set to "bicor", minModuleSize set to 100, and mergeCutHeight set to 0.25. Gene modules were identified using the dynamic tree cut method, and the correlation and significance of each module's eigengene with the phenotype were calculated. Ultimately, this process enabled us to identify the gene modules most significantly associated with specific traits and the genes within these modules. Elastic Net Regression Elastic Net regression is a widely-used regularization technique in high-dimensional data analysis that combines the strengths of Lasso regression (L1 regularization) and Ridge regression (L2 regularization) [ 24 ]. Its core idea lies in addressing multicollinearity and variable selection issues in high-dimensional data by incorporating both regularization terms [ 24 , 25 ]. In this study, we employed Elastic Net regression to identify key genes associated with specific groups, utilizing cross-validation to select optimal regularization parameters (alpha and lambda). First, we divided the data into 5-fold cross-validation sets using the caret package to ensure the robustness of model evaluation. For each fold, we conducted a grid search over different alpha values ranging from 0 to 1 with a step size of 0.1. The cv.glmnet function was used to perform binomial logistic regression cross-validation, selecting the model with the lowest classification error on the validation set. Finally, the model parameters with the highest accuracy across all folds were chosen to fit the final Elastic Net model on the entire dataset. By analyzing the model coefficients, we identified genes with non-zero coefficients as the key genes. To evaluate the classification performance of the model, we utilized the "pROC" package [ 26 ] to generate ROC curves and calculate the AUC values. This comprehensive approach demonstrated the effectiveness of Elastic Net in gene selection and classification tasks. Validation and Evaluation of CSA-Signature Genes To validate and assess the CSA-signature genes, we first obtain the intersection of key module genes from WGCNA, Elastic Net results, MCC (Mutual Coherence Criterion), and Degree algorithm results using a Venn diagram. This intersection represents the CSA-signature genes that are consistently identified by multiple methods. Immune Infiltration Analysis To evaluate the landscape of immune infiltration, we first conducted a quantitative analysis of the immune cell composition within the test dataset using the "CIBERSORT" algorithm [ 27 ]. Subsequently, we delved into the correlation between the expression levels of CSA-signature genes and the abundance of various immune cell populations. Specifically, we calculated the Pearson correlation coefficients and their corresponding p-values between gene expression levels and immune cell densities. Correlations with p-values less than 0.05 were considered statistically significant. Finally, we assessed the relationship between the CSA-signature genes and immune checkpoints using Pearson analysis. To visually represent these correlations, we generated a heatmap using the "ggplot2" package. Transcription Factor Screening and Evaluation To identify key transcription factors (TFs) that regulate the cellular senescence-associated signature genes (CSA-signature genes), we utilized the transcription factor prediction module of the NetworkAnalyst 3.0 web platform ( https://www.networkanalyst.ca ). This platform offers robust analytical tools for delving into regulatory networks embedded within gene expression data [ 28 ]. Initially, we imported the CSA-signature genes into the NetworkAnalyst platform to obtain a list of potential transcription factors. Subsequently, we evaluated the expression levels of these predicted TFs across different groups within the two datasets. Identifying Candidate Drugs To pinpoint potential candidate drugs, we submitted the CSA-signature genes to the Enrichr website ( https://www.amp.pharm.mssm.edu/Enrichr/ ) and downloaded the information on target drugs associated with these genes. Subsequently, we ranked the candidate drugs based on a comprehensive score, arranged from highest to lowest. This comprehensive score reflects the degree of association between each small molecule drug and the studied genes. Drugs with a significant level (P-value < 0.05) and a high comprehensive score were deemed prominent candidates for further consideration. Statistical Analysis Statistical analyses were performed using R (version 4.3.2). To estimate the differences in expression between two groups, the Wilcoxon rank-sum test was employed. A P-value of less than 0.05 was considered statistically significant. Results Cell Senescence-Associated DEGs and Functional Enrichment Analysis In the dataset GSE136661, mRNA sequencing data from both primary tumor and recurrent tumor groups successfully passed quality control measures, revealing no significant outliers and substantial gene expression differences between the two groups (Fig. 2 A-C). Consequently, all samples were included in subsequent analyses. Differential expression analysis yielded 1827 differentially expressed genes (DEGs) (Fig. 2 D), of which 847 were downregulated and 980 were upregulated. Further comparison with a curated set of 503 cell senescence-related genes identified 48 cell senescence-associated differentially expressed genes (CSA-DEGs) (Fig. 2 E), comprising 35 upregulated and 13 downregulated genes. Enrichment analysis of the CSA-DEGs revealed several insightful findings. GO biological process enrichment highlighted significant enrichment in processes such as cellular senescence, cell cycle, DNA replication, and stem cell proliferation (Fig. 2 F). In KEGG pathway enrichment analysis, enriched pathways included cellular senescence, cell cycle, apoptosis, and the P53 signaling pathway (Fig. 2 G). These results suggest that cellular senescence mechanisms may play a pivotal role in meningioma recurrence. WGCNA Key Module Genes During the WGCNA, we first ensured the quality of the data, confirming the absence of significant outlier samples (Fig. 3 A). Subsequently, we selected an optimal soft-thresholding power of 12 to construct the co-expression network using the one-step method, resulting in the identification of four distinct modules (Fig. 3 B-C). Correlation analysis revealed that the MEgreen module exhibited the highest correlation with the phenotype of interest (r = 0.42, p = 2e-08, Fig. 3 D). Within the MEgreen module, we identified 251 genes (detailed in Supplementary Material 1), which were further subjected to enrichment analysis. The results of Biological Process (BP) and KEGG pathway enrichment analyses highlighted terms such as cellular senescence, cell cycle, apoptosis, and the P53 signaling pathway (Fig. 3 E, note: original figure reference adjusted for context). These findings underscore the intimate relationship between cellular senescence and the recurrence phenotype in meningiomas, suggesting that the genes within the MEgreen module may play crucial roles in mediating this process. PPI Network Construction and Functional Enrichment Analysis Initially, we intersected the genes within the MEgreen module with the CSA-DEGs, resulting in the identification of 25 CSA-Hub genes (Fig. 4 A). Subsequently, utilizing the STRING database, we constructed a PPI (Protein-Protein Interaction) network for the CSA-DEGs. This network comprises 25 nodes interconnected by 246 edges, demonstrating a statistically significant PPI enrichment with a p-value of < 1.0e-16. To evaluate the importance of individual genes within this network, we employed the 'cytoHubba' plugin to score all genes based on commonly used algorithms: Degree and Betweenness. This analysis identified key genes and their corresponding scores (Fig. 4 B), with detailed results provided in Supplementary Material 1. Following this, we conducted GO functional enrichment and KEGG pathway enrichment analyses to delve into the biological pathways implicated in meningioma recurrence. The GO biological process enrichment analysis uncovered significant enrichments in crucial cellular processes such as cell cycle transition, DNA damage-responsive signal transduction, and DNA replication (Fig. 4 C). Meanwhile, the KEGG pathway enrichment analysis highlighted essential pathways including cellular senescence, apoptosis, and the P53 signaling pathway (Fig. 4 D). These findings collectively emphasize the pivotal role of cellular senescence in the mechanisms governing meningioma recurrence, providing valuable insights into the underlying biological processes and pathways. Elastic Net Model Analysis Firstly, we conducted a Principal Component Analysis (PCA) on the expression matrix of CSA-Hub genes, resulting in a clear differentiation between the two groups of samples (Fig. 5 A). Subsequently, through the establishment of an Elastic Net model, we identified the top 20 genes ranked by their importance (Fig. 5 B). This model demonstrated robust performance in distinguishing between primary and recurrent meningiomas, as evidenced by a high Area Under the Curve (AUC) value (AUC = 0.816) (Fig. 5 C). Next, we intersected the top 10 genes ranked by the Degree and Betweenness algorithms with those ranked by the Elastic Net model's importance, ultimately pinpointing four CSA-signature genes: CDK1, FOXM1, MYBL2, and BIRC5 (Fig. 5 D). Further GO enrichment analysis of these CSA signature genes revealed crucial biological processes such as cell cycle transition and DNA damage-responsive signal transduction (Fig. 5 