Enhancing the Understanding of Ferroptosis Mechanisms in Glioblastoma: An Integrated Approach Utilizing Single-Cell Sequencing and Mendelian Randomization Methods

Enhancing the Understanding of Ferroptosis Mechanisms in Glioblastoma: An Integrated Approach Utilizing Single-Cell Sequencing and Mendelian Randomization Methods | 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 Research Article Enhancing the Understanding of Ferroptosis Mechanisms in Glioblastoma: An Integrated Approach Utilizing Single-Cell Sequencing and Mendelian Randomization Methods Menghao Liu, Wencai Wang, Hui Liu, Zun Wang, Zijie Xiong, Xianfeng Li, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5925744/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 : Ferroptosis, a novel form of regulated cell death, has emerged as a significant research focus due to its involvement in various cancers. This study combines single-cell RNA sequencing with Mendelian randomization (MR) methods to identify genetic factors that are associated with ferroptosis and explore their potential impact on the pathogenesis of glioblastoma (GBM). Methods : MR and multiple validation methods were used to identify ferroptosis-related genes in glioblastoma. Single-cell analysis was performed to evaluate the expression levels of ferroptosis markers across different glioblastoma cell types. Additionally, gene enrichment analysis was conducted to explore gene functions, while survival analysis examined the relationship between gene expression and patient prognosis. Immune cell infiltration analysis was also carried out for genes associated with prognosis. Results : MR and sensitivity analyses identified 9 ferroptosis-related genes that are associated with GBM: SIRT1, KDM3B, VCP, GPT2, PRDX6, CISD2, TP53, FLT3, and FANCD2. Co-localization analysis showed a significant association between the VCP gene and GBM. Single-cell analysis revealed that PRDX6 is highly expressed in tumor tissues. Gene enrichment analysis highlighted the biological processes such as cell metabolism, DNA repair, ubiquitination, and autophagy in the occurrence and progression of GBM. Immune infiltration and survival analyses suggest that CISD2 was related to CD8+ and CD4+ T cell infiltration, and may affect patient prognosis. Conclusions : This study combines MR analysis and single-cell analysis to reveal the crucial role of ferroptosis genes in GBM. These genes influence the occurrence and development of GBM by regulating processes such as metabolism, DNA repair, oxidative stress, and autophagy. Among them, PRDX6 plays an important role in microscopic research, while ubiquitination and autophagy are key drivers of GBM progression. The CISD2 gene may influence patient prognosis by regulating T cell infiltration, thereby promoting immune tolerance mechanisms in tumor cells. Ferroptosis GBM Mendelian Randomization PRDX6 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 6 Figure 7 1. Introduction GBM is the most aggressive primary brain tumor and widely regarded as incurable due to its rapid progression and resistance to conventional therapies[1]. The prognosis for patients with GBM remains poor, with a median survival of approximately 15 months, despite aggressive treatment strategies that include maximal safe surgical resection, adjuvant radiotherapy and concurrent temozolomide chemotherapy [2]. Therefore, there is an urgent need to investigate the mechanisms of GBM development further and find new therapeutic approaches. Ferroptosis is a programmed cell death mechanism closely related to ROS accumulation and lipid peroxidation [3]. Unlike traditional apoptosis or necrosis, the occurrence of ferroptosis depends on the accumulation of intracellular iron and the level of pro-oxidative stress [4]. In recent years, researchers have gradually recognized the importance of ferroptosis in GBM, altered iron metabolism may promote the survival and proliferation of tumor cells thus affecting the prognosis of the disease , and the activation of the ferroptosis pathway may also serve as an alternative apoptotic pathway in GBM, reducing chemotherapeutic drug resistance and providing new drug targets[5] [6].Therefore, revealing a deeper understanding of the molecular mechanisms of ferroptosis in GBM is important for the development of new therapeutic strategies. Our study utilized the MR method, which uses genetic variation as an instrumental variable to study causality [7] . By MR analysis, the influence of confounding factors can be effectively reduced and the causal effect of ferroptosis on GBM can be assessed more accurately. Using single-cell RNA sequencing technology allows us to analyze gene expression profiles at the single-cell level [8]. 2. Materials And Methods 2.1 Exposure data An overview of the study design is shown in Fig.1. In this study, we generated a complete list of 564 human genes associated with ferroptosis from the FerrDb database (zhounan.org/ferrdb/current) (Supplementary Table1) [9]. To further investigate the structure of genes affecting ferroptosis, we used the eQTLGen Consortium repository (https: www.eQTLgen.org/cis-equals) and extracted cis-acting expression quantitative trait loci eQTL close to 1,000 kb of coding sequences of identified genes associated with ferroptosis.[10] Subsequent steps included pinpointing single nucleotide polymorphism (SNP) loci within a 100 kb genomic interval flanking each ferroptosis-related gene. To ensure the accuracy of our findings, we implemented a rigorous quality control program. The protocol involves the following selection criteria: (1) P -value threshold: a stringent P 10 was enforced to avoid weak instrumental variables that could introduce bias in MR estimates; (3) Linkage disequilibrium (LD) pruning: LD pruning was performed to mitigate potential confounding effects caused by LD. This process involves trimming with an r2 threshold of less than 0.1, as recommended for the European cohort of the 1000 Genomes Project. These criteria help to mitigate the effects of weak instrumental variables and ensure the robustness of our genetic associations. Finally, we completed the identification of 370 significant eQTLs for ferroptosis-related genes. Fig.1 Research process overview 2.2 Outcome data We searched the R11 version of the FinnGen database for a dataset titled “Brain GBM” (https://risteys.finngen.fi/endpoints/C3_GBM_EXALLC). The comprehensive dataset consisted of data from 378 individuals diagnosed with GBM, and a control group of 345,118 participants. The sex distribution of the study population was 39% female and 61% male. The median age at first illness was 65.8 years for the entire cohort. 2.3 MR analysis To explore the causal relationship between iron-toxicity-associated eQTL and GBM, we performed a two-sample MR analysis using the “TwoSampleMR” R package version 0.5.11 (https://mrcieu.github.io/TwoSampleMR/index.html) and adopted the Inverse variance weighting (IVW) method to derive the results. We considered P <0.05 as the threshold for statistical significance and applied the false discovery rate (FDR) method with a cutoff of <0.05 to correct for multiple comparisons. 2.4 Sensitivity analysis -horizontal pleiotropy MR - Egger regression analysis was carried out to evaluate horizontal pleiotropy. A significant deviation of the Egger intercept from zero indicates the presence of horizontal pleiotropy, which would potentially disrupt the hypothesis testing of the Mendelian randomization analysis. A p-value greater than 0.05 was considered indicative of no significant horizontal pleiotropy[11]. 2.5 Sensitivity analysis - heterogeneity assessment Heterogeneity was assessed by evaluating the variability among instrumental variables using the Cochran Q statistic[12], as calculated with the MR_heterogeneity function in the TwoSampleMR R package. This statistic helps determine the consistency of the instrumental variable effects across studies. To assess heterogeneity in the IVW (Inverse Variance Weighting) method, we used the following formula = ∑ₖ wₖ (βₖ - β)².If the p-value>0.05, it suggests no significant heterogeneity, thereby supporting the robustness and reliability of the MR results. 2.6 Co-positioning analysis We performed co-localization analysis using the coloc R package (version 5.2.2) to identify genetic variants shared between eQTLs associated with ferrous iron metabolism and GWAS signals in GBM via the coloc.abf function. [13] . This analysis was designed to determine whether a single genetic variant affects both gene expression and disease phenotype, thereby revealing potential causal pathways. 