Neddylation-related gene signature predicts the prognosis and is associated with immune infiltration of glioma

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Neddylation, a post-translational modification similar to ubiquitination, is involved in tumorigenesis and progression. However, there were limited studies focused on the neddylation in glioma. Therefore, we aimed to explore the potential role of neddylation in glioma. Methods In this study, neddylation-related genes (NRGs) were identified and were used to construct a prognostic signature for glioma patients. Based on this prognostic index, we also explored the differences in clinical features, mutational landscape, immune cell infiltration between high-risk and low-risk groups. Next, single-cell RNA sequencing analysis was further performed to verify the expression of these genes in NRG signature. At last, one gene selected from the NRG signature were validated by in vitro experiments. Results Seven genes (TOP2A, F2R, UST, HSPA1B, LGALS3BP, UROS, and OSBPL11) were identified to construct the NRG signature, which was able to successfully classify glioma patients into high-risk and low-risk groups. A nomogram based on the NRG signature and other prognostic factors were developed to accurately predict the prognosis of glioma. Significant differences in prognosis, mutational landscape, immune cell infiltration were found between distinct groups. Moreover, in vitro experiments illustrated that knockdown of HSPA1B could inhibit the proliferation, migration, and invasion of glioma cells and also inhibit the polarization of M2 macrophages. Conclusion These findings provide new insights into understanding the relationship between NRGs and glioma development and identify novel biomarkers may help to guiding precise treatments to glioma. Glioma Neddylation HSPA1B Tumor immunity Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Introduction Glioma is the most prevalent malignant tumor that originates from central nervous system [ 1 ] . Classically, glioma can be classified into lower-grade glioma (LGG, grade 2–3), and glioblastoma multiforme (GBM, grade 4). However, the 2021 World Health Organization (WHO) classification of central nervous system tumors divided adult diffuse glioma into three categories: astrocytoma IDH-mutant (grade 2–4), oligodendroglioma IDH-mutant and 1p/19q-codeleted (grade 2–3) and GBM IDH-wildtype (grade 4) [ 2 ] . The treatment of glioma main includes maximal safe surgical resection followed by radiotherapy chemotherapy. Although these treatments have improved the overall survival (OS) in patients with glioma, the prognosis is still unsatisfactory, in particular the median OS of GBM is less than 12 months [ 3 ] . Therefore, it is urgently to identify novel biomarkers and elucidate the underlying mechanisms involved in developing new therapeutic strategies and improving prognosis. Neddylation, a post-translational modification similar to ubiquitination, adds the neddylation-like protein NEDD8 (neural precursor cell expressed, developmentally downregulated 8) to specific substrate proteins [ 4 ] . Neddylation plays an important role in regulating a variety of biological processes, such cell cycle, DNA damage response, and innate immune responses [ 5 ] . Lately, studies showed that protein modification by neddylation is overactivated in many cancers, such as lung cancer, liver cancer, nasopharyngeal carcinoma and glioblastoma [ 6 ] . Hua et al. reported that the neddylation inhibitor MLN4924 (Pevonedistat) could inhibit the proliferation of GBM [ 7 ] . These findings suggest that neddylation and neddylation-related genes (NRGs) are crucial for glioma progression. Therefore, a detailed investigation into the roles of neddylation and NRGs may aid in the development of new therapeutic strategies and improve the prognosis of glioma patients. This study used the TCGA (The Cancer Genome Atlas) and CGGA (Chinese Glioma Genome Atlas) databases for obtaining clinical and mRNA expression data of glioma. NRGs retrieved from The Human Gene Database (GeneCards database) were identified. Then, this study developed and validated NRG-based signature for predicting OS of glioma. Then, a nomogram based on the signature was established to predict glioma prognosis. Subsequently, the relationship between the risk score and tumor immunity was also investigated. Finally, in vitro experiments were also used to validate findings of this study. Materials and methods Dataset and Source NRGs were downloaded from The Human Gene Database( https://www.genecards.org/ ). Differential gene expression matrix of normal tissue and glioma samples was collected in UCSC Xena ( http://xena.ucsc.edu/ ). The mRNA expression data and corresponding clinical data of glioma patients were obtained from the TCGA data portal ( https://portal.gdc.cancer.gov/ ). The clinical information of glioma patients for the external validation set was downloaded from the CGGA database ( http://www.cgga.org.cn/ ). Some of the gene expression information from normal brain tissues was collected from the Genotype-Tissue Expression (GTEx) database ( https://www.gtexportal.org/home/ ). The downloaded data were consolidated with the Perl program. All data were collated and merged through R 4.2.2. The flowchart of this study was shown in Fig. 1 . Construction of a Prognostic Signature based on NRGs Utilizing the criteria of a False Discovery Rate (FDR) filter set at 0.05 and a log 2 -fold change (log 2 FC) of 2, we identified differential genes between tumor and normal tissues using the GTEx and TCGA datasets. The “VennDiagram” package was employed to visually represent the intersection of NRGs with the identified differential genes. Subsequently, NRGs with a P -value < 0.05 were further filtered to discern genes with prognostic significance. Pre-installed and utilized packages included “Survival”, “caret”, “glmnet”, “survminer”, and “timeROC”, fifty percent of the sorted cohort data were allocated into the training group, with the remaining samples assigned to the test group. The filtering condition for univariate Cox analysis was set at “ P -value < 0.05” due to the considerable number of genes. NRGs data underwent Lasso (Least absolute shrinkage and selection operator) regression and cross-validation to identify the most fitting combination with minimal error. Subsequently, risk scores for both the training and test groups were computed using the formula: Risk score= \(\sum _{i=1}^{n}({\beta }_{i}\times {x}_{i})\) Differential P -values were obtained by comparing the survival disparities between two distinct risk groups. Subsequently, the accuracy of the modified signature was evaluated by calculating the area under the ROC (receiver operating characteristic) curve. Prognostic value of the neddylation-related prognostic signature For glioma patients in the TCGA and CGGA datasets, a risk score was calculated using the NRG-based signature, and the resulting scores were utilized to generate a survival heatmap employing the “pheatmap” package. Subsequent analyses compared OS and progression-free survival (PFS) among the distinct groups. Patients were stratified into different subgroups based on clinical-pathological characteristics such as age, gender, and grade. Box plots depicting the correlation between risk scores and these clinical variables were created using the “ggpubr” and “limma” packages. Following this, both univariate and multivariate Cox regression analyses were performed on risk scores and relevant clinical variables, including age, gender, and grade. Significantly independent prognostic variables were selected to construct a nomogram, predicting the probability of OS at 1, 3, and 5 years. The “rms” package was employed to calculate the concordance index (C-index) for the nomogram, assessing its discriminatory ability. Subsequently, ROC curves and decision curves were plotted to evaluate the prognostic predictive accuracy of both the risk score and the nomogram. Finally, the concordance index was visualized using the “dplyr”, “survival”, “rms”, and “pec” packages. Functional analysis of significantly differentially expressed genes Differential genes were subjected to Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis to observe the enrichment patterns of differentially expressed genes. The top 30 most significant pathways were selected for presentation. Functional and pathway enrichment were separately assessed in the high-risk and low-risk groups through Gene Set Enrichment Analysis (GSEA), and the top five most crucial pathways were displayed. Relationship analysis between NRG-based signature with immunity and molecular alteration Using the ESTIMATE (Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data) algorithm, StromalScore, ImmuneScore, and EstimateScore were calculated and compared between high-risk and low-risk groups. The CIBERSORT algorithm was employed to compute the relative percentages of immune cells and immune function scores in each group. Drug sensitivity prediction Utilizing the “oncoppredict” and “parallel” packages, drug sensitivity prediction and scoring were performed for each glioma patient in TCGA. Subsequently, the “limma”, “ggplot”, and “ggpubr” packages were employed to predict differences in drug responsiveness between the two groups. Single-cell RNA sequencing analysis The single-cell RNA sequencing (scRNA-seq) analysis was used to investigate the mechanisms of key genes at the single-cell level. We downloaded the glioma scRNA-seq dataset in the CGGA database. The Seurat R package was utilized to analyze the single-cell data. The “CreateSeuratObject” function was employed to create a Seurat object to store the data matrix. Quality control was performed according to the basic standards: (1) genes detected in more than 3 cells; (2) cells with more than 200 total detected genes and less than 5% of mitochondrial genes. The “NormalizeData” function was applied to normalize the data and the function “FindVariableGenes” was used to identify the top 2000 variable genes. Principal component analysis (PCA) and the t-distributed stochastic neighbor embedding (t-SNE) algorithm were performed dimensionality reduction. The “SingleR” package and manual annotation were employed to annotate the cell types and the “FeaturePlot” function was used for visualization. Cell culture and real-time quantitative polymerase chain reaction (RT-qPCR) Glioma cell lines (U251 and T98G) were sourced from the Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education at Central South University, Changsha, China. These cells were cultured in high-glucose DMEM (Gibco) supplemented with 10% fetal bovine serum. Small interfering RNAs (siRNAs) targeting the HSPA1B gene were obtained from RiboBio Corporation (Guangzhou, China). Real-time quantitative