E). Additionally, KEGG pathway enrichment analysis illuminated the associations of these genes with essential pathways including cellular senescence, apoptosis, and the P53 signaling pathway (Fig. 5 F). Furthermore, Wikipathway enrichment analysis indicated their involvement in pathways such as DNA IR Damage and Cell Response Via ATR, CKAP4 signaling, and IL-24 signaling (Fig. 5 G). Lastly, through MSigDB Hallmark enrichment analysis, we discovered that these genes are primarily associated with processes like G2/M checkpoint, E2F Targets, and Mitotic Spindle (Fig. 5 H). These findings not only reinforce the pivotal role of CSA-signature genes in meningioma recurrence but also suggest that they might play a significant part in this process by influencing cellular proliferation and DNA repair mechanisms. Immunoinfiltration Analysis We employed the CIBERSORT algorithm to quantitatively assess the infiltration of immune cells within tumor samples. Initially, the CIBERSORT analysis revealed significant heterogeneity in immune cell infiltration among meningioma samples (Fig. 6 A), with notable differences in the distribution of various immune cell types between primary and recurrent tumor groups (Fig. 6 B). Specifically, in the recurrent tumor group, there was a marked increase in the proportions of neutrophils and M0 macrophages (p < 0.01), accompanied by a significant decrease in the proportions of eosinophils and naive B cells (p < 0.05). Subsequently, we examined the correlation between the CSA-signature genes and the proportions of immune cells. The results indicated a close association between these CSA-signature genes and the aforementioned changes in immune cell proportions (Fig. 6 C). Lastly, we discovered a significant correlation between all CSA-signature genes and the V-domain Ig suppressor of T cell activation receptor (VSIR) gene (p = 0.05) (Fig. 6 D). Through this analysis, we have uncovered distinct differences in immune cell infiltration between primary and recurrent meningiomas, and identified the potential roles of CSA-signature genes within the tumor immune microenvironment. Validation and Evaluation of CSA-Signature Genes Initially, we conducted a differential analysis of the CSA-signature genes within the dataset GSE136661. The results demonstrated that the mRNA expressions of CDK1, FOXM1, MYBL2, and BIRC5 were significantly higher in recurrent meningiomas compared to primary meningiomas (Fig. 7 A). Furthermore, these four CSA-signature genes exhibited excellent diagnostic performance, with AUC values exceeding 0.8 (Fig. 7 B). These findings were validated in the dataset GSE173825 (Figs. 7 C-D), further underscoring the reliability and generalizability of our results. Additionally, the data processing details for the dataset GSE173825 are provided in Supplementary Material 2. Exploration and Evaluation of Transcription Factors To gain further insights into the regulatory mechanisms of the CSA-signature genes, we conducted an exploration and evaluation of relevant transcription factors. Utilizing the TRRUST and JASPAR databases, we identified candidate transcription factors that potentially regulate the expression of CDK1, FOXM1, MYBL2, and BIRC5 (Figs. 7 E-F). Among these, KLF4 and E2F1 emerged as potential regulators. Subsequently, we performed differential analysis of the mRNA expression levels of KLF4 and E2F1 in the datasets GSE136661 and GSE173825. Our findings revealed that the expression of E2F1 was significantly different between primary and recurrent meningiomas, whereas no significant difference was observed in KLF4 expression (Figs. 7 G-H). This suggests that E2F1 may play a crucial role in modulating the expression of the CSA-signature genes and potentially contributing to the differences observed in the immune microenvironment between primary and recurrent meningiomas. Identification of Candidate Drugs To determine potential therapeutic agents for treating recurrent meningiomas, we employed the Enrichr website and inputted the CSA-signature genes for drug screening. Leveraging the enrichment analysis from the DsigDB database, we generated a list of the top 10 candidate drugs based on their Combined Score, as outlined in Table 1 . These candidate drugs encompass a diverse range of chemical structures and mechanisms of action, offering a broad spectrum of therapeutic options.These drugs have demonstrated varying degrees of anti-meningioma activity in vitro and in vivo studies, with Dasatinib and Rapamycin emerging as particularly promising candidates due to their notable potential for further development into clinical therapeutic agents. Table 1 Top 10 Candidate Drugs Selected through DsigDB Enrichment Analysis Term Adjusted P-value Combined Score Genes Phytoestrogens CTD 00007437 1.04E-08 1935924.864 CDK1;BIRC5;MYBL2;FOXM1 LUCANTHONE CTD 00006227 2.22E-06 1440253.305 CDK1;BIRC5;MYBL2;FOXM1 Dasatinib CTD 00004330 4.64E-05 1150486.005 CDK1;BIRC5;MYBL2;FOXM1 troglitazone CTD 00002415 9.87E-05 1061016.686 CDK1;BIRC5;MYBL2;FOXM1 Enterolactone CTD 00001393 3.92E-04 921506.0298 CDK1;BIRC5;MYBL2;FOXM1 rapamycin CTD 00007350 4.36E-04 2869.860506 CDK1;BIRC5;FOXM1 roscovitine CTD 00003426 5.34E-04 8747.566939 CDK1;BIRC5 7-Hydroxystaurosporine CTD 00002331 5.34E-04 8021.859107 CDK1;BIRC5 Fulvestrant CTD 00002740 5.34E-04 2142.505433 CDK1;BIRC5;FOXM1 testosterone CTD 00006844 5.34E-04 832973.8493 CDK1;BIRC5;MYBL2;FOXM1 Discussion In this study, we have not only unveiled the potential role of cellular senescence in meningioma recurrence but also elucidated the significance of CSA-signature genes through an integrative approach encompassing various analytical methods such as WGCNA, elastic net, and PPI-related algorithms. Furthermore, we have systematically delved into their functions and mechanisms from perspectives including immune infiltration, functional enrichment, and transcriptional factor regulation, thereby offering crucial biological insights and clinical implications. Cellular senescence emerges as a crucial factor in the recurrence mechanisms of meningioma. Previous studies have demonstrated its dual role in tumor suppression and cancer progression [ 29 , 30 ]. In this study, we observed significant alterations in the expression of CSA-DEGs in recurrent meningiomas, particularly the upregulation of CSA-signature genes, suggesting their potential key role in tumor recurrence. To further elucidate the functions of these genes, we utilized WGCNA to identify critical modules associated with meningioma recurrence, notably the MEgreen module. Genes within this module were enriched in biological processes related to cellular senescence, cell cycle regulation, apoptosis, and the P53 signaling pathway, further reinforcing the significance of cellular senescence in meningioma recurrence. Subsequently, we conducted GO and KEGG enrichment analyses on CSA-DEGs, CSA-related Hub genes, and CSA-signature genes, revealing their primary involvement in crucial biological processes such as the cell cycle, DNA replication, and the P53 signaling pathway. This indicates that CSA-signature genes may play a pivotal role in meningioma recurrence by regulating these core biological processes. Additionally, Wikipathway enrichment analysis provided a novel perspective, suggesting that CSA-signature genes may be implicated in the radiotherapy resistance mechanisms of recurrent meningiomas, particularly through the ATR pathway [ 31 ]. These findings offer a systematic view, enhancing our understanding of the vital mechanisms of cellular senescence in meningioma recurrence. Meningiomas are not constrained by the blood-brain barrier, allowing surrounding immune cells to infiltrate these tumors. The composition of immune infiltration within the tumor microenvironment is intimately linked to tumor progression [ 32 ]. CIBERSORT analysis has revealed remarkable heterogeneity in immune cell infiltration patterns among meningioma samples, with notable increases in the proportions of neutrophils and M0 macrophages in recurrent meningiomas. These alterations may be associated with an immunosuppressive state within the tumor microenvironment [ 33 , 34 ].Moreover, CSA-signature genes exhibit a close correlation with changes in immune cell proportions, particularly displaying significant positive correlations with M0 macrophages and Treg cells. This suggests that these genes may play a role in meningioma recurrence by modulating the immune microenvironment, consistent with previous studies [ 35 ]. Intriguingly, CSA-signature genes display negative correlations with immune checkpoint molecules, notably VSIR (V-domain Ig suppressor of T cell activation receptor), a crucial immune checkpoint that plays a pivotal role in regulating immune responses [ 36 ]. This finding implies that CSA-signature genes may influence tumor immune evasion mechanisms by modulating the expression of immune checkpoint molecules. Through this analysis, we have not only uncovered significant differences in immune cell infiltration between primary and recurrent meningiomas but also identified the potential role of CSA-signature genes in the tumor immune microenvironment. These discoveries offer new avenues for further research into meningioma immunotherapy and provide a theoretical foundation for developing therapeutic strategies targeting specific immune cell types and immune checkpoint molecules. The identification of four key CSA-signature genes—CDK1, FOXM1, MYBL2, and BIRC5—through an elastic net model is a significant finding, as