Identification and validation of SUN modification-related anti-PD-1 immunotherapy-resistance signatures to predict prognosis and immune microenvironment status in glioblastoma

Identification and validation of SUN modification-related anti-PD-1 immunotherapy-resistance signatures to predict prognosis and immune microenvironment status in glioblastoma | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Identification and validation of SUN modification-related anti-PD-1 immunotherapy-resistance signatures to predict prognosis and immune microenvironment status in glioblastoma Hong Zhang, Meiyan Gao, Zhen Gao, Li Yao, Hong Sun, Huqing Wang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6838293/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 29 Nov, 2025 Read the published version in BMC Cancer → Version 1 posted 11 You are reading this latest preprint version Abstract Background: Ubiquitination, SUMOylation, and neddylation (collectively termed SUN modifications) play crucial roles in cancer pathogenesis and immunotherapy resistance. This study investigated the prognostic significance of these modifications in glioblastoma (GBM). Methods: Key genes associated with SUN modifications and anti-PD-1 resistance were identified using integrated bioinformatic approaches, including differential expression analysis, Weighted Gene Co-expression Network Analysis (WGCNA), and machine learning algorithms. The expression levels of identified genes were subsequently validated in GBM cell lines using RT-qPCR and Western blotting. A prognostic risk model was constructed based on the key genes. Single-cell RNA sequencing (scRNA-seq) and spatial transcriptome analysis were further employed to characterize gene expression patterns. Results: Six prognostic genes (PLK2, CDC73, PSMC2, SOCS3, ETV4, and LMO7) were identified. CDC73, PSMC2, SOCS3, and ETV4 were upregulated, while PLK2 and LMO7 were downregulated in GBM cells. The six-gene prognostic risk model demonstrated excellent predictive performance, achieving an Area Under the Curve (AUC) exceeding 0.9. The derived risk score exhibited significant correlations with clinical features, immune infiltration levels, and drug sensitivity profiles. Furthermore, scRNA-seq and spatial transcriptome analysis revealed high SOCS3 expression specifically in monocytes and macrophages, suggesting its potential role in mediating the activity of these immune cells to influence tumor progression and drug sensitivity in GBM. Conclusion: This study established a robust six-gene prognostic model related to SUN modifications and anti-PD-1 therapy in GBM. The model demonstrates strong predictive ability and correlates with clinically relevant parameters, highlighting its potential utility for survival prediction and guiding therapeutic management decisions in GBM patients. Glioblastoma posttranslational modification SUMOylation ubiquitination neddylation immune microenvironment Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1 Introduction Glioblastoma (GBM) commonly referred to the GBM (grade 4), which was updated to be glioblastoma, IDH-wildtype in the WHO 2021 CNS Tumor classification [ 1 ]. GBM and malignant gliomas constitute the most prevalent and highly fatal brain tumors in the adult population, with annual incidence of 5.26 per million population or 17 thousand new diagnoses per year [ 2 ]. GBM has a dismal prognosis, with a median survival duration of fewer than two years following diagnosis [ 3 ]. The therapy methods for GBM contains surgical resection, radiotherapy, chemotherapy, and target therapy [ 4 ]. Although with various therapy option, the survival rate of GBM is still less than 5% [ 5 ]. GBM exhibits great inter-tumor and intra-tumor heterogeneity [ 6 ], so, the prognosis of GBM in clinic remains difficulties. Therefore, there is a pressing need to discover more efficacious and novel prognostic biomarkers for GBM in order to ameliorate its prognosis. Posttranslational modifications (PTMs) are the biochemical modifications of proteins after protein biosynthesis, which control the protein abundance and function exceed inherent transcriptional regulation [ 7 ]. PTMs could modulate protein biosynthesis process via modification like phosphoryl, methyl, acetyl, and glycosyl. Recently, aberrant regulatory roles of PTMs in diseases are proposed. For example, USP36 has been demonstrated to promote tumorigenesis and drug resistance in GBM through deubiquitination [ 8 ]. Furthermore, targeting PTMs alter, a novel therapeutic method is also proposed. The latest study by Yue et al. demonstrated that suppressing lactylation in GBM could increase its sensitivity to cancer therapy [ 9 ]. Immune checkpoint blockade, particularly anti-programmed cell death protein-1 (PD-1)/PD-1 ligand-1 immunotherapy, exhibits considerable promise in the management of diverse cancer types, encompassing GBM [ 10 ]. However, still less than 10% of patients show an objective response to anti-PD-1 therapy [ 10 ]. PTM of PD-1 has been demonstrated to be a potential target for cancer immunotherapy, as it affects the anti-tumor immunity of T cells [ 11 – 13 ]. These evidences indicate the crucial role of PTMs in GBM progression and anti-PD-1 therapeutic research, prompting us to explore PTMs related biomarkers in GBM to predict the GBM occurrence and anti-PD-1 therapeutic efficiency. Ubiquitination, alongside small ubiquitin-like modifier (SUMOylation) and neuronal precursor cell-expressed developmentally down-regulated protein 8 modification (neddylation), collectively constitute the three primary types of PTMs known as SUN [ 14 ]. Previous studies have highlighted the critical role of SUN in cancer cell apoptosis, the cell cycle, and other biological processes. For example, it is reported that the SUMOylation modification of HNRNPK at the specific site interferes its DNA-binding ability, and thus promotes GBM invasion [ 15 ]. In some studies, several SUN-related prognostic signatures based on SUN has been exploited, such as a two-gene SUMOylation signature in prostate cancer [ 16 ] and a three-gene ubiquitination signature in pancreatic ductal adenocarcinoma [ 17 ]. Nevertheless, a systematic investigation for SUN-related prognostic signature in GBM remains absent to date. Considering the crucial role of SUN-related genes in the GBM development and antiPD-1 therapy, we hypotheses that there were key SUN genes which can be used for survival predicting and drug therapeutic outcomes in GBM. In our study, SUN modified anti-PD-1 immunotherapy resistance genes (SUNIRDEGs) were identified using bioinformatic tools through generating data from public database; and the main SUNIRDEGs were validated utilizing RT-qPCR and WB analysis. Following, a prognostic risk scoring system was developed utilizing the key genes, which might be efficient in predicting the disease and therapeutic outcomes. The research framework is depicted in Fig. 1 . 2 Methods 2.1 Data source The GBM data was retrieved from The Cancer Genome Atlas (TCGA)-GBM database [ 18 ] and China Glioma Genome Atlas (CGGA) database. Then the human gene annotation files (GRCh38. P14) were retrieved from the GENCODE database [ 19 ]. Based on the annotation files, data from TCGA-GBM and CGGA database was preprocessing as listed below: (1) samples featuring missing or zero survival time were omitted; (2) samples were removed if they contained more than 50% missing values or unexpressed genes; (3) all expression values underwent log 2 (X + 1) transformation. Subsequently, a total of 166 GBM samples were retrieved from the TCGA-GBM database. A total of 85 and 133 samples were obtained from CGGA-325 and CGGA-693 datasets in CGGA database, respectively. Furthermore, data for 207 normal samples were downloaded from Genotype-Tissue Expression (GTEx) database. For the consistency and standardization, all the above data were uniformly processed using the Toil process of UCSC XENA [ 20 ]. Finally, Anti-PD-1 immunotherapy data GBM-PRJNA482620 were obtained from a previously literature [ 21 ], which included 17 patients with Nonresponse and 17 patients with Response. 2.2 Differential expression analysis Limma package [ 22 ] was adopted to select the differential expressed genes (DEGs) between GBM and normal tissues. The criteria for selection were designed at FDR 1. 2.3 Weighted Gene Co-expression Network Analysis (WGCNA) WGCNA analysis[ 23 ] was utilized to determine the gene modules which were highly associated with GBM based on GBM-PRJNA482620 dataset. Firstly, after comprehensive consideration to ensure the robustness of the network, construction efficiency and biological significance, the top 5000 genes in GBM-PRJNA482620 were selected based on the mean absolute deviation (MAD). Next, WGCNA package [ 23 ] was utilized to identify the gene modules which were highly linked to anti-PD-1 immunotherapy, with Nonresponse and Response as the phenotypic characteristics. 2.4 Identification of SUN modified immunotherapy-resistance genes The SUN modified gene set was extracted from the Molecular Signatures Database (MsigDB) with the keywords "SUMOylation, ubiquitination, and neddylation". The related pathway genes from REACTOME_SUMOYLATION, REACTOME_PROTEIN_UBIQUITINATION and REACTOME_NEDDYLATION were utilized as SUN modified genes for the subsequent analysis. DEGs were intersecting with SUN modified genes and anti-PD-1 immunotherapy genes, namely DEGs-SUN genes and DEGs-drug resistance genes, respectively. Pearson correlation analysis was performed on the intersection results of the two parts of genes to identify SUN modified anti-PD-1 immunotherapy resistance genes (SUNIRDEGs), with the threshold of |R| > 0.4 and P < 0.001. 2.5 Identification of prognostic related genes Based on the expression levels of above SUNIRDEGs in TCGA-GBM, and combined the clinical OS survival, univariate cox was performed to evaluate the prognostic value of each gene utilizing survival package [ 24 ] in R software. P < 0.05 was deliberated as the threshold to filter genes with significant prognostic relevance. 2.6 Exploitation and evaluation of the prognostic gene signature Ten different machine learning algorithms [ 25 ] were performed to exploit the optimal predicting model. All the predicting models were obtained by LOOCV framework fitting based on TCGA-GBM data, and then these models were validated in CGGA-325 and CGGA-693 datasets. C-index was calculated to select the optimal models. In accordance with the median value of the risk score, the cohort of patients with GBM was stratified into high-risk and low-risk groups. Following this, Kaplan-Meier survival analysis was conducted to assess the prognostic implications. Furthermore, both calibration and ROC curves were constructed to assess the prognostic impact of the risk score. Finally, to examine whether our prognostic risk score model possesses superior predictive effects, we also compared the SUNIRDEGs prognostic signature with 10 published signatures predicting patient outcomes [ 26 ]. To make the signatures comparable, the same method was utilized to calculate and transform the C-index of published prognostic signatures in TCGA and CGGA datasets. 2.7 Construction of prognostic nomogram model To ascertain the independent prognostic factors, Cox regression analyses, encompassing both univariate and multivariate approaches, were executed on clinical variables, specifically including the risk score, age, gender, and IDH status. The clinical characteristics with significant correlation with clinical prognosis were selected ( P < 0.05). The nomogram model was constructed utilizing the rms package (Version 6.8-0) [ 27 ] within the R software framework. 2.8 The differences of genome, immune feature, and function GBM mutation data were obtained from TCGA database for the subsequent analysis. The maftools package (version 2.17.0) [ 28 ] was adopted to select the mutation frequency of the top20 genes. To further observe the correlation between risk score and immune infiltrating cells, three immune microenvironment analysis algorithms ESTIMATE [ 29 ], CIBERSORT [ 30 ], and ssGSEA [ 31 ] were utilized to evaluate the immune infiltration levels of each GBM sample. Based on the corresponding subset h.all.v2023.2.Hs.symbols.gmt in MSigDB database, GSVA algorithm [ 32 ] was performed to estimate the hallmark enrichment score of all GBM samples to investigate the differential KEGG pathways between the risk groups. 