UBE2C is associated with Prognosis of Immunosuppression and Cell Invasiveness in Glioma

UBE2C is associated with Prognosis of Immunosuppression and Cell Invasiveness in Glioma | 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 UBE2C is associated with Prognosis of Immunosuppression and Cell Invasiveness in Glioma Hao Feng, Anhui Fu, Rong Yang, Fei Qiao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4019424/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Glioma is one of the common tumors of the central nervous system, which presents difficulties in clinical diagnosis and treatment due to its characteristics of immunosuppression and cell invasion phenotypes. The condition and prognosis of glioma may be predicted during the process of diagnosis and treatment, it will be more conducive to timely intervention or evaluation of glioma. Methods Differential or risk genes were analyzed based on TCGA (The Cancer Genome Atlas) - glioma samples, selecting relative typical biological processes based on enrichment analysis of their common genes. Target gene UBE2C were obtained by the expression correlation and differential expression analysis for the enrichment results. UBE2C were evaluated by clinical grading, survival prognosis and cell experiments. GSEA (Gene Set Enrichment Analysis) analysis based on GEO (Gene Expression Omnibus) data sets and the above conclusions were verified. The correlation of UBE2C with immune invasion, immune checkpoint and cell invasiveness of gliomas was analyzed by TCGA-glioma data and STRING, respectively. Results Our results suggests that the high expression and risk of UBE2C in gliomas may be a factor that promotes malignant phenotype of tumor cells. The immune phenotype shows that IL6 and IL10 may be the key nodes affecting the immunosuppressive phenotype of glioma. Further, the invasion genes from the MMP family can be correlated with immunosuppressive phenotypes via UBE2C-IL6/IL10 axis, especially displayed by MMP2/MMP9. Conclusion The UBE2C may systemic effects the malignant phenotype, immunosuppression and cell invasiveness of tumors systematically, which reflects UBE2C as a potential biomarker and therapeutic target for glioma. Glioma Biomarker UBE2C Th2 cells Immune checkpoint MMP family Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction As the main type of central nervous system tumor, brain glioma is also one of the most prone to malignant progression of tumors, which seriously endangers people's nervous system health [ 1 – 3 ]. Currently, although this disease can be treated with surgery, radiotherapy and chemotherapy, the tumor is rapidly progressive, which prone to metastasis and immune checkpoints suppression [ 2 , 4 ]. For example, the glioblastoma with a high degree of malignancy contains the above adverse characteristics [ 5 ]. Therefore, it is necessary to seek or explore relatively effective markers to effectively predict and evaluate the prognosis of patients with this tumor. Based on the malignant progression and high-risk characteristics of glioma, clarifying the correlation between this biomarker and the above characteristics becomes an important indicator of effective evaluation and prognosis [ 6 , 7 ], and it may even be used as a potential target for glioma intervention. The malignant phenotype of gliomas usually includes a highly aggressive and immunosuppressive phenotype, which is often a difficulty in clinical diagnosis and treatment [ 8 , 9 ]. Therefore, the search for valuable biomarkers has become an important task for the effective prediction and prognosis of the disease. Compared with normal tissues, high-risk genes in glioma samples may be important factors in the adaptive survival or malignancy process of tumor cells [ 10 ]. UBE2C with relatively high expression in tumors may become typical risk factors. Studies have reported that UBE2C may play a role in promoting cancer, such as brain cancer, breast cancer, cervical cancer, pancreatic cancer and liver cancer [ 11 , 12 ]. Based on this characteristic and combined with the malignant phenotype of glioma, multifaceted or systematic analysis can reflect that the expression level of this gene and may be a key factor affecting tumor progression [ 6 , 13 ]. Such as immunosuppressive and highly aggressive phenotypes in glioma may be related to this gene, and thus become the key to the progression of tumor malignancy [ 6 , 14 ]. These aspects mainly manifested by the systematic association of this gene with immune infiltrating cells, immune checkpoints and invasion-related genes [ 15 ]. Therefore, evaluating the intrinsic and extrinsic risk of these genes for glioma from a systemic perspective may be beneficial for obtaining valuable biomarkers. Although there has been some progress in the study of biomarkers for glioma, systematic research on the relationship between risk genes and immunosuppressive or invasive phenotypes is rarely reported. Based on the TCGA database and GEO-sourced clinical glioma sample data (including glioblastoma multiforme and low-grade glioma), this study demonstrated that UBE2C gene could be used as a biomarker for glioma via systemic analysis, screening and evaluation. This gene has established a significant correlation with immunoinfiltrated Th2 cells, which may influence immunosuppressive phenotypes and tumor cell invasion via IL6 and IL10. In summary, based on these results, it can be reflected that the high expression of UBE2C in glioma may serve as a relatively typical risk factor and may become a valuable prognostic marker. Materials and Methods Glioma sample The glioma samples involved