these genes demonstrate exceptional diagnostic performance in distinguishing between primary and recurrent meningiomas, validated in external datasets. Prior research underscores the significance of these genes in meningioma biology: CDK1 is notably associated with meningioma recurrence and biological aggressiveness [ 37 ], FOXM1 is linked to the enhancement of meningioma malignant features [ 38 , 39 ], MYBL2 mutations are correlated with high-grade meningiomas [ 40 ], and a NanoString targeted gene expression panel study on meningioma progression highlights the crucial roles of FOXM1, MYBL2, and BIRC5 [ 41 ].Further enrichment analysis reveals that these genes are primarily involved in crucial biological processes such as cell cycle transition and DNA damage response, suggesting they may play a pivotal role in meningioma recurrence by regulating cell proliferation and DNA repair. To gain deeper insights into their regulatory mechanisms, we explored potential transcription factors for these CSA-signature genes and identified E2F1 as a candidate. E2F1 is a key transcription factor involved in cell proliferation, differentiation, and apoptosis [ 42 ] and is currently considered a potential therapeutic target in various cancers, including colorectal, breast, and gastric cancers [ 42 – 44 ]. By analyzing the co-expression relationship between E2F1 and the CSA-signature genes in the two datasets, we ensure the reliability of its regulatory role. This discovery not only enhances our understanding of the molecular mechanisms underlying meningioma recurrence but also opens up new avenues for developing targeted therapies that can potentially disrupt the oncogenic pathways driven by these genes and their regulators. Utilizing the Enrichr database, we have identified several potential therapeutic drugs. Among them, Dasatinib and Rapamycin have demonstrated robust anti-meningioma activity [ 45 , 46 ], making them worthy of further investigation and development as clinical treatment options. No relevant reports exist for the other drugs at this time. These candidate drugs offer novel therapeutic strategies with the potential to improve the prognosis for patients with meningiomas. Study limitation and Future Prospects Despite the insights gained from this study into the potential role of cell aging-related genes in meningioma recurrence, there are still some limitations. Firstly, our analysis is based on publicly available datasets, and future studies are needed to validate our findings in larger, independent cohorts. Secondly, while we have preliminarily identified candidate drugs, further in vivo and in vitro experiments are required to assess their safety and efficacy. Conclusion In conclusion, through systematic analysis, this study has revealed the pivotal role of cellular aging in the recurrence mechanism of meningioma and provided new avenues for future therapeutic strategies. These findings not only deepen our understanding of the recurrence mechanisms of meningioma but also offer potential biomarkers and drug targets for individualized treatment. Abbreviations CSA- genes Cellular Senescence-associated Genes GEO Gene Expression Omnibus. DEG Differentially expressed gene. GO Gene Ontology. KEGG Kyoto Encyclopedia of Genes and Genomes. WGCNA Weighted Gene Co-expression Network Analysis. PPI Protein-Protein Interaction. Declarations Acknowledgements Not applicable. Authors’ contributions JHH and CHL analyzed the data and wrote this manuscript. YC, and YBK assisted in analyzing the data and revising the manuscript. JHH, YC critically read and edited the manuscript. Availability of data and materials The data that support the findings of this study are available from the corresponding author upon reasonable request. GSE43292: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc= GSE136661 GSE163154: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc= GSE173825 Cell Senescence Database: http://csgene.bioinfo-minzhao.org Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests There is no competing interest to be declared by the authors. References Gittleman, H. R. et al. Trends in central nervous system tumor incidence relative to other common cancers in adults, adolescents, and children in the United States, 2000 to 2010. Cancer . 121 , 102–112 (2015). Ostrom, Q. T. et al. CBTRUS Statistical Report: Primary brain and other central nervous system tumors diagnosed in the United States in 2010–2014. Neuro Oncol. 19 , v1–v88 (2017). Materi, J., Mampre, D., Ehresman, J., Rincon-Torroella, J. & Chaichana, K. L. Predictors of recurrence and high growth rate of residual meningiomas after subtotal resection. J. Neurosurg. 134 , 410–416 (2021). 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Comprehensive Molecular Profiling Identifies FOXM1 as a Key Transcription Factor for Meningioma Proliferation. Cell. Rep. 22 , 3672–3683 (2018). Kim, E. et al. Characterization and comparison of genomic profiles between primary cancer cell lines and parent atypical meningioma tumors. Cancer Cell. Int. 20 , 345 (2020). Maier, A. D. et al. Gene expression analysis during progression of malignant meningioma compared to benign meningioma. J. Neurosurg. 138 , 1302–1312 (2023). Fang, Z., Lin, M., Li, C., Liu, H. & Gong, C. A comprehensive review of the roles of E2F1 in colon cancer. Am. J. Cancer Res. 10 , 757–768 (2020). Lin, X. et al. CHPF promotes gastric cancer tumorigenesis through the activation of E2F1. Cell. Death Dis. 12 , 876 (2021). Farra, R., Dapas, B., Grassi, M., Benedetti, F. & Grassi, G. E2F1 as a molecular drug target in ovarian cancer. Expert Opin. Ther. Targets . 23 , 161–164 (2019). Sagers, J. E. et al. Combination therapy with mTOR kinase inhibitor and dasatinib as a novel therapeutic strategy for vestibular schwannoma. Sci. Rep. 10 , 4211 (2020). Angus, S. P. et al. EPH receptor signaling as a novel therapeutic target in NF2-deficient meningioma. Neuro Oncol. 20 , 1185–1196 (2018). Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial1.xlsx SupplementaryMaterial2.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5126255","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":379297785,"identity":"5588bdfa-fbc0-4c0a-af83-d29c0e69e4f9","order_by":0,"name":"Jian-huang Huang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzElEQVRIiWNgGAWjYJACZjDJ3sBwgCjlPHAtPAdI1iKRQKSj7Nmbn24ubLPLk498/vBwQQ2DPL8YAct4eI6Z3Z7ZllxseDvH4PCMYwyGM2cTsI5HIsHsNm8bc+LG2TkMh3nYGBIMbhPSIv/8G1BLfeLGmccfHOb5R4wWCR6QLYcT50swGBzmbSNGy5mcsts8544nbuAB+oW3T4KwX9jbj2+7zVNWnTi//fjjzzzfbOT5pQlogQODA2BKgkjlICDfQILiUTAKRsEoGFkAAHs0Q1jfHcxKAAAAAElFTkSuQmCC","orcid":"","institution":"Affiliated Hospital of Putian University","correspondingAuthor":true,"prefix":"","firstName":"Jian-huang","middleName":"","lastName":"Huang","suffix":""},{"id":379297786,"identity":"9bcf46d2-59f4-4a53-aeab-a2a7db55b4ab","order_by":1,"name":"Yao Chen","email":"","orcid":"","institution":"Affiliated Hospital of Putian University","correspondingAuthor":false,"prefix":"","firstName":"Yao","middleName":"","lastName":"Chen","suffix":""},{"id":379297787,"identity":"310aaac8-fe8b-4558-b4a2-9c436553e60f","order_by":2,"name":"Yuan-bao Kang","email":"","orcid":"","institution":"Affiliated Hospital of Putian University","correspondingAuthor":false,"prefix":"","firstName":"Yuan-bao","middleName":"","lastName":"Kang","suffix":""},{"id":379297788,"identity":"b23fd054-797c-4563-a878-47163bc2969d","order_by":3,"name":"Cai-hou Lin","email":"","orcid":"","institution":"Fujian Medical University Union Hospital","correspondingAuthor":false,"prefix":"","firstName":"Cai-hou","middleName":"","lastName":"Lin","suffix":""}],"badges":[],"createdAt":"2024-09-21 02:39:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5126255/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5126255/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":71470674,"identity":"2a64e9a1-c2b7-4b6a-bfe0-26bd045165d4","added_by":"auto","created_at":"2024-12-16 04:01:43","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":890681,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTechnical Roadmap of This Study\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5126255/v1/f426f63db7d74f66a58a0a82.jpg"},{"id":71470676,"identity":"ae61846a-7c25-435d-886f-b52adfd67216","added_by":"auto","created_at":"2024-12-16 04:01:43","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1288206,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferentially Expressed Genes (DEGs) Related to Cellular Senescence and Functional Enrichment Analysis. \u003c/strong\u003eA-C. Sample data quality validation from dataset GSE136661 shows no significant outliers, with clear differentiation between the two groups. D. Volcano plot depicting the DEGs identified in dataset GSE136661. E. Venn diagram illustrating the intersection of DEGs from dataset GSE136661 and cellular senescence-associated genes, yielding the CSA-DEGs. F-G. Bubble plot presenting the results of GO functional enrichment and KEGG pathway enrichment analysis for the CSA-DEGs. CSA-DEGs: Cellular Senescence-Associated DEGs.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5126255/v1/e922ac632b774179d447ebc5.jpg"},{"id":71471782,"identity":"9a7645ec-441d-47d3-b1c0-1c1afe4e383e","added_by":"auto","created_at":"2024-12-16 04:17:43","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":996004,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification of Key Module Genes Using WGCNA. \u003c/strong\u003eA. Dendrogram indicating no significant outliers in the data. B. Soft-thresholding power analysis suggests an optimal soft-thresholding power of 12. C-D. WGCNA yields four modules, with MEgreen showing the strongest correlation with the trait of interest. E. Bubble plot displaying the results of enrichment analysis for genes within the MEgreen module.