2.7 Single - cell transcriptomic clarification of the ferroptosis-related genes We downloaded the GBM single-cell RNA sequencing dataset GSE27137 from the GEO database, which contains tumor tissue from five patients. The data were processed using the Seurat software package, and quality control was first performed with filtering criteria including (1) gene number per cell greater than 200 and less than 7000; (2) Retain cells with unique molecular identifier count greater than 1000 and remove the top 3% of cells with the highest content. (3) mitochondrial gene expression accounting for less than 10%; and 4) erythrocyte gene expression accounting for less than 3%. Then, 3000 highly variable genes were screened by normalization using the normalize function and downscaled by principal component analysis (PCA). To remove the batch effect, data integration was performed using the Harmony function. Finally, UMAP dimensionality reduction and the Leiden clustering algorithm were applied to cluster the cells, revealing the gene expression characteristics of different cell populations. We downloaded the GBM single-cell RNA sequencing dataset GSE27137 from the GEO database, which contains tumor tissue from five patients. The data were processed using the Seurat software package, and quality control was first performed with filtering criteria including (1) gene number per cell greater than 200 and less than 7000; (2) UMI count greater than 1000 and removal of the top 3% of cells with the highest content; (3) mitochondrial gene expression accounting for less than 10%; and (4) erythrocyte gene expression accounting for less than 3% [14]. Then, 3000 highly variable genes were screened by normalization using the normalize function and downscaled by principal component analysis. To remove the batch effect, data integration was performed using the Harmony algorithm. Finally, the t-SNE downscaling and Leiden clustering algorithm were applied to cluster the cells. Cluster-specific marker genes were identified using the FindAllMarkers function for differential analysis, combined with the CellMarker database (http://bio-bigdata.hrbmu.edu.cn/CellMarker/) to annotate cell types [15]. UMAP maps were generated to visualize the distribution of gene expression patterns. Annotation was performed using specific gene collections: oligodendrocytes (MBP, TFPLP1, MAG, MOG, CLDN11, APOD), endothelial cells (CLDN5, VWF, CD34), macrophages (APOC1, CD163, F13A1), microglia (CX3CR1, P2RY12, P2RY13), neutrophils (IL1R2, CXCR2, FPR2), lymphocytes (CD3D, CD3E, IGHG1, IGHG3, CD79A), dendritic cells (HLA-DQA1, HLA-DPB1), neuroglial/neuronal cells (FABP7, PTPRZ1), and vascular wall cells (RGS5, PDGFRB, NOTCH3). In addition, TP53, MKI67, TOP2A, and cell cycle markers were used to identify possible tumor cells. [16]. 2.8 Survival Analysis The gene expression matrix and survival data of GBM patients were downloaded from the TCGA database, and the gene expression matrix was normalized using the R package limma. Based on the expression levels of CISD2, the samples were categorized into high-expression and low-expression groups. Subsequently, the R package survival was employed to analyze the relationship between CISD2 expression levels and survival time. 2.9 Gene Enrichment Analysis In this study, we utilized GO and KEGG enrichment analysis through the DAVID database (https://david.ncifcrf.gov/home.jsp) to explore the functions and biological significance of key genes, as well as to investigate the interactions and regulatory relationships within biological systems. 2.10 Immune infiltration analysis According to the expression levels of CISD2, 160 GBM samples were divided into high-expression and low-expression groups. Immune cell infiltration analysis was performed using the R package immunedeconv, and box plots and correlation network diagrams were generated to visualize the results[17]. 3. Results 3.1 Mendelian randomization, sensitivity analysis revealed significantly associated ferroptosis-related genes First, we constructed a MR framework by using 370 ferroptosis-related genes as exposure variables and the GBM data from the FinnGen database as outcome variables. MR analysis was performed and found 10 ferroptosis-related genes strongly associated with GBM by the criteria: ( P < 0.05, FDR< 0.05). After assessment of pleiotropy and heterogeneity of these 10 genes, results showed that TRIM26 showed significant heterogeneity ( P 0.05) or heterogeneity ( P >0.05), thus confirming their validity (Table 1, Fig.2). Table 1.MR estimates for the association between ferroptosis genes and GBM. exposure outcome method heterogeneity pleiotropy Q Q_df Q_pval egger_intercept se pval TRIM26 GBM MR Egger 39.644 27 0.055 0.089 0.073 0.229 TRIM26 GBM IVW 41.865 28 0.045 FLT3 GBM MR Egger 2.001 4 0.736 0.123 0.127 0.387 FLT3 GBM IVW 2.940 5 0.709 VCP GBM MR Egger 31.476 41 0.858 -0.011 0.037 0.767 VCP GBM IVW 31.565 42 0.880 KDM3B GBM MR Egger 27.270 51 0.997 -0.007 0.034 0.838 KDM3B GBM IVW 27.312 52 0.998 SIRT1 GBM MR Egger 66.191 96 0.991 -0.018 0.032 0.579 SIRT1 GBM IVW 66.501 97 0.992 GPT2 GBM MR Egger 13.293 16 0.651 -0.012 0.083 0.882 GPT2 GBM IVW 13.316 17 0.715 TP53 GBM MR Egger 0.015 1 0.903 0.366 1.244 0.818 TP53 GBM IVW 0.101 2 0.951 PRDX6 GBM MR Egger 54.275 72 0.941 -0.064 0.045 0.160 PRDX6 GBM IVW 56.291 73 0.926 CISD2 GBM MR Egger 17.415 21 0.686 -0.074 0.063 0.251 CISD2 GBM IVW 18.811 22 0.657 FANCD2 GBM MR Egger 30.297 40 0.867 -0.044 0.036 0.228 FANCD2 GBM IVW 31.798 41 0.848 MR, Mendelian randomization; IVW, inverse variance weighting. Fig.2 Forest plot depicting the relationship between ferroptosis-related genes and GBM outcomes. Of these genes, SIRT1 (OR 0.75; 95% CI 0.70-0.81; FDR value 2.34E-11), VCP (OR 0.55; 95% CI 0.44-0.68; FDR value 2.84E-05), GPT2 (OR 0.43; 95% CI 0.31-0.62; FDR value 1.34E-03), CISD2 (OR 0.49; 95% CI 0.35-0.67; FDR value 3.90E-03), FLT3 (OR 0.15; 95% CI 0.07-0.37; FDR value 9.39E-03), FANCD2 (OR 0.64; 95% CI 0.51-0.80; FDR value 3.51E-02) reduced GBM risk, while genes KDM3B (OR 1.52; 95% CI 1.31-1.76; FDR value 7.57E-06), PRDX6 (OR 1.49; 95% CI 1.27-1.75; FDR value 3.61E-04), TP53 (OR 8.95; 95% CI 3.23-24.80; FDR value 9.29E-03) increased GBM risk (Fig.3). Fig.3 Volcano Plot Display of Effect Size and Significance of Ferroptosis Genes 3.2 Colocalization analysis reveals shared genetic loci Co-localization analysis identifies shared genetic loci. By employing the coloc method for co-localization analysis, we quantified the genetic overlap between GBM-associated and ferroptosis-associated eQTLs. Notably, the posterior probability (PP.H4.abf) for the VCP gene exceeded 0.6, suggesting that it may represent a pathogenic variant associated with GBM. (Table 2, Fig.4). Table 2. Results of colocalization analysis of 9 ferroptosis genes in GBM. id nsnps PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf SIRT1 102 0 0.6337 0 0.0072 0.3591 KDM3B 55 1.28E-234 0.8821 6.18E-237 0.0041 0.1138 VCP 44 1.48E-299 0.3549 1.06E-301 0.0019 0.6432 GPT2 20 4.50E-50 0.8430 1.10E-52 0.0019 0.1551 PRDX6 78 3.03E-222 0.9458 2.31E-224 0.0072 0.0470 CISD2 23 8.57E-111 0.7190 2.57E-113 0.0019 0.2791 TP53 4 7.41E-15 0.6885 7.91E-18 0.0004 0.3111 FLT3 6 2.93E-12 0.8481 2.78E-15 0.0007 0.1513 FANCD2 43 0 0.9186 0 0.0041 0.0773 Fig.4 Co-localization Map of eQTL of Gene VCP and GBM 3.3 Expression distribution of key genes in GBM tissues Through analysis of the single - cell RNA - sequencing data from the GSE162631 dataset [18], we found that 51,575 cells could be clearly classified into 9 different cell sub - populations, namely lymphocytes, monocytes, natural killer cells, neutrophils, plasma cells, platelets, red blood cells and T cells. Using the R package Seurat, we comprehensively processed these single - cell data and visualized them (Fig.5A-B). Subsequently, we constructed UMAP plots for genes related to ferroptosis toxicity (SIRT1, KDM3B, CISD2, VCP, GPT2, PRDX6, TP53, FLT3 and FANCD2). The results showed that the expression levels of FLT3, SIRT1 and KDM3B were relatively low in almost all cell types. In contrast, the expression levels of VCP and PRDX6 were relatively high in all cell types. It is particularly noteworthy that the expression level of CISD2 peaked in tumor - associated macrophages, which was significantly higher than that in other cell types. Figure5. Expression atlas of 9 ferroptosis-related genes in single-cell sequencing of GBM (A) UMAP clustering distribution of GBM cells; (B) UMAP visualization and annotation of GBM cell types; (C) UMAP expression patterns of 9 ferroptosis genes in GBM cells. 3.4 Survival analysis The expression levels of SIRT1, KDM3B, VCP, GPT2, PRDX6, TP53, FLT3 and FANCD2 were not associated with the prognosis of GBM ( P > 0.05). In contrast to the group with high expression levels, a lower expression level of CISD2 was found to prolong the survival time of patients (Fig.6A-I). Fig.6 Survival analysis of 9 ferroptosis genes. (A-I) Survival KM curves for FANCD2, KDM3B, PRDX6, FLT3, SIRT1, VCP, GTP2, TP53, CISD2. 3.5 Enrichment analysis The GO enrichment analysis consisted of three sections (biological process, cellular component, and molecular function) and the bubble plots for each gene show the top 10 significantly enriched functional items (Fig.7A). The top three significant biological process items were (1) organelle disassembly, (2) oxidative stress response, and (3) cellular response to oxidative stress We also found that the most important cellular component (CC) was a characteristic structure within PML virus-infected cells". These vesicles are formed within JC virus-infected glial cells [19]. The top two important molecular functions are ubiquitin-protein ligase binding and ubiquitin-like protein ligase binding. The most important pathways analyzed in KEEG enrichment were mitochondrial autophagy and cellular senescence (Fig.7B). 3.6 Immune infiltration analysis Immune infiltration analysis results showed that under conditions of high CISD2 expression, the infiltration level of CD8+ T cells in tumor samples significantly increases, while the infiltration level of CD4+ T cells in the tumor samples significantly decreases (Fig.7C). In the schematic diagram, red lines represent a negative correlation between gene expression and immune scores, while green lines indicate a positive correlation between the two. The more intense the red or green color, the stronger the correlation between them; the larger the circle, the stronger the correlation (Fig.7D). Fig.7. Biological functional and pathway enrichment analysis of 9 ferroptosis genes and impact of CISD2 gene expression on immune infiltration patterns. (A) GO analysis of 9 ferroptosis genes.; (B) KEGG analysis of 9 ferroptosis genes; (C)Difference in immune cell infiltration between high and low CISD2 gene expression tumor samples; (D) CISD2 gene expression and its correlation network with tumor immune cell infiltration. 