in this study were taken from the TCGA database ( https://www.cancer.gov/ccg/research/genome-sequencing/tcga ), which contained 174 Glioblastoma multiforme, 532 low-grade gliomas, and 5 adjacent controls. In addition, there is a glioma sequencing dataset (GSE12657) from the GEO (Gene Expression Omnibus) database ( https://www.ncbi.nlm.nih.gov/geo/ ), which contains five adjacent controls and seven Glioblastoma patients. Cell Culture and Transfection The HBE, LN229, U87MG, A172 and U251MG cells involved in this experiment were all purchased from ATCC cell bank ( http://www.atcc.org ). All the cell lines were cultured in medium: DMEM (90%, Gibco) + FBS (10% Fetal calf serum, Gibco) + 1% PS (Penicillin streptomycin combination, Gibco), 37℃, 5% CO2. Cell transfection that transfection agents used for PolyPlus ( https://www.polyplus-sartorius.com/ ), siRNA was synthesized by Geneparma company ( https://www.genepharma.com/ ). Please refer to the instructions of transfection reagent for the procedure of transfection experiment. Wound Healing and Trans-Well Assays Tumor cells were seeded into 12 - well plates and scratched with a 10 µL pipette tip after reaching confluency. The plates washed thrice with PBS, cultured in serum-free medium DMEM for 24 h, and photographed. Cell migration assays, 10 4 cells were spread into the filter of a 24 - well plate and cultured in serum-free medium DMEM for 48 h. Filters were then fixed with a neutral formaldehyde solution (4%) and stained with crystal violet. Colony Formation Assays and Cell Proliferation Colony formation assay, cells were plated at a density of 1000 cells/well into 12-well plates and cultured for 14 days, then fixed with neutral formaldehyde (4%) and stained with crystal violet. Cell proliferation assay was by seeding 5 × 10 3 cells/well into a 96-well plate. Proliferation activity (Absorption in OD450 nm) was measured over four days using the CCK-8 method as per the manufacturer's instructions. Western Blot Protein samples extracted from cells were used for Western blotting, following a standard method. Reagents and consumables used in this process included PVDF membranes (Millipore, USA); anti-UBE2C (Rabbit, ab252940) (Abcam, USA); anti-GAPDH (Rabbit, ab181602) (Abcam, USA); TBST buffer (Biosharp, China); Running buffer (Biosharp, China); Transmembrane buffer (Biosharp, China); and Substrate luminescent liquid (Biosharp, China), Goat anti-Rabbit HRP (H + L, ab6721) (Abcam, USA). RNA Isolation and Real-Time PCR Total RNA from tumor cells was extracted using the Trizol reagent method following standard procedures. Reverse transcription of total RNA was performed using a two-step kit (TaKaRa, Japan), and real-time qPCR experiments were conducted using the SYBR fluorescent dye kit (TaKaRa, Japan). The primers used in this procedure included: UBE2C (F: 5'- CATCAGAACCAGCTCAACAGT − 3'; R: 5'- GTTGCAGAGTAAGCT CCAGCA − 3'); GAPDH (F: 5′- ACAACTTTGGTATCGTGGAAGG − 3′; R: 5′- GCC ATCACGCCACAGTTTC − 3′). Immunohistochemical (IHC) The glioma IHC images involved in this study are all from The Human Protein Atlas database ( https://www.proteinatlas.org/ ), which contains the expression of existing related proteins and the IHC results of tumor pathological tissues (all images contain the sample number). Our conclusion is based on these image data for analysis and demonstration. GlioVis analysis GlioVis online analysis (Visualization Tools for Glioma Datasets, http://gliovis.bioinfo . cnio.es/), which is an important database that adopted for data visualization and analysis to explore glioma [ 16 ]. Meanwhile, the normalized gene expression, this database includes information on glioma molecular pathology and glioma subtypes, which are important tools for online analysis. KEGG and GO enrichment analysis The enrichment analyses involved in this study included KEGG (Kyoto Encyclopedia of Genes and Genomes) and GO (Gene Ontology). RNA-sequencing expression (level 3) profiles and corresponding clinical information for glioma were downloaded from the TCGA dataset ( https://portal.gdc.com ). Using the limma package in the R software to study the differentially expressed mRNA. “Adjusted P 1 or Log 2 FC < − 1” were defined as the threshold for the differential expression of mRNAs. In addition, online Metascape analysis ( https://metascape.org/g p/index.html#/main/step1) also serves as an important reference for enrichment analysis results [ 17 ]. Gene Set Enrichment Analyses (GSEA) Dataset GSE12657 was downloaded from the GEO database and then GSEA ( http://software.broadinstitute.org/gsea/index.jsp ) enrichment analysis was performed. The overall differential genes in this dataset were used as the data source for analysis without additional conditional screening. GSEA enrichments were estimated by normalized enrichment score (NES) [ 18 ]. The significance of the results was assessed with FDR < 0.25 level, P < 0.05, and FDR < 0.25 levels. Network system analysis Online network analysis between multiple genes is performed using STRING ( https://cn.stringdb.org/cgi/input?sessionId=bsNTRVpzqx25&input_page_show_search=on ), and adjustments and layout optimization are made on this result. At the same time, the systematic analysis based on Gene Mania ( https://genemania.org/ ) is used as a reference for the above results. Bioinformatics and statistical analysis Gene expression differential analysis, clinical grading significance, survival prognosis analysis (K-M survival curve), immune infiltration analysis, gene expression correlation analysis, Cox risk regression analysis (Univariate and multivariate regression analysis) and