2.9 The differences of drug sensitivity and immunotherapy response The sensitivity of each GBM samples to the chemotherapy drugs were evaluated based on the Genomics of Drug Sensitivity in Cancer (GDSC) database [ 33 ]. pRRophetic package (version 0.5) [ 34 ] in R software was utilized to quantify IC50. TIDE [ 35 ] was utilized to calculate the sample immunotherapy response score. Furthermore, immunophenoscore (IPS) was utilized to calculate the scores of four different immunophenotypes: antigen presentation (MHC molecules), effector cells (EC), suppressor cells (SC), and immune checkpoints (CP) [ 36 ]. Finally, based on the anti-PD-1 immunotherapy data from GBM-PRJNA482620, the links between risk score and immunotherapy response was evaluated to predict the immunotherapy for patients with GBM. 2.10 Key genes expression in various immune cells Here, the single-cell RNA sequencing (scRNA-seq) data of GBM was retrieved from the Tumor Immune Signature Consortium Hub (TISCH2) database [ 37 ]. Combined with the spatial transcriptomics data from the spatial omics resource in cancer (SORC) [ 38 ], the expression levels of the key genes in different cells were evaluated. 2.11 Identification of molecular subgroups of GBM Based on the key gene expression data obtained, ConsensusClusterPlus package [ 39 ] in R software was utilized to conduct unsupervised clustering analysis to identify the molecular subgroups of GBM. The clustering cluster number was set as 2 to 9 k; the clustering method was chosen as KMeans algorithm; and the distance was calculated as Euclidean distance. At the same time, Kaplan-Meier curve was employed to acclimate to observe the survival of samples between subgroups. 2.12 Cell culture Cell lines, including Human brain astrocytes (SVG p12) and various human glioma cell lines (SHG44, U87, and U251), were obtained from the Cell Bank/Stem Cell Bank of the Chinese Academy of Sciences. These cells were subsequently cultivated in Dulbecco's Modified Eagle's Medium (DMEM) supplemented with 10% fetal bovine serum (FBS) and 1% penicillin-streptomycin. 2.13 RT-qPCR Total RNA was isolated using Trizol reagent (Invitrogen), adhering to the manufacturer's guidelines. RNA concentration was accurately determined with a NanoDrop ND-1000 spectrophotometer (NanoDrop Technologies). Following this, reverse transcription was conducted to convert RNA into cDNA, employing SuperScript IV reverse transcriptase (Thermo Fisher Scientific, Waltham, MA, USA). For quantitative PCR (qPCR) analysis, Platinum™ Taq DNA Polymerase High Fidelity (Thermo Fisher Scientific) was utilized on an Applied Biosystems 7500 Real-Time PCR System (Applied Biosystems, Foster City, CA). The qPCR protocol encompassed an initial denaturation at 95°C for 30 seconds, followed by 40 amplification cycles consisting of denaturation at 95°C for 15 seconds, primer annealing at 60°C for 30 seconds, and DNA extension at 75°C for 60 seconds. The primer sequences used for qPCR analysis are listed in Table 1 , and gene expression levels were normalized to glyceraldehyde-3-phosphate dehydrogenase (GAPDH) as an internal reference. Table 1 The primers. Primers Primer sequence (5’ to 3’) Length (bp) GAPDH-S GGAAGCTTGTCATCAATGGAAATC 168 GAPDH-A TGATGACCCTTTTGGCTCCC CDC73-S TGACACTGAAATCTGTAACGGAGG 104 CDC73-A ATTTGGGGGAGGTCTTGCTT PLK2-S GCAGTAGAAGGTCAATGGCTCA 84 PLK2-A CAATCTGCCTGAGGTAGTATCGAAC PSMC2-S GTATTAAAGAATCTGACACTGGCCT 153 PSMC2-A CGTTGATAATGTATTTTGGGTCCTC SOCS3-S CCTACTGAACCCTCCTCCGA 242 SOCS3-A TGGTCCAGGAACTCCCGAAT LMO7-S GCCCACAGGATTCTATGCTTCTT 242 LMO7-A TCCCACTGACTGACCTGTTACG ETV4-S AAGGAGACATCAAGCAGGAAGG 180 ETV4-A CGACCTCCTCAGGCTCAATG 2.14 Western blotting (WB) WB was used for detection of protein levels of the key genes (PLK2, CDC73, PSMC2, SOCS3, ETV4, and LMO7). Total protein was isolated using RIPA lysis buffer, and then the protein concentration was quantified using a bicinchoninic acid (BCA) kit. The membranes were incubated with primary antibodies at 4°C overnight, followed by incubation with second antibody. The primary antibodies used in this assay were as follows: anti- PLK2 Antibody (1: 1000, 15956-1-AP, Proteintech Group, Wuhan, China), anti- CDC73 Antibody (1: 1000, 66490-1-Ig, Proteintech Group, Wuhan, China), anti- PSMC2 Antibody (1: 1000, GB113120, Servicebio, Wuhan, China), anti- SOCS3 Antibody (1: 1000, GB113792, Servicebio, Wuhan, China), anti- ETV4 Antibody (1: 1000, 10684-1-AP, Proteintech Group, Wuhan, China), anti- LMO7Antibody (1: 1000, 29392-1-AP, Proteintech Group, Wuhan, China), anti- GAPDH Antibody (1: 1000, GB15004, Servicebio, Wuhan, China), The second antibody used in this assay were HRP goat anti-rabbit (1: 3000, GB23303, Servicebio, Wuhan, China) and HRP goat anti-mouse (1: 3000, GB23301, Servicebio, Wuhan, China). Protein quantification normalization was established utilizing GAPDH as the endogenous reference. Immunoreactive bands were subsequently detected through enhanced chemiluminescence substrates (ECL, Sigma-Aldrich) and subjected to densitometric quantification employing ImageJ software suite (v1.8.0, NIH, USA). 2.15 Statistical analysis Statistical analyses were conducted using R software (version 4.3.3). To compare differences between the two groups, the Wilcoxon rank-sum test was applied. Correlations between the groups were assessed using the Spearman correlation method. A P -value less than 0.05 was viewed statistically significant. 3 Results 3.1 Identification of SUN modified immunotherapy-resistance genes In differential expression analysis, 6159 DEGs between GBM and control were determined, with 4381 upregulated, and 1777 downregulated (Fig. 2 A). In the WGCNA, hierarchical clustering displayed that SRR8281245 sample was clearly deviant (Fig. 2 B), which were deleted in the following analysis. Appropriate soft threshold was chosen as 7 and all the GBM samples were divided into 13 modules (Fig. 2 C-D). Following, yellow-green and red modules were noticeably positively correlated with Nonresponse (Fig. 2 E) and 595 genes included in the two modules were served as the immunotherapy-resistance genes. After interesting analysis, a total of 200 intersecting DEGs-SUN and 198 DEGs-drug resistance genes were obtained (Fig. 2 F). Finally, 355 cross-linked genes were obtained as SUNIRDEGs after integration and deduplication. 3.2 Exploitation of the prognostic gene signature All the 355 cross-linked SUNIRDEGs were involved in the univariate cox analysis. Finally, 33 prognostic SUNIRDEGs were confirmed to be markedly correlated with the overall survival (OS) (Fig. 3 A). Then, based on the 33 prognostic SUNIRDEGs, 10 algorithms were performed to select an optimal algorithm to exploit the prognostic signature. The results demonstrated that lasso + RSF exhibited best performance in prognosis in all the three datasets, with highest average C-index as 0.761 ( Figure S1 ). Finally, through further Lasso and RSF filtration, 33 prognostic SUNIRDEGs were optimized to six key signature genes: PLK2, CDC73, PSMC2, SOCS3, ETV4, and LMO7 (Fig. 3 B-D). Based on the six key signature genes, the risk score model was formulated as the prognostic signature. 3.3 Evaluation of the prognostic gene signature in the TCGA-GBM dataset, all GBM samples were categorized into high- and low-risk cohorts based on the median risk score. Kaplan-Meier survival analysis demonstrated that patients in the high-risk cohort had noticeably worse OS compared to those in the low-risk cohort (Fig. 3 E, P < 0.0001). The ROC curve exerted that the predicting ability of the risk score model were excellent, with AUC above 0.9 (Fig. 3 F). The predicting ability of the risk score model were confirmed both in CGGA-325 and CGGA-693 dataset (Fig. 3 E and 3 F). To further evaluate the predictive performance of risk score, its predictive power was compared with signatures in precious literatures and the results illustrated that our risk score model had better predictive performance than almost all the signatures in each dataset (including metacohort) (Fig. 3 G). These results comprehensively implied that the risk score model related to SUN modified immunotherapy-resistance genes in our study displayed fantastic predicting ability in OS of patients with GBM and might be a robust signature in GBM prognosis. 3.4 Construction of prognostic nomogram model The clinical analysis suggested substantially different risk score between different IDH statues, showing as higher risk score in IDH wild-type (IDHwt) group than that in IDH mutation (IDHmut) group (Fig. 4 A, P < 0.001). Kaplan-Meier analysis displayed that IDHwt was correlated with shorter survival time (Fig. 4 B). Postoperative temozolomide (TMZ) combined with chemoradiotherapy is the first choice for therapy of GBM [ 40 ]. Our findings demonstrated that risk score was higher in patients who did not receive TMZ therapy (Fig. 4 C). MGMTp methylation status can predict the sensitivity of patients with GBM to TMZ therapy [ 41 ]. In our results, patients who did not develop MGMTp methylation had a higher risk score (Fig. 4 D). These results indicated that risk score was obviously associated with the clinic features of patients with GBM and might be an effective indicator in clinic. In the subsequent analysis, univariate and multivariate cox analysis implied that risk score and IDH status were independent predictive factors in GBM (Fig. 4 E). The independent factors risk score and IDH status were further involved into the nomogram (Fig. 4 F). The calibration curve implied that the gradients between measured OS and nomogram-predicted OS were close to 1 (Fig. 4 G), thereby demonstrating the efficacy of the nomogram. And the Kaplan-Meier survival curve revealed an inverse correlation between the nomogram score and survival probability (Fig. 4 H). Furthermore, the ROC curve also indicated the robust predictive capacity of nomogram in OS, with AUC above 0.9 (Fig. 4 I). 3.5 The differences of genome, immune feature, and function Gene mutation occurred in both high- and low-risk groups and the mutation frequency type mainly was missense mutation and SNP ( Figure S2 ). The ESTIMATE analysis revealed significant differences in immune, stromal, ESTIMATE scores, and tumor purity between the two risk groups, with the high-risk group exhibiting elevated immune, stromal, and ESTIMATE scores, as well as reduced tumor purity (Fig. 5 A, P < 0.05), indicating active immune response under high-risk score. Moreover, immune cell infiltration levels were also investigated utilizing CIBERSORT and ssGSEA (Fig. 5 B and 5 C). The analysis revealed significant variations in immune infiltration levels between the two risk groups, with the high-risk group exhibiting elevated levels of regulatory T cells and natural killer cells compared to the low-risk group (Fig. 5 C, P < 0.01). Functional annotation indicated that the pathways distinguishing the two risk groups were primarily associated with oncogenic processes, including the MAPK, NF-kappa B, NOD-like receptor, p53, and PI3K-Akt signaling pathways (Fig. 5 D). 3.6 The differences of drug sensitivity and immunotherapy response Drug sensitivity analysis indicated significant differences of 25 drugs in IC50 values between the two risk groups. Notably, Rapamycin, CGP.60474, and AZD6244 exhibited substantially higher IC50 values in the low-risk group compared to the high-risk group (Fig. 6 A, P < 0.01), indicating reduced drug sensitivity in the high-risk cohort. Furthermore, the high-risk group had a significantly higher TIDE score than the low-risk group (Fig. 6 B, P < 0.0001), implying that GBM in the high-risk group may exhibit enhanced immune evasion and a diminished response to ICB therapy. Additionally, IPS analysis displayed that suppressor cells (SC) and immune checkpoints (CP) were significantly more abundant in the low-risk group compared to the high-risk group (Fig. 6 C, P < 0.05), implying potential sensitivity to ICB treatment in the low-risk cohort. Moreover, the risk score was markedly higher in the Nonresponse group compared to the Response group (Fig. 6 D, P < 0.01) and high-risk group contained more Nonresponse patients (Fig. 6 E, P < 0.05). High risk score was substantially linked to poor prognosis (Fig. 6 F, P < 0.01). 3.7 The expression validation of the key genes in glioblastoma The expression levels of the six identified SUN modified immunotherapy resistance genes were investigated in the Gene Expression Profiling Interactive Analysis (GEPIA), Gene Expression Omnibus (GEO, GSE16011 dataset), and Human Protein Atlas database (HPA) databases. The results indicated that CDC73, PSMC2, SOCS3, and ETV4 were substantially upregulated in tumor group than that in control group, whereas PLK2 and LMO7 were substantially downregulated in tumor group than that in control group (Fig. 7 A-C). RT-qPCR and WB validation also validated that CDC73, PSMC2, SOCS3, and ETV4 were substantially upregulated in GBM cells (SHG-44, U87, and U251) than that in control astrocytes group (SVG p12), whereas PLK2 and LMO7 were substantially downregulated in GBM cells than that in control microglia (Fig. 7 D, 7 E and S3, P < 0.05). 