4. Discussion MR analysis revealed that nine ferroptosis-related genes were closely associated with GBM, confirming the genetic susceptibility of ferroptosis-related genes in GBM progression [20]. GBM is highly metabolically reprogrammed, in which tumor cells rely on multiple metabolic pathways to maintain rapid proliferation and growth [21]. SIRT1 plays a key role in cellular regulation, and key antioxidant enzymes and DNA repair factors are precisely regulated through deacetylation to enhance cellular antioxidant and DNA repair performance[22].In the event of oxidative stress, it can repair DNA damage in time, stabilize the genome and prevent and delay the process of GBM. At the same time, SIRT1 is deeply involved in regulating mitophagy, accurately guiding the brain and other nervous systems to remove damaged proteins, helping cells remove harmful substances, and ensuring normal function under stress, providing new ideas for exploring the pathogenesis and prevention of GBM [23]. VCP promotes the breakdown of protein aggregates and the removal of dysfunctional organelles, which is particularly important for maintaining cellular homeostasis in cells and organs, especially in the nervous system. These are key events that prevent dysfunction of the brain and other parts of the nervous system[24]. Previous studies have identified VCP/p97 (VCP, valence protein) as a novel regulator of autophagosome biosynthesis, in which VCP regulates the induction of autophagy in two ways. Autophagy is a key cellular process for the removal of harmful protein material, and upregulation of autophagy may help protect against neurodegenerative diseases [25].The results of colocalization analysis confirmed the shared genetic pattern between GBM and ferroptosis, further emphasizing the interweaving of the genetic framework of GBM with the mechanism of ferroptosis [26]. The mono-ubiquitination of Fancd2 is crucial for repairing DNA interstrand crosslinks. The ubiquitinated Fancd2 recruits the Fan1 nuclease to stalled replication forks, enabling crosslink repair, preventing chromosomal abnormalities, and playing a broader role in cancer prevention. [27]. All of these mechanisms may reduce the occurrence of GBM. High expression of PRDX6 is associated with poor prognosis in glioma. PRDX1 and PRDX6 were positively correlated with different immune cell populations in low-grade gliomas and to a lesser extent in GBM [28].Several studies have shown that the expression of CISD2 is associated with the clinical characteristics of aging-related diseases and cancer patients, and is considered to be a novel biomarker for the diagnosis and prognosis of human diseases. Regulating the expression or function of CISD2 may be a potential strategy for the treatment of different diseases [29]. At first glance, the results of the survival analysis seem to be contrary to those of the MR analysis. However, the essential difference lies in the OR and the hazard ratio (HR). The HR represents the relative risk considering the time factor, while the OR is related to the probability ratio of the occurrence of GBM. The survival analysis examines the correlation between genes and survival rates, whereas the MR assesses the causal relationship of disease onset. A plausible explanation is that the high expression of CISD2 prior to the onset of GBM reduces the risk of GBM occurrence, but the persistent high expression after disease onset is associated with a poor prognosis of GBM. Therefore, these two analytical methods do not lead to contradictory conclusions. Instead, this may reflect the differences between the two methods as well as those between the observed data and the hypothesized data [30]. Inhibitory receptors are key regulators of immune cell function. In the immune infiltration analysis of CISD2, GBM cells may leverage changes in the expression of CISD2 to regulate their own antigen presentation mechanisms. On one hand, CISD2 may influence the expression or function of proteins involved in antigen processing and presentation within tumor cells, leading to alterations in the formation and presentation of tumor antigen peptide-MHC class I complexes. This, in turn, may affect the recognition and killing of tumor cells by CD8+ T cells. On the other hand, the negative regulation of CD4+ T cells by CISD2 could impair the immune system's ability to recognize and process tumor antigens, making it easier for tumor cells to evade immune surveillance. This ultimately results in reduced antitumor activity and negatively impacts the patient's prognosis [31]. 5. Limitations Although a variety of methods were used in this study, differences in ethnic and geographic factors may limit the applicability of the results to differences in ethnic and geographic factors may limit the applicability of the results to populations in other regions, as the sample of the MR study was mainly from the Nordic population. In addition, the dual effects of gene expression, the complex interaction between genes and the environment, and the choice of different analytical methods may lead to some inconsistency in the research conclusions. Therefore, studies should combine broader clinical data, in-depth molecular mechanism elucidation, and animal models to achieve the same results in the future. Therefore, future studies should combine broader clinical data, in-depth molecular mechanisms, elucidation, and animal models to further explore the biological basis and clinical relevance of these observations. 6. Conclusion In this study, MR and single-cell analysis were combined to reveal the role of ferroptosis-related genes in the development and progression of GBM. These genes may affect the occurrence and progression of GBM by regulating metabolism, DNA repair, oxidative stress response, and autophagy, which provides a new perspective for understanding the molecular basis of GBM. Abbreviations CC cellular component; FDR false discovery rate; HR hazard ratio; IVW inverse variance weighting; LD linkage disequilibrium; MR mendelian randomization; PCA principal component analysis; SNP single nucleotide polymorphism. Declarations Acknowledgments We thank the investigators and participants of the original GWAS. We are grateful for all GWAS sharing summary data used in this study. Statement of Ethics Ethical approval and consent are not required for this study in accordance with local or national guidelines. Conflict of Interest Statement The authors declare that they have no competing interests. Funding Sources: NO Funding. Author Contributions Menghao Liu, Wencai Wang, Haicheng Yang, and Xianfeng Li: designed the research; Menghao Liu, Wencai Wang and Hui Liu: collected and organized data; Menghao Liu, and Zun Wang: analyzed the data; Wencai Wang, Menghao Liu, Zun Wang, Hui Liu, and Zijie Xiong: drafted the manuscript; Menghao Liu, Wencai Wang, Haicheng Yang, and Xianfeng Li: contributed to the critical revision of the manuscript. All authors contributed to the manuscript and approved the submitted version. Supplementary Materials Supplementary Table 1: List of ferroptosis-related genes The detailed list of 564 ferroptosis-related genes is available in the attached file "Attachment_S1.xlsx". To access this attachment, please [describe the access method, e.g., contact the corresponding author or download from the paper - associated website]. 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Identification of drug targets for Sjogren's syndrome: multi-omics Mendelian randomization and colocalization analyses. Front Immunol. 2024;15:1419363. 10.3389/fimmu.2024.1419363 . Lachaud C, Moreno A, Marchesi F, Toth R, Blow JJ, Rouse J. Ubiquitinated Fancd2 recruits Fan1 to stalled replication forks to prevent genome instability. Science. 2016;351(6275):846–9. 10.1126/science.aad5634 . Pankiewicz JE, Diaz JR, Marta-Ariza M, Lizinczyk AM, Franco LA, Sadowski MJ. Peroxiredoxin 6 mediates protective function of astrocytes in Abeta proteostasis. Mol Neurodegener. 2020;15(1):50. 10.1186/s13024-020-00401-8 . Liao HY, Liao B, Zhang HH. CISD2 plays a role in age-related diseases and cancer. Biomed Pharmacother. 2021;138:111472. 10.1016/j.biopha.2021.111472 . Liang J, Meng Q, Zhao W, Tong P, Li P, Zhao Y, et al. An expression based REST signature predicts patient survival and therapeutic response for glioblastoma multiforme. Sci Rep. 2016;6:34556. 10.1038/srep34556 . Ahmady F, Curpen P, Perriman L, Fonseca Teixeira A, Wu S, Zhu H-J, et al. Reduced T and NK Cell Activity in Glioblastoma Patients Correlates with TIM-3 and BAT3 Dysregulation. Cells. 2024;13(21). 