nomogram analysis (Calibration curves) in gliomas were all implemented by R v4.0.3 software package. All the analysis results were represented by Spearman as the correlation coefficient ( R ), and P < 0.05 was the significant result. Results Function of Differentially Expressed Genes and Risk Genes in Glioma Identifying the relatively typical biological processes in gliomas is the basis for seeking markers. Therefore, we first conducted differential gene enrichment analysis (KEGG and GO) based on glioma (containing the glioblastoma multiforme and low-grade glioma) sample data from TCGA database. Based on significantly upregulated genes, the enrichment results of both KEGG and GO reflected entries related to cell cycle regulation. We combined with the characteristics of tumor cell cycle, it was suggested that this process is the basis of malignant phenotype or progression of tumor cells. (Fig. 1 A, B, where the red arrow is pointing ). Meanwhile, there were no typical characteristic items in the significantly down-regulated gene enrichment results ( Supplementary Fig. 1A, B ). To further explore the commonality between the function of risk genes and up-regulated genes, 8344 risk genes (HR > 1, P 1, P < 0.05) for Venn diagram analysis. The results showed that there were 679 genes with common characteristics (Fig. 1 C). Enrichment analysis of these genes revealed four biological processes associated with the cell cycle in the top 5 entries (Fig. 1 D, where the red arrow is pointing ). These results suggest that upregulated risk genes in gliomas may also be involved in cell cycle processes. Based on the above enrichment results, we conducted Venn diagram analysis for the genes from these four items, which found that 27 genes were the common part (Fig. 1 E). Correlation analysis of these genes in glioma showed that 17 genes were strongly correlated with each other ( R > 0.8, P < 0.05), and all showed high risk at high expression levels (Fig. 1 F, G). These results confirmed the high-risk characteristics for the selected genes. Meanwhile, Cox regression analysis showed that 17 genes were significant risk factors in univariate analysis, and the multivariate prognostic analysis shows that CHEK1, UBE2C and BUB1 were independent prognostic risk factors ( Supplementary Fig. 1C, D , HR > 1, P < 0.05). In addition, according to the expression level of 17 genes in glioma, UBE2C had the most significant upregulation (Fig. 1 H, *** P < 0.001). In summary, UBE2C may be a risk factor and significantly upregulated in gliomas, which may be the internal cause of malignant phenotype of tumors. Clinical Classification and Prognostic Significance of UBE2C in Glioma UBE2C was selected as a typical representative gene. To further verify its clinical significance in glioma, these samples from the TCGA database were clinically classified ( Supplementary Table 1 ). The results showed that the expression level of UBE2C was significantly up regulated with the progression of the disease in both WHO classification (G2, G3, G4) and primary therapy outcome (CR, Complete response; PR, Partial Response; SD, Stable disease; PD, Progressive disease) (Fig. 2 A, B, ** P < 0.01, *** P < 0.001). Among the pathological grades of glioma, the malignant degree of glioblastoma was the highest [ 5 , 19 ], and the expression level of UBE2C was significantly higher than that of the other three types (Fig. 2 C, *** P < 0.001). In terms of age and survival status, glioma patients over 60 years of age and those who had died showed significant UBE2C upregulation (Fig. 2 D, E, *** P < 0.001). These features can also be demonstrated by the results of Cox regression analysis of clinical grading ( Supplementary Table 2 ). These results suggest that the expression level of UBE2C is significantly different in the clinical grades of glioma, which can significantly affect the prognosis of these clinical grades. In addition, the ROC Curve (Receiver Operating Characteristic Curve) analysis of UBE2C in glioma samples showed that the AUC (Area Under ROC Curve, CI: 0.935–0.993) value was 0.964. This result indicates that UBE2C gene has good diagnostic value for glioma (Fig. 2 F). To assess the prognostic value of UBE2C in glioma, survival curves (K-M) with OS (Overall Survival), DSS (Disease Specific Survival), and PFI (Progress Free Interval) were performed on these clinical samples, respectively. The results showed that the three types of K-M curves showed that the survival rate of the group with high expression of UBE2C was relatively low, that is, the expression level of this gene was significantly negatively correlated with the survival rate of patients (Fig. 2 G-I). In conclusion, the differences in the expression of UBE2C in clinical classification and survival prognosis can reflect the related characteristics of disease progression, and thus show certain clinical diagnosis and prognostic value. The Intrinsic Effect of UBE2C in Glioma Based on the clinical significance of UBE2C in gliomas, we need to further explore the internal influence of this gene in gliomas, which can reflect the value of UBE2C as a marker of endogenous adverse factors. We need to further explore the major biological processes involved in UBE2C by enrichment analyzing the genes that were significantly positively correlated with UBE2C ( R > 0.6, adj. P < 0.05). The results show that the TOP 20 items presented mainly include two biological processes, which are cell cycle regulation (red arrow) and DNA behavior (blue arrow) (Fig. 3 A). Meanwhile, GSEA analysis of differential genes based on TCGA verified the significance of cell