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5126255/v1/8b4087de79eb12f95ee1fbeb.jpg"},{"id":71471490,"identity":"de12e986-01df-4ff2-a4f4-cac19d911afe","added_by":"auto","created_at":"2024-12-16 04:09:43","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":554036,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConstruction of PPI Network and Functional Enrichment Analysis. \u003c/strong\u003eA. Venn diagram depicting the intersection between genes in the MEgreen module and CSA-DEGs, resulting in 25 CSA-Hub genes. B. PPI network of CSA-related genes generated using the Degree algorithm. C-D. Bubble plots displaying the results of GO and KEGG enrichment analysis for CSA-related genes.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5126255/v1/23382d5634c1bac5009aa995.jpg"},{"id":71471492,"identity":"97ad59dd-c950-4467-9f52-1dccbcf48e2d","added_by":"auto","created_at":"2024-12-16 04:09:43","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1230534,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eElastic Net Selection of Key Genes and Enrichment Analysis of CSA-Signature Genes. \u003c/strong\u003eA. PCA plot demonstrating the ability of CSA-Hub genes to distinguish between primary and recurrent meningiomas. B. Ranking of gene importance based on the elastic net model. C. ROC curve illustrating the excellent discriminative ability of the elastic net model. D. Venn diagram showcasing the identification of four CSA-signature genes. E-H. Bubble plots presenting the results of enrichment analysis for the CSA-signature genes.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5126255/v1/3ad8b01e0ec448c25a7dd0cf.jpg"},{"id":71470681,"identity":"73e844fe-3258-44dd-a793-95e9ef73e235","added_by":"auto","created_at":"2024-12-16 04:01:44","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":840921,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImmune Infiltration Analysis in Meningiomas. \u003c/strong\u003eA. Heatmap depicting significant heterogeneity in immune cell infiltration between primary and recurrent meningioma samples. B. Bar chart displaying differences in immune cell proportions between primary and recurrent meningioma groups. C. Heatmap showing the correlation between CSA-signature genes and immune cell types. D. Heatmap illustrating the correlation between CSA-signature genes and immune checkpoints.\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5126255/v1/02443c1770fc10c6e36cfd26.jpg"},{"id":71470682,"identity":"81291e1e-3a6d-4032-bc1f-4b7d6873ffd8","added_by":"auto","created_at":"2024-12-16 04:01:44","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":897565,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eValidation of CSA-Signature Genes and Exploration of Candidate Transcription Factors. \u003c/strong\u003eA. Bar chart displaying the differential expression analysis of CSA-signature genes in dataset GSE136661. B. ROC curve demonstrating the excellent diagnostic performance of CSA-signature genes in dataset GSE136661. C-D. Differential expression analysis and diagnostic performance of CSA-signature genes in dataset GSE173825. E-F. Identification of candidate transcription factors using the TRRUST and JASPAR databases. G-H. Validation of candidate transcription factors in datasets GSE136661 and GSE173825.\u003c/p\u003e","description":"","filename":"Figure7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5126255/v1/a5dcb9a303ca1cc8835a4ca9.jpg"},{"id":73659745,"identity":"73f4e1cd-5f80-42ff-9be5-b8ec41fec486","added_by":"auto","created_at":"2025-01-13 10:54:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7924416,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5126255/v1/41da3aed-0172-4d12-92b9-15ec4a94fa8b.pdf"},{"id":71470675,"identity":"706d2a2e-712a-45f0-ae7c-481a8ee02b5f","added_by":"auto","created_at":"2024-12-16 04:01:43","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":46917,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5126255/v1/21ab62ceab96bb82aa3f2d37.xlsx"},{"id":71472937,"identity":"4205989b-5383-4a15-b18d-06f6976dc92d","added_by":"auto","created_at":"2024-12-16 04:25:43","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":302488,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial2.docx","url":"https://assets-eu.researchsquare.com/files/rs-5126255/v1/7fcabb3826429dc8a279e475.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Dissecting the Role of Cellular Senescence in Meningioma Recurrence: Integrative Bioinformatics and Elastic Network Modeling","fulltext":[{"header":"Introduction","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003eRevised for the Style of Nature:\u003c/h2\u003e \u003cp\u003eMeningiomas, the most prevalent primary tumors of the central nervous system, constitute 53% of non-malignant CNS neoplasms, with an incidence rate of 7.86 cases per 100,000 individuals annually [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Current standard-of-care treatments for meningiomas encompass surgical resection and radiotherapy. Notably, WHO grading and the extent of surgical resection significantly impact recurrence rates and survival outcomes [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Although most meningiomas are benign, tumors in surgically challenging locations (e.g., skull base meningiomas) or those of higher grades often exhibit brain invasion and a high propensity for recurrence, even after multiple rounds of surgery, chemotherapy, and radiotherapy. Studies report recurrence rates of approximately 90% for these subtypes [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Recurrence of meningiomas poses significant therapeutic challenges and adversely affects patient survival, with 10-year overall survival rates of 81.4% for non-malignant meningiomas and 57.1% for malignant ones, particularly dismal at 0% for grade III meningiomas [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Furthermore, a striking 20% of WHO grade I meningiomas recur following complete resection [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], underscoring the existence of underlying biological mechanisms yet to be elucidated.\u003c/p\u003e \u003cp\u003eThe surgical resection or radiotherapy of recurrent meningiomas remains a formidable challenge, prompting the frequent consideration of systemic therapies. However, meningiomas have historically been understudied diseases, with evidence for systemic treatments generally scarce [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Several compounds have been explored in small prospective studies, but while preliminary evidence suggests antitumor activity in patients with recurrent meningiomas, subsequent trials have failed to confirm significant clinical benefits [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Thus, unraveling the underlying biological mechanisms and investigating novel targeted therapeutic strategies hold paramount importance. Delving deeper into these avenues has the potential to yield transformative treatments and improve outcomes for patients with recurrent meningiomas.\u003c/p\u003e \u003cp\u003eIn recent years, cellular senescence strategies have garnered considerable attention, yet their investigation in the context of meningiomas remains inadequate. Cellular senescence, a process that not only contributes to aging but also underlies numerous age-related diseases such as atherosclerosis, neuropsychiatric disorders, chronic nephritis, and cancer [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], has emerged as a key player in meningioma pathogenesis. The incidence of meningiomas markedly increases with age [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], highlighting a strong association between meningiomas and cellular senescence [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Defined as an irreversible state of cell cycle arrest, cellular senescence serves as a critical tumor-suppressive mechanism [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Some scholars even posit that escape from senescence is a prerequisite for tumors to progress towards overt malignancy [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Moreover, senescence in tumor cells can foster the emergence of more aggressive variants, particularly when induced by chemotherapy, which can reprogram cancer cells with stem-like properties, enhancing their invasiveness, therapy resistance, and recurrence potential [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Notably, the elimination of senescent cells delays tumorigenesis [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Therefore, elucidating the role of cellular senescence in meningioma recurrence mechanisms warrants further investigation. In this study, we employ a combined approach utilizing bioinformatics and elastic net models to interrogate recurrent meningioma data from public repositories, aiming to clarify the contribution of cellular senescence in meningioma recurrence and provide insights for targeted meningioma therapies.