10.3390/cells13211777 . Additional Declarations No competing interests reported. Supplementary Files AttachmentS1.xlsx 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-5925744","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":415139376,"identity":"36d5e179-452f-4d5c-9eb6-d5c4a8255259","order_by":0,"name":"Menghao Liu","email":"","orcid":"","institution":"The Second Affiliated Hospital of Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Menghao","middleName":"","lastName":"Liu","suffix":""},{"id":415139378,"identity":"b46f9b17-2785-47b6-93f6-7abe91d9d50a","order_by":1,"name":"Wencai Wang","email":"","orcid":"","institution":"The Second Affiliated Hospital of Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Wencai","middleName":"","lastName":"Wang","suffix":""},{"id":415139379,"identity":"ee55781b-1fe3-4626-be8b-4009b8116a1d","order_by":2,"name":"Hui Liu","email":"","orcid":"","institution":"The Second Affiliated Hospital of Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Hui","middleName":"","lastName":"Liu","suffix":""},{"id":415139380,"identity":"d46a3ef5-c667-429f-9023-ea8e8cc55947","order_by":3,"name":"Zun Wang","email":"","orcid":"","institution":"The Second Affiliated Hospital of Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zun","middleName":"","lastName":"Wang","suffix":""},{"id":415139381,"identity":"0eb675df-e49d-4dab-a7e1-5c833e60c1d8","order_by":4,"name":"Zijie Xiong","email":"","orcid":"","institution":"The Second Affiliated Hospital of Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zijie","middleName":"","lastName":"Xiong","suffix":""},{"id":415139382,"identity":"deb336cd-e634-4985-afa2-0cde5f0e47e7","order_by":5,"name":"Xianfeng Li","email":"","orcid":"","institution":"The Second Affiliated Hospital of Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xianfeng","middleName":"","lastName":"Li","suffix":""},{"id":415139383,"identity":"b8b6423a-cdd0-4e05-bf55-cfcde825965b","order_by":6,"name":"Haicheng Yang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2ElEQVRIiWNgGAWjYBACNvbmgw8//JCoZ+NvPvggoaKGsBY+nmPJxpI9Ngn8EseSDR6cOUZYi5yEj5oED1tagmRDjprkwxZmIhwmwcNsIMFzOM/gwBm2isQGNgb+9u4E/Fqkew8+KLA4XGxwuPfYjcQdMgwSZ85uwK9F5lwyyBbGDQfOpd1IPMPGYCCRS0CLRI4Z0C8gLTlmBYltzERrSUuc2ZBjxkCcFmggG4MCWSLhzDEegn6Rb4dEpRwoKj/+qKiR42/vxa8FA/CQpnwUjIJRMApGAVYAAFC5TH9qf/1DAAAAAElFTkSuQmCC","orcid":"","institution":"The Second Affiliated Hospital of Harbin Medical University","correspondingAuthor":true,"prefix":"","firstName":"Haicheng","middleName":"","lastName":"Yang","suffix":""}],"badges":[],"createdAt":"2025-01-29 17:08:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5925744/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5925744/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":77014410,"identity":"9fecd01e-d082-4d63-8eac-2d0f3d7653e8","added_by":"auto","created_at":"2025-02-24 09:51:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":344715,"visible":true,"origin":"","legend":"\u003cp\u003eResearch process overview\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5925744/v1/4c2c45b0520d573c6ec536d9.png"},{"id":77018046,"identity":"5bc76454-fcd5-41ee-8e6e-964ed98a50be","added_by":"auto","created_at":"2025-02-24 10:23:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":959731,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot depicting the relationship between ferroptosis-related genes and GBM outcomes.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5925744/v1/ec451eb3ba678989cd884f96.png"},{"id":77015983,"identity":"ff30788a-41d8-4bfb-b490-079023dc2466","added_by":"auto","created_at":"2025-02-24 09:59:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":218810,"visible":true,"origin":"","legend":"\u003cp\u003eVolcano Plot Display of Effect Size and Significance of Ferroptosis Genes\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5925744/v1/947b25b3e930c10669e53f27.png"},{"id":77015988,"identity":"26c08f67-c493-4670-830e-f9e56e13cd96","added_by":"auto","created_at":"2025-02-24 09:59:32","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":161018,"visible":true,"origin":"","legend":"\u003cp\u003eCo-localization Map of eQTL of Gene VCP and GBM\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-5925744/v1/124389e0d9f5fba7c10c33cb.png"},{"id":77017549,"identity":"2daf8c20-b6a5-478f-81f1-a405b0e2c7e3","added_by":"auto","created_at":"2025-02-24 10:15:32","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":926569,"visible":true,"origin":"","legend":"\u003cp\u003eSurvival analysis of 9 ferroptosis genes. (A-I) Survival KM curves for FANCD2, KDM3B, PRDX6, FLT3, SIRT1, VCP, GTP2, TP53, CISD2.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-5925744/v1/99b3347c41164126ad9cefcf.png"},{"id":77014418,"identity":"9bd42a4d-b270-48f6-bab7-acda90ae7a99","added_by":"auto","created_at":"2025-02-24 09:51:32","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":221302,"visible":true,"origin":"","legend":"\u003cp\u003eBiological functional and pathway enrichment analysis of 9 ferroptosis genes and impact of CISD2 gene expression on immune infiltration patterns. (A) GO analysis of 9 ferroptosis genes.; (B) KEGG analysis of 9 ferroptosis genes; (C)Difference in immune cell infiltration between high and low CISD2 gene expression tumor samples; (D) CISD2 gene expression and its correlation network with tumor immune cell infiltration.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-5925744/v1/79e332d768efc98c4bf7039a.png"},{"id":82804074,"identity":"fadd2939-e36f-413e-a4b8-6bb7ac7e445e","added_by":"auto","created_at":"2025-05-15 12:01:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3697318,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5925744/v1/946c388f-f526-4e6c-88d4-68905d1e60eb.pdf"},{"id":77014406,"identity":"32ae2e09-8116-47ae-b117-94931e4242f7","added_by":"auto","created_at":"2025-02-24 09:51:31","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":17235,"visible":true,"origin":"","legend":"","description":"","filename":"AttachmentS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5925744/v1/2f590d68fc8e98a69b2355eb.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Enhancing the Understanding of Ferroptosis Mechanisms in Glioblastoma: An Integrated Approach Utilizing Single-Cell Sequencing and Mendelian Randomization Methods","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eGBM is the most aggressive primary brain tumor and widely regarded as incurable due to its rapid progression and resistance to conventional therapies[1]. The prognosis for patients with GBM remains poor, with a median survival of approximately 15 months, despite aggressive treatment strategies that include maximal safe surgical resection, adjuvant radiotherapy and concurrent temozolomide \u0026nbsp;chemotherapy [2]. Therefore, there is an urgent need to investigate the mechanisms of GBM development further and find new therapeutic approaches.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFerroptosis is a programmed cell death mechanism closely related to ROS accumulation and lipid peroxidation\u0026nbsp;[3]. Unlike traditional apoptosis or necrosis, the occurrence of ferroptosis depends on the accumulation of intracellular iron and the level of pro-oxidative stress\u0026nbsp;[4]. In recent years, researchers have gradually recognized the importance of ferroptosis in GBM, altered iron metabolism may promote the survival and proliferation of tumor cells thus affecting the prognosis of the disease , and the activation of the ferroptosis pathway may also serve as an alternative apoptotic pathway in GBM, reducing chemotherapeutic drug resistance and providing new drug targets[5]\u0026nbsp;[6].Therefore, revealing a deeper understanding of the molecular mechanisms of ferroptosis in GBM is important for the development of new therapeutic strategies.\u003c/p\u003e\n\u003cp\u003eOur study utilized the MR method, which uses genetic variation as an instrumental variable to study causality [7] . By MR analysis, the influence of confounding factors can be effectively reduced and the causal effect of ferroptosis on GBM can be assessed more accurately. Using single-cell RNA sequencing technology allows us to analyze gene expression profiles at the single-cell level [8].\u003c/p\u003e"},{"header":"2. Materials And Methods","content":"\u003cp\u003e\u003cstrong\u003e2.1 Exposure data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAn overview of the study design is shown in Fig.1. In this study, we generated a complete list of 564 human genes associated with ferroptosis from the FerrDb database (zhounan.org/ferrdb/current) (Supplementary Table1) [9]. To further investigate the structure of genes affecting ferroptosis, we used the eQTLGen Consortium repository (https: www.eQTLgen.org/cis-equals) and extracted cis-acting expression quantitative trait loci eQTL close to 1,000 kb of coding sequences of identified genes associated with ferroptosis.[10] Subsequent steps included pinpointing single nucleotide polymorphism (SNP) loci within a 100 kb genomic interval flanking each ferroptosis-related gene. To ensure the accuracy of our findings, we implemented a rigorous quality control program. The protocol involves the following selection criteria: (1) \u003cem\u003eP\u003c/em\u003e-value threshold: a stringent \u003cem\u003eP\u003c/em\u003e \u0026lt; 1 \u0026times; 10\u003csup\u003e-5\u0026nbsp;\u003c/sup\u003ewas applied to the SNP-gene associations to improve the reliability of the identified eQTLs; (2) F-statistics: A minimum F statistic\u0026gt;10 was enforced to avoid weak instrumental variables that could introduce bias in MR estimates; (3) Linkage disequilibrium (LD) pruning: LD pruning was performed to mitigate potential confounding effects caused by LD. This process involves trimming with an r2 threshold of less than 0.1, as recommended for the European cohort of the 1000 Genomes Project. These criteria help to mitigate the effects of weak instrumental variables and ensure the robustness of our genetic associations. Finally, we completed the identification of 370 significant eQTLs for\u0026nbsp;ferroptosis-related genes.