3.8 The expression of the key genes in various immune cells scRNA-seq data implied differential expression of the six key genes across various immune cell types (Fig. 8 A). Among which, SOCS3 was highly expressed in monocytes and macrophages. Subsequent spatial transcriptome data based on SORC database also revealed that SOCS3 was highly expressed in monocytes and macrophages (Fig. 8 B). Subtype analysis divided all the GBM samples into two clusters: cluster 1 (69 samples) and cluster 2 (97 samples) (Fig. 8 C); and cluster 2 possessed worse survival and contained more high-risk samples (Fig. 8 D and 8 E, P < 0.01). 4 Discussion Many crucial life activities are mediated by ubiquitin and ubiquitin-like alterations, and their dysfunction is also associated with pathological progression such as immunological dysfunction and tumor progression [ 42 ]. PTM of PD-1 has been identified as a promising target for cancer immunotherapy, influencing the anti-tumor immune response of T cells [ 11 – 13 ]. Hence, early identification and prediction of diagnosis promote better survival and treatment efficacy. In our study, through WGCNA and machine learning algorithms, six SUN modified immunotherapy resistance genes were confirmed to involve in GBM prognosis: PLK2, CDC73, PSMC2, SOCS3, ETV4, and LMO7. Among which, CDC73, PSMC2, SOCS3, and ETV4 were substantially upregulated; whereas PLK2 and LMO7 were substantially downregulated in glioblastoma cells than that in control. Based on these six optimal genes, a prognostic risk model was generated, which exhibited excellent prognostic significance with AUC above 0.9. Through comparison of our risk score with ten previous published risk score, our risk score possessed best predicting ability in GBM, with better sensitivity and specificity in clinic application. The six key genes exhibit significant role in GBM. The polo-like kinases (PLKs) constitute a family of serine-threonine kinases that play regulatory roles in various cellular processes. PLK2 is deliberated a potential inhibitory factor in tumors. PLK2 is down-regulated in GBM, and its upregulation links to poor prognosis [ 43 ], which is coincidence with our study. PLK2 might be a novel chemotherapy-resistant biomarker and therapeutic target in GBM. For example, Alafate et al. demonstrated that PLK2 inhibition could stimulate acquired resistance to TMZ through activation of PLK2/Notch axis [ 44 ]. Furthermore, the latest evidences indicates that PLK2 is also involved in various PTM process, including phosphorylation and ubiquitination [ 45 , 46 ]. Tan et al. demonstrated that DYRK1A-mediated phosphorylation of PLK2 regulates the proliferation and invasion of GBM cells [ 43 ]. Ge et al. demonstrated that PLK2 can inhibit oxidative stress through phosphorylating GSK3β in ischemia-reperfusion injury [ 46 ]. CDC73, full named cell division cycle 73, has been demonstrated to regulate Notch-induced T-cell leukemia cells [ 47 ] in various disorders. For example, in esophageal cancer, CDC73 acts as a tumor-promoting factor [ 48 ]. CDC73 was upregulated in esophageal cancer, and its downregulation effectively hinders the proliferation and growth of esophageal cancer cells. In parathyroid cancer, CDC73 is a tumor suppressor gene, and variant of CDC73 is related to risk of parathyroid carcinoma [ 49 ]. Nevertheless, the specific role of CDC73 in GBM have not been clarified in GBM. A recent study demonstrated that CDC73 involve in ubiquitin-proteasome degradation in hyperparathyroidism-jaw tumor syndrome [ 50 ]. This evidence implied that CDC73 might affect GBM progression through regulation of ubiquitination, and the underline mediatory role should be clarified in future. PSMC2 is an important gene in proteasome complex. A pan-cancer analysis has suggested that PSMC2 serves as a reliable prognostic biomarker for predicting the response to immunotherapy [ 51 ]. Elevated expression of PSMC2 has been observed in gliomas and is associated with a poor prognosis for patients with this disease [ 52 ]; inhibition of PSMC2 can inhibit the cancer progression and drug resistance through regulation of immune microenvironment and cell autophagy [ 53 , 54 ]. Evidence from other cancers also indicated the positive role of PSMC2 knockdown in tumor progression [ 55 , 56 ]. SOCS3 represents a promising target for the treatment of metabolic disorders [ 57 ]. In GBM, SOCS3 is reported to be related to chemotherapy radiotherapy resistance acquisition [ 58 , 59 ]. Overexpression of SOCS3 is found in GBM [ 58 , 60 ], which is coincidence with our results. SOCS3 mainly regulate the GBM drug sensitivity through JAK/STAT phosphorylation signaling [ 60 – 62 ]. Furthermore, SOCS3 exhibits as an onco-immunological biomarker in GBM, attributing to its immune regulation role [ 63 ]. Goswami et al. demonstrated that Kdm6b absence enhances antigen presentation and anti-PD1 efficacy in myeloid cells by inhibition of Socs3 [ 64 ]. Hence, SOCS3 might also be an important immunotherapy target in GBM. E-twenty-six-specific sequence variant transcription factor 4 (ETV4) has also been demonstrated to involve in tumor progressions. In hepatocellular carcinoma, ETV4 elevation facilitates tumor metastasis by upregulating PD-L1 [ 65 ]. In Multiple Myeloma, ETV4-dependent transcriptional plasticity can maintain MYC expression and is related to drug resistance [ 66 ]. In GBM, ETV4 is upregulated and its knockdown promote the autophagy and cell apoptosis trough inhibition of PI3K/AKT/mTOR signaling pathways [ 67 ]. Furthermore, the phosphorylation regulation roles of ETV4 are also been reported in several cancers [ 68 , 69 ]. LIM domain only 7 (LMO7) gene played crucial roles in regulating cell growth, differentiation, protein localization, signal transduction, and intracellular protein complex assembly, establishing it as a hallmark gene in cancer[ 70 ]. Research has indicated an inverse relationship between LMO7 expression and the progression as well as prognosis of human lung adenocarcinoma [ 71 ]. In pancreatic ductal carcinoma, LMO7 is reported to regulate the T cell differentiation and chemotaxis and thus achieve the immune escape [ 72 ]. Nevertheless, its function in GBM remains unexplored. In summary, there is currently a dearth of evidence linking these six genes to ubiquitination, SUMOylation, and Neddylation in GBM. Our study identified CDC73, PSMC2, SOCS3, and ETV4 as pivotal SUN-modified genes that contribute to resistance to anti-PD-1 therapy. Notably, these genes were also observed to be overexpressed in GBM cells relative to microglia. Conversely, PLK2 and LMO7 were downregulated. Suggesting that these six genes are appropriate to develop a predictive model, and the excellent predictive performance of the resulting risk scoring model further supports this point. In our investigation, high risk score indicated a worse survival. The AUC value of the risk score model was above 0.9, which indicated a relatively high predicting efficiency, which exhibited potential application ability in predicting the survival of patients with GBM in clinic. That is, a patient with a higher risk score is likely to have a lower probability of survival. Furthermore, our results proved that the risk score constructed by the above six key genes revealed remarkably correlation with clinic features like IDH mutation, TMZ therapy, and MGMTp methylation. This results further demonstrated the advantages of the risk score in predicting GBM. Furthermore, GBM are highly heterogenetic. We examined the correlation between risk score and gene mutation. The results displayed that PTEN and TP53 were the main mutation genes. Among which, TP53 is a critical gene in normal tumor growth and its mutation predicts cancer deteriorate and drug resistance [ 73 , 74 ]. Moreover, although p53 is known to undergo SUMOylation, the precise function of this modification in oncogenesis remains elusive. More importantly, we found elevated immune cell infiltration levels in high-risk GBM, like higher regulatory T cell and natural killer cells, indicating immune microenvironment alteration. Moreover, 25 drugs displayed different IC50 between the two different risk status, indicating different sensitivity of patients with GBM to different drugs. Higher IC50 of Rapamycin, CGP.60474, and AZD6244 were found in low-risk score, indicating GBM in low-risk group exhibited greater sensitivity to drugs. A higher TIDE score was associated with high-risk status, suggesting an increased likelihood of immune escape and a suboptimal response to ICB. These results imply that risks core could indicate the immune status and guide the selection of clinical drugs for GBM. Finally, we estimated the expression levels of the key genes across various immune cell types. scRNA-seq and spatial transcriptome analysis demonstrated that SOCS3 was highly expressed in monocytes and macrophages. SOCS3 is reported to be a modulator of macrophage, which could drive macrophage inflammatory responses and modulate the efficiency of phagocytic processes [ 75 ], indicating that SOCS3 might affect tumor progression and drug sensitivity through mediate the macrophages and monocytes in GBM. In summary, we identified six genes associated with SUN-modified anti-PD-1 resistance in GBM and constructed a promising prognostic model. Our prognostic risk score system exhibited superior performance compared to ten previously published signatures. However, several limitations exist. First, our data comes from multiple databases and online sources, which might increase the data diversity and complexity. Second, the algorithm for bioinformatics analysis itself may have some unknown limitations. Third, our whole investigations and analyses largely depend on retrospective data. Fourth, the precise regulatory mechanism of the six key genes and the predicting ability of six-gene prognostic model should be explored and validated in larger cohorts and experiments in future. 5 Conclusion A six-gene prognostic model related to ubiquitination, SUMOylation, and neddylation and anti-PD-1 response is constructed in GBM, and the model exhibits excellent predictive properties in the clinical survival and drug sensitivity, which is benefit for accurate prediction and refined treatment in clinic. Future research should focus on exploring the crucial role and potential mechanism of the model and model genes in GBM. Declarations Acknowledgments We would like to acknowledge with appreciation the contributions of the TCGA, GTEx, HPA, and CGGA databases. Funding This work was supported by funds from the National Natural Science Foundation of China (81070999), Shaanxi Provincial Key R&D Program (2022SF-380) and Shaanxi Provincial Health Research Fund (2022A006). Author contributions All authors (Hong Zhang, Meiyan Gao, Zhen Gao, Li Yao, Hong Sun, Huqing Wang, Ru Zhang, Shuqin Zhan) had complete access to the study data and are responsible for ensuring the data's integrity and the accuracy of its analysis. Hong Zhang: Writing-Original draft preparation, Conceptualization, Methodology. Meiyan Gao and Zhen Gao: Writing-Reviewing and Editing, Software, Visualization. Li Yao, Hong Sun, Huqing Wang and Ru Zhang: Writing-Reviewing and Editing, Investigation, Data curation. Shuqin Zhan: Writing-Reviewing and Editing, Conceptualization, Funding. Data availability Data is provided within the manuscript or supplementary information files. Ethics approval and consent to participate The current study investigated the publicly available data, and no ethical approval was required. All methods were carried out in accordance with the Declaration of Helsinki. Consent for publication Not Applicable. Competing interests The authors declare that the research was conducted in the absence of any commercial or financial associations that could be construed as a potential source of conflict of interest. References Louis DN, Perry A, Wesseling P, Brat DJ, Cree IA, Figarella-Branger D, Hawkins C, Ng HK, Pfister SM, Reifenberger G et al : The 2021 WHO Classification of Tumors of the Central Nervous System: a summary . Neuro-oncology 2021, 23 (8):1231-1251. Omuro A, DeAngelis LM: Glioblastoma and other malignant gliomas: a clinical review . Jama 2013, 310 (17):1842-1850. Hira VVV, Van Noorden CJF, Molenaar RJ: CXCR4 Antagonists as Stem Cell Mobilizers and Therapy Sensitizers for Acute Myeloid Leukemia and Glioblastoma? 