\u003c/p\u003e \u003c/div\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eBulk-RNA Sequencing Data Sources\u003c/h2\u003e \u003cp\u003eWe commence by outlining the study's workflow in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The bulk-RNA sequencing transcriptome data for meningiomas were sourced from the Gene Expression Omnibus (GEO) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Our search strategy encompassed the following criteria: (1) a keyword search for \"Meningioma\"; (2) selection of \"Expression profiling by array\" under the Study type option; (3) samples derived from Homo sapiens; and (4) datasets encompassing both primary and recurrent meningioma samples. Specifically, the mRNA sequencing for dataset GSE136661 was performed on the GPL20301 platform, comprising 15 recurrent meningioma samples and 145 primary meningioma samples. Meanwhile, dataset GSE173825, based on the GPL16791 platform, contained 5 recurrent meningioma samples and 3 primary meningioma samples. For this study, GSE136661 served as the discovery set, while GSE173825 functioned as the validation set. The Cellular Senescence Gene Database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://csgene.bioinfo-minzhao.org\u003c/span\u003e\u003cspan address=\"http://csgene.bioinfo-minzhao.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) provided a list of 503 genes associated with cellular senescence.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDifferential Expression Analysis\u003c/h3\u003e\n\u003cp\u003eOur initial analysis of the bulk-RNA sequencing datasets was conducted using R software (version 4.3.2). Prior to data analysis, rigorous data cleaning procedures were implemented, including normalization using the \"NormalizeBetweenArrays\" function and subsequent log2 transformation. Differentially expressed genes (DEGs) were identified utilizing the \"Limma\" package [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Initially, a design matrix was constructed to reflect the relationships between experimental and control groups. Subsequently, the lmFit function was employed to fit a linear model, and the eBayes function was applied for empirical Bayes moderation to calculate statistical significance. We established selection criteria of |log2 fold change| \u0026gt; 0.5 and adjusted P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 to identify DEGs. Finally, the results were visually represented through volcano plots and heatmaps.\u003c/p\u003e\n\u003ch3\u003eIdentification of Cell Senescence-Associated Differentially Expressed Genes\u003c/h3\u003e\n\u003cp\u003eTo pinpoint differentially expressed genes that are intimately linked to cellular senescence, we employed a Venn diagram approach. By comparing the identified DEGs with a known set of genes associated with cellular senescence, we filtered out those DEGs that overlapped with the senescence-related gene list. This process allowed us to precisely identify genes that play pivotal roles in the cellular senescence process.\u003c/p\u003e\n\u003ch3\u003eGene Enrichment Analysis\u003c/h3\u003e\n\u003cp\u003eTo gain deeper insights into the differential genes, we utilized the \"clusterProfiler\" package (version 4.10) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] for ID conversion and subsequent enrichment analysis. This encompassed Gene Ontology Biological Process (GO_BP) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Entries with an FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered significantly enriched, providing valuable insights into the biological processes and pathways associated with the differential genes.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eConstruction of Protein-Protein Interaction (PPI) Network\u003c/h2\u003e \u003cp\u003eTo construct a protein-protein interaction (PPI) network encompassing the cell senescence-associated differentially expressed genes (CSA-DEGs), we leveraged the STRING database version 12 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://string-db.org\u003c/span\u003e\u003cspan address=\"http://string-db.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). During network construction, we set the \"minimum required interaction score\" to 0.4 to ensure the reliability of interactions and opted to hide disconnected nodes for a simplified network structure. This approach yielded interaction scores among the CSA-DEGs. Subsequently, these data were imported into Cytoscape software version 3.9.1 [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] for further visualization. To identify key genes within the network, we employed the \"cytoHubba\" plugin [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] in Cytoscape to score all genes. Specifically, we ranked genes using the Betweenness and Degree algorithms, ultimately selecting the top 10 genes ranked by both methods as our key genes.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eWeighted Gene Co-expression Network Analysis (WGCNA)\u003c/h3\u003e\n\u003cp\u003eWGCNA is a systems biology approach used to identify gene modules closely related to specific phenotypes and explore the interrelationships among these genes. In this study, we employed the R package \"WGCNA\" [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] for our analysis. Initially, the GSE136661 expression data was cleaned and processed, with missing values handled and the top 15,000 genes with the highest median absolute deviation selected as the data source for analysis. Next, hierarchical clustering of samples was performed to assess similarity among samples and identify outliers for exclusion.\u003c/p\u003e \u003cp\u003eSelecting an optimal soft-thresholding power, a crucial step in constructing a scale-free network, was done to ensure the network's stability. We set the networkType parameter to \"unsigned\" and RsquaredCut to 0.9. Subsequently, a co-expression network was constructed using the one-step method, with corType set to \"bicor\", minModuleSize set to 100, and mergeCutHeight set to 0.25. Gene modules were identified using the dynamic tree cut method, and the correlation and significance of each module's eigengene with the phenotype were calculated.\u003c/p\u003e \u003cp\u003eUltimately, this process enabled us to identify the gene modules most significantly associated with specific traits and the genes within these modules.\u003c/p\u003e\n\u003ch3\u003eElastic Net Regression\u003c/h3\u003e\n\u003cp\u003eElastic Net regression is a widely-used regularization technique in high-dimensional data analysis that combines the strengths of Lasso regression (L1 regularization) and Ridge regression (L2 regularization) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Its core idea lies in addressing multicollinearity and variable selection issues in high-dimensional data by incorporating both regularization terms [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In this study, we employed Elastic Net regression to identify key genes associated with specific groups, utilizing cross-validation to select optimal regularization parameters (alpha and lambda).\u003c/p\u003e \u003cp\u003eFirst, we divided the data into 5-fold cross-validation sets using the caret package to ensure the robustness of model evaluation. For each fold, we conducted a grid search over different alpha values ranging from 0 to 1 with a step size of 0.1. The cv.glmnet function was used to perform binomial logistic regression cross-validation, selecting the model with the lowest classification error on the validation set. Finally, the model parameters with the highest accuracy across all folds were chosen to fit the final Elastic Net model on the entire dataset.\u003c/p\u003e \u003cp\u003eBy analyzing the model coefficients, we identified genes with non-zero coefficients as the key genes. To evaluate the classification performance of the model, we utilized the \"pROC\" package [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] to generate ROC curves and calculate the AUC values. This comprehensive approach demonstrated the effectiveness of Elastic Net in gene selection and classification tasks.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eValidation and Evaluation of CSA-Signature Genes\u003c/h2\u003e \u003cp\u003eTo validate and assess the CSA-signature genes, we first obtain the intersection of key module genes from WGCNA, Elastic Net results, MCC (Mutual Coherence Criterion), and Degree algorithm results using a Venn diagram. This intersection represents the CSA-signature genes that are consistently identified by multiple methods.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eImmune Infiltration Analysis\u003c/h2\u003e \u003cp\u003eTo evaluate the landscape of immune infiltration, we first conducted a quantitative analysis of the immune cell composition within the test dataset using the \"CIBERSORT\" algorithm [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Subsequently, we delved into the correlation between the expression levels of CSA-signature genes and the abundance of various immune cell populations. Specifically, we calculated the Pearson correlation coefficients and their corresponding p-values between gene expression levels and immune cell densities. Correlations with p-values less than 0.05 were considered statistically significant. Finally, we assessed the relationship between the CSA-signature genes and immune checkpoints using Pearson analysis. To visually represent these correlations, we generated a heatmap using the \"ggplot2\" package.