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Fig.1 Research process overview \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Outcome data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe searched the R11 version of the FinnGen database for a dataset titled \u0026ldquo;Brain GBM\u0026rdquo;\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e(https://risteys.finngen.fi/endpoints/C3_GBM_EXALLC). The comprehensive dataset consisted of data from 378 individuals diagnosed with GBM, and a control group of 345,118 participants. The sex distribution of the study population was 39% female and 61% male. The median age at first illness was 65.8 years for the entire cohort.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 MR analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo explore the causal relationship between iron-toxicity-associated eQTL and GBM, we performed a two-sample MR analysis using the \u0026ldquo;TwoSampleMR\u0026rdquo; R package version 0.5.11 (https://mrcieu.github.io/TwoSampleMR/index.html) and adopted the Inverse variance weighting (IVW) method to derive the results. We considered \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt;0.05 as the threshold for statistical significance and applied the false discovery rate (FDR) method with a cutoff of \u0026lt;0.05 to correct for multiple comparisons.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Sensitivity analysis -horizontal pleiotropy\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMR - Egger regression analysis was carried out to evaluate horizontal pleiotropy. A significant deviation of the Egger intercept from zero indicates the presence of horizontal pleiotropy, which would potentially disrupt the hypothesis testing of the Mendelian randomization analysis. A p-value greater than 0.05 was considered indicative of no significant horizontal pleiotropy[11].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5 Sensitivity analysis - heterogeneity assessment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHeterogeneity was assessed by evaluating the variability among instrumental variables using the Cochran Q statistic[12], as calculated with the MR_heterogeneity function in the TwoSampleMR R package. This statistic helps determine the consistency of the instrumental variable effects across studies. To assess heterogeneity in the IVW (Inverse Variance Weighting) method, we used the following formula = \u0026sum;ₖ wₖ (\u0026beta;ₖ - \u0026beta;)\u0026sup2;.If the p-value\u0026gt;0.05, it suggests no significant heterogeneity, thereby supporting the robustness and reliability of the MR results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6 Co-positioning analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe performed co-localization analysis using the coloc R package (version 5.2.2) to identify genetic variants shared between eQTLs associated with ferrous iron metabolism and GWAS signals in GBM via the coloc.abf function.\u0026nbsp;[13] . This analysis was designed to determine whether a single genetic variant affects both gene expression and disease phenotype, thereby revealing potential causal pathways.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.7 Single - cell transcriptomic clarification of the ferroptosis-related genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe downloaded the GBM single-cell RNA sequencing dataset GSE27137 from the GEO database, which contains tumor tissue from five patients. The data were processed using the Seurat software package, and quality control was first performed with filtering criteria including (1) gene number per cell greater than 200 and less than 7000; (2) Retain cells with unique molecular identifier count greater than 1000 and remove the top 3% of cells with the highest content. (3) mitochondrial gene expression accounting for less than 10%; and 4) erythrocyte gene expression accounting for less than 3%. Then, 3000 highly variable genes were screened by normalization using the normalize function and downscaled by principal component analysis (PCA). To remove the batch effect, data integration was performed using the Harmony function. Finally, UMAP dimensionality reduction and the Leiden clustering algorithm were applied to cluster the cells, revealing the gene expression characteristics of different cell populations. We downloaded the GBM single-cell RNA sequencing dataset GSE27137 from the GEO database, which contains tumor tissue from five patients. The data were processed using the Seurat software package, and quality control was first performed with filtering criteria including (1) gene number per cell greater than 200 and less than 7000; (2) UMI count greater than 1000 and removal of the top 3% of cells with the highest content; (3) mitochondrial gene expression accounting for less than 10%; and (4) erythrocyte gene expression accounting for less than 3%\u0026nbsp;[14]. Then, 3000 highly variable genes were screened by normalization using the normalize function and downscaled by principal component analysis. To remove the batch effect, data integration was performed using the Harmony algorithm. Finally, the t-SNE downscaling and Leiden clustering algorithm were applied to cluster the cells.\u003c/p\u003e\n\u003cp\u003eCluster-specific marker genes were identified using the FindAllMarkers function for differential analysis, combined with the CellMarker database (http://bio-bigdata.hrbmu.edu.cn/CellMarker/) \u0026nbsp;to annotate cell types\u0026nbsp;[15]. UMAP maps were generated to visualize the distribution of gene expression patterns. Annotation was performed using specific gene collections: oligodendrocytes (MBP, TFPLP1, MAG, MOG, CLDN11, APOD), endothelial cells (CLDN5, VWF, CD34), macrophages (APOC1, CD163, F13A1), microglia (CX3CR1, P2RY12, P2RY13), neutrophils (IL1R2, CXCR2, FPR2), lymphocytes (CD3D, CD3E, IGHG1, IGHG3, CD79A), dendritic cells (HLA-DQA1, HLA-DPB1), neuroglial/neuronal cells (FABP7, PTPRZ1), and vascular wall cells (RGS5, PDGFRB, NOTCH3). In addition, TP53, MKI67, TOP2A, and cell cycle markers were used to identify possible tumor cells.\u0026nbsp;[16].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.8 Survival Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe gene expression matrix and survival data of GBM patients were downloaded from the TCGA database, and the gene expression matrix was normalized using the R package limma. Based on the expression levels of CISD2, the samples were categorized into high-expression and low-expression groups. Subsequently, the R package survival was employed to analyze the relationship between CISD2 expression levels and survival time.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.9 Gene Enrichment Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, we utilized GO and KEGG enrichment analysis through the DAVID database (https://david.ncifcrf.gov/home.jsp) to explore the functions and biological significance of key genes, as well as to investigate the interactions and regulatory relationships within biological systems.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.10 Immune infiltration analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to the expression levels of CISD2, 160 GBM samples were divided into high-expression and low-expression groups. Immune cell infiltration analysis was performed using the R package immunedeconv, and box plots and correlation network diagrams were generated to visualize the results[17].\u0026nbsp;\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 Mendelian randomization, sensitivity analysis revealed significantly associated ferroptosis-related genes\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFirst, we constructed a MR framework by using 370 ferroptosis-related genes as exposure variables and the GBM data from the FinnGen database as outcome variables. MR analysis was performed and found 10 ferroptosis-related genes strongly associated with GBM by the criteria: (\u003cem\u003eP\u003c/em\u003e\u0026lt; 0.05, FDR\u0026lt; 0.05). After assessment of pleiotropy and heterogeneity of these 10 genes, results showed that TRIM26 showed significant heterogeneity (\u003cem\u003eP\u003c/em\u003e\u0026lt; 0.05), and the remaining 9 genes strongly associated with GBM did not show significant pleiotropy (\u003cem\u003eP\u003c/em\u003e\u0026gt;0.05) or heterogeneity (\u003cem\u003eP\u003c/em\u003e\u0026gt;0.05), thus confirming their validity (Table 1, Fig.2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 1.MR estimates for the association between ferroptosis genes and GBM.