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Supplementary Files Supplementaryfile1FigureS1andS2.docx Supplementaryfile2FigureS3.tif Cite Share Download PDF Status: Published Journal Publication published 29 Nov, 2025 Read the published version in BMC Cancer → Version 1 posted Editorial decision: Revision requested 18 Aug, 2025 Reviews received at journal 16 Aug, 2025 Reviews received at journal 13 Aug, 2025 Reviewers agreed at journal 11 Aug, 2025 Reviews received at journal 21 Jul, 2025 Reviewers agreed at journal 09 Jul, 2025 Reviewers agreed at journal 01 Jul, 2025 Reviewers invited by journal 10 Jun, 2025 Editor assigned by journal 09 Jun, 2025 Submission checks completed at journal 09 Jun, 2025 First submitted to journal 06 Jun, 2025 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-6838293","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":469263350,"identity":"fe8fcad0-f684-49e8-85bc-70a3c351d276","order_by":0,"name":"Hong Zhang","email":"","orcid":"","institution":"The Second Affiliated Hospital of Xi'an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Hong","middleName":"","lastName":"Zhang","suffix":""},{"id":469263351,"identity":"dfe8e8a9-339b-442a-8c04-859299d02f50","order_by":1,"name":"Meiyan Gao","email":"","orcid":"","institution":"Shaanxi Provincial Hospital of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Meiyan","middleName":"","lastName":"Gao","suffix":""},{"id":469263353,"identity":"eb4b62a2-e341-4634-ba21-bccbe32e2d75","order_by":2,"name":"Zhen Gao","email":"","orcid":"","institution":"The Second Affiliated Hospital of Xi'an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Zhen","middleName":"","lastName":"Gao","suffix":""},{"id":469263357,"identity":"22a87be6-9b87-428a-a1aa-320a9a1211fa","order_by":3,"name":"Li Yao","email":"","orcid":"","institution":"The Second Affiliated Hospital of Xi'an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Yao","suffix":""},{"id":469263358,"identity":"f660dea7-8551-4633-bad5-872dcc3716bb","order_by":4,"name":"Hong Sun","email":"","orcid":"","institution":"The Second Affiliated Hospital of Xi'an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Hong","middleName":"","lastName":"Sun","suffix":""},{"id":469263359,"identity":"b71ba337-2cac-45ef-ac84-b287e6d07e31","order_by":5,"name":"Huqing Wang","email":"","orcid":"","institution":"The Second Affiliated Hospital of Xi'an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Huqing","middleName":"","lastName":"Wang","suffix":""},{"id":469263360,"identity":"301b8dbd-582c-4be0-9d31-024fbb3297e6","order_by":6,"name":"Ru Zhang","email":"","orcid":"","institution":"The Second Affiliated Hospital of Xi'an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Ru","middleName":"","lastName":"Zhang","suffix":""},{"id":469263361,"identity":"b0733b8e-db52-464d-9798-e3d8319394bd","order_by":7,"name":"Shuqin Zhan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2ElEQVRIie3RsQrCMBCA4ZOCU6Rri1CfQIgEhILoqzQITn2Aji1KJ90d+hAFoep2EsgUdwcXl05dxFVEu+nUuAnm3z/ukgMwmX4wGwSKe/Qgtj1HPeImkiNR6LlrGegRKhTDzgIZjUOquZmUAbrxmeegrqcKxl4/bhCtpUAc7Eq+tVYbP4MpG2IDsZzXFK4svo+PRZcA8qKJtHsVxUNq8RzDUo8QUPSQpIJRDNt6xAEZCFCz+pOZn1GNt0xQiBtEo/qUl1MVjb1G8jmS6J7mjXwrTCaT6S96Ail9UEbc8V2PAAAAAElFTkSuQmCC","orcid":"","institution":"The Second Affiliated Hospital of Xi'an Jiaotong University","correspondingAuthor":true,"prefix":"","firstName":"Shuqin","middleName":"","lastName":"Zhan","suffix":""}],"badges":[],"createdAt":"2025-06-06 15:38:34","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6838293/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6838293/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12885-025-15345-9","type":"published","date":"2025-11-29T15:57:45+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":84547470,"identity":"a4dc95f0-51d7-4f1e-b6c8-5d6f02f1bad9","added_by":"auto","created_at":"2025-06-13 09:26:35","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3454539,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe flowchart of the study.\u003c/strong\u003e DEGs, differential expressed genes; SUN, ubiquitination, small ubiquitin-related modifiers (SUMOylation), and neddylation; WGCNA, Weighted gene co-expression network analysis.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6838293/v1/2148b8ab401b25df10819eec.png"},{"id":84545880,"identity":"8ca7747d-4c7a-451e-919a-084cf5a7591a","added_by":"auto","created_at":"2025-06-13 09:18:35","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":998904,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification of SUN modified immunotherapy-resistance genes.\u003c/strong\u003e (A) Volcano of the differential expressed genes DEGs between GBM and control groups. (B) Sample level clustering. (C) Scale-free soft threshold distribution. The horizontal axis represents the weight parameter power value; the vertical axis of the left panel is Scale Free Topology Model Fit, that is, signed R^2. The higher the square of the correlation coefficient, the closer the network is to the scale-free distribution; the vertical axis of the right panel represents the mean of all adjacency functions. (D) Module clustering tree. Different colors represent different modules. (E) Heat map of correlation between modules and clinical traits. The vertical axis represents different modules; the horizontal axis represents different traits; and each square represents the correlation coefficient and P value. (F) Venn analysis.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6838293/v1/c461b248562c47056284df03.png"},{"id":84545878,"identity":"5611e04f-14ad-4d0b-89d1-be36ce16cc8d","added_by":"auto","created_at":"2025-06-13 09:18:35","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1677256,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExploitation of the prognostic gene signature.\u003c/strong\u003e (A) Univariate cox analysis for selection of prognostic genes. (B) LASSO analysis. Left panel represents the coefficients. Right panel represents the selection of λ. The two dashed lines indicate two special values of λ: lambda.min on the left and lambda.1se on the right. (C) RSF algorithm to determine the number of gene number with the least error, and the importance of the six most valuable gene feature. (D) Univariate cox analysis for the six key genes to construct the risk score model. (E) Kaplan-Meier analysis for the risk score. (F) Receiver operator characteristic (ROC) to evaluate the effectiveness of risk score model. (G) Comparation for the signature in our results and the ten publications, showing in C index. *, \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.05; **, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01. ***, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001. ****, \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-6838293/v1/79deabec71de7fb50f4d6902.png"},{"id":84547471,"identity":"7f233a55-aaa8-4d2c-a5cf-028db2beff14","added_by":"auto","created_at":"2025-06-13 09:26:35","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1499965,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation of the risk score with clinic features and construction of nomogram model.\u003c/strong\u003e(A) Differences of risk score between IDHwt and IDHmut groups. (B) Kaplan-Meier analysis between IDHwt and IDHmut groups. (C) Differences of risk score between in patients with or without TMZ therapy. (D) Differences of risk score between different MGMTp methylation status. (E) Univariate and multivariate cox correlation analysis for selection of the independent prognostic factors. (F) Construction of the nomogram model. (G) Calibration curve for evaluation of the nomogram model. (H) Kaplan-Meier analysis for different nomogram score. (I) The ROC curve for evaluation of the nomogram model. ns, not significant; *, \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.05; **, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01. ***, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001. ****, \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-6838293/v1/bdb3c44180888c072f0d8beb.png"},{"id":84545882,"identity":"40270b0c-47fc-4b32-8b47-9c958efe9c47","added_by":"auto","created_at":"2025-06-13 09:18:35","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":375007,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe differences of immune feature, and function between different GBM groups. \u003c/strong\u003e(A) ESTIMATE algorithm for investigation of differences of stromal score, immune score, ESTIMATE score, and tumor purity between different risk groups. (B) CIBERSORT algorithm for investigation of differences of immune cells between high- and low-risk groups. (C) ssGSEA algorithm for investigation of the differences of immune cells between high- and low-risk groups. (D) The differences of KEGG pathways between high-and low-risk groups (www.kegg.jp/kegg/kegg1.html). ns, not significant; *, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; **, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01. ***, \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.001. ****, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-6838293/v1/db5303cc33acadadfd068a0b.png"},{"id":84547834,"identity":"68885a54-69a0-4391-9fdf-d07b356f34bf","added_by":"auto","created_at":"2025-06-13 09:34:35","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":680838,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe differences of drug sensitivity and immunotherapy response between different GBM groups.\u003c/strong\u003e(A) The differences of IC50 of drugs between high-and low-risk groups. (B) The differences of TIDE between high-and low-risk groups. (C) The differences of IPS score between high-and low-risk groups. (D) The differences of risk score between different immunotherapy groups. (E) The distribution of the patients with response or non-response to ICB treatment between high- and low-risk groups. (F) Kaplan-Meier analysis. ns, not significant; *, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; **, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01. ***, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001. ****, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-6838293/v1/b776e2cbc748f81e95fe15d8.png"},{"id":84547474,"identity":"c0c51eb1-29ca-4c6c-9e74-d551970ad63f","added_by":"auto","created_at":"2025-06-13 09:26:35","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":11762535,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe expression validation of the key genes in glioblastoma.\u003c/strong\u003e (A) The expression of CDC73, PSMC2, SOCS3, ETV4, PLK2, and LMO7 between control and tumor in the GEPIA database. (B) The expression of CDC73, PSMC2, SOCS3, ETV4, PLK2, and LMO7 between control and tumor in the GEO database. (C) The expression of CDC73, PSMC2, SOCS3, ETV4, PLK2, and LMO7 between control and tumor in the HPA database (https://www.proteinatlas.org/), the control group: histologically normal cerebral cortex tissues, the tumor group: pathologically confirmed glioma specimens. (D) The expression levels of CDC73, PSMC2, SOCS3, ETV4, PLK2, and LMO7 in human glioblastoma cells (SHG-44, U87, and U251) and control human astrocytes (SVG p12) detected by RT-qPCR analysis. (E) The expression levels of CDC73, PSMC2, SOCS3, ETV4, PLK2, and LMO7 in human glioblastoma cells (SHG-44, U87, and U251) and control human astrocytes (SVG p12) detected by western blotting analysis. *, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; **, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01; ***, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001; ****, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-6838293/v1/8f618aaf2766bddc017f635c.png"},{"id":84547476,"identity":"62113313-4399-4128-b5d2-feda2792d8af","added_by":"auto","created_at":"2025-06-13 09:26:36","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":2839072,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe expression of the key genes in various immune cells.\u003c/strong\u003e (A) Cell distribution and model gene distribution in annotation cell based on the scRNA-sequencing in GSE162631 datasets. (B) SOCS3 expression patterns in spatial transcriptome analysis. (C) Unsupervised clustering result, all the samples were divided into two groups. (D) Kaplan-Meier analysis. (E) Correlation between subtypes and risk score.