PCR was performed using established protocols, with primers sourced from Sangon (Shanghai, China). The sequences used for qPCR were as follows: for HSPA1B, the forward primer was 5’- CAAGAAGGACATCAGCCAGAACAAGA-3’ and the reverse primer was 5’- CGGAACAGGTCGGAGCACAG-3’. The Cell Counting Kit-8 (CCK8) and Colony Formation Assays The CCK8 assay and colony formation assay are essential techniques utilized in biomedical research to evaluate cell viability and proliferation, respectively. In the CCK8 assay, cells are digested and resuspended in culture medium containing 10% serum, followed by plating at a density of 2000–4000 cells per well in a 96-well plate. The CCK8 assay solution is then added every 24 hours to measure absorbance changes, allowing for daily monitoring of cell proliferation. Meanwhile, in the colony formation assay, cells are plated at the same density in a 6-well plate, with 3–4 replicate wells per group. After an incubation period of 1–2 weeks, colonies are observed and counted to assess proliferation. At an appropriate time point, cells are fixed, stained with crystal violet solution, and the colonies are counted to analyze differences in colony numbers. These assays provide valuable insights into cellular behavior and response to various experimental conditions. Wound-healing assay Glioma cells were replated after digestion. Upon reaching 80–90% confluency, the cell monolayer was scratched using a pipette tip, followed by washing with PBS and further incubation in culture medium. Images were captured at 0- and 24-hours post-scratching, and differences in cell migration between groups were quantified. Cell migration assay Transwell chambers were coated with Matrigel gel, and cells were plated at a density of 1 × 10 4 cells per well using serum-free culture medium, with serum-containing medium added to the lower chamber. After 48 hours of incubation, cells were fixed and stained, and the number of migrated cells was compared between different groups. Macrophage differentiation and co-culture system Firstly, 100 ng/ml PMA (Phorbol-12-myristate-13 acetate) was used to induce THP-1 monocyte to M0 macrophages. Then, the co-culture system was established by glioma cells (upper chamber) and M0 macrophages (lower chamber). Finally, the M0 macrophages were harvested for further analysis. Immunofluorescence staining Immunofluorescence staining was performed as described previously [ 8 ] . Primary antibodies were CD68 (1:100; mouse; Proteintech 66,231-2-Ig) and CD163 (1:100; rabbit; Proteintech 16,646-1-AP) antibodies. The secondary antibodies were Alexa Fluor 568-conjugated donkey anti-mouse secondary antibody (1:500, Invitrogen) and Alexa Fluor 488-conjugated donkey anti-rabbit secondary antibody (1:500, Invitrogen). DAPI (1:500, Sigma, United States) staining was used to label the nuclei. Results Construction and assessment of the NRG-based signature We first utilized the GTEx and TCGA databases to screen 1085 genes associated with glioma prognosis, which were then intersected with 2299 genes related to neddylation, resulting in a total of 108 NRGs associated with glioma prognosis (Fig. 2 A). Genes exhibiting abnormal expression were filtered using |log 2 FC|=2 criteria (Fig. 2 B). Hazard ratios of several NRGs such as DNAJB6, SMN1, and RPN2 were presented using forest plots (Fig. 2 C). Next, multivariate Cox regression analysis and the LASSO regression algorithm were employed to identify the most suitable combination of NRGs for prognostic prediction (Fig. 2 D, E). Consequently, 7 NRGs, including TOP2A, F2R, UST, HSPA1B, LGALS3BP, UROS, and OSBPL11, were selected for inclusion in the construction of the prognostic signature ( Table 1 ). Construction and assessment of the signature in TCGA Using the prognostic signature, risk scores were computed for each sample, stratifying patients into low- and high-risk groups based on the median risk score of the training set. The distribution of risk scores and expression levels among glioma patients in the training set is depicted in Fig. 3 A. Notably, the scatter plot distribution of survival time indicates a positive correlation between poorer prognosis and higher risk scores. Furthermore, analysis of risk scores, survival time, and expression levels across both testing and entire cohorts yielded results consistent with those observed in the training set ( Fig. 3 B, C ) . Kaplan-Meier analysis revealed significantly shorter OS times in the high-risk group compared to the low-risk group across all subsets ( Fig. 3 D-F ) . Interestingly, Kaplan-Meier analysis indicated significantly longer PFS among high-risk glioma patients compared to low-risk patients ( Fig. 3 G-I ) . These results underscore the robustness of our signature in predicting prognosis among glioma patients. Assessment of the signature in CGGA We further evaluated our signature in the CGGA325 and CGGA693 glioma datasets. Patients with high-risk scores exhibited significantly shorter survival times. A gene heatmap depicted the expression characteristics of the seven genes modeled across all patients ( Fig. 4 A, B ) . The prognostic analysis also aligns with the results from TCGA, showing significantly shorter OS in patients classified as high-risk group ( Fig. 4 C, D ) . Stratified analysis of glioma To ascertain the relationship between our prognostic risk score and clinical features, we conducted a correlation analysis of commonly used clinical characteristics, including age, gender, WHO grade, 1p/19q co-deletion status, IDH1 mutant status and ATRX mutant status. The results indicate a significant correlation between age, glioma grade, IDH1 mutation status, and 1p/19q codeletion status with our risk score ( Fig. 5 A-F ) . To determine the independent prognostic factors associated with glioma patient outcomes, we conducted univariate and multivariate Cox regression analyses on potential predictive factors. The results of the analysis revealed significantly increased hazard ratios for age, glioma grade, 1p/19q codeletion status, IDH1 mutation status, ATRX mutation status, and risk score ( Fig. 5 G ) . However, the multivariate regression analysis revealed that ATRX mutation status (P = 0.281) did not reach statistical significance, while other factors such as age (HR = 1.045, 95% CI = 1.030–1.060, P < 0.001), grade (HR = 2.110, 95% CI = 1.553–2.867, P < 0.001), 1p/19q codeletion status (HR = 0.352, 95% CI = 0.199–0.622, P < 0.001), IDH1 mutation status (HR = 1.837, 95% CI = 1.146–2.944, P = 0.011), and risk score (HR = 1.012, 95% CI = 1.003–1.020, P = 0.007) demonstrated statistical significance ( Fig. 5 H ) . Nomogram for predicting glioma prognosis The prognostic charts forecasting the 1-year, 3-year, and 5-year survival rates for the TCGA cohort displayed AUC (area under the curve) values of 0.870, 0.890, and 0.807, respectively ( Fig. 6 A ) . In the CGGA325 cohort, the AUC values for the 1-year, 3-year, and 5-year survival rates were 0.773, 0.851, and 0.864, respectively ( Fig. 6 B ) . Similarly, in the CGGA693 cohort, the AUC values for the 1-year, 3-year, and 5-year survival rates were 0.697, 0.735, and 0.726, respectively ( Fig. 6 C ) . The signature (AUC = 0.890) appeared to provide more precise prognosis prediction compared to clinical factors such as age (AUC = 0.804), grade (AUC = 0.772), 1p/19q codeletion status (AUC = 0.621), and IDH1 mutation status (AUC = 0.785) ( Fig. 6 D ) . Additionally, we computed the concordance index for these clinical variables spanning from 1 to 5 years, and the risk score exhibited the largest AUC, underscoring the importance of our prognostic signature ( Fig. 6 E ) . Next, we developed a nomogram incorporating IDH1 status, risk score, grade, 1p/19q status, and age to forecast the prognosis of glioma patients ( Fig. 6 F ) . The nomogram exhibited predictive accuracy with a concordance index of 0.861 (95% confidence interval: 0.835–0.888) ( Fig. 6 G ) . Functional enrichment analysis After data compilation, we conducted GO, KEGG, and GSEA functional analyses separately. The top three relevant biological processes (BP) in the graphene oxide analysis were organelle fission, nuclear division, and embryonic organ development. Simultaneously, cellular components (CC) and molecular functions (MF) were associated with collagen-containing extracellular matrix, endoplasmic reticulum lumen, chromosomal region, as well as extracellular matrix structural constituent, glycosaminoglycan binding, and peptidase regulator activity, respectively ( Fig. 7 A ) . Moreover, KEGG pathway analysis revealed significant pathways such as “focal adhesion” and “pathways in cancer”, further elucidating the potential regulatory mechanisms underlying tumors ( Fig. 7 B ) . Finally, GSEA analysis was performed on both high-risk and low-risk groups to compare differences in enriched signaling pathways. Pathways such as anterior posterior pattern specification, embryonic, focal adhesion, graft-versus-host disease, and systemic lupus erythematosus seemed to be more associated with the high-risk group ( Fig. 7 C ) , while pathways including glutamate receptor signaling pathway, positive regulation of excitatory postsynaptic potential, regulation of neuronal synaptic plasticity, neuron projection membrane, and neurotransmitter receptor complex appeared to be more associated with the low-risk group ( Fig. 7 D ) . Correlation analysis of tumor immunity and molecule alteration We utilized the ESTIMATE algorithm and found that the high-risk group exhibited higher StromlScore, ImmuneScore, and EstimateScore compared to the low-risk group ( Fig. 8 A ) . Subsequently, we examined the composition of various immune cells and observed a higher proportion of macrophages (including macrophage M2) in the high-risk group ( Fig. 8 B, C ) . Furthermore, we conducted a comprehensive analysis of immune-related functional disparities. Significant differences were observed between the high- and low-risk groups in functions related to APC (antigen-presenting cell) co-inhibition, CD8 + T cells, HLA (human leukocyte antigen), and other immune-related functions ( Fig. 8 D ) . We compared the differences in Tumor Mutational Burden (TMB) between the high- and low-risk groups and found that the total TMB was significantly higher in the high-risk group than in the low-risk group ( Fig. 8 E ) . Notably, the low TMB and low risk score group (L-TMB + low risk) exhibited the highest survival probability ( Fig. 8 F, G ) . The mutation landscape plot revealed that the high-risk group had fewer IDH1 mutations but more mutations in EGFR and PTEN, all of which are molecular features associated with poor prognosis ( Fig. 8 H ) . Finally, we screened for potential drugs that could aid in the treatment of glioma (Figure S1 ) . Analysis of gene expressions at the single-cell level Further, we performed