cycle and DNA behavior, respectively (NES > 1, adj. P < 0.05, FDR q 1, adj. P < 0.05, FDR q < 0.25). Therefore, the above results indicate that UBE2C may satisfy the adaptive survival of tumor cells in glioma mainly via the regulation of cell cycle and DNA behavior. Summarizing the above results, it can be inferred that UBE2C gene may be related to malignant proliferation of glioma cells. To verify this association, we separately display the IHC (Immunohistochemistry) images from The Human Protein Atlas (THPA) database. Since Ki67, PCNA and MCM7 are common markers reflecting tumor cell proliferation [ 20 ], they are related to the replication behavior of nuclear DNA. Therefore, according to the IHC of these three proliferative markers, the nuclear coloring degree of tumor region is deeper than adjacent region (Fig. 4 B-D). Meanwhile, the staining trend of UBE2C cells in tumor region was consistent with these three proliferative markers (Fig. 4 A). In addition, the scatter plot of correlation analysis shows that UBE2C had a significant positive correlation with Ki67, PCNA and MCM7, respectively (Fig. 4 E-G). In summary, the expression level of UBE2C is related to the proliferation of tumor cells, and it may be an internal factor affecting the adaptive survival of tumor cells. Effect of UBE2C Expression on Malignant Phenotype for Glioma Cell To verify the expression of UBE2C in glioma cell lines, our analysis based on the mRNA data collected in the CCLE database (Cancer Cell Line Encyclopedia), which showed that there was little difference in the expression of UBE2C in 48 brain cancer cell lines ( Supplementary Fig. 3A ). However, the differences between the common glioma cell lines LN229 and U251MG were relatively obvious ( Supplementary Fig. 3A , where the red arrow is pointing). The expression of UBE2C in 4 glioma cell lines (LN229, U87MG, A172 and U251MG) was detected by qPCR and Western blot, respectively. The results showed that the mRNA expression level of UBEC in these four cell lines was significantly higher than that of normal glioma cells (HEB), but there was no difference among the four types ( Supplementary Fig. 3B , ** P < 0.01, *** P < 0.001). In addition, the expression of UBE2C protein in the four types of glioma cell was similar to mRNA levels ( Supplementary Fig. 3C , * P < 0.05, ** P < 0.01, *** P < 0.001). In conclusion, the relatively high expression level of UBE2C in glioma cell lines was consistent with TCGA data and IHC results. Based on the difference of UBE2C expression in glioma (tissue and cellular level) and its relevance to the clinical prognosis of this tumor, we need to further verify the effects of UBE2C on the malignant phenotype for the glioma at the cellular level. UBE2C was highly expressed in glioma cell lines LN229 and U251MG (Fig. 4 H, J). Therefore, we achieved knockdown of UBE2C protein expression via transfecting these two cells with siRNA. The results showed that siRNA3 had the most significant knockdown effect on these two types of cells (Fig. 5 A, B, * P < 0.05, ** P < 0.01). Since the knockdown effects of siRNA3 above, cell scratch experiments showed that the knockdown of UBE2C in both LN229 and U251MG cells could significantly inhibit the healing ability of these two cells (Fig. 5 C, D, ** P < 0.01, *** P < 0.001). In addition, when UBE2C is knocked down in LN229 and U251MG cells, it can significantly inhibit the migration ability of these two cells (Fig. 5 E, F, ** P < 0.01). These results indicate that the expression level of UBE2C is positively correlated with the migration ability of tumor cell lines LN229 and U251MG. In addition, the cell cloning assay of LN229 and U251MG showed that when UBE2C was knocked down, it could significantly inhibit the ability of clonal formation for the two tumor cells (Fig. 5 G, H, ** P < 0.01, *** P < 0.001). Meanwhile, the cell proliferation curve assay (OD450, CCK8) showed that the knockdown of UBE2C could significantly inhibit the proliferation ability of these two types of cells (Fig. 5 I, J, * P < 0.05, ** P < 0.01). Therefore, these results indicate that the expression level of UBE2C can significantly affect the malignant phenotype of LN229 and U251MG cells. The External Effects of UBE2C in Glioma As an intrinsic risk factor affecting the progression of glioma, UBE2C may be involved in the regulation of cell cycle to positively link the proliferative activity of tumor cells. However, we also need to understand its correlation with external factors. Based on TCGA data, we analyzed the correlation between UBE2C and 24 types of immune cell infiltration in glioma. The results showed that the expression level of UBE2C was most significantly correlated with the immune infiltration of Th2 cells (Fig. 6 A, R = 0.871, *** P < 0.001). Therefore, we are concerned that this feature may be the focus as UBE2C is linked to external factors of the tumor. According to the secretory phenotype of Th2 cells [ 21 , 22 ] and the trend of UBE2C expression, and combined with their correlation analysis, we can see that IL6 (R = 0.396, P < 0.01) and IL10 (R = 0.338, P < 0.01) are the most significant (Fig. 6 B, C). In conclusion, UBE2C may be closely related to the immune infiltration of Th2 cells. It has been reported that IL6 or IL10 can induce immunosuppressive phenotypes in the tumor microenvironment, which leads to the immune escape mechanism of tumor cells [ 23 ]. Therefore, for malignant progression and poor prognosis of glioma, the correlation between UBE2C and external adverse factors may reflect the essence behind this phenomenon. We analyzed the correlation between