\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-6838293/v1/acdb2f2653ab86ec9520aa26.png"},{"id":97179684,"identity":"e00804bb-21e6-4786-92e9-8b2d3517d2ae","added_by":"auto","created_at":"2025-12-01 16:16:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":18579861,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6838293/v1/dde999da-3926-4182-b19a-5774fdfa3611.pdf"},{"id":84545881,"identity":"a23acb08-a377-4fc8-8ecc-3787d4710745","added_by":"auto","created_at":"2025-06-13 09:18:35","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1060378,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfile1FigureS1andS2.docx","url":"https://assets-eu.researchsquare.com/files/rs-6838293/v1/2c4511ab788301bad9b1fd95.docx"},{"id":84545918,"identity":"98b34a46-e9b7-43a6-9986-76612b6ebfe3","added_by":"auto","created_at":"2025-06-13 09:18:38","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":52446696,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfile2FigureS3.tif","url":"https://assets-eu.researchsquare.com/files/rs-6838293/v1/66335363301a943c18021e45.tif"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eIdentification and validation of SUN modification-related anti-PD-1 immunotherapy-resistance signatures to predict prognosis and immune microenvironment status in glioblastoma\u003c/p\u003e","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eGlioblastoma (GBM) commonly referred to the GBM (grade 4), which was updated to be glioblastoma, IDH-wildtype in the WHO 2021 CNS Tumor classification [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. GBM and malignant gliomas constitute the most prevalent and highly fatal brain tumors in the adult population, with annual incidence of 5.26 per million population or 17 thousand new diagnoses per year [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. GBM has a dismal prognosis, with a median survival duration of fewer than two years following diagnosis [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The therapy methods for GBM contains surgical resection, radiotherapy, chemotherapy, and target therapy [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Although with various therapy option, the survival rate of GBM is still less than 5% [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. GBM exhibits great inter-tumor and intra-tumor heterogeneity [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], so, the prognosis of GBM in clinic remains difficulties. Therefore, there is a pressing need to discover more efficacious and novel prognostic biomarkers for GBM in order to ameliorate its prognosis.\u003c/p\u003e \u003cp\u003ePosttranslational modifications (PTMs) are the biochemical modifications of proteins after protein biosynthesis, which control the protein abundance and function exceed inherent transcriptional regulation [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. PTMs could modulate protein biosynthesis process via modification like phosphoryl, methyl, acetyl, and glycosyl. Recently, aberrant regulatory roles of PTMs in diseases are proposed. For example, USP36 has been demonstrated to promote tumorigenesis and drug resistance in GBM through deubiquitination [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Furthermore, targeting PTMs alter, a novel therapeutic method is also proposed. The latest study by Yue et al. demonstrated that suppressing lactylation in GBM could increase its sensitivity to cancer therapy [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Immune checkpoint blockade, particularly anti-programmed cell death protein-1 (PD-1)/PD-1 ligand-1 immunotherapy, exhibits considerable promise in the management of diverse cancer types, encompassing GBM [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, still less than 10% of patients show an objective response to anti-PD-1 therapy [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. PTM of PD-1 has been demonstrated to be a potential target for cancer immunotherapy, as it affects the anti-tumor immunity of T cells [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. These evidences indicate the crucial role of PTMs in GBM progression and anti-PD-1 therapeutic research, prompting us to explore PTMs related biomarkers in GBM to predict the GBM occurrence and anti-PD-1 therapeutic efficiency.\u003c/p\u003e \u003cp\u003eUbiquitination, alongside small ubiquitin-like modifier (SUMOylation) and neuronal precursor cell-expressed developmentally down-regulated protein 8 modification (neddylation), collectively constitute the three primary types of PTMs known as SUN [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Previous studies have highlighted the critical role of SUN in cancer cell apoptosis, the cell cycle, and other biological processes. For example, it is reported that the SUMOylation modification of HNRNPK at the specific site interferes its DNA-binding ability, and thus promotes GBM invasion [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In some studies, several SUN-related prognostic signatures based on SUN has been exploited, such as a two-gene SUMOylation signature in prostate cancer [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] and a three-gene ubiquitination signature in pancreatic ductal adenocarcinoma [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Nevertheless, a systematic investigation for SUN-related prognostic signature in GBM remains absent to date.\u003c/p\u003e \u003cp\u003eConsidering the crucial role of SUN-related genes in the GBM development and antiPD-1 therapy, we hypotheses that there were key SUN genes which can be used for survival predicting and drug therapeutic outcomes in GBM. In our study, SUN modified anti-PD-1 immunotherapy resistance genes (SUNIRDEGs) were identified using bioinformatic tools through generating data from public database; and the main SUNIRDEGs were validated utilizing RT-qPCR and WB analysis. Following, a prognostic risk scoring system was developed utilizing the key genes, which might be efficient in predicting the disease and therapeutic outcomes. The research framework is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"2 Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data source\u003c/h2\u003e \u003cp\u003eThe GBM data was retrieved from The Cancer Genome Atlas (TCGA)-GBM database [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] and China Glioma Genome Atlas (CGGA) database. Then the human gene annotation files (GRCh38. P14) were retrieved from the GENCODE database [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Based on the annotation files, data from TCGA-GBM and CGGA database was preprocessing as listed below: (1) samples featuring missing or zero survival time were omitted; (2) samples were removed if they contained more than 50% missing values or unexpressed genes; (3) all expression values underwent log\u003csub\u003e2\u003c/sub\u003e(X\u0026thinsp;+\u0026thinsp;1) transformation. Subsequently, a total of 166 GBM samples were retrieved from the TCGA-GBM database. A total of 85 and 133 samples were obtained from CGGA-325 and CGGA-693 datasets in CGGA database, respectively. Furthermore, data for 207 normal samples were downloaded from Genotype-Tissue Expression (GTEx) database. For the consistency and standardization, all the above data were uniformly processed using the Toil process of UCSC XENA [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFinally, Anti-PD-1 immunotherapy data GBM-PRJNA482620 were obtained from a previously literature [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], which included 17 patients with Nonresponse and 17 patients with Response.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Differential expression analysis\u003c/h2\u003e \u003cp\u003eLimma package [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] was adopted to select the differential expressed genes (DEGs) between GBM and normal tissues. The criteria for selection were designed at FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |log\u003csub\u003e2\u003c/sub\u003e FC| \u0026gt; 1.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Weighted Gene Co-expression Network Analysis (WGCNA)\u003c/h2\u003e \u003cp\u003eWGCNA analysis[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] was utilized to determine the gene modules which were highly associated with GBM based on GBM-PRJNA482620 dataset. Firstly, after comprehensive consideration to ensure the robustness of the network, construction efficiency and biological significance, the top 5000 genes in GBM-PRJNA482620 were selected based on the mean absolute deviation (MAD). Next, WGCNA package [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] was utilized to identify the gene modules which were highly linked to anti-PD-1 immunotherapy, with Nonresponse and Response as the phenotypic characteristics.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Identification of SUN modified immunotherapy-resistance genes\u003c/h2\u003e \u003cp\u003eThe SUN modified gene set was extracted from the Molecular Signatures Database (MsigDB) with the keywords \"SUMOylation, ubiquitination, and neddylation\". The related pathway genes from REACTOME_SUMOYLATION, REACTOME_PROTEIN_UBIQUITINATION and REACTOME_NEDDYLATION were utilized as SUN modified genes for the subsequent analysis.\u003c/p\u003e \u003cp\u003eDEGs were intersecting with SUN modified genes and anti-PD-1 immunotherapy genes, namely DEGs-SUN genes and DEGs-drug resistance genes, respectively. Pearson correlation analysis was performed on the intersection results of the two parts of genes to identify SUN modified anti-PD-1 immunotherapy resistance genes (SUNIRDEGs), with the threshold of |R| \u0026gt; 0.4 and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Identification of prognostic related genes\u003c/h2\u003e \u003cp\u003eBased on the expression levels of above SUNIRDEGs in TCGA-GBM, and combined the clinical OS survival, univariate cox was performed to evaluate the prognostic value of each gene utilizing survival package [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] in R software. \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was deliberated as the threshold to filter genes with significant prognostic relevance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Exploitation and evaluation of the prognostic gene signature\u003c/h2\u003e \u003cp\u003eTen different machine learning algorithms [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] were performed to exploit the optimal predicting model. All the predicting models were obtained by LOOCV framework fitting based on TCGA-GBM data, and then these models were validated in CGGA-325 and CGGA-693 datasets. C-index was calculated to select the optimal models.\u003c/p\u003e \u003cp\u003eIn accordance with the median value of the risk score, the cohort of patients with GBM was stratified into high-risk and low-risk groups. Following this, Kaplan-Meier survival analysis was conducted to assess the prognostic implications. Furthermore, both calibration and ROC curves were constructed to assess the prognostic impact of the risk score. Finally, to examine whether our prognostic risk score model possesses superior predictive effects, we also compared the SUNIRDEGs prognostic signature with 10 published signatures predicting patient outcomes [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. To make the signatures comparable, the same method was utilized to calculate and transform the C-index of published prognostic signatures in TCGA and CGGA datasets.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Construction of prognostic nomogram model\u003c/h2\u003e \u003cp\u003eTo ascertain the independent prognostic factors, Cox regression analyses, encompassing both univariate and multivariate approaches, were executed on clinical variables, specifically including the risk score, age, gender, and IDH status. The clinical characteristics with significant correlation with clinical prognosis were selected (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The nomogram model was constructed utilizing the rms package (Version 6.8-0) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] within the R software framework.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 The differences of genome, immune feature, and function\u003c/h2\u003e \u003cp\u003eGBM mutation data were obtained from TCGA database for the subsequent analysis. The maftools package (version 2.17.0) [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] was adopted to select the mutation frequency of the top20 genes.