scRNA-seq analysis to further analyze the expression of the gene signature at a single cell level. The expression characteristics of the CGGA scRNA-seq dataset after quality control and filtering are showed in Fig. 9 A. The top 1500 highly variable genes are shown in Fig. 9 B. After PCA analysis, the top 20 PCs with P < 0.05 were selected for further analysis. Totally, 15 clusters of cells were visualized by the t-SNE dimensionality reduction algorithm ( Fig. 9 C ) . Moreover, 5 major cell types (astrocytes, monocytes, macrophages, T cells, and epithelial cells) were identified ( Fig. 9 C ) . As Fig. 9 E illustrated, genes like LGALS3BP were expressed in most cell types, whereas HSPA1B was mostly expressed in macrophages and monocytes. Gene like UST and OSBPL11 was expressed very low in specific cell types. HSPA1B promotes the proliferation, migration and invasion of glioma cells and macrophage polarization We investigated the correlation between HSPA1B expression and the malignant phenotype of glioma cells. Utilizing small interfering RNA (siRNA), we downregulated the expression levels of HSPA1B in U251 and T98G cells (Fig. 10 A). Subsequent CCK8 and colony formation assays revealed that the proliferation capacity of U251 and T98G cells was inhibited upon downregulation of HSPA1B expression (Fig. 10 B, C). Transwell and wound-healing assays demonstrated that the invasive and migratory abilities of U251 and T98G cells were suppressed following HSPA1B knockdown (Fig. 10 D, E). Moreover, immunofluorescence staining analysis showed that the fluorescence intensity ratio of CD163 and CD68 was considerably lower in the si-HSPA1B group than in the control group in both U251 and T98G cell (Supplementary Fig. 2A-B) . The statistical analyses were revealed in (Supplementary Fig. 2C-D) . These results showed that HSPA1B promotes tumor-associated macrophage polarization in glioma cells. Discussion Neddylation is a post-translational modification in which a neddylation-like molecule NEDD8 is covalently attached to a lysine residue within a substrate protein [ 9 ] . Firstly, NEDD8 precursor undergoes proteolytic processing to maturation with exposing the C-terminal Gly residue. Next, the mature NEDD8 undergoes adenosine triphosphate (ATP)-dependent activation catalyzed by the NEDD8-activating E1 enzyme (NAE). After activation, a trans-thiolationed attaches NEDD8 to an E2-conjugating enzyme. Finally, NEDD8 was covalent attached to the lysine residue of the substrate protein, which catalyzed by the substrate-specific E3 ligase [ 9 ] . Neddylation plays a crucial regulatory role in various diseases particularly neurodegenerative disorders [ 10 , 11 ] . Researches focused on tumors has revealed that most neddylation pathway proteins are over-activated in various cancers, targeting neddylation becomes an emerging approach for the treatment of these cancers [ 6 ] . Several neddylation inhibitors have been developed for cancer treatment, of which MLN4924 (pevonedistat) has entered clinical trials. Although studies showed MLN4924 could inhibit the proliferation of glioblastoma cells, other studies focused on the neddylation in glioma was limited [ 12 , 13 ] . Therefore, it is of great significance to further explore neddylation process in glioma for the development of new therapeutic strategies. In this study, we first identified 108 potentially important NRGs in glioma through bioinformatics analysis. Then, the univariate Cox regression analysis and the LASSO regression algorithm were employed to establish a prognostic prediction signature based on these NRGs and TCGA dataset. Moreover, this signature was validated in both TCGA and CGGA databases. Among these genes, TOP2A, F2R, HSPA1B and LGALS3BP were confirmed as risk-associated genes with their high expression closely associated with poor prognosis. In contrast, UST, UROS and OSBPL11 were identified as protective genes. TOP2A, namely DNA topoisomerase II alpha, plays an important role in altering DNA topology [ 14 ] . Liu et al. found that TOP2A could activate the Wnt/β-catenin pathway in glioma and promote cell growth, migration, and invasion [ 15 ] . F2R encodes coagulation factor II thrombin receptor which is a ligand of thrombin [ 16 ] . Consistent with our findings, F2R has been reported to be correlated with worse prognosis or the development of glioma [ 17 ] . HSPA1B, as a member of heat shock protein 70 family, can stabilize existing proteins against aggregation and mediate the folding of newly translated proteins in the cytosol and in organelles [ 18 ] . HSPA1B, as a subtypes of HSP70, is a promising antitumor target in many cancers [ 19 ] . However, the investigations of HSP20 in glioma research is rare. LGALS3BP, namely galectin-3-binding protein, is a glycosylated protein overexpressed in various human tumors and is a potential novel diagnostic biomarker and therapeutic target in GBM deserving further validation [ 20 ] . UST encodes uronyl 2-sulfotransferase which transfers sulfate to the 2-position of uronyl residue. Jiang et al. has found that UST is a favorable prognostic biomarker for glioma patients but lack of experimental validation [ 21 ] . UROS encodes uroporphyrinogen III synthase which catalyzes the fourth step of porphyrin biosynthesis in the heme biosynthetic pathway and the study of UROS in cancer research is very limited [ 22 ] . OSBPL11 encodes a member of the oxysterol-binding protein (OSBP) family, which serves a role in lipid metabolism. Previous study showed that down-regulated OSBPL11 may be a potential indicator for hepatocellular carcinoma [ 23 ] . Then, functional enrichment and GSEA analyses were performed between high-risk and low-risk groups. The results showed that extracelluar matrix and focal adhesion were significant enriched. Extracelluar matrix is an important component of glioma microenvironment and focal adhesion is closely correlated with the migration and invasion of glioma. Thus, neddylation modification may play an important role in the crosstalk between the glioma cells and their microenvironment. Next, we compared the immune scores, stromal scores, immune cell distributions and TMB between high- and low-risk groups. Stromal and immune scores in the high-risk group were higher than that in the low-risk group, which indicate that tumor stromal cell strongly facilitated the progression of tumor and glioma is surrounded with an immune-excluded microenvironment [ 8 ] . Tumor-associated macrophages (TAMs) are rich in the tumor microenvironment and could interact with glioma cells to promote the progression of glioma [ 24 ] . M1 and M2 are two different polarized phenotypes of macrophages and M2 macrophages are usually considered to participate in immune suppression and tumor development [ 25 ] . Consistently, we found that TAMs especially M2 macrophages were high expressed in high-risk group, which indicate that the NRG signature could reflect the immune state of glioma. Previous study has been revealed that high TMB reflects high tumor proliferative activity and glioma patients with high TMB often have a shorter OS [ 26 ] . We found that patients in the high-risk group have significant high TMBs and patients with high TMBs and risk scores have the shortest OS. These results indicated that our NRG signature could combined with TMB to better predict the prognosis of glioma patients. We further analyze the expression of the NRG signature at a single cell level and found that HSPA1B is mainly expressed in macrophages. Considering that HSPA1B was poorly studied in previous glioma study, we selected HSPA1B for experimental validation. Our results revealed that HSPA1B could promote the migration, invasive and proliferation of glioma cells. Moreover, we further confirmed that HSPA1B could promote M2 macrophage polarization. These results indicated that HSPA1B may serve as a novel target for glioma treatment. Conclusion In summary, we constructed the NRG signature that could stratify glioma patients with completely different prognoses and immune microenvironment. We also found that HSPA1B, one important gene in the NRG, could promote the migration, invasive, proliferation and macrophage polarization in glioma. These findings provide novel insights in the role of neddylation in glioma development which may contribute to guiding more precise therapeutic strategies. Abbreviations AUC Area Under the Curve BP Biological Process CC Cellular Component CGGA Chinese Glioma Genome Atlas ESTIMATE Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data FDR False Discovery Rate GBM Glioblastoma Multiforme GO Gene Ontology GSEA Gene Set Enrichment Analysis KEGG Kyoto Encyclopedia of Genes and Genomes LASSO Least Absolute Shrinkage and Selection Operator MF Molecular Function OS Overall Survival PCA Principal Component Analysis PFS Progression-Free Survival ROC Receiver Operating Characteristic TCGA The Cancer Genome Atlas TMB Tumor Mutation Burden t-SNE t-distributed Stochastic Neighbor Embedding WHO World Health Organization Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Data Availability Statement The data source of the manuscript is from the public database. The details were displayed in the materials and methods section. Other data could contact with the corresponding authors. Funding Statement This study was supported by the National Natural Science Foundation of China (81472355), and Provincial Natural Science Foundation of Hunan (2022JJ30931). Author Contributions CR and XJ conceived the study. ZJ, WY, GT, HH, LW, WL, and ZW collected and analyzed data and visualized figures. ZJ and WY wrote the manuscript. All authors reviewed and approved the submitted manuscript. Acknowledgments We sincerely thank the CGGA and TCGA databases for freely providing the transcriptomic information of glioma samples. References Liu Y, Ali H, Khan F, et al. Epigenetic regulation of tumor-immune symbiosis in glioma [J]. Trends Mol Med; 2024. Horbinski C, Berger T, Packer RJ, et al. Clinical implications of the 2021 edition of the WHO classification of central nervous system tumours [J]. Nat Rev Neurol. 2022;18(9):515–29. Smith K, Nakaji P, Thomas T, et al. 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Tang W, Lo CWS, Ma W, et al. Revealing the role of SPP1(+) macrophages in glioma prognosis and therapeutic targeting by investigating tumor-associated macrophage landscape in grade 2 and 3 gliomas [J]. Cell Biosci. 2024;14(1):37. Li M, Xu H, Qi Y, et al. Tumor-derived exosomes deliver the tumor suppressor miR-3591-3p to induce M2 macrophage polarization and promote glioma progression [J]. Oncogene. 2022;41(41):4618–32. Yin W, Jiang X, Tan J, et al. Development and Validation of a Tumor Mutation Burden-Related Immune Prognostic Model for Lower-Grade Glioma [J]. Front Oncol. 