the expression of UBE2C and 14 immune checkpoints in glioma, and the results showed that eight immune checkpoints (CD80, SIGLEC7, LAG3, CD28, PDCD1LG2, PDCD1, HAVCR2, SIGLEC15) were significantly positively correlated with UBE2C (Fig. 6 D, E, R > 0.3, P < 0.001). Based on the correlation analysis of Th2 cells secretion phenotype and immune checkpoints, we found that IL6 and IL10 were most significantly correlated with various immune checkpoints, among which six immune checkpoints were more typical (where the red arrow is pointing) (Fig. 6 F, R > 0.5, * P < 0.05). These results suggest that secretory phenotypes IL6 and IL10 from Th2 cells may be important factors affecting immunosuppressive phenotypes in gliomas. Based on the systematic correlation characteristics of UBE2C - IL6/IL10 - immune checkpoint axis, we further analyzed the prognostic value of glioma. The results showed that the expression levels of 13 genes, such as IL6/IL10 and immune checkpoint, were significantly negatively correlated with the survival rate of patients (Fig. 7 A). Meanwhile, IHC images from the THPA database displayed relatively typical expressions of six proteins, which indicated that IL6, IL10, PDCD1LG2, PDCD1, HAVCR2 and CD80 were upregulated in glioma tissues, respectively (Fig. 7 B-G). Taking together, the expression levels of UBE2C-related immune infiltrating Th2 cells and immune checkpoints can significantly affect the survival and prognosis of glioma, which suggesting that UBE2C may be an external risk factor to predict tumor progression. Correction of UBE2C-Related Risk Factors with Cell Invasion in Glioma Since the high aggressiveness of glioma cells has been a major difficulty in the treatment of this disease [ 24 , 25 ], we should also consider its association with cell aggressiveness phenotype when exploring the internal and external correlation of UBE2C. It has been reported that the invasiveness of tumor cells is correlated with soluble immune checkpoints, which reflects that immune checkpoints in the immune microenvironment can significantly affects the invasive phenotype of tumor cells [ 26 ]. Therefore, we performed network and correlation analyses of between MMPs (Matrix Metalloproteinase) family genes and immune checkpoints, which due to the MMP family plays an important role in the invasion of tumor cells [ 27 ]. Based on these backgrounds and analysis, we found that UBE2C-related risk genes (IL6, IL10 and immune checkpoints) were significantly positively correlated with MMP1, -2, -3, -7, -8, -9, -10, -11, -12, -13, -14 and − 19, respectively (Fig. 8 A, * P < 0.05). In addition, online analysis by STRING showed that UBE2C, IL6, IL10 and MMP9 acted as a “linker” that between immune checkpoints and the MMP family (Fig. 8 B). Subsequently, after verifying the correlation between seven MMPs (The most typical seven MMPs, which including MMP1, -2, -7, -9, -11, -14, -19) and immune infiltration in glioma, it was found that MMP2, MMP9 and MMP11 had the most significant correlation with Th2 cells in glioma ( R > 0.5, P < 0.001) (Fig. 8 C-E, Supplementary Fig. 4A-D ). In conclusion, UBE2C, IL6 and IL10 may be the link between immune checkpoint and MMP family. This result also confirmed that the immune invasive phenotype (IL6, IL10 and immune checkpoints) of Th2 may be related to the aggressiveness of tumor cells. Based on these findings, we need to further clarify the prognosis of these seven MMPs for the glioma patients. K-M survival analysis (OS) showed that the expression levels of seven MMPs were significantly negatively correlated with the survival rate of glioma patients, and the MMP2, MMP9 and MMP11 were more typical (Fig. 8 F-H, Supplementary Fig. 4E-H ). IHC images from the THPA database showed that MMP2 and MMP14 expression differences were most significant in gliomas (MMP1 and MMP19 data were not included) ( Supplementary Fig. 5A-E ). Finally, multivariate Cox regression models of UBE2C, IL6, IL10, MMP2, MMP9 and MMP11 were analyzed by nomogram to predict 1-, 3-, and 5-year survival probabilities. The results showed that the contribution value of UBE2C, IL10 and MMP2 was the largest (total score ratio: 250/280 = 89.3%), which may be used as a more accurate multi-factor prediction model for glioma patients, and the prediction model was consistent with the conclusion, that is, UBE2C was correlated with immune invasion and cell invasion (Fig. 8 I). Meanwhile, the calibration curve verifies that the 1-, 3-, and 5-year survival probability curves fit well with the ideal line based on the same conditions, which indicates that the multi-factor prediction model is reliable (Fig. 8 J). In conclusion, among the risk factors for glioma, UBE2C may be systematically associated with IL10 and MMP2, which become a better predictive model. Therefore, UBE2C may be an ideal biomarker for the prognosis of glial patients. Discussion Glioma is a major tumor that seriously threatens the health of the nervous system, which includes a variety of diseases with different degrees of malignancy [ 1 , 28 , 29 ]. At present, the difficulty of clinical treatment of glioma is the malignant progression and poor prognosis from the tumor [ 1 , 30 ]. However, there has been some progress in the study of gliomas, especially in the molecular mechanisms of the tumor microenvironment and cell invasion [ 31 – 33 ]. However, the search for effective diagnosis and treatment of clinical tumors and prognosis assessment is still one of the tasks that need to be solved [ 2 , 6 ]. The clinical application of biomarkers for glioma is mainly reflected in pathological diagnosis, prediction