\u003c/p\u003e \u003cp\u003eTo further observe the correlation between risk score and immune infiltrating cells, three immune microenvironment analysis algorithms ESTIMATE [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], CIBERSORT [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], and ssGSEA [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] were utilized to evaluate the immune infiltration levels of each GBM sample.\u003c/p\u003e \u003cp\u003eBased on the corresponding subset h.all.v2023.2.Hs.symbols.gmt in MSigDB database, GSVA algorithm [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] was performed to estimate the hallmark enrichment score of all GBM samples to investigate the differential KEGG pathways between the risk groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9 The differences of drug sensitivity and immunotherapy response\u003c/h2\u003e \u003cp\u003eThe sensitivity of each GBM samples to the chemotherapy drugs were evaluated based on the Genomics of Drug Sensitivity in Cancer (GDSC) database [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. pRRophetic package (version 0.5) [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] in R software was utilized to quantify IC50.\u003c/p\u003e \u003cp\u003eTIDE [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] was utilized to calculate the sample immunotherapy response score. Furthermore, immunophenoscore (IPS) was utilized to calculate the scores of four different immunophenotypes: antigen presentation (MHC molecules), effector cells (EC), suppressor cells (SC), and immune checkpoints (CP) [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Finally, based on the anti-PD-1 immunotherapy data from GBM-PRJNA482620, the links between risk score and immunotherapy response was evaluated to predict the immunotherapy for patients with GBM.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.10 Key genes expression in various immune cells\u003c/h2\u003e \u003cp\u003eHere, the single-cell RNA sequencing (scRNA-seq) data of GBM was retrieved from the Tumor Immune Signature Consortium Hub (TISCH2) database [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Combined with the spatial transcriptomics data from the spatial omics resource in cancer (SORC) [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], the expression levels of the key genes in different cells were evaluated.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.11 Identification of molecular subgroups of GBM\u003c/h2\u003e \u003cp\u003eBased on the key gene expression data obtained, ConsensusClusterPlus package [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] in R software was utilized to conduct unsupervised clustering analysis to identify the molecular subgroups of GBM. The clustering cluster number was set as 2 to 9 k; the clustering method was chosen as KMeans algorithm; and the distance was calculated as Euclidean distance. At the same time, Kaplan-Meier curve was employed to acclimate to observe the survival of samples between subgroups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.12 Cell culture\u003c/h2\u003e \u003cp\u003eCell lines, including Human brain astrocytes (SVG p12) and various human glioma cell lines (SHG44, U87, and U251), were obtained from the Cell Bank/Stem Cell Bank of the Chinese Academy of Sciences. These cells were subsequently cultivated in Dulbecco's Modified Eagle's Medium (DMEM) supplemented with 10% fetal bovine serum (FBS) and 1% penicillin-streptomycin.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e2.13 RT-qPCR\u003c/h2\u003e \u003cp\u003eTotal RNA was isolated using Trizol reagent (Invitrogen), adhering to the manufacturer's guidelines. RNA concentration was accurately determined with a NanoDrop ND-1000 spectrophotometer (NanoDrop Technologies). Following this, reverse transcription was conducted to convert RNA into cDNA, employing SuperScript IV reverse transcriptase (Thermo Fisher Scientific, Waltham, MA, USA). For quantitative PCR (qPCR) analysis, Platinum\u0026trade; Taq DNA Polymerase High Fidelity (Thermo Fisher Scientific) was utilized on an Applied Biosystems 7500 Real-Time PCR System (Applied Biosystems, Foster City, CA). The qPCR protocol encompassed an initial denaturation at 95\u0026deg;C for 30 seconds, followed by 40 amplification cycles consisting of denaturation at 95\u0026deg;C for 15 seconds, primer annealing at 60\u0026deg;C for 30 seconds, and DNA extension at 75\u0026deg;C for 60 seconds. The primer sequences used for qPCR analysis are listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, and gene expression levels were normalized to glyceraldehyde-3-phosphate dehydrogenase (GAPDH) as an internal reference.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe primers.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimers\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimer sequence (5\u0026rsquo; to 3\u0026rsquo;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLength (bp)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGAPDH-S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGGAAGCTTGTCATCAATGGAAATC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e168\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGAPDH-A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTGATGACCCTTTTGGCTCCC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDC73-S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTGACACTGAAATCTGTAACGGAGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDC73-A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eATTTGGGGGAGGTCTTGCTT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLK2-S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGCAGTAGAAGGTCAATGGCTCA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLK2-A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCAATCTGCCTGAGGTAGTATCGAAC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePSMC2-S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGTATTAAAGAATCTGACACTGGCCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e153\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePSMC2-A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCGTTGATAATGTATTTTGGGTCCTC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSOCS3-S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCCTACTGAACCCTCCTCCGA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e242\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSOCS3-A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTGGTCCAGGAACTCCCGAAT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLMO7-S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGCCCACAGGATTCTATGCTTCTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e242\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLMO7-A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTCCCACTGACTGACCTGTTACG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eETV4-S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAAGGAGACATCAAGCAGGAAGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eETV4-A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCGACCTCCTCAGGCTCAATG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e2.14 Western blotting (WB)\u003c/h2\u003e \u003cp\u003eWB was used for detection of protein levels of the key genes (PLK2, CDC73, PSMC2, SOCS3, ETV4, and LMO7). Total protein was isolated using RIPA lysis buffer, and then the protein concentration was quantified using a bicinchoninic acid (BCA) kit. The membranes were incubated with primary antibodies at 4\u0026deg;C overnight, followed by incubation with second antibody. The primary antibodies used in this assay were as follows: anti- PLK2 Antibody (1: 1000, 15956-1-AP, Proteintech Group, Wuhan, China), anti- CDC73 Antibody (1: 1000, 66490-1-Ig, Proteintech Group, Wuhan, China), anti- PSMC2 Antibody (1: 1000, GB113120, Servicebio, Wuhan, China), anti- SOCS3 Antibody (1: 1000, GB113792, Servicebio, Wuhan, China), anti- ETV4 Antibody (1: 1000, 10684-1-AP, Proteintech Group, Wuhan, China), anti- LMO7Antibody (1: 1000, 29392-1-AP, Proteintech Group, Wuhan, China), anti- GAPDH Antibody (1: 1000, GB15004, Servicebio, Wuhan, China), The second antibody used in this assay were HRP goat anti-rabbit (1: 3000, GB23303, Servicebio, Wuhan, China) and HRP goat anti-mouse (1: 3000, GB23301, Servicebio, Wuhan, China). Protein quantification normalization was established utilizing GAPDH as the endogenous reference. Immunoreactive bands were subsequently detected through enhanced chemiluminescence substrates (ECL, Sigma-Aldrich) and subjected to densitometric quantification employing ImageJ software suite (v1.8.0, NIH, USA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e2.15 Statistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were conducted using R software (version 4.3.3). To compare differences between the two groups, the Wilcoxon rank-sum test was applied. Correlations between the groups were assessed using the Spearman correlation method. A \u003cem\u003eP\u003c/em\u003e-value less than 0.05 was viewed statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Identification of SUN modified immunotherapy-resistance genes\u003c/h2\u003e \u003cp\u003eIn differential expression analysis, 6159 DEGs between GBM and control were determined, with 4381 upregulated, and 1777 downregulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). In the WGCNA, hierarchical clustering displayed that SRR8281245 sample was clearly deviant (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB), which were deleted in the following analysis. Appropriate soft threshold was chosen as 7 and all the GBM samples were divided into 13 modules (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC-D). Following, yellow-green and red modules were noticeably positively correlated with Nonresponse (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE) and 595 genes included in the two modules were served as the immunotherapy-resistance genes. After interesting analysis, a total of 200 intersecting DEGs-SUN and 198 DEGs-drug resistance genes were obtained (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF). Finally, 355 cross-linked genes were obtained as SUNIRDEGs after integration and deduplication.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Exploitation of the prognostic gene signature\u003c/h2\u003e \u003cp\u003eAll the 355 cross-linked SUNIRDEGs were involved in the univariate cox analysis. Finally, 33 prognostic SUNIRDEGs were confirmed to be markedly correlated with the overall survival (OS) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Then, based on the 33 prognostic SUNIRDEGs, 10 algorithms were performed to select an optimal algorithm to exploit the prognostic signature. The results demonstrated that lasso\u0026thinsp;+\u0026thinsp;RSF exhibited best performance in prognosis in all the three datasets, with highest average C-index as 0.761 (\u003cb\u003eFigure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). Finally, through further Lasso and RSF filtration, 33 prognostic SUNIRDEGs were optimized to six key signature genes: PLK2, CDC73, PSMC2, SOCS3, ETV4, and LMO7 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB-D). Based on the six key signature genes, the risk score model was formulated as the prognostic signature.