2020;10:1409. Additional Declarations No competing interests reported. Supplementary Files table.docx supplementaryfile.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4209486","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":287062155,"identity":"144f34f3-63f3-4b80-a750-c8da500970a7","order_by":0,"name":"Zhipeng Jiang","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"prefix":"","firstName":"Zhipeng","middleName":"","lastName":"Jiang","suffix":""},{"id":287062157,"identity":"dad9a8a3-0450-49f0-9aa3-193e5759fd35","order_by":1,"name":"Wen Yin","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"prefix":"","firstName":"Wen","middleName":"","lastName":"Yin","suffix":""},{"id":287062158,"identity":"e34a64ef-6d88-42d1-b90a-b61bced99970","order_by":2,"name":"Guihua Tang","email":"","orcid":"","institution":"Hunan Provincial People's Hospital (The first affiliated hospital of Hunan Normal University, The College of Clinical Medicine of Human Normal University)","correspondingAuthor":false,"prefix":"","firstName":"Guihua","middleName":"","lastName":"Tang","suffix":""},{"id":287062159,"identity":"16e9558d-77a8-4a3e-a716-249a3e00bf86","order_by":3,"name":"Youwei Guo","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"prefix":"","firstName":"Youwei","middleName":"","lastName":"Guo","suffix":""},{"id":287062160,"identity":"bdec634d-ef2e-470e-af92-b2cac1c54783","order_by":4,"name":"HaiLong Huang","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"prefix":"","firstName":"HaiLong","middleName":"","lastName":"Huang","suffix":""},{"id":287062161,"identity":"d839ba17-403a-41ce-817a-39098449db38","order_by":5,"name":"Zihan Wang","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"prefix":"","firstName":"Zihan","middleName":"","lastName":"Wang","suffix":""},{"id":287062163,"identity":"f2f99ad0-97ad-4392-b686-9bb8ac4a920b","order_by":6,"name":"Lei Wang","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"Wang","suffix":""},{"id":287062165,"identity":"fba94bd3-2947-48ff-b620-554255f8a09c","order_by":7,"name":"Weidong Liu","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"prefix":"","firstName":"Weidong","middleName":"","lastName":"Liu","suffix":""},{"id":287062167,"identity":"219c38e0-cca9-4cd8-86bb-171daced7262","order_by":8,"name":"Xingjun Jiang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsUlEQVRIie3PMQrCQBCF4QkBbUa2naDgFdbKCEGvshJJlUMMpLDJAdaLWE9IrwdIb530KQx6gUknuF89P28XIAh+UMTggCBDY3heUmwSL/PG2syyU97G3r0oHZ9oQaJ+KDUPq6Ugwg73McfJ7a5Jrjwl1OGBZRGvVEkFU2IfaMVpk8+KkzlJLXlKkmPim0r3l50vzx2Nx5MxVdMPqoTxsqbvICvuJ1tYtkOvuw2CIPhXb42dNEpCfqP4AAAAAElFTkSuQmCC","orcid":"","institution":"Central South University","correspondingAuthor":true,"prefix":"","firstName":"Xingjun","middleName":"","lastName":"Jiang","suffix":""},{"id":287062168,"identity":"57122984-e39f-407c-8515-9623d6ed4a8a","order_by":9,"name":"Caiping Ren","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"prefix":"","firstName":"Caiping","middleName":"","lastName":"Ren","suffix":""}],"badges":[],"createdAt":"2024-04-03 02:51:02","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4209486/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4209486/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54314963,"identity":"00e1c5f9-9fa1-4643-a60e-8851820c0e4d","added_by":"auto","created_at":"2024-04-08 17:41:40","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":254020,"visible":true,"origin":"","legend":"\u003cp\u003eThe flow chart of research design.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4209486/v1/f1073fb7a10fb67721721140.png"},{"id":54314964,"identity":"8f3e4ed0-11c3-4c46-9774-e83635fbc4b8","added_by":"auto","created_at":"2024-04-08 17:41:40","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1738290,"visible":true,"origin":"","legend":"\u003cp\u003eSelection of the prognostic-related NRGs. (A) Intersection of NRGs and differential genes associated with glioma. (B) The volcano plot depicting differentially expressed genes in glioma. (C) The forest plot illustrating prognostic association of NRGs with glioma. (D, E) Construction of the NRGs-based signature.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4209486/v1/b5a301f40a3634087d1cd7dd.png"},{"id":54314988,"identity":"9bc352d6-6402-422b-9ae3-4e7cba835955","added_by":"auto","created_at":"2024-04-08 17:41:41","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":943943,"visible":true,"origin":"","legend":"\u003cp\u003eConstruction of the prognostic signature in the TCGA set.\u003cstrong\u003e \u003c/strong\u003e(A-C) The risk scores, survival time distributions, and heatmaps based on the neddylation-related signature were computed. (D-F) Kaplan-Meier analysis was conducted to reveal the OS between the high-risk and low-risk groups in the training set, test set, and entire TCGA cohort. (G-I) Kaplan-Meier analysis was performed to reveal the PFS between the high-risk and low-risk groups in the training set, test set, and entire TCGA cohort.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4209486/v1/fa33cea852fd883d85f71bf0.png"},{"id":54314966,"identity":"0ec8a49c-da28-47ab-a9d7-0856be58cc53","added_by":"auto","created_at":"2024-04-08 17:41:40","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1031748,"visible":true,"origin":"","legend":"\u003cp\u003eValidation of the prognostic signature in the CGGA sets. (A-C) The risk scores, survival time distributions, and heatmaps based on the neddylation-related signature were computed in CGGA_325 and CGGA_693. (D-F) Kaplan-Meier analysis was conducted to reveal the OS between the high-risk and low-risk groups in CGGA cohorts.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4209486/v1/8d1631bdee9689cf157146cb.png"},{"id":54314973,"identity":"d5b3d0fb-0bd3-4dfb-aa4e-1c485fa4efcc","added_by":"auto","created_at":"2024-04-08 17:41:41","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":334007,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of clinical features and prognostic risk factors.\u003cstrong\u003e \u003c/strong\u003e(A-F) Associations between the risk score and clinical characteristics were examined. (G, H) Forest plots displaying the results of univariate and multivariate Cox regression analyses for prognostic factors.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4209486/v1/855ac7430e85ad2b3c75414a.png"},{"id":54314972,"identity":"241a78e0-ac4b-4882-a410-16861641c943","added_by":"auto","created_at":"2024-04-08 17:41:41","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":641483,"visible":true,"origin":"","legend":"\u003cp\u003eConstruction of nomogram of patients with glioma and test of its predictive ability. (A-C) The AUCs of TCGA, CGGA325 and CGGA693 for 1-, 3-, and 5-year OS rates. (D, E) The predictive accuracy of clinical factors. (F) Construct a nomogram in glioma patients. (G) The predictive accuracy of the nomogram.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-4209486/v1/4444def9d790d2c51d28a505.png"},{"id":54314965,"identity":"89af6dca-0b31-4bf3-be26-f02f01627ad3","added_by":"auto","created_at":"2024-04-08 17:41:40","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1192683,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional enrichment analysis.\u003cstrong\u003e \u003c/strong\u003e(A) GO analysis of the signature. (B) KEGG analysis of the signature. (C, D) GSEA analysis of the signature.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-4209486/v1/e4734c937dc8dbab0301b4fe.png"},{"id":54314969,"identity":"dcb4cac3-e671-4f90-8585-0192ad056696","added_by":"auto","created_at":"2024-04-08 17:41:40","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1263933,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of the NRGs-based signature in immune features and molecules alteration.\u003cstrong\u003e \u003c/strong\u003e(A) The comparison of TME-related scores between high- and low-risk groups. (B-D) The comparison of Differences in immune cell types between two groups. (E-G) The analysis of the association between the signature and TMB. (H) The differences in mutation landscape between the two groups. (*\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001)\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-4209486/v1/d5e39a7ec8c22e9ac190f386.png"},{"id":54314970,"identity":"d8c55c41-7508-4266-adb4-fd3610719bbf","added_by":"auto","created_at":"2024-04-08 17:41:41","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":3192961,"visible":true,"origin":"","legend":"\u003cp\u003eValidation of the expression of the NRG signature using scRNA-seq data. (A) Quality control and normalization. (B) The volcano plot of highly variable genes (the top 10 genes are marked). (C) tSNE algorithm classified cells into 15 clusters. (D) The cell types of the various clusters. (E) In total, 15 cell clusters were annotated into five cell subtypes using the tSNE algorithm. (F) The distribution of the NRG signature in different cell types.\u003c/p\u003e","description":"","filename":"Figure9.png","url":"https://assets-eu.researchsquare.com/files/rs-4209486/v1/f18221ab7bb5c5ad4a9a30ec.png"},{"id":54314971,"identity":"2f1f9ae4-fce7-4932-b56a-48984eddc8e1","added_by":"auto","created_at":"2024-04-08 17:41:41","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":1367221,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between HSPA1B and Malignant Phenotype of Glioma Cells. (A) Efficiency of HSPA1B gene knockdown after siRNA treatment. (B, C) The CCK-8 assay and colony formation assay demonstrate HSPA1B could promote glioma cell proliferation. (D, E) Wound-healing and Transwell assays demonstrate that HSPA1B enhances the migration and invasion capabilities of glioma cells. (*\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.005)\u003c/p\u003e","description":"","filename":"Figure10.png","url":"https://assets-eu.researchsquare.com/files/rs-4209486/v1/7a2df7db64479511e684abe0.png"},{"id":76910005,"identity":"e974ba12-b348-49c2-89ad-7c852a017bd4","added_by":"auto","created_at":"2025-02-22 07:17:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":11965950,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4209486/v1/861be1b2-2453-469e-97ae-18d572c3b715.pdf"},{"id":54314968,"identity":"f73be39f-604a-4bd4-8dea-1b3f39474dbe","added_by":"auto","created_at":"2024-04-08 17:41:40","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":16469,"visible":true,"origin":"","legend":"","description":"","filename":"table.docx","url":"https://assets-eu.researchsquare.com/files/rs-4209486/v1/18f8273958d15f88aa82b953.docx"},{"id":54314967,"identity":"b9615ed3-0631-48c4-86af-236aefdfae44","added_by":"auto","created_at":"2024-04-08 17:41:40","extension":"docx","order_by":15,"title":"","display":"","copyAsset":false,"role":"supplement","size":1289879,"visible":true,"origin":"","legend":"","description":"","filename":"supplementaryfile.docx","url":"https://assets-eu.researchsquare.com/files/rs-4209486/v1/67f1a56a32efeafddc390471.