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eexposure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eoutcome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003emethod\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003eheterogeneity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003epleiotropy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eQ_df\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eQ_pval\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eegger_intercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ese\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003epval\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTRIM26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGBM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e39.644\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.055\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.089\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.073\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.229\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTRIM26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGBM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e41.865\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.045\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFLT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGBM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.001\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.736\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.123\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.127\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.387\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFLT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGBM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.940\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.709\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eVCP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGBM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e31.476\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.858\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.011\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.037\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.767\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eVCP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGBM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e31.565\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.880\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eKDM3B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGBM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e27.270\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.997\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.007\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.034\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.838\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eKDM3B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGBM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e27.312\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.998\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSIRT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGBM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e66.191\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.991\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.018\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.032\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.579\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSIRT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGBM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e66.501\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.992\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGPT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGBM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13.293\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.651\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.012\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.083\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.882\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGPT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGBM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13.316\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.715\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTP53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGBM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.015\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.903\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.366\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.244\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.818\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTP53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGBM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.101\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.951\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePRDX6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGBM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e54.275\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.941\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.064\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.045\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.160\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePRDX6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGBM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e56.291\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.926\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCISD2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGBM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e17.415\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.686\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.074\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.063\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.251\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCISD2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGBM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18.811\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.657\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFANCD2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGBM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e30.297\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.867\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.044\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.036\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.228\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFANCD2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGBM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e31.798\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.848\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eMR, Mendelian randomization; IVW, inverse variance weighting.\u003c/p\u003e\n\u003cp\u003eFig.2 Forest plot depicting the relationship between ferroptosis-related genes and GBM outcomes.\u003c/p\u003e\n\u003cp\u003eOf these genes, SIRT1 (OR 0.75; 95% CI 0.70-0.81; FDR value 2.34E-11), VCP (OR 0.55; 95% CI 0.44-0.68; FDR value 2.84E-05), GPT2 (OR 0.43; 95% CI 0.31-0.62; FDR value 1.34E-03), CISD2 (OR 0.49; 95% CI 0.35-0.67; FDR value 3.90E-03), FLT3 (OR 0.15; 95% CI 0.07-0.37; FDR value 9.39E-03), FANCD2 (OR 0.64; 95% CI 0.51-0.80; FDR value 3.51E-02) reduced GBM risk, while genes KDM3B (OR 1.52; 95% CI 1.31-1.76; FDR value 7.57E-06), PRDX6 (OR 1.49; 95% CI 1.27-1.75; FDR value 3.61E-04), TP53 (OR 8.95; 95% CI 3.23-24.80; FDR value 9.29E-03) increased GBM risk (Fig.3).\u003c/p\u003e\n\u003cp\u003eFig.3 Volcano Plot Display of Effect Size and Significance of Ferroptosis Genes\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Colocalization analysis reveals shared genetic loci\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCo-localization analysis identifies shared genetic loci. By employing the coloc method for co-localization analysis, we quantified the genetic overlap between GBM-associated and ferroptosis-associated eQTLs. Notably, the posterior probability (PP.H4.abf) for the VCP gene exceeded 0.6, suggesting that it may represent a pathogenic variant associated with GBM. (Table 2, Fig.4).\u003c/p\u003e\n\u003cp\u003eTable 2. Results of colocalization analysis of 9 ferroptosis genes in GBM.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"588\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003eid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 44px;\"\u003e\n \u003cp\u003ensnps\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003ePP.H0.abf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003ePP.H1.abf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003ePP.H2.abf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003ePP.H3.abf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp; PP.H4.abf\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003eSIRT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 44px;\"\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.6337\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.0072\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.3591\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003eKDM3B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 44px;\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003e1.28E-234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.8821\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e6.18E-237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.0041\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.1138\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003eVCP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 44px;\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003e1.48E-299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.3549\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e1.06E-301\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.0019\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.6432\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003eGPT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 