and prognosis assessment, which is also based on the gene expression profile, mutant and the expression difference of risk genes between tumor cells and normal cells to screen [ 6 , 34 , 35 ]. The purpose of this study was to obtain candidate markers via the differential expression of risk genes in the overall sample of gliomas (including glioblastoma and low-grade gliomas) (Fig. 1 , 2 , Supplementary Fig. 1 ). It is characterized by the possibility of considering the range of gliomas with different degrees of malignancy, which should be an important angle to search for potential biomarkers of gliomas. Currently, non-coding RNA, circulating exosome factors and cerebrospinal fluid can be the scope of seeking glioma biomarkers [ 7 , 34 , 36 – 38 ]. However, this study suggests that in addition to circulatory source factor expression differences and prognostic analysis, systematic analysis and evaluation based on markers are also key to validate their reliability [ 39 , 40 ], such as assessing the intrinsic and extrinsic association and risk of marker genes, which are also needed. Therefore, a relatively typical marker - UBE2C was selected based on systemic intrinsic factors such as differential expression genes, risk genes, cell cycle and DNA replication in glioma. However, in terms of the reliability of prognostic markers, validation of external associations is still necessary. Studies have reported that UBE2C, as an E2 ubiquitin binding enzyme, is involved in the regulation of glioma autophagy, chemotherapy resistance, aggressiveness, and poor prognosis [ 41 – 43 ]. However, systematic analysis to evaluate this gene as a potential marker has not been reported. Based on this background, this study not only analyzed the prognostic effect of UBE2C in glioma from multiple perspectives (Fig. 1 , 2 ), but also found that this gene is related to cell cycle and DNA behavior (Fig. 3 , 4 ). These biological processes may be the intrinsic reasons for glioma adaptive survival, and the correlation between UBE2C and malignant phenotypes of glioma cell also reflects this inference (Fig. 5 ). In addition, since the immunosuppression and cell invasion phenotypes of glioma are difficult in clinical diagnosis and treatment [ 44 , 45 ], the evaluation of the application value of biomarkers should also focus on these two aspects [ 46 – 48 ]. In this study, UBE2C was found to be significantly positively correlated with immune infiltration of Th2 cells in gliomas (Fig. 6 A-C). Therefore, according to the secretory phenotype of Th2 cells, it is speculated that Th2 cells may be an important factor leading to immunosuppressive phenotype in the glioma microenvironment, which has been reported in relevant studies [ 49 , 50 ]. Our correlation analysis results confirmed the conjecture that IL6 and IL10 were significantly positively correlated with most immune checkpoints (Fig. 6 F). This conclusion confirms the extrinsic association between UBE2C and glioma, which may be a risk factor for the adaptive progression of tumor cells. The aggressiveness of tumor cells is also one of the main malignant phenotypes of glioma, which may be regulated and influenced by the tumor microenvironment [ 51 , 52 ]. It has been reported that immune checkpoints can affect the expression of MMPs in tumor cells, which promoting the aggressiveness of tumor cells [ 53 , 54 ]. Therefore, exploring the correlation between UBE2C and MMP gene family is also an important aspect of the external correlation between UBE2C and glioma. In this study, the network or association analysis of between immune checkpoints and MMP families showed that IL6, IL10 and MMP9 correlated with UBE2C may be a “linker” of two extrinsic phenotypes, which is reflected that a significant correlation (Fig. 8 A, B). This feature highlights the potential value of UBE2C as a biomarker for glioma. In summary, we identified UBE2C as a potential prognostic biomarker for glioma, and these results demonstrated the internal or external correlation and effects of UBE2C in glioma, but this causal relationship need to be further verified and explored by experiments. Based on the prognostic value and correlation of UBE2C, it may systematically reveal the factors of glioma cells' adaptive survival in the tumor microenvironment, and further reflect that UBE2C may be a potential target for the treatment of this disease. Declarations Competing interests The authors declare that they have no competing interests. Author contributions Study concept and design: HF and FQ; Acquisition of data: HF, AF and RY; Analysis and interpretation of data: HF, AF; Statistical analysis: HF; Drafting of the manuscript: HF and FQ; Manuscript check: HF, RY and FQ; Critical revision and final approval of the manuscript: FQ. All authors contributed to the article and approved the submitted version. Ethics Statement The studies involving data and platform were reviewed and approved by The Institutional Research Ethics Committee of Nanchong Central Hospital. Consent for publication All personal data and samples involved in this study have been obtained with their knowledge and permission for publication. Data Availability Statement All datasets generated and analyzed during the current study are available from the corresponding authors on request. Funding This study was not supported by any funding. Acknowledgements We are especially grateful for the relevant personnel of the Nanchong Central Hospital/Department of Neurosurgery for their platform and help. References Lapointe S, Perry A, Butowski NA: Primary brain tumours in adults. Lancet 2018, 392:432–446. Yang K, Wu Z, Zhang H, Zhang N, Wu W, Wang Z, Dai Z, Zhang X, Zhang L, Peng Y, et al: Glioma targeted therapy: insight into future of molecular approaches. Mol Cancer 2022, 21:39. 