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Neddylation-related gene signature predicts the prognosis and is associated with immune infiltration of glioma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGlioma is the most prevalent malignant tumor that originates from central nervous system\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Classically, glioma can be classified into lower-grade glioma (LGG, grade 2\u0026ndash;3), and glioblastoma multiforme (GBM, grade 4). However, the 2021 World Health Organization (WHO) classification of central nervous system tumors divided adult diffuse glioma into three categories: astrocytoma IDH-mutant (grade 2\u0026ndash;4), oligodendroglioma IDH-mutant and 1p/19q-codeleted (grade 2\u0026ndash;3) and GBM IDH-wildtype (grade 4)\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. The treatment of glioma main includes maximal safe surgical resection followed by radiotherapy chemotherapy. Although these treatments have improved the overall survival (OS) in patients with glioma, the prognosis is still unsatisfactory, in particular the median OS of GBM is less than 12 months\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Therefore, it is urgently to identify novel biomarkers and elucidate the underlying mechanisms involved in developing new therapeutic strategies and improving prognosis.\u003c/p\u003e \u003cp\u003eNeddylation, a post-translational modification similar to ubiquitination, adds the neddylation-like protein NEDD8 (neural precursor cell expressed, developmentally downregulated 8) to specific substrate proteins\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Neddylation plays an important role in regulating a variety of biological processes, such cell cycle, DNA damage response, and innate immune responses\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Lately, studies showed that protein modification by neddylation is overactivated in many cancers, such as lung cancer, liver cancer, nasopharyngeal carcinoma and glioblastoma\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Hua et al. reported that the neddylation inhibitor MLN4924 (Pevonedistat) could inhibit the proliferation of GBM\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. These findings suggest that neddylation and neddylation-related genes (NRGs) are crucial for glioma progression. Therefore, a detailed investigation into the roles of neddylation and NRGs may aid in the development of new therapeutic strategies and improve the prognosis of glioma patients.\u003c/p\u003e \u003cp\u003eThis study used the TCGA (The Cancer Genome Atlas) and CGGA (Chinese Glioma Genome Atlas) databases for obtaining clinical and mRNA expression data of glioma. NRGs retrieved from The Human Gene Database (GeneCards database) were identified. Then, this study developed and validated NRG-based signature for predicting OS of glioma. Then, a nomogram based on the signature was established to predict glioma prognosis. Subsequently, the relationship between the risk score and tumor immunity was also investigated. Finally, \u003cem\u003ein vitro\u003c/em\u003e experiments were also used to validate findings of this study.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eDataset and Source\u003c/h2\u003e \u003cp\u003eNRGs were downloaded from The Human Gene Database( \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.genecards.org/\u003c/span\u003e\u003cspan address=\"https://www.genecards.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e ). Differential gene expression matrix of normal tissue and glioma samples was collected in UCSC Xena ( \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://xena.ucsc.edu/\u003c/span\u003e\u003cspan address=\"http://xena.ucsc.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e ). The mRNA expression data and corresponding clinical data of glioma patients were obtained from the TCGA data portal ( \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov/\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e ). The clinical information of glioma patients for the external validation set was downloaded from the CGGA database ( \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.cgga.org.cn/\u003c/span\u003e\u003cspan address=\"http://www.cgga.org.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e ). Some of the gene expression information from normal brain tissues was collected from the Genotype-Tissue Expression (GTEx) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gtexportal.org/home/\u003c/span\u003e\u003cspan address=\"https://www.gtexportal.org/home/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e ). The downloaded data were consolidated with the Perl program. All data were collated and merged through R 4.2.2. The flowchart of this study was shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eConstruction of a Prognostic Signature based on NRGs\u003c/h2\u003e \u003cp\u003eUtilizing the criteria of a False Discovery Rate (FDR) filter set at 0.05 and a log\u003csub\u003e2\u003c/sub\u003e-fold change (log\u003csub\u003e2\u003c/sub\u003eFC) of 2, we identified differential genes between tumor and normal tissues using the GTEx and TCGA datasets. The \u0026ldquo;VennDiagram\u0026rdquo; package was employed to visually represent the intersection of NRGs with the identified differential genes. Subsequently, NRGs with a \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were further filtered to discern genes with prognostic significance. Pre-installed and utilized packages included \u0026ldquo;Survival\u0026rdquo;, \u0026ldquo;caret\u0026rdquo;, \u0026ldquo;glmnet\u0026rdquo;, \u0026ldquo;survminer\u0026rdquo;, and \u0026ldquo;timeROC\u0026rdquo;, fifty percent of the sorted cohort data were allocated into the training group, with the remaining samples assigned to the test group. The filtering condition for univariate Cox analysis was set at \u0026ldquo;\u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u0026rdquo; due to the considerable number of genes. NRGs data underwent Lasso (Least absolute shrinkage and selection operator) regression and cross-validation to identify the most fitting combination with minimal error. Subsequently, risk scores for both the training and test groups were computed using the formula:\u003c/p\u003e \u003cp\u003eRisk score=\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\sum _{i=1}^{n}({\\beta }_{i}\\times {x}_{i})\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eDifferential \u003cem\u003eP\u003c/em\u003e-values were obtained by comparing the survival disparities between two distinct risk groups. Subsequently, the accuracy of the modified signature was evaluated by calculating the area under the ROC (receiver operating characteristic) curve.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003ePrognostic value of the neddylation-related prognostic signature\u003c/h2\u003e \u003cp\u003eFor glioma patients in the TCGA and CGGA datasets, a risk score was calculated using the NRG-based signature, and the resulting scores were utilized to generate a survival heatmap employing the \u0026ldquo;pheatmap\u0026rdquo; package. Subsequent analyses compared OS and progression-free survival (PFS) among the distinct groups. Patients were stratified into different subgroups based on clinical-pathological characteristics such as age, gender, and grade. Box plots depicting the correlation between risk scores and these clinical variables were created using the \u0026ldquo;ggpubr\u0026rdquo; and \u0026ldquo;limma\u0026rdquo; packages. Following this, both univariate and multivariate Cox regression analyses were performed on risk scores and relevant clinical variables, including age, gender, and grade. Significantly independent prognostic variables were selected to construct a nomogram, predicting the probability of OS at 1, 3, and 5 years. The \u0026ldquo;rms\u0026rdquo; package was employed to calculate the concordance index (C-index) for the nomogram, assessing its discriminatory ability. Subsequently, ROC curves and decision curves were plotted to evaluate the prognostic predictive accuracy of both the risk score and the nomogram. Finally, the concordance index was visualized using the \u0026ldquo;dplyr\u0026rdquo;, \u0026ldquo;survival\u0026rdquo;, \u0026ldquo;rms\u0026rdquo;, and \u0026ldquo;pec\u0026rdquo; packages.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eFunctional analysis of significantly differentially expressed genes\u003c/h2\u003e \u003cp\u003eDifferential genes were subjected to Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis to observe the enrichment patterns of differentially expressed genes. The top 30 most significant pathways were selected for presentation. Functional and pathway enrichment were separately assessed in the high-risk and low-risk groups through Gene Set Enrichment Analysis (GSEA), and the top five most crucial pathways were displayed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eRelationship analysis between NRG-based signature with immunity and molecular alteration\u003c/h2\u003e \u003cp\u003eUsing the ESTIMATE (Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data) algorithm, StromalScore, ImmuneScore, and EstimateScore were calculated and compared between high-risk and low-risk groups. The CIBERSORT algorithm was employed to compute the relative percentages of immune cells and immune function scores in each group.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eDrug sensitivity prediction\u003c/h2\u003e \u003cp\u003eUtilizing the \u0026ldquo;oncoppredict\u0026rdquo; and \u0026ldquo;parallel\u0026rdquo; packages, drug sensitivity prediction and scoring were performed for each glioma patient in TCGA. Subsequently, the \u0026ldquo;limma\u0026rdquo;, \u0026ldquo;ggplot\u0026rdquo;, and \u0026ldquo;ggpubr\u0026rdquo; packages were employed to predict differences in drug responsiveness between the two groups.\u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003eSingle-cell RNA sequencing analysis\u003c/h2\u003e \u003cp\u003eThe single-cell RNA sequencing (scRNA-seq) analysis was used to investigate the mechanisms of key genes at the single-cell level. We downloaded the glioma scRNA-seq dataset in the CGGA database. The Seurat R package was utilized to analyze the single-cell data. The \u0026ldquo;CreateSeuratObject\u0026rdquo; function was employed to create a Seurat object to store the data matrix. Quality control was performed according to the basic standards: (1) genes detected in more than 3 cells; (2) cells with more than 200 total detected genes and less than 5% of mitochondrial genes. The \u0026ldquo;NormalizeData\u0026rdquo; function was applied to normalize the data and the function \u0026ldquo;FindVariableGenes\u0026rdquo; was used to identify the top 2000 variable genes. Principal component analysis (PCA) and the t-distributed stochastic neighbor embedding (t-SNE) algorithm were performed dimensionality reduction. The \u0026ldquo;SingleR\u0026rdquo; package and manual annotation were employed to annotate the cell types and the \u0026ldquo;FeaturePlot\u0026rdquo; function was used for visualization.