44px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003e4.50E-50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.8430\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e1.10E-52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.0019\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.1551\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003ePRDX6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 44px;\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003e3.03E-222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.9458\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e2.31E-224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.0072\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.0470\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003eCISD2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 44px;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003e8.57E-111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.7190\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e2.57E-113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.0019\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.2791\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003eTP53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 44px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003e7.41E-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.6885\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e7.91E-18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.0004\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.3111\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003eFLT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 44px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003e2.93E-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.8481\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e2.78E-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.0007\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.1513\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003eFANCD2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 44px;\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.9186\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.0041\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.0773\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;Fig.4 Co-localization Map of eQTL of Gene VCP and GBM\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Expression distribution of key genes in GBM tissues\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThrough analysis of the single - cell RNA - sequencing data from the GSE162631 dataset [18], we found that 51,575 cells could be clearly classified into 9 different cell sub - populations, namely lymphocytes, monocytes, natural killer cells, neutrophils, plasma cells, platelets, red blood cells and T cells. Using the R package Seurat, we comprehensively processed these single - cell data and visualized them (Fig.5A-B). Subsequently, we constructed UMAP plots for genes related to ferroptosis toxicity (SIRT1, KDM3B, CISD2, VCP, GPT2, PRDX6, TP53, FLT3 and FANCD2). The results showed that the expression levels of FLT3, SIRT1 and KDM3B were relatively low in almost all cell types. In contrast, the expression levels of VCP and PRDX6 were relatively high in all cell types. It is particularly noteworthy that the expression level of CISD2 peaked in tumor - associated macrophages, which was significantly higher than that in other cell types.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFigure5. Expression atlas of 9 ferroptosis-related genes in single-cell sequencing of GBM (A)\u0026nbsp;UMAP clustering distribution of GBM cells; (B)\u0026nbsp;UMAP visualization and annotation of GBM cell types; (C)\u0026nbsp;UMAP expression patterns of 9 ferroptosis genes in GBM cells.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Survival analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe expression levels of SIRT1, KDM3B, VCP, GPT2, PRDX6, TP53, FLT3 and FANCD2 were not associated with the prognosis of GBM (\u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05). In contrast to the group with high expression levels, a lower expression level of CISD2 was found to prolong the survival time of patients (Fig.6A-I).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Fig.6 Survival analysis of 9 ferroptosis genes. (A-I) Survival KM curves for FANCD2, KDM3B, PRDX6, FLT3, SIRT1, VCP, GTP2, TP53, CISD2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5 Enrichment analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe GO enrichment analysis consisted of three sections (biological process, cellular component, and molecular function) and the bubble plots for each gene show the top 10 significantly enriched functional items (Fig.7A). The top three significant biological process items were (1) organelle disassembly, (2) oxidative stress response, and (3) cellular response to oxidative stress We also found that the most important cellular component (CC) was a characteristic structure within PML virus-infected cells\u0026quot;. These vesicles are formed within JC virus-infected glial cells\u0026nbsp;[19]. The top two important molecular functions are ubiquitin-protein ligase binding and ubiquitin-like protein ligase binding. The most important pathways analyzed in KEEG enrichment were mitochondrial autophagy and cellular senescence (Fig.7B).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.6 Immune infiltration analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eImmune infiltration analysis results showed that under conditions of high CISD2 expression, the infiltration level of CD8+ T cells in tumor samples significantly increases, while the infiltration level of CD4+ T cells in the tumor samples significantly decreases (Fig.7C). In the schematic diagram, red lines represent a negative correlation between gene expression and immune scores, while green lines indicate a positive correlation between the two. The more intense the red or green color, the stronger the correlation between them; the larger the circle, the stronger the correlation (Fig.7D).\u003c/p\u003e\n\u003cp\u003eFig.7. Biological functional and pathway enrichment analysis of 9 ferroptosis genes and impact of CISD2 gene expression on immune infiltration patterns. (A) GO analysis of 9 ferroptosis genes.; (B) KEGG analysis of 9 ferroptosis genes; (C)Difference in immune cell infiltration between high and low CISD2 gene expression tumor samples; (D) CISD2 gene expression and its correlation network with tumor immune cell infiltration.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eMR analysis revealed that nine ferroptosis-related genes were closely associated with GBM, confirming the genetic susceptibility of ferroptosis-related genes in GBM progression\u0026nbsp;[20]. GBM is highly metabolically reprogrammed, in which tumor cells rely on multiple metabolic pathways to maintain rapid proliferation and growth\u0026nbsp;[21]. SIRT1 plays a key role in cellular regulation, and key antioxidant enzymes and DNA repair factors are precisely regulated through deacetylation to enhance cellular antioxidant and DNA repair performance[22].In the event of oxidative stress, it can repair DNA damage in time, stabilize the genome and prevent and delay the process of GBM. At the same time, SIRT1 is deeply involved in regulating mitophagy, accurately guiding the brain and other nervous systems to remove damaged proteins, helping cells remove harmful substances, and ensuring normal function under stress, providing new ideas for exploring the pathogenesis and prevention of GBM\u0026nbsp;[23]. VCP promotes the breakdown of protein aggregates and the removal of dysfunctional organelles, which is particularly important for maintaining cellular homeostasis in cells and organs, especially in the nervous system. These are key events that prevent dysfunction of the brain and other parts of the nervous system[24].\u0026nbsp;Previous studies have identified VCP/p97 (VCP, valence protein) as a novel regulator of autophagosome biosynthesis, in which VCP regulates the induction of autophagy in two ways. Autophagy is a key cellular process for the removal of harmful protein material, and upregulation of autophagy may help protect against neurodegenerative diseases\u0026nbsp;[25].The results of colocalization analysis confirmed the shared genetic pattern between GBM and ferroptosis, further emphasizing the interweaving of the genetic framework of GBM with the mechanism of ferroptosis\u0026nbsp;[26].\u0026nbsp;The mono-ubiquitination of Fancd2 is crucial for repairing DNA interstrand crosslinks. The ubiquitinated Fancd2 recruits the Fan1 nuclease to stalled replication forks, enabling crosslink repair, preventing chromosomal abnormalities, and playing a broader role in cancer prevention.\u0026nbsp;[27].\u0026nbsp;All of these mechanisms may reduce the occurrence of GBM. High expression of PRDX6 is associated with poor prognosis in glioma. PRDX1 and PRDX6 were positively correlated with different immune cell populations in low-grade gliomas and to a lesser extent in GBM\u0026nbsp;[28].Several studies have shown that the expression of CISD2 is associated with the clinical characteristics of aging-related diseases and cancer patients, and is considered to be a novel biomarker for the diagnosis and prognosis of human diseases. Regulating the expression or function of CISD2 may be a potential strategy for the treatment of different diseases\u0026nbsp;[29].