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Cheng J, Meng J, Zhu L, Peng Y: Exosomal noncoding RNAs in Glioma: biological functions and potential clinical applications. Mol Cancer 2020, 19:66. Xiao M, Wang X, Yan M, Chen W: A systematic evaluation for the potential translation of CD166-related expression as a cancer biomarker. Expert Rev Mol Diagn 2016, 16:925–932. Jin S, Ye Q, Hong Y, Dai W, Zhang C, Liu W, Guo Y, Zhu D, Zhang Z, Chen S, et al: A systematic evaluation of stool DNA preparation protocols for colorectal cancer screening via analysis of DNA methylation biomarkers. Clin Chem Lab Med 2020, 59:91–99. Guo L, Ding Z, Huang N, Huang Z, Zhang N, Xia Z: Forkhead Box M1 positively regulates UBE2C and protects glioma cells from autophagic death. Cell Cycle 2017, 16:1705–1718. Alafate W, Zuo J, Deng Z, Guo X, Wu W, Zhang W, Xie W, Wang M, Wang J: Combined elevation of AURKB and UBE2C predicts severe outcomes and therapy resistance in glioma. Pathol Res Pract 2019, 215:152557. Ma R, Kang X, Zhang G, Fang F, Du Y, Lv H: High expression of UBE2C is associated with the aggressive progression and poor outcome of malignant glioma. Oncol Lett 2016, 11:2300–2304. Zang L, Kondengaden SM, Che F, Wang L, Heng X: Potential Epigenetic-Based Therapeutic Targets for Glioma. Front Mol Neurosci 2018, 11:408. Xu X, Li L, Luo L, Shu L, Si X, Chen Z, Xia W, Huang J, Liu Y, Shao A, Ke Y: Opportunities and challenges of glioma organoids. Cell Commun Signal 2021, 19:102. Chen R, Smith-Cohn M, Cohen AL, Colman H: Glioma Subclassifications and Their Clinical Significance. Neurotherapeutics 2017, 14:284–297. Nikiforova MN, Hamilton RL: Molecular diagnostics of gliomas. Arch Pathol Lab Med 2011, 135:558–568. Senhaji N, Squalli Houssaini A, Lamrabet S, Louati S, Bennis S: Molecular and Circulating Biomarkers in Patients with Glioblastoma. Int J Mol Sci 2022, 23. Esparvarinha M, Madadi S, Aslanian-Kalkhoran L, Nickho H, Dolati S, Pia H, Danaii S, Taghavi S, Yousefi M: Dominant immune cells in pregnancy and pregnancy complications: T helper cells (TH1/TH2, TH17/Treg cells), NK cells, MDSCs, and the immune checkpoints. Cell Biol Int 2023, 47:507–519. Lee WS, Yang H, Chon HJ, Kim C: Combination of anti-angiogenic therapy and immune checkpoint blockade normalizes vascular-immune crosstalk to potentiate cancer immunity. Exp Mol Med 2020, 52:1475–1485. Chen J, Liu G, Wang X, Hong H, Li T, Li L, Wang H, Xie J, Li B, Li T, et al: Glioblastoma stem cell-specific histamine secretion drives pro-angiogenic tumor microenvironment remodeling. Cell Stem Cell 2022, 29:1531–1546 e1537. Dapash M, Hou D, Castro B, Lee-Chang C, Lesniak MS: The Interplay between Glioblastoma and Its Microenvironment. Cells 2021, 10. Wang T, He Z, Yuan CS, Deng ZW, Li F, Chen XG, Liu Y: MMP-responsive transformation nanomaterials with IAP antagonist to boost immune checkpoint therapy. J Control Release 2022, 343:765–776. Ye Y, Kuang X, Xie Z, Liang L, Zhang Z, Zhang Y, Ma F, Gao Q, Chang R, Lee HH, et al: Small-molecule MMP2/MMP9 inhibitor SB-3CT modulates tumor immune surveillance by regulating PD-L1. Genome Med 2020, 12:83. Additional Declarations No competing interests reported. Supplementary Files SupplementaryFigure1.tiff Supplementary Figure 1. (A, B) KEGG and GO enrichment analysis bubble maps based on differential genes (TCGA-glioma, down-regulation), respectively; (C, D)Represents Forest maps of univariate and multivariate Cox regression analyses based on 17 genes, respectively. SupplementaryFigure2.tiff Supplementary Figure 2. (A, B) Represents the results of GSEA enrichment analysis based on dataset GSE12657, which includes biological processes of cell cycle and DNA behavior, respectively. SupplementaryFigure3.tiff Supplementary Figure 3. (A) UBE2C mRNA expression in 48 human glioma cell lines from the CCLE database; (B) The expression level of UBE2C gene in five cell lines was detected by qPCR; (C) The expression level of UBE2C gene in five cell lines was detected by Western blot. * P < 0.05, ** P < 0.01, *** P < 0.001. SupplementaryFigure4.tiff Supplementary Figure 4. (A-D) Indicates the correlation analysis of MMP1, MMP7, MMP14 and MMP19 with immune cell infiltration in TCGA-glioma, respectively; (E-H)Represents survival prognosis analysis for MMP1, MMP7, MMP14 and MMP19 in TCGA-glioma, respectively. SupplementaryFigure5.tiff Supplementary Figure 5. (A-E) The expression level of MMP2, MMP7, MMP9, MMP11 and MMP14 in glioma tissues was displayed by IHC images from THPA database, respectively (MMP1, MMP4 and MMP19 MMP1, MMP4 and MMP19 are not included in the THPA database). 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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-4019424","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":276686424,"identity":"2f275cdf-fd45-42f0-8e9b-0a8283d50c6a","order_by":0,"name":"Hao Feng","email":"","orcid":"","institution":"Nanchong Central Hospital/The Second Clinical Medical College, North Sichuan Medical College, Department of Neurosurgery, Nanchong City, Sichuan Province","correspondingAuthor":false,"prefix":"","firstName":"Hao","middleName":"","lastName":"Feng","suffix":""},{"id":276686425,"identity":"0d7c3e18-bf6e-4b4f-987f-def18cfcb8f1","order_by":1,"name":"Anhui Fu","email":"","orcid":"","institution":"Nanchong Central Hospital/The Second Clinical Medical College, North Sichuan Medical College, Department of Neurosurgery, Nanchong City, Sichuan