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eTranscription Factor Screening and Evaluation\u003c/h2\u003e \u003cp\u003eTo identify key transcription factors (TFs) that regulate the cellular senescence-associated signature genes (CSA-signature genes), we utilized the transcription factor prediction module of the NetworkAnalyst 3.0 web platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.networkanalyst.ca\u003c/span\u003e\u003cspan address=\"https://www.networkanalyst.ca\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). This platform offers robust analytical tools for delving into regulatory networks embedded within gene expression data [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Initially, we imported the CSA-signature genes into the NetworkAnalyst platform to obtain a list of potential transcription factors. Subsequently, we evaluated the expression levels of these predicted TFs across different groups within the two datasets.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eIdentifying Candidate Drugs\u003c/h2\u003e \u003cp\u003eTo pinpoint potential candidate drugs, we submitted the CSA-signature genes to the Enrichr website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.amp.pharm.mssm.edu/Enrichr/\u003c/span\u003e\u003cspan address=\"https://www.amp.pharm.mssm.edu/Enrichr/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and downloaded the information on target drugs associated with these genes. Subsequently, we ranked the candidate drugs based on a comprehensive score, arranged from highest to lowest. This comprehensive score reflects the degree of association between each small molecule drug and the studied genes. Drugs with a significant level (P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and a high comprehensive score were deemed prominent candidates for further consideration.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed using R (version 4.3.2). To estimate the differences in expression between two groups, the Wilcoxon rank-sum test was employed. A P-value of less than 0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eCell Senescence-Associated DEGs and Functional Enrichment Analysis\u003c/h2\u003e \u003cp\u003eIn the dataset GSE136661, mRNA sequencing data from both primary tumor and recurrent tumor groups successfully passed quality control measures, revealing no significant outliers and substantial gene expression differences between the two groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-C). Consequently, all samples were included in subsequent analyses. Differential expression analysis yielded 1827 differentially expressed genes (DEGs) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD), of which 847 were downregulated and 980 were upregulated. Further comparison with a curated set of 503 cell senescence-related genes identified 48 cell senescence-associated differentially expressed genes (CSA-DEGs) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE), comprising 35 upregulated and 13 downregulated genes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eEnrichment analysis of the CSA-DEGs revealed several insightful findings. GO biological process enrichment highlighted significant enrichment in processes such as cellular senescence, cell cycle, DNA replication, and stem cell proliferation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF). In KEGG pathway enrichment analysis, enriched pathways included cellular senescence, cell cycle, apoptosis, and the P53 signaling pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG). These results suggest that cellular senescence mechanisms may play a pivotal role in meningioma recurrence.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eWGCNA Key Module Genes\u003c/h2\u003e \u003cp\u003eDuring the WGCNA, we first ensured the quality of the data, confirming the absence of significant outlier samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Subsequently, we selected an optimal soft-thresholding power of 12 to construct the co-expression network using the one-step method, resulting in the identification of four distinct modules (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB-C). Correlation analysis revealed that the MEgreen module exhibited the highest correlation with the phenotype of interest (r\u0026thinsp;=\u0026thinsp;0.42, p\u0026thinsp;=\u0026thinsp;2e-08, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWithin the MEgreen module, we identified 251 genes (detailed in Supplementary Material 1), which were further subjected to enrichment analysis. The results of Biological Process (BP) and KEGG pathway enrichment analyses highlighted terms such as cellular senescence, cell cycle, apoptosis, and the P53 signaling pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE, note: original figure reference adjusted for context). These findings underscore the intimate relationship between cellular senescence and the recurrence phenotype in meningiomas, suggesting that the genes within the MEgreen module may play crucial roles in mediating this process.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003ePPI Network Construction and Functional Enrichment Analysis\u003c/h2\u003e \u003cp\u003eInitially, we intersected the genes within the MEgreen module with the CSA-DEGs, resulting in the identification of 25 CSA-Hub genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Subsequently, utilizing the STRING database, we constructed a PPI (Protein-Protein Interaction) network for the CSA-DEGs. This network comprises 25 nodes interconnected by 246 edges, demonstrating a statistically significant PPI enrichment with a p-value of \u0026lt;\u0026thinsp;1.0e-16.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo evaluate the importance of individual genes within this network, we employed the 'cytoHubba' plugin to score all genes based on commonly used algorithms: Degree and Betweenness. This analysis identified key genes and their corresponding scores (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB), with detailed results provided in Supplementary Material 1.\u003c/p\u003e \u003cp\u003eFollowing this, we conducted GO functional enrichment and KEGG pathway enrichment analyses to delve into the biological pathways implicated in meningioma recurrence. The GO biological process enrichment analysis uncovered significant enrichments in crucial cellular processes such as cell cycle transition, DNA damage-responsive signal transduction, and DNA replication (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003eMeanwhile, the KEGG pathway enrichment analysis highlighted essential pathways including cellular senescence, apoptosis, and the P53 signaling pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). These findings collectively emphasize the pivotal role of cellular senescence in the mechanisms governing meningioma recurrence, providing valuable insights into the underlying biological processes and pathways.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eElastic Net Model Analysis\u003c/h2\u003e \u003cp\u003eFirstly, we conducted a Principal Component Analysis (PCA) on the expression matrix of CSA-Hub genes, resulting in a clear differentiation between the two groups of samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Subsequently, through the establishment of an Elastic Net model, we identified the top 20 genes ranked by their importance (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). This model demonstrated robust performance in distinguishing between primary and recurrent meningiomas, as evidenced by a high Area Under the Curve (AUC) value (AUC\u0026thinsp;=\u0026thinsp;0.816) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). Next, we intersected the top 10 genes ranked by the Degree and Betweenness algorithms with those ranked by the Elastic Net model's importance, ultimately pinpointing four CSA-signature genes: CDK1, FOXM1, MYBL2, and BIRC5 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). Further GO enrichment analysis of these CSA signature genes revealed crucial biological processes such as cell cycle transition and DNA damage-responsive signal transduction (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). Additionally, KEGG pathway enrichment analysis illuminated the associations of these genes with essential pathways including cellular senescence, apoptosis, and the P53 signaling pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF). Furthermore, Wikipathway enrichment analysis indicated their involvement in pathways such as DNA IR Damage and Cell Response Via ATR, CKAP4 signaling, and IL-24 signaling (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eG). Lastly, through MSigDB Hallmark enrichment analysis, we discovered that these genes are primarily associated with processes like G2/M checkpoint, E2F Targets, and Mitotic Spindle (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eH). These findings not only reinforce the pivotal role of CSA-signature genes in meningioma recurrence but also suggest that they might play a significant part in this process by influencing cellular proliferation and DNA repair mechanisms.