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Evaluation of the prognostic gene signature\u003c/h2\u003e \u003cp\u003ein the TCGA-GBM dataset, all GBM samples were categorized into high- and low-risk cohorts based on the median risk score. Kaplan-Meier survival analysis demonstrated that patients in the high-risk cohort had noticeably worse OS compared to those in the low-risk cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). The ROC curve exerted that the predicting ability of the risk score model were excellent, with AUC above 0.9 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF). The predicting ability of the risk score model were confirmed both in CGGA-325 and CGGA-693 dataset (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF). To further evaluate the predictive performance of risk score, its predictive power was compared with signatures in precious literatures and the results illustrated that our risk score model had better predictive performance than almost all the signatures in each dataset (including metacohort) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG). These results comprehensively implied that the risk score model related to SUN modified immunotherapy-resistance genes in our study displayed fantastic predicting ability in OS of patients with GBM and might be a robust signature in GBM prognosis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Construction of prognostic nomogram model\u003c/h2\u003e \u003cp\u003eThe clinical analysis suggested substantially different risk score between different IDH statues, showing as higher risk score in IDH wild-type (IDHwt) group than that in IDH mutation (IDHmut) group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Kaplan-Meier analysis displayed that IDHwt was correlated with shorter survival time (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Postoperative temozolomide (TMZ) combined with chemoradiotherapy is the first choice for therapy of GBM [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Our findings demonstrated that risk score was higher in patients who did not receive TMZ therapy (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). MGMTp methylation status can predict the sensitivity of patients with GBM to TMZ therapy [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. In our results, patients who did not develop MGMTp methylation had a higher risk score (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). These results indicated that risk score was obviously associated with the clinic features of patients with GBM and might be an effective indicator in clinic.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the subsequent analysis, univariate and multivariate cox analysis implied that risk score and IDH status were independent predictive factors in GBM (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). The independent factors risk score and IDH status were further involved into the nomogram (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF). The calibration curve implied that the gradients between measured OS and nomogram-predicted OS were close to 1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG), thereby demonstrating the efficacy of the nomogram. And the Kaplan-Meier survival curve revealed an inverse correlation between the nomogram score and survival probability (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eH). Furthermore, the ROC curve also indicated the robust predictive capacity of nomogram in OS, with AUC above 0.9 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eI).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e3.5 The differences of genome, immune feature, and function\u003c/h2\u003e \u003cp\u003eGene mutation occurred in both high- and low-risk groups and the mutation frequency type mainly was missense mutation and SNP (\u003cb\u003eFigure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/b\u003e). The ESTIMATE analysis revealed significant differences in immune, stromal, ESTIMATE scores, and tumor purity between the two risk groups, with the high-risk group exhibiting elevated immune, stromal, and ESTIMATE scores, as well as reduced tumor purity (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating active immune response under high-risk score. Moreover, immune cell infiltration levels were also investigated utilizing CIBERSORT and ssGSEA (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). The analysis revealed significant variations in immune infiltration levels between the two risk groups, with the high-risk group exhibiting elevated levels of regulatory T cells and natural killer cells compared to the low-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Functional annotation indicated that the pathways distinguishing the two risk groups were primarily associated with oncogenic processes, including the MAPK, NF-kappa B, NOD-like receptor, p53, and PI3K-Akt signaling pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e3.6 The differences of drug sensitivity and immunotherapy response\u003c/h2\u003e \u003cp\u003eDrug sensitivity analysis indicated significant differences of 25 drugs in IC50 values between the two risk groups. Notably, Rapamycin, CGP.60474, and AZD6244 exhibited substantially higher IC50 values in the low-risk group compared to the high-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), indicating reduced drug sensitivity in the high-risk cohort. Furthermore, the high-risk group had a significantly higher TIDE score than the low-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), implying that GBM in the high-risk group may exhibit enhanced immune evasion and a diminished response to ICB therapy. Additionally, IPS analysis displayed that suppressor cells (SC) and immune checkpoints (CP) were significantly more abundant in the low-risk group compared to the high-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), implying potential sensitivity to ICB treatment in the low-risk cohort. Moreover, the risk score was markedly higher in the Nonresponse group compared to the Response group (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and high-risk group contained more Nonresponse patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). High risk score was substantially linked to poor prognosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e3.7 The expression validation of the key genes in glioblastoma\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eThe expression levels of the six identified SUN modified immunotherapy resistance genes were investigated in the Gene Expression Profiling Interactive Analysis (GEPIA), Gene Expression Omnibus (GEO, GSE16011 dataset), and Human Protein Atlas database (HPA) databases. The results indicated that CDC73, PSMC2, SOCS3, and ETV4 were substantially upregulated in tumor group than that in control group, whereas PLK2 and LMO7 were substantially downregulated in tumor group than that in control group (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA-C). RT-qPCR and WB validation also validated that CDC73, PSMC2, SOCS3, and ETV4 were substantially upregulated in GBM cells (SHG-44, U87, and U251) than that in control astrocytes group (SVG p12), whereas PLK2 and LMO7 were substantially downregulated in GBM cells than that in control microglia (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD, \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE and S3, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e3.8 The expression of the key genes in various immune cells\u003c/h2\u003e \u003cp\u003escRNA-seq data implied differential expression of the six key genes across various immune cell types (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA). Among which, SOCS3 was highly expressed in monocytes and macrophages. Subsequent spatial transcriptome data based on SORC database also revealed that SOCS3 was highly expressed in monocytes and macrophages (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB). Subtype analysis divided all the GBM samples into two clusters: cluster 1 (69 samples) and cluster 2 (97 samples) (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC); and cluster 2 possessed worse survival and contained more high-risk samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eD and \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eE, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eMany crucial life activities are mediated by ubiquitin and ubiquitin-like alterations, and their dysfunction is also associated with pathological progression such as immunological dysfunction and tumor progression [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. PTM of PD-1 has been identified as a promising target for cancer immunotherapy, influencing the anti-tumor immune response of T cells [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Hence, early identification and prediction of diagnosis promote better survival and treatment efficacy. In our study, through WGCNA and machine learning algorithms, six SUN modified immunotherapy resistance genes were confirmed to involve in GBM prognosis: PLK2, CDC73, PSMC2, SOCS3, ETV4, and LMO7. Among which, CDC73, PSMC2, SOCS3, and ETV4 were substantially upregulated; whereas PLK2 and LMO7 were substantially downregulated in glioblastoma cells than that in control. Based on these six optimal genes, a prognostic risk model was generated, which exhibited excellent prognostic significance with AUC above 0.9. Through comparison of our risk score with ten previous published risk score, our risk score possessed best predicting ability in GBM, with better sensitivity and specificity in clinic application.\u003c/p\u003e \u003cp\u003eThe six key genes exhibit significant role in GBM. The polo-like kinases (PLKs) constitute a family of serine-threonine kinases that play regulatory roles in various cellular processes. PLK2 is deliberated a potential inhibitory factor in tumors. PLK2 is down-regulated in GBM, and its upregulation links to poor prognosis [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], which is coincidence with our study. PLK2 might be a novel chemotherapy-resistant biomarker and therapeutic target in GBM. For example, Alafate et al. demonstrated that PLK2 inhibition could stimulate acquired resistance to TMZ through activation of PLK2/Notch axis [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Furthermore, the latest evidences indicates that PLK2 is also involved in various PTM process, including phosphorylation and ubiquitination [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Tan et al. demonstrated that DYRK1A-mediated phosphorylation of PLK2 regulates the proliferation and invasion of GBM cells [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Ge et al. demonstrated that PLK2 can inhibit oxidative stress through phosphorylating GSK3β in ischemia-reperfusion injury [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. CDC73, full named cell division cycle 73, has been demonstrated to regulate Notch-induced T-cell leukemia cells [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e] in various disorders. For example, in esophageal cancer, CDC73 acts as a tumor-promoting factor [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. CDC73 was upregulated in esophageal cancer, and its downregulation effectively hinders the proliferation and growth of esophageal cancer cells. In parathyroid cancer, CDC73 is a tumor suppressor gene, and variant of CDC73 is related to risk of parathyroid carcinoma [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Nevertheless, the specific role of CDC73 in GBM have not been clarified in GBM. A recent study demonstrated that CDC73 involve in ubiquitin-proteasome degradation in hyperparathyroidism-jaw tumor syndrome [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. This evidence implied that CDC73 might affect GBM progression through regulation of ubiquitination, and the underline mediatory role should be clarified in future. PSMC2 is an important gene in proteasome complex. A pan-cancer analysis has suggested that PSMC2 serves as a reliable prognostic biomarker for predicting the response to immunotherapy [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Elevated expression of PSMC2 has been observed in gliomas and is associated with a poor prognosis for patients with this disease [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]; inhibition of PSMC2 can inhibit the cancer progression and drug resistance through regulation of immune microenvironment and cell autophagy [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Evidence from other cancers also indicated the positive role of PSMC2 knockdown in tumor progression [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. SOCS3 represents a promising target for the treatment of metabolic disorders [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. In GBM, SOCS3 is reported to be related to chemotherapy radiotherapy resistance acquisition [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. Overexpression of SOCS3 is found in GBM [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e], which is coincidence with our results. SOCS3 mainly regulate the GBM drug sensitivity through JAK/STAT phosphorylation signaling [\u003cspan additionalcitationids=\"CR61\" citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. Furthermore, SOCS3 exhibits as an onco-immunological biomarker in GBM, attributing to its immune regulation role [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. Goswami et al. demonstrated that Kdm6b absence enhances antigen presentation and anti-PD1 efficacy in myeloid cells by