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAt first glance, the results of the survival analysis seem to be contrary to those of the MR analysis. However, the essential difference lies in the OR and the hazard ratio (HR). The HR represents the relative risk considering the time factor, while the OR is related to the probability ratio of the occurrence of GBM. The survival analysis examines the correlation between genes and survival rates, whereas the MR assesses the causal relationship of disease onset. A plausible explanation is that the high expression of CISD2 prior to the onset of GBM reduces the risk of GBM occurrence, but the persistent high expression after disease onset is associated with a poor prognosis of GBM. Therefore, these two analytical methods do not lead to contradictory conclusions. Instead, this may reflect the differences between the two methods as well as those between the observed data and the hypothesized data [30]. Inhibitory receptors are key regulators of immune cell function. In the immune infiltration analysis of CISD2, GBM cells may leverage changes in the expression of CISD2 to regulate their own antigen presentation mechanisms. On one hand, CISD2 may influence the expression or function of proteins involved in antigen processing and presentation within tumor cells, leading to alterations in the formation and presentation of tumor antigen peptide-MHC class I complexes. This, in turn, may affect the recognition and killing of tumor cells by CD8+ T cells. On the other hand, the negative regulation of CD4+ T cells by CISD2 could impair the immune system's ability to recognize and process tumor antigens, making it easier for tumor cells to evade immune surveillance. This ultimately results in reduced antitumor activity and negatively impacts the patient's prognosis [31].\u0026nbsp;\u003c/p\u003e"},{"header":"5. Limitations","content":"\u003cp\u003eAlthough a variety of methods were used in this study, differences in ethnic and geographic factors may limit the applicability of the results to differences in ethnic and geographic factors may limit the applicability of the results to populations in other regions, as the sample of the MR study was mainly from the Nordic population. In addition, the dual effects of gene expression, the complex interaction between genes and the environment, and the choice of different analytical methods may lead to some inconsistency in the research conclusions. Therefore, studies should combine broader clinical data, in-depth molecular mechanism elucidation, and animal models to achieve the same results in the future. Therefore, future studies should combine broader clinical data, in-depth molecular mechanisms, elucidation, and animal models to further explore the biological basis and clinical relevance of these observations.\u003c/p\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eIn this study, MR and single-cell analysis were combined to reveal the role of ferroptosis-related genes in the development and progression of GBM. These genes may affect the occurrence and progression of GBM by regulating metabolism, DNA repair, oxidative stress response, and autophagy, which provides a new perspective for understanding the molecular basis of GBM.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; cellular component;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFDR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; false discovery rate;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;hazard ratio;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIVW \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; inverse variance weighting;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLD \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;linkage disequilibrium;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; mendelian randomization;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePCA \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;principal component analysis;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSNP \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;single nucleotide polymorphism.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the investigators and participants of the original GWAS. We are grateful for all GWAS sharing summary data used in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatement of Ethics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval and consent are not required for this study in accordance with local or national guidelines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Sources:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNO Funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMenghao Liu, Wencai Wang, Haicheng Yang, and Xianfeng Li: designed the research; Menghao Liu, Wencai Wang and Hui Liu: collected and organized data; Menghao Liu, and Zun Wang: analyzed the data; Wencai Wang, Menghao Liu, Zun Wang, Hui Liu, and Zijie Xiong: drafted the manuscript; Menghao Liu, Wencai Wang, Haicheng Yang, and Xianfeng Li: contributed to the critical revision of the manuscript. All authors contributed to the manuscript and approved the submitted version.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupplementary Table 1: List of ferroptosis-related genes\u003cbr\u003e\u0026nbsp;The detailed list of 564 ferroptosis-related genes is available in the attached file \"Attachment_S1.xlsx\". To access this attachment, please [describe the access method, e.g., contact the corresponding author or download from the paper - associated website].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed during this study are included in this article. Further enquiries can be directed to the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eVarn FS, Johnson KC, Martinek J, Huse JT, Nasrallah MP, Wesseling P, et al. 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Cells. 2024;13(21). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/cells13211777\u003c/span\u003e\u003cspan address=\"10.3390/cells13211777\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\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":"Ferroptosis, GBM, Mendelian Randomization, PRDX6","lastPublishedDoi":"10.21203/rs.3.rs-5925744/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5925744/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: Ferroptosis, a novel form of regulated cell death, has emerged as a significant research focus due to its involvement in various cancers. This study combines single-cell RNA sequencing with Mendelian randomization (MR) methods to identify genetic factors that are associated with ferroptosis and explore their potential impact on the pathogenesis of glioblastoma (GBM).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: MR and multiple validation methods were used to identify ferroptosis-related genes in glioblastoma. Single-cell analysis was performed to evaluate the expression levels of ferroptosis markers across different glioblastoma cell types. Additionally, gene enrichment analysis was conducted to explore gene functions, while survival analysis examined the relationship between gene expression and patient prognosis. Immune cell infiltration analysis was also carried out for genes associated with prognosis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: MR and sensitivity analyses identified 9 ferroptosis-related genes that are associated with GBM: SIRT1, KDM3B, VCP, GPT2, PRDX6, CISD2, TP53, FLT3, and FANCD2. Co-localization analysis showed a significant association between the VCP gene and GBM. Single-cell analysis revealed that PRDX6 is highly expressed in tumor tissues. Gene enrichment analysis highlighted the biological processes such as cell metabolism, DNA repair, ubiquitination, and autophagy in the occurrence and progression of GBM. Immune infiltration and survival analyses suggest that CISD2 was related to CD8+ and CD4+ T cell infiltration, and may affect patient prognosis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e: This study combines MR analysis and single-cell analysis to reveal the crucial role of ferroptosis genes in GBM. These genes influence the occurrence and development of GBM by regulating processes such as metabolism, DNA repair, oxidative stress, and autophagy. Among them, PRDX6 plays an important role in microscopic research, while ubiquitination and autophagy are key drivers of GBM progression. The CISD2 gene may influence patient prognosis by regulating T cell infiltration, thereby promoting immune tolerance mechanisms in tumor cells.\u003c/p\u003e","manuscriptTitle":"Enhancing the Understanding of Ferroptosis Mechanisms in Glioblastoma: An Integrated Approach Utilizing Single-Cell Sequencing and Mendelian Randomization Methods","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-24 09:51:26","doi":"10.21203/rs.3.rs-5925744/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":"05360579-1332-4667-bdd9-dbd4ac16fe31","owner":[],"postedDate":"February 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-05-15T11:53:47+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-24 09:51:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5925744","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5925744","identity":"rs-5925744","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}