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eImmunoinfiltration Analysis\u003c/h2\u003e \u003cp\u003eWe employed the CIBERSORT algorithm to quantitatively assess the infiltration of immune cells within tumor samples. Initially, the CIBERSORT analysis revealed significant heterogeneity in immune cell infiltration among meningioma samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA), with notable differences in the distribution of various immune cell types between primary and recurrent tumor groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). Specifically, in the recurrent tumor group, there was a marked increase in the proportions of neutrophils and M0 macrophages (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), accompanied by a significant decrease in the proportions of eosinophils and naive B cells (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Subsequently, we examined the correlation between the CSA-signature genes and the proportions of immune cells. The results indicated a close association between these CSA-signature genes and the aforementioned changes in immune cell proportions (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). Lastly, we discovered a significant correlation between all CSA-signature genes and the V-domain Ig suppressor of T cell activation receptor (VSIR) gene (p\u0026thinsp;=\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). Through this analysis, we have uncovered distinct differences in immune cell infiltration between primary and recurrent meningiomas, and identified the potential roles of CSA-signature genes within the tumor immune microenvironment.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eValidation and Evaluation of CSA-Signature Genes\u003c/h2\u003e \u003cp\u003eInitially, we conducted a differential analysis of the CSA-signature genes within the dataset GSE136661. The results demonstrated that the mRNA expressions of CDK1, FOXM1, MYBL2, and BIRC5 were significantly higher in recurrent meningiomas compared to primary meningiomas (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). Furthermore, these four CSA-signature genes exhibited excellent diagnostic performance, with AUC values exceeding 0.8 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). These findings were validated in the dataset GSE173825 (Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC-D), further underscoring the reliability and generalizability of our results. Additionally, the data processing details for the dataset GSE173825 are provided in Supplementary Material 2.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eExploration and Evaluation of Transcription Factors\u003c/h2\u003e \u003cp\u003eTo gain further insights into the regulatory mechanisms of the CSA-signature genes, we conducted an exploration and evaluation of relevant transcription factors. Utilizing the TRRUST and JASPAR databases, we identified candidate transcription factors that potentially regulate the expression of CDK1, FOXM1, MYBL2, and BIRC5 (Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE-F). Among these, KLF4 and E2F1 emerged as potential regulators. Subsequently, we performed differential analysis of the mRNA expression levels of KLF4 and E2F1 in the datasets GSE136661 and GSE173825. Our findings revealed that the expression of E2F1 was significantly different between primary and recurrent meningiomas, whereas no significant difference was observed in KLF4 expression (Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eG-H). This suggests that E2F1 may play a crucial role in modulating the expression of the CSA-signature genes and potentially contributing to the differences observed in the immune microenvironment between primary and recurrent meningiomas.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of Candidate Drugs\u003c/h2\u003e \u003cp\u003eTo determine potential therapeutic agents for treating recurrent meningiomas, we employed the Enrichr website and inputted the CSA-signature genes for drug screening. Leveraging the enrichment analysis from the DsigDB database, we generated a list of the top 10 candidate drugs based on their Combined Score, as outlined in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. These candidate drugs encompass a diverse range of chemical structures and mechanisms of action, offering a broad spectrum of therapeutic options.These drugs have demonstrated varying degrees of anti-meningioma activity in vitro and in vivo studies, with Dasatinib and Rapamycin emerging as particularly promising candidates due to their notable potential for further development into clinical therapeutic agents.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTop 10 Candidate Drugs Selected through DsigDB Enrichment Analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTerm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdjusted P-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCombined Score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGenes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhytoestrogens CTD 00007437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.04E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1935924.864\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCDK1;BIRC5;MYBL2;FOXM1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLUCANTHONE CTD 00006227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.22E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1440253.305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCDK1;BIRC5;MYBL2;FOXM1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDasatinib CTD 00004330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.64E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1150486.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCDK1;BIRC5;MYBL2;FOXM1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etroglitazone CTD 00002415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.87E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1061016.686\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCDK1;BIRC5;MYBL2;FOXM1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnterolactone CTD 00001393\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.92E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e921506.0298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCDK1;BIRC5;MYBL2;FOXM1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003erapamycin CTD 00007350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.36E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2869.860506\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCDK1;BIRC5;FOXM1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eroscovitine CTD 00003426\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.34E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8747.566939\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCDK1;BIRC5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7-Hydroxystaurosporine CTD 00002331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.34E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8021.859107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCDK1;BIRC5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFulvestrant CTD 00002740\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.34E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2142.505433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCDK1;BIRC5;FOXM1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etestosterone CTD 00006844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.34E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e832973.8493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCDK1;BIRC5;MYBL2;FOXM1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we have not only unveiled the potential role of cellular senescence in meningioma recurrence but also elucidated the significance of CSA-signature genes through an integrative approach encompassing various analytical methods such as WGCNA, elastic net, and PPI-related algorithms. Furthermore, we have systematically delved into their functions and mechanisms from perspectives including immune infiltration, functional enrichment, and transcriptional factor regulation, thereby offering crucial biological insights and clinical implications.\u003c/p\u003e \u003cp\u003eCellular senescence emerges as a crucial factor in the recurrence mechanisms of meningioma. Previous studies have demonstrated its dual role in tumor suppression and cancer progression [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. In this study, we observed significant alterations in the expression of CSA-DEGs in recurrent meningiomas, particularly the upregulation of CSA-signature genes, suggesting their potential key role in tumor recurrence. To further elucidate the functions of these genes, we utilized WGCNA to identify critical modules associated with meningioma recurrence, notably the MEgreen module. Genes within this module were enriched in biological processes related to cellular senescence, cell cycle regulation, apoptosis, and the P53 signaling pathway, further reinforcing the significance of cellular senescence in meningioma recurrence. Subsequently, we conducted GO and KEGG enrichment analyses on CSA-DEGs, CSA-related Hub genes, and CSA-signature genes, revealing their primary involvement in crucial biological processes such as the cell cycle, DNA replication, and the P53 signaling pathway. This indicates that CSA-signature genes may play a pivotal role in meningioma recurrence by regulating these core biological processes. Additionally, Wikipathway enrichment analysis provided a novel perspective, suggesting that CSA-signature genes may be implicated in the radiotherapy resistance mechanisms of recurrent meningiomas, particularly through the ATR pathway [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. These findings offer a systematic view, enhancing our understanding of the vital mechanisms of cellular senescence in meningioma recurrence.