inhibition of Socs3 [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. Hence, SOCS3 might also be an important immunotherapy target in GBM. E-twenty-six-specific sequence variant transcription factor 4 (ETV4) has also been demonstrated to involve in tumor progressions. In hepatocellular carcinoma, ETV4 elevation facilitates tumor metastasis by upregulating PD-L1 [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. In Multiple Myeloma, ETV4-dependent transcriptional plasticity can maintain MYC expression and is related to drug resistance [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. In GBM, ETV4 is upregulated and its knockdown promote the autophagy and cell apoptosis trough inhibition of PI3K/AKT/mTOR signaling pathways [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. Furthermore, the phosphorylation regulation roles of ETV4 are also been reported in several cancers [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. LIM domain only 7 (LMO7) gene played crucial roles in regulating cell growth, differentiation, protein localization, signal transduction, and intracellular protein complex assembly, establishing it as a hallmark gene in cancer[\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. Research has indicated an inverse relationship between LMO7 expression and the progression as well as prognosis of human lung adenocarcinoma [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. In pancreatic ductal carcinoma, LMO7 is reported to regulate the T cell differentiation and chemotaxis and thus achieve the immune escape [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]. Nevertheless, its function in GBM remains unexplored. In summary, there is currently a dearth of evidence linking these six genes to ubiquitination, SUMOylation, and Neddylation in GBM. Our study identified CDC73, PSMC2, SOCS3, and ETV4 as pivotal SUN-modified genes that contribute to resistance to anti-PD-1 therapy. Notably, these genes were also observed to be overexpressed in GBM cells relative to microglia. Conversely, PLK2 and LMO7 were downregulated. Suggesting that these six genes are appropriate to develop a predictive model, and the excellent predictive performance of the resulting risk scoring model further supports this point.\u003c/p\u003e \u003cp\u003eIn our investigation, high risk score indicated a worse survival. The AUC value of the risk score model was above 0.9, which indicated a relatively high predicting efficiency, which exhibited potential application ability in predicting the survival of patients with GBM in clinic. That is, a patient with a higher risk score is likely to have a lower probability of survival. Furthermore, our results proved that the risk score constructed by the above six key genes revealed remarkably correlation with clinic features like IDH mutation, TMZ therapy, and MGMTp methylation. This results further demonstrated the advantages of the risk score in predicting GBM. Furthermore, GBM are highly heterogenetic. We examined the correlation between risk score and gene mutation. The results displayed that PTEN and TP53 were the main mutation genes. Among which, TP53 is a critical gene in normal tumor growth and its mutation predicts cancer deteriorate and drug resistance [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]. Moreover, although p53 is known to undergo SUMOylation, the precise function of this modification in oncogenesis remains elusive.\u003c/p\u003e \u003cp\u003eMore importantly, we found elevated immune cell infiltration levels in high-risk GBM, like higher regulatory T cell and natural killer cells, indicating immune microenvironment alteration. Moreover, 25 drugs displayed different IC50 between the two different risk status, indicating different sensitivity of patients with GBM to different drugs. Higher IC50 of Rapamycin, CGP.60474, and AZD6244 were found in low-risk score, indicating GBM in low-risk group exhibited greater sensitivity to drugs. A higher TIDE score was associated with high-risk status, suggesting an increased likelihood of immune escape and a suboptimal response to ICB. These results imply that risks core could indicate the immune status and guide the selection of clinical drugs for GBM. Finally, we estimated the expression levels of the key genes across various immune cell types. scRNA-seq and spatial transcriptome analysis demonstrated that SOCS3 was highly expressed in monocytes and macrophages. SOCS3 is reported to be a modulator of macrophage, which could drive macrophage inflammatory responses and modulate the efficiency of phagocytic processes [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e], indicating that SOCS3 might affect tumor progression and drug sensitivity through mediate the macrophages and monocytes in GBM.\u003c/p\u003e \u003cp\u003eIn summary, we identified six genes associated with SUN-modified anti-PD-1 resistance in GBM and constructed a promising prognostic model. Our prognostic risk score system exhibited superior performance compared to ten previously published signatures. However, several limitations exist. First, our data comes from multiple databases and online sources, which might increase the data diversity and complexity. Second, the algorithm for bioinformatics analysis itself may have some unknown limitations. Third, our whole investigations and analyses largely depend on retrospective data. Fourth, the precise regulatory mechanism of the six key genes and the predicting ability of six-gene prognostic model should be explored and validated in larger cohorts and experiments in future.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eA six-gene prognostic model related to ubiquitination, SUMOylation, and neddylation and anti-PD-1 response is constructed in GBM, and the model exhibits excellent predictive properties in the clinical survival and drug sensitivity, which is benefit for accurate prediction and refined treatment in clinic. Future research should focus on exploring the crucial role and potential mechanism of the model and model genes in GBM.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to acknowledge with appreciation the contributions of the TCGA, GTEx, HPA, and CGGA databases.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by funds from the National Natural Science Foundation of China (81070999), Shaanxi Provincial Key R\u0026amp;D Program (2022SF-380) and Shaanxi Provincial Health Research Fund (2022A006).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors (Hong Zhang, Meiyan Gao, Zhen Gao, Li Yao, Hong Sun, Huqing Wang, Ru Zhang, Shuqin Zhan) had complete access to the study data and are responsible for ensuring the data\u0026apos;s integrity and the accuracy of its analysis. Hong Zhang: Writing-Original draft preparation, Conceptualization, Methodology. Meiyan Gao and Zhen Gao: Writing-Reviewing and Editing, Software, Visualization. Li Yao, Hong Sun, Huqing Wang and Ru Zhang: Writing-Reviewing and Editing, Investigation, Data curation. Shuqin Zhan: Writing-Reviewing and Editing, Conceptualization, Funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData is provided within the manuscript or supplementary information files.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe current study investigated the publicly available data, and no ethical approval was required. All methods were carried out in accordance with the Declaration of Helsinki.\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 the research was conducted in the absence of any commercial or financial associations that could be construed as a potential source of conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLouis DN, Perry A, Wesseling P, Brat DJ, Cree IA, Figarella-Branger D, Hawkins C, Ng HK, Pfister SM, Reifenberger G\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eThe 2021 WHO Classification of Tumors of the Central Nervous System: a summary\u003c/strong\u003e. \u003cem\u003eNeuro-oncology \u003c/em\u003e2021, \u003cstrong\u003e23\u003c/strong\u003e(8):1231-1251.\u003c/li\u003e\n\u003cli\u003eOmuro A, 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\u003cstrong\u003e100\u003c/strong\u003e(4):771-780.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Glioblastoma, posttranslational modification, SUMOylation, ubiquitination, neddylation, immune microenvironment","lastPublishedDoi":"10.21203/rs.3.rs-6838293/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6838293/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Ubiquitination, SUMOylation, and neddylation (collectively termed SUN modifications) play crucial roles in cancer pathogenesis and immunotherapy resistance. This study investigated the prognostic significance of these modifications in glioblastoma (GBM).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eKey genes associated with SUN modifications and anti-PD-1 resistance were identified using integrated bioinformatic approaches, including differential expression analysis, Weighted Gene Co-expression Network Analysis (WGCNA), and machine learning algorithms. The expression levels of identified genes were subsequently validated in GBM cell lines using RT-qPCR and Western blotting. A prognostic risk model was constructed based on the key genes. Single-cell RNA sequencing (scRNA-seq) and spatial transcriptome analysis were further employed to characterize gene expression patterns.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eSix prognostic genes (PLK2, CDC73, PSMC2, SOCS3, ETV4, and LMO7) were identified. CDC73, PSMC2, SOCS3, and ETV4 were upregulated, while PLK2 and LMO7 were downregulated in GBM cells. The six-gene prognostic risk model demonstrated excellent predictive performance, achieving an Area Under the Curve (AUC) exceeding 0.9. The derived risk score exhibited significant correlations with clinical features, immune infiltration levels, and drug sensitivity profiles. Furthermore, scRNA-seq and spatial transcriptome analysis revealed high SOCS3 expression specifically in monocytes and macrophages, suggesting its potential role in mediating the activity of these immune cells to influence tumor progression and drug sensitivity in GBM.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eThis study established a robust six-gene prognostic model related to SUN modifications and anti-PD-1 therapy in GBM. The model demonstrates strong predictive ability and correlates with clinically relevant parameters, highlighting its potential utility for survival prediction and guiding therapeutic management decisions in GBM patients.\u003c/p\u003e","manuscriptTitle":"Identification and validation of SUN modification-related anti-PD-1 immunotherapy-resistance signatures to predict prognosis and immune microenvironment status in glioblastoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-13 09:18:30","doi":"10.21203/rs.3.rs-6838293/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-18T10:28:10+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-16T14:11:13+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-13T06:02:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"53642019039492852388216813471362862702","date":"2025-08-12T01:37:15+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-21T07:44:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"129556682277466984246557582975543921775","date":"2025-07-09T07:37:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"87507889632077034156838106837815305259","date":"2025-07-01T08:11:19+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-10T10:43:08+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-09T23:23:23+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-09T23:23:04+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cancer","date":"2025-06-06T15:28:44+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8b5ac12e-550a-49b2-8a21-0eb64e3fe6ab","owner":[],"postedDate":"June 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-01T16:13:11+00:00","versionOfRecord":{"articleIdentity":"rs-6838293","link":"https://doi.org/10.1186/s12885-025-15345-9","journal":{"identity":"bmc-cancer","isVorOnly":false,"title":"BMC Cancer"},"publishedOn":"2025-11-29 15:57:45","publishedOnDateReadable":"November 29th, 2025"},"versionCreatedAt":"2025-06-13 09:18:30","video":"","vorDoi":"10.1186/s12885-025-15345-9","vorDoiUrl":"https://doi.org/10.1186/s12885-025-15345-9","workflowStages":[]},"version":"v1","identity":"rs-6838293","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6838293","identity":"rs-6838293","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}