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eCell culture and real-time quantitative polymerase chain reaction (RT-qPCR)\u003c/h2\u003e \u003cp\u003eGlioma cell lines (U251 and T98G) were sourced from the Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education at Central South University, Changsha, China. These cells were cultured in high-glucose DMEM (Gibco) supplemented with 10% fetal bovine serum. Small interfering RNAs (siRNAs) targeting the HSPA1B gene were obtained from RiboBio Corporation (Guangzhou, China). Real-time quantitative PCR was performed using established protocols, with primers sourced from Sangon (Shanghai, China). The sequences used for qPCR were as follows: for HSPA1B, the forward primer was 5\u0026rsquo;- CAAGAAGGACATCAGCCAGAACAAGA-3\u0026rsquo; and the reverse primer was 5\u0026rsquo;- CGGAACAGGTCGGAGCACAG-3\u0026rsquo;.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eThe Cell Counting Kit-8 (CCK8) and Colony Formation Assays\u003c/h2\u003e \u003cp\u003eThe CCK8 assay and colony formation assay are essential techniques utilized in biomedical research to evaluate cell viability and proliferation, respectively. In the CCK8 assay, cells are digested and resuspended in culture medium containing 10% serum, followed by plating at a density of 2000\u0026ndash;4000 cells per well in a 96-well plate. The CCK8 assay solution is then added every 24 hours to measure absorbance changes, allowing for daily monitoring of cell proliferation. Meanwhile, in the colony formation assay, cells are plated at the same density in a 6-well plate, with 3\u0026ndash;4 replicate wells per group. After an incubation period of 1\u0026ndash;2 weeks, colonies are observed and counted to assess proliferation. At an appropriate time point, cells are fixed, stained with crystal violet solution, and the colonies are counted to analyze differences in colony numbers. These assays provide valuable insights into cellular behavior and response to various experimental conditions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eWound-healing assay\u003c/h2\u003e \u003cp\u003eGlioma cells were replated after digestion. Upon reaching 80\u0026ndash;90% confluency, the cell monolayer was scratched using a pipette tip, followed by washing with PBS and further incubation in culture medium. Images were captured at 0- and 24-hours post-scratching, and differences in cell migration between groups were quantified.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eCell migration assay\u003c/h2\u003e \u003cp\u003eTranswell chambers were coated with Matrigel gel, and cells were plated at a density of 1 \u0026times; 10\u003csup\u003e4\u003c/sup\u003e cells per well using serum-free culture medium, with serum-containing medium added to the lower chamber. After 48 hours of incubation, cells were fixed and stained, and the number of migrated cells was compared between different groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eMacrophage differentiation and co-culture system\u003c/h2\u003e \u003cp\u003eFirstly, 100 ng/ml PMA (Phorbol-12-myristate-13 acetate) was used to induce THP-1 monocyte to M0 macrophages. Then, the co-culture system was established by glioma cells (upper chamber) and M0 macrophages (lower chamber). Finally, the M0 macrophages were harvested for further analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eImmunofluorescence staining\u003c/h2\u003e \u003cp\u003eImmunofluorescence staining was performed as described previously\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. Primary antibodies were CD68 (1:100; mouse; Proteintech 66,231-2-Ig) and CD163 (1:100; rabbit; Proteintech 16,646-1-AP) antibodies. The secondary antibodies were Alexa Fluor 568-conjugated donkey anti-mouse secondary antibody (1:500, Invitrogen) and Alexa Fluor 488-conjugated donkey anti-rabbit secondary antibody (1:500, Invitrogen). DAPI (1:500, Sigma, United States) staining was used to label the nuclei.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eConstruction and assessment of the NRG-based signature\u003c/h2\u003e \u003cp\u003eWe first utilized the GTEx and TCGA databases to screen 1085 genes associated with glioma prognosis, which were then intersected with 2299 genes related to neddylation, resulting in a total of 108 NRGs associated with glioma prognosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Genes exhibiting abnormal expression were filtered using |log\u003csub\u003e2\u003c/sub\u003eFC|=2 criteria (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Hazard ratios of several NRGs such as DNAJB6, SMN1, and RPN2 were presented using forest plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Next, multivariate Cox regression analysis and the LASSO regression algorithm were employed to identify the most suitable combination of NRGs for prognostic prediction (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD, E). Consequently, 7 NRGs, including TOP2A, F2R, UST, HSPA1B, LGALS3BP, UROS, and OSBPL11, were selected for inclusion in the construction of the prognostic signature (\u003cb\u003eTable\u0026nbsp;1\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eConstruction and assessment of the signature in TCGA\u003c/h2\u003e \u003cp\u003eUsing the prognostic signature, risk scores were computed for each sample, stratifying patients into low- and high-risk groups based on the median risk score of the training set. The distribution of risk scores and expression levels among glioma patients in the training set is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA. Notably, the scatter plot distribution of survival time indicates a positive correlation between poorer prognosis and higher risk scores. Furthermore, analysis of risk scores, survival time, and expression levels across both testing and entire cohorts yielded results consistent with those observed in the training set \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, C\u003cb\u003e)\u003c/b\u003e. Kaplan-Meier analysis revealed significantly shorter OS times in the high-risk group compared to the low-risk group across all subsets \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD-F\u003cb\u003e)\u003c/b\u003e. Interestingly, Kaplan-Meier analysis indicated significantly longer PFS among high-risk glioma patients compared to low-risk patients \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG-I\u003cb\u003e)\u003c/b\u003e. These results underscore the robustness of our signature in predicting prognosis among glioma patients.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eAssessment of the signature in CGGA\u003c/h2\u003e \u003cp\u003eWe further evaluated our signature in the CGGA325 and CGGA693 glioma datasets. Patients with high-risk scores exhibited significantly shorter survival times. A gene heatmap depicted the expression characteristics of the seven genes modeled across all patients \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, B\u003cb\u003e)\u003c/b\u003e. The prognostic analysis also aligns with the results from TCGA, showing significantly shorter OS in patients classified as high-risk group \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC, D\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eStratified analysis of glioma\u003c/h2\u003e \u003cp\u003eTo ascertain the relationship between our prognostic risk score and clinical features, we conducted a correlation analysis of commonly used clinical characteristics, including age, gender, WHO grade, 1p/19q co-deletion status, IDH1 mutant status and ATRX mutant status. The results indicate a significant correlation between age, glioma grade, IDH1 mutation status, and 1p/19q codeletion status with our risk score \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA-F\u003cb\u003e)\u003c/b\u003e. To determine the independent prognostic factors associated with glioma patient outcomes, we conducted univariate and multivariate Cox regression analyses on potential predictive factors. The results of the analysis revealed significantly increased hazard ratios for age, glioma grade, 1p/19q codeletion status, IDH1 mutation status, ATRX mutation status, and risk score \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eG\u003cb\u003e)\u003c/b\u003e. However, the multivariate regression analysis revealed that ATRX mutation status (P\u0026thinsp;=\u0026thinsp;0.281) did not reach statistical significance, while other factors such as age (HR\u0026thinsp;=\u0026thinsp;1.045, 95% CI\u0026thinsp;=\u0026thinsp;1.030\u0026ndash;1.060, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), grade (HR\u0026thinsp;=\u0026thinsp;2.110, 95% CI\u0026thinsp;=\u0026thinsp;1.553\u0026ndash;2.867, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), 1p/19q codeletion status (HR\u0026thinsp;=\u0026thinsp;0.352, 95% CI\u0026thinsp;=\u0026thinsp;0.199\u0026ndash;0.622, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), IDH1 mutation status (HR\u0026thinsp;=\u0026thinsp;1.837, 95% CI\u0026thinsp;=\u0026thinsp;1.146\u0026ndash;2.944, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011), and risk score (HR\u0026thinsp;=\u0026thinsp;1.012, 95% CI\u0026thinsp;=\u0026thinsp;1.003\u0026ndash;1.020, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007) demonstrated statistical significance \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eH\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eNomogram for predicting glioma prognosis\u003c/h2\u003e \u003cp\u003eThe prognostic charts forecasting the 1-year, 3-year, and 5-year survival rates for the TCGA cohort displayed AUC (area under the curve) values of 0.870, 0.890, and 0.807, respectively \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. In the CGGA325 cohort, the AUC values for the 1-year, 3-year, and 5-year survival rates were 0.773, 0.851, and 0.864, respectively \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e. Similarly, in the CGGA693 cohort, the AUC values for the 1-year, 3-year, and 5-year survival rates were 0.697, 0.735, and 0.726, respectively \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC\u003cb\u003e)\u003c/b\u003e. The signature (AUC\u0026thinsp;=\u0026thinsp;0.890) appeared to provide more precise prognosis prediction compared to clinical factors such as age (AUC\u0026thinsp;=\u0026thinsp;0.804), grade (AUC\u0026thinsp;=\u0026thinsp;0.772), 1p/19q codeletion status (AUC\u0026thinsp;=\u0026thinsp;0.621), and IDH1 mutation status (AUC\u0026thinsp;=\u0026thinsp;0.785) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD\u003cb\u003e)\u003c/b\u003e. Additionally, we computed the concordance index for these clinical variables spanning from 1 to 5 years, and the risk score exhibited the largest AUC, underscoring the importance of our prognostic signature \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNext, we developed a nomogram incorporating IDH1 status, risk score, grade, 1p/19q status, and age to forecast the prognosis of glioma patients \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF\u003cb\u003e)\u003c/b\u003e. The nomogram exhibited predictive accuracy with a concordance index of 0.861 (95% confidence interval: 0.835\u0026ndash;0.888) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eG\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eFunctional enrichment analysis\u003c/h2\u003e \u003cp\u003eAfter data compilation, we conducted GO, KEGG, and GSEA functional analyses separately. The top three relevant biological processes (BP) in the graphene oxide analysis were organelle fission, nuclear division, and embryonic organ development. Simultaneously, cellular components (CC) and molecular functions (MF) were associated with collagen-containing extracellular matrix, endoplasmic reticulum lumen, chromosomal region, as well as extracellular matrix structural constituent, glycosaminoglycan binding, and peptidase regulator activity, respectively \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. Moreover, KEGG pathway analysis revealed significant pathways such as \u0026ldquo;focal adhesion\u0026rdquo; and \u0026ldquo;pathways in cancer\u0026rdquo;, further elucidating the potential regulatory mechanisms underlying tumors \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e. Finally, GSEA analysis was performed on both high-risk and low-risk groups to compare differences in enriched signaling pathways. Pathways such as anterior posterior pattern specification, embryonic, focal adhesion, graft-versus-host disease, and systemic lupus erythematosus seemed to be more associated with the high-risk group \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC\u003cb\u003e)\u003c/b\u003e, while pathways including glutamate receptor signaling pathway, positive regulation of excitatory postsynaptic potential, regulation of neuronal synaptic plasticity, neuron projection membrane, and neurotransmitter receptor complex appeared to be more associated with the low-risk group \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eCorrelation analysis of tumor immunity and molecule alteration\u003c/h2\u003e \u003cp\u003eWe utilized the ESTIMATE algorithm and found that the high-risk group exhibited higher StromlScore, ImmuneScore, and EstimateScore compared to the low-risk group \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. Subsequently, we examined the composition of various immune cells and observed a higher proportion of macrophages (including macrophage M2) in the high-risk group \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB, C\u003cb\u003e)\u003c/b\u003e. Furthermore, we conducted a comprehensive analysis of immune-related functional disparities. Significant differences were observed between the high- and low-risk groups in functions related to APC (antigen-presenting cell) co-inhibition, CD8\u0026thinsp;+\u0026thinsp;T cells, HLA (human leukocyte antigen), and other immune-related functions \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eD\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe compared the differences in Tumor Mutational Burden (TMB) between the high- and low-risk groups and found that the total TMB was significantly higher in the high-risk group than in the low-risk group \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eE\u003cb\u003e)\u003c/b\u003e. Notably, the low TMB and low risk score group (L-TMB\u0026thinsp;+\u0026thinsp;low risk) exhibited the highest survival probability \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eF, G\u003cb\u003e)\u003c/b\u003e. The mutation landscape plot revealed that the high-risk group had fewer IDH1 mutations but more mutations in EGFR and PTEN, all of which are molecular features associated with poor prognosis \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eH\u003cb\u003e)\u003c/b\u003e. Finally, we screened for potential drugs that could aid in the treatment of glioma \u003cb\u003e(Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e)\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of gene expressions at the single-cell level\u003c/h2\u003e \u003cp\u003eFurther, we performed scRNA-seq analysis to further analyze the expression of the gene signature at a single cell level. The expression characteristics of the CGGA scRNA-seq dataset after quality control and filtering are showed in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA. The top 1500 highly variable genes are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB. After PCA analysis, the top 20 PCs with \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were selected for further analysis. Totally, 15 clusters of cells were visualized by the t-SNE dimensionality reduction algorithm \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eC\u003cb\u003e)\u003c/b\u003e. Moreover, 5 major cell types (astrocytes, monocytes, macrophages, T cells, and epithelial cells) were identified \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eC\u003cb\u003e)\u003c/b\u003e. As Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eE illustrated, genes like LGALS3BP were expressed in most cell types, whereas HSPA1B was mostly expressed in macrophages and monocytes. Gene like UST and OSBPL11 was expressed very low in specific cell types.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eHSPA1B promotes the proliferation, migration and invasion of glioma cells and macrophage polarization\u003c/h2\u003e \u003cp\u003eWe investigated the correlation between HSPA1B expression and the malignant phenotype of glioma cells. Utilizing small interfering RNA (siRNA), we downregulated the expression levels of HSPA1B in U251 and T98G cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eA). Subsequent CCK8 and colony formation assays revealed that the proliferation capacity of U251 and T98G cells was inhibited upon downregulation of HSPA1B expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eB, C). Transwell and wound-healing assays demonstrated that the invasive and migratory abilities of U251 and T98G cells were suppressed following HSPA1B knockdown (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eD, E). Moreover, immunofluorescence staining analysis showed that the fluorescence intensity ratio of CD163 and CD68 was considerably lower in the si-HSPA1B group than in the control group in both U251 and T98G cell \u003cb\u003e(Supplementary Fig.\u0026nbsp;2A-B)\u003c/b\u003e. The statistical analyses were revealed in \u003cb\u003e(Supplementary Fig.\u0026nbsp;2C-D)\u003c/b\u003e. These results showed that HSPA1B promotes tumor-associated macrophage polarization in glioma cells.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eNeddylation is a post-translational modification in which a neddylation-like molecule NEDD8 is covalently attached to a lysine residue within a substrate protein\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Firstly, NEDD8 precursor undergoes proteolytic processing to maturation with exposing the C-terminal Gly residue. Next, the mature NEDD8 undergoes adenosine triphosphate (ATP)-dependent activation catalyzed by the NEDD8-activating E1 enzyme (NAE). After activation, a trans-thiolationed attaches NEDD8 to an E2-conjugating enzyme. Finally, NEDD8 was covalent attached to the lysine residue of the substrate protein, which catalyzed by the substrate-specific E3 ligase\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Neddylation plays a crucial regulatory role in various diseases particularly neurodegenerative disorders\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Researches focused on tumors has revealed that most neddylation pathway proteins are over-activated in various cancers, targeting neddylation becomes an emerging approach for the treatment of these cancers\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Several neddylation inhibitors have been developed for cancer treatment, of which MLN4924 (pevonedistat) has entered clinical trials. Although studies showed MLN4924 could inhibit the proliferation of glioblastoma cells, other studies focused on the neddylation in glioma was limited\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Therefore, it is of great significance to further explore neddylation process in glioma for the development of new therapeutic strategies.\u003c/p\u003e \u003cp\u003eIn this study, we first identified 108 potentially important NRGs in glioma through bioinformatics analysis. Then, the univariate Cox regression analysis and the LASSO regression algorithm were employed to establish a prognostic prediction signature based on these NRGs and TCGA dataset. Moreover, this signature was validated in both TCGA and CGGA databases. Among these genes, TOP2A, F2R, HSPA1B and LGALS3BP were confirmed as risk-associated genes with their high expression closely associated with poor prognosis. In contrast, UST, UROS and OSBPL11 were identified as protective genes. TOP2A, namely DNA topoisomerase II alpha, plays an important role in altering DNA topology\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. Liu et al. found that TOP2A could activate the Wnt/β-catenin pathway in glioma and promote cell growth, migration, and invasion\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. F2R encodes coagulation factor II thrombin receptor which is a ligand of thrombin\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. Consistent with our findings, F2R has been reported to be correlated with worse prognosis or the development of glioma\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. HSPA1B, as a member of heat shock protein 70 family, can stabilize existing proteins against aggregation and mediate the folding of newly translated proteins in the cytosol and in organelles\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. HSPA1B, as a subtypes of HSP70, is a promising antitumor target in many cancers\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. However, the investigations of HSP20 in glioma research is rare. LGALS3BP, namely galectin-3-binding protein, is a glycosylated protein overexpressed in various human tumors and is a potential novel diagnostic biomarker and therapeutic target in GBM deserving further validation\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. UST encodes uronyl 2-sulfotransferase which transfers sulfate to the 2-position of