\u003c/p\u003e \u003cp\u003eMeningiomas are not constrained by the blood-brain barrier, allowing surrounding immune cells to infiltrate these tumors. The composition of immune infiltration within the tumor microenvironment is intimately linked to tumor progression [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. CIBERSORT analysis has revealed remarkable heterogeneity in immune cell infiltration patterns among meningioma samples, with notable increases in the proportions of neutrophils and M0 macrophages in recurrent meningiomas. These alterations may be associated with an immunosuppressive state within the tumor microenvironment [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].Moreover, CSA-signature genes exhibit a close correlation with changes in immune cell proportions, particularly displaying significant positive correlations with M0 macrophages and Treg cells. This suggests that these genes may play a role in meningioma recurrence by modulating the immune microenvironment, consistent with previous studies [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Intriguingly, CSA-signature genes display negative correlations with immune checkpoint molecules, notably VSIR (V-domain Ig suppressor of T cell activation receptor), a crucial immune checkpoint that plays a pivotal role in regulating immune responses [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. This finding implies that CSA-signature genes may influence tumor immune evasion mechanisms by modulating the expression of immune checkpoint molecules.\u003c/p\u003e \u003cp\u003eThrough this analysis, we have not only uncovered significant differences in immune cell infiltration between primary and recurrent meningiomas but also identified the potential role of CSA-signature genes in the tumor immune microenvironment. These discoveries offer new avenues for further research into meningioma immunotherapy and provide a theoretical foundation for developing therapeutic strategies targeting specific immune cell types and immune checkpoint molecules.\u003c/p\u003e \u003cp\u003eThe identification of four key CSA-signature genes\u0026mdash;CDK1, FOXM1, MYBL2, and BIRC5\u0026mdash;through an elastic net model is a significant finding, as these genes demonstrate exceptional diagnostic performance in distinguishing between primary and recurrent meningiomas, validated in external datasets. Prior research underscores the significance of these genes in meningioma biology: CDK1 is notably associated with meningioma recurrence and biological aggressiveness [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], FOXM1 is linked to the enhancement of meningioma malignant features [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], MYBL2 mutations are correlated with high-grade meningiomas [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], and a NanoString targeted gene expression panel study on meningioma progression highlights the crucial roles of FOXM1, MYBL2, and BIRC5 [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].Further enrichment analysis reveals that these genes are primarily involved in crucial biological processes such as cell cycle transition and DNA damage response, suggesting they may play a pivotal role in meningioma recurrence by regulating cell proliferation and DNA repair. To gain deeper insights into their regulatory mechanisms, we explored potential transcription factors for these CSA-signature genes and identified E2F1 as a candidate. E2F1 is a key transcription factor involved in cell proliferation, differentiation, and apoptosis [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] and is currently considered a potential therapeutic target in various cancers, including colorectal, breast, and gastric cancers [\u003cspan additionalcitationids=\"CR43\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. By analyzing the co-expression relationship between E2F1 and the CSA-signature genes in the two datasets, we ensure the reliability of its regulatory role. This discovery not only enhances our understanding of the molecular mechanisms underlying meningioma recurrence but also opens up new avenues for developing targeted therapies that can potentially disrupt the oncogenic pathways driven by these genes and their regulators.\u003c/p\u003e \u003cp\u003eUtilizing the Enrichr database, we have identified several potential therapeutic drugs. Among them, Dasatinib and Rapamycin have demonstrated robust anti-meningioma activity [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], making them worthy of further investigation and development as clinical treatment options. No relevant reports exist for the other drugs at this time. These candidate drugs offer novel therapeutic strategies with the potential to improve the prognosis for patients with meningiomas.\u003c/p\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003eStudy limitation and Future Prospects\u003c/h2\u003e \u003cp\u003eDespite the insights gained from this study into the potential role of cell aging-related genes in meningioma recurrence, there are still some limitations. Firstly, our analysis is based on publicly available datasets, and future studies are needed to validate our findings in larger, independent cohorts. Secondly, while we have preliminarily identified candidate drugs, further in vivo and in vitro experiments are required to assess their safety and efficacy.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, through systematic analysis, this study has revealed the pivotal role of cellular aging in the recurrence mechanism of meningioma and provided new avenues for future therapeutic strategies. These findings not only deepen our understanding of the recurrence mechanisms of meningioma but also offer potential biomarkers and drug targets for individualized treatment.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eCSA-\u003c/strong\u003e\u003cstrong\u003egenes\u003c/strong\u003e Cellular Senescence-associated Genes\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGEO\u003c/strong\u003e Gene Expression Omnibus.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDEG\u003c/strong\u003e Differentially expressed gene.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGO\u003c/strong\u003e Gene Ontology.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eKEGG\u003c/strong\u003e Kyoto Encyclopedia of Genes and Genomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWGCNA\u003c/strong\u003e Weighted Gene Co-expression Network Analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePPI\u003c/strong\u003e Protein-Protein Interaction.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJHH and CHL analyzed the data and wrote this manuscript. YC, and YBK assisted in analyzing the data and revising the manuscript. JHH, YC critically read and edited the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003eGSE43292: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc= GSE136661\u003c/p\u003e\n\u003cp\u003eGSE163154: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc= GSE173825\u003c/p\u003e\n\u003cp\u003eCell Senescence Database: http://csgene.bioinfo-minzhao.org\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere is no competing interest to be declared by the authors.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGittleman, H. 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EPH receptor signaling as a novel therapeutic target in NF2-deficient meningioma. \u003cem\u003eNeuro Oncol.\u003c/em\u003e \u003cb\u003e20\u003c/b\u003e, 1185\u0026ndash;1196 (2018).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Meningioma recurrence, Immune Infiltration, Elastic Net, Cellular Senescence","lastPublishedDoi":"10.21203/rs.3.rs-5126255/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5126255/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eCellular 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.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe 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.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eCDK1, FOXM1, MYBL2, and BIRC5 emerged as key CSA-genes related to cell cycle and DNA damage. Recurrent meningiomas showed immune heterogeneity, with CSA-genes correlating with immune infiltration and checkpoint molecules. E2F1 was predicted as a regulator. Dasatinib and Rapamycin showed promising anti-meningioma potential.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eOur 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.\u003c/p\u003e","manuscriptTitle":"Dissecting the Role of Cellular Senescence in Meningioma Recurrence: Integrative Bioinformatics and Elastic Network Modeling","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-16 04:01:38","doi":"10.21203/rs.3.rs-5126255/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7f26f77a-ea3f-40c0-9463-22aeef2c4710","owner":[],"postedDate":"December 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":40385773,"name":"Biological sciences/Cancer"},{"id":40385774,"name":"Biological sciences/Cell biology"},{"id":40385775,"name":"Biological sciences/Genetics"},{"id":40385776,"name":"Biological sciences/Neuroscience"},{"id":40385777,"name":"Health sciences/Biomarkers"}],"tags":[],"updatedAt":"2025-02-21T05:53:17+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-16 04:01:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5126255","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5126255","identity":"rs-5126255","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}