uronyl residue. Jiang et al. has found that UST is a favorable prognostic biomarker for glioma patients but lack of experimental validation\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. UROS encodes uroporphyrinogen III synthase which catalyzes the fourth step of porphyrin biosynthesis in the heme biosynthetic pathway and the study of UROS in cancer research is very limited\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. OSBPL11 encodes a member of the oxysterol-binding protein (OSBP) family, which serves a role in lipid metabolism. Previous study showed that down-regulated OSBPL11 may be a potential indicator for hepatocellular carcinoma\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThen, functional enrichment and GSEA analyses were performed between high-risk and low-risk groups. The results showed that extracelluar matrix and focal adhesion were significant enriched. Extracelluar matrix is an important component of glioma microenvironment and focal adhesion is closely correlated with the migration and invasion of glioma. Thus, neddylation modification may play an important role in the crosstalk between the glioma cells and their microenvironment. Next, we compared the immune scores, stromal scores, immune cell distributions and TMB between high- and low-risk groups. Stromal and immune scores in the high-risk group were higher than that in the low-risk group, which indicate that tumor stromal cell strongly facilitated the progression of tumor and glioma is surrounded with an immune-excluded microenvironment\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. Tumor-associated macrophages (TAMs) are rich in the tumor microenvironment and could interact with glioma cells to promote the progression of glioma\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. M1 and M2 are two different polarized phenotypes of macrophages and M2 macrophages are usually considered to participate in immune suppression and tumor development\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. Consistently, we found that TAMs especially M2 macrophages were high expressed in high-risk group, which indicate that the NRG signature could reflect the immune state of glioma. Previous study has been revealed that high TMB reflects high tumor proliferative activity and glioma patients with high TMB often have a shorter OS\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. We found that patients in the high-risk group have significant high TMBs and patients with high TMBs and risk scores have the shortest OS. These results indicated that our NRG signature could combined with TMB to better predict the prognosis of glioma patients.\u003c/p\u003e \u003cp\u003eWe further analyze the expression of the NRG signature at a single cell level and found that HSPA1B is mainly expressed in macrophages. Considering that HSPA1B was poorly studied in previous glioma study, we selected HSPA1B for experimental validation. Our results revealed that HSPA1B could promote the migration, invasive and proliferation of glioma cells. Moreover, we further confirmed that HSPA1B could promote M2 macrophage polarization. These results indicated that HSPA1B may serve as a novel target for glioma treatment.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, we constructed the NRG signature that could stratify glioma patients with completely different prognoses and immune microenvironment. We also found that HSPA1B, one important gene in the NRG, could promote the migration, invasive, proliferation and macrophage polarization in glioma. These findings provide novel insights in the role of neddylation in glioma development which may contribute to guiding more precise therapeutic strategies.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAUC \u0026nbsp; \u0026nbsp; Area Under the Curve\u003c/p\u003e\n\u003cp\u003eBP \u0026nbsp; \u0026nbsp;Biological Process\u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCC \u0026nbsp; \u0026nbsp;\u0026nbsp;Cellular Component\u003c/p\u003e\n\u003cp\u003eCGGA \u0026nbsp; Chinese Glioma Genome Atlas\u003c/p\u003e\n\u003cp\u003eESTIMATE \u0026nbsp; Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data\u003c/p\u003e\n\u003cp\u003eFDR \u0026nbsp; \u0026nbsp;False Discovery Rate\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGBM \u0026nbsp; \u0026nbsp;Glioblastoma Multiforme\u003c/p\u003e\n\u003cp\u003eGO \u0026nbsp; \u0026nbsp;Gene Ontology\u003c/p\u003e\n\u003cp\u003eGSEA \u0026nbsp; \u0026nbsp; Gene Set Enrichment Analysis\u003c/p\u003e\n\u003cp\u003eKEGG \u0026nbsp; \u0026nbsp;Kyoto Encyclopedia of Genes and Genomes\u003c/p\u003e\n\u003cp\u003eLASSO \u0026nbsp; \u0026nbsp; Least Absolute Shrinkage and Selection Operator\u003c/p\u003e\n\u003cp\u003eMF \u0026nbsp; \u0026nbsp;Molecular Function\u003c/p\u003e\n\u003cp\u003eOS \u0026nbsp; \u0026nbsp; Overall Survival\u003c/p\u003e\n\u003cp\u003ePCA \u0026nbsp;\u0026nbsp;Principal Component Analysis\u003c/p\u003e\n\u003cp\u003ePFS \u0026nbsp; \u0026nbsp;Progression-Free Survival\u003c/p\u003e\n\u003cp\u003eROC \u0026nbsp; \u0026nbsp;Receiver Operating Characteristic\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTCGA \u0026nbsp; \u0026nbsp;The Cancer Genome Atlas\u003c/p\u003e\n\u003cp\u003eTMB \u0026nbsp; \u0026nbsp;Tumor Mutation Burden\u003c/p\u003e\n\u003cp\u003et-SNE \u0026nbsp; \u0026nbsp;t-distributed Stochastic Neighbor Embedding\u003c/p\u003e\n\u003cp\u003eWHO \u0026nbsp; \u0026nbsp;World Health Organization\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data source of the manuscript is from the public database. The details were displayed in the materials and methods section. Other data could contact with the corresponding authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the National Natural Science Foundation of China (81472355), and Provincial Natural Science Foundation of Hunan (2022JJ30931).\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCR and XJ conceived the study. ZJ, WY, GT, HH, LW, WL, and ZW collected and analyzed data and visualized figures. ZJ and WY wrote the manuscript. All authors reviewed and approved the submitted manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe sincerely thank the CGGA and TCGA databases for freely providing the transcriptomic information of glioma samples.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLiu Y, Ali H, Khan F, et al. Epigenetic regulation of tumor-immune symbiosis in glioma [J]. Trends Mol Med; 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHorbinski C, Berger T, Packer RJ, et al. Clinical implications of the 2021 edition of the WHO classification of central nervous system tumours [J]. Nat Rev Neurol. 2022;18(9):515\u0026ndash;29.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSmith K, Nakaji P, Thomas T, et al. 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Causal effect of porphyria biomarkers on alcohol-related hepatocellular carcinoma through Mendelian Randomization [J]. PLoS ONE. 2024;19(3):e0299536.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeng H, Yan Z, Zeng X, et al. Serum and tissue proteomic signatures of patients with hepatocellular carcinoma using 2\u0026ndash;D gel electrophoresis [J]. Mol Med Rep. 2019;20(2):1025\u0026ndash;38.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTang W, Lo CWS, Ma W, et al. Revealing the role of SPP1(+) macrophages in glioma prognosis and therapeutic targeting by investigating tumor-associated macrophage landscape in grade 2 and 3 gliomas [J]. Cell Biosci. 2024;14(1):37.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi M, Xu H, Qi Y, et al. Tumor-derived exosomes deliver the tumor suppressor miR-3591-3p to induce M2 macrophage polarization and promote glioma progression [J]. Oncogene. 2022;41(41):4618\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYin W, Jiang X, Tan J, et al. Development and Validation of a Tumor Mutation Burden-Related Immune Prognostic Model for Lower-Grade Glioma [J]. Front Oncol. 2020;10:1409.\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":"Glioma, Neddylation, HSPA1B, Tumor immunity","lastPublishedDoi":"10.21203/rs.3.rs-4209486/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4209486/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eGlioma is the most prevalent malignant tumor that originates from central nervous system. Neddylation, a post-translational modification similar to ubiquitination, is involved in tumorigenesis and progression. However, there were limited studies focused on the neddylation in glioma. Therefore, we aimed to explore the potential role of neddylation in glioma.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn this study, neddylation-related genes (NRGs) were identified and were used to construct a prognostic signature for glioma patients. Based on this prognostic index, we also explored the differences in clinical features, mutational landscape, immune cell infiltration between high-risk and low-risk groups. Next, single-cell RNA sequencing analysis was further performed to verify the expression of these genes in NRG signature. At last, one gene selected from the NRG signature were validated by \u003cem\u003ein vitro\u003c/em\u003e experiments.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eSeven genes (TOP2A, F2R, UST, HSPA1B, LGALS3BP, UROS, and OSBPL11) were identified to construct the NRG signature, which was able to successfully classify glioma patients into high-risk and low-risk groups. A nomogram based on the NRG signature and other prognostic factors were developed to accurately predict the prognosis of glioma. Significant differences in prognosis, mutational landscape, immune cell infiltration were found between distinct groups. Moreover, in vitro experiments illustrated that knockdown of HSPA1B could inhibit the proliferation, migration, and invasion of glioma cells and also inhibit the polarization of M2 macrophages.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThese findings provide new insights into understanding the relationship between NRGs and glioma development and identify novel biomarkers may help to guiding precise treatments to glioma.\u003c/p\u003e","manuscriptTitle":"Neddylation-related gene signature predicts the prognosis and is associated with immune infiltration of glioma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-08 17:41:35","doi":"10.21203/rs.3.rs-4209486/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":"ca941121-6957-4ef4-b49b-6a659926d00f","owner":[],"postedDate":"April 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-22T07:08:50+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-08 17:41:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4209486","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4209486","identity":"rs-4209486","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}