Province","correspondingAuthor":false,"prefix":"","firstName":"Anhui","middleName":"","lastName":"Fu","suffix":""},{"id":276686426,"identity":"1da99a2d-e3b1-4e00-819b-3ca92b20115f","order_by":2,"name":"Rong Yang","email":"","orcid":"","institution":"Nanchong Central Hospital/The Second Clinical Medical College, North Sichuan Medical College, Department of Neurosurgery, Nanchong City, Sichuan Province","correspondingAuthor":false,"prefix":"","firstName":"Rong","middleName":"","lastName":"Yang","suffix":""},{"id":276686427,"identity":"b1ed5165-057f-453d-aa63-cbec9a2d4d88","order_by":3,"name":"Fei Qiao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3ElEQVRIiWNgGAWjYBAC9gYGBmYIk43xQUJFDWEtPAcQWpgNHpw5RpoWNsmHLcxEaGHvPfy6oMYmccONtLSKxAY2Bv727gT8WnjOpVnPOJaWOLPn2LEbiTtkGCTOnN2AV4u9RI6ZMQ/b4dx+9va2G4ln2BgMJHLxa+GRfwPU8u9wbhsze1tBYhszEVokeIwf87aBbGk7xkCcFp4cM2bevrR6oF+SJRLOHOMh6Bce9jPGn3m+2Rgb3Egz/PijokaOv70XvxYgYJNAMYOQchBg/kCMqlEwCkbBKBjBAAA/3Ea+kDrnwQAAAABJRU5ErkJggg==","orcid":"","institution":"Nanchong Central Hospital/The Second Clinical Medical College, North Sichuan Medical College, Department of Neurosurgery, Nanchong City, Sichuan Province","correspondingAuthor":true,"prefix":"","firstName":"Fei","middleName":"","lastName":"Qiao","suffix":""}],"badges":[],"createdAt":"2024-03-06 05:29:37","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4019424/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4019424/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":52255488,"identity":"93e3658e-15a5-418c-862c-048c94d1bfc3","added_by":"auto","created_at":"2024-03-08 10:34:05","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":273809,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eScreening of target genes in gliomas based on TCGA.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A, B)\u003c/strong\u003e KEGG and GO enrichment analysis of differential genes (Upregulation, Log\u003csub\u003e2\u003c/sub\u003eFC \u0026gt; 1, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05), respectively; \u003cstrong\u003e(C)\u003c/strong\u003e Represents Venn diagram analysis of risk genes (HR \u0026gt; 1, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05) and up-regulated genes (Log\u003csub\u003e2\u003c/sub\u003eFC \u0026gt; 1, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05); \u003cstrong\u003e(D)\u003c/strong\u003e Enrichment analysis of 679 common genes from (C); \u003cstrong\u003e(E)\u003c/strong\u003e Venn diagram analysis of genes from the four biological processes in (D) (where the red arrow is pointing); \u003cstrong\u003e(F)\u003c/strong\u003e Analysis of expression associations among 27 genes from (E); \u003cstrong\u003e(G)\u003c/strong\u003e Co-expression and risk analysis of 17 genes from (F); \u003cstrong\u003e(H)\u003c/strong\u003e Differential expression analysis of 17 genes (The red box indicates that UBE2C has the highest differential expression). **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"OnlineFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4019424/v1/c35df36c52a6268c395568ad.png"},{"id":52255354,"identity":"3aa071db-b547-4682-bba8-03d1912e76dc","added_by":"auto","created_at":"2024-03-08 10:26:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":142573,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eClinical grading and prognostic analysis of UBE2C gene in TCGA-glioma samples\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A) \u003c/strong\u003eThe\u003cstrong\u003e \u003c/strong\u003eclinical grading based on WHO; \u003cstrong\u003e(B)\u003c/strong\u003e Represents the primary therapy outcome of glioma patients; \u003cstrong\u003e(C)\u003c/strong\u003e Indicates the histological type of glioma samples; \u003cstrong\u003e(D)\u003c/strong\u003e Represents the age division of glioma patients; \u003cstrong\u003e(E) \u003c/strong\u003eRepresents the survival status of glioma patients; \u003cstrong\u003e(F)\u003c/strong\u003e Represents ROC curve analysis of glioma samples; \u003cstrong\u003e(G-I)\u003c/strong\u003e Respectively represent survival prognosis analysis based on TCGA-glioma with OS, DSS, PFI, respectively. **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"OnlineFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4019424/v1/1ccbc50b7903c9f90d9206e9.png"},{"id":52255355,"identity":"6b9f13b2-5dca-4f53-869f-f3d3ac8df272","added_by":"auto","created_at":"2024-03-08 10:26:04","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":145979,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEnrichment analysis of genes positively related to UBE2C\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003eRepresents enrichment analysis based on UBE2C positively related genes (red arrow represents cell cycle related processes, blue arrow represents DNA behavior related processes). \u003cstrong\u003e(B, C)\u003c/strong\u003e Represents the network analysis based on the results of the enrichment analysis in (A); \u003cstrong\u003e(D, E)\u003c/strong\u003e Represents a differential gene GSEA enrichment assay validation based on TCGA-glioma, which are cell cycle and DNA behavior processes, respectively. NES \u0026gt; 1, adj. \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.05, FDR q \u0026lt; 0.25.\u003c/p\u003e","description":"","filename":"OnlineFigure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4019424/v1/3e145dc83e0872498c220b71.png"},{"id":52255487,"identity":"1057afbf-bdb8-4c1a-87e4-26d5d30d3711","added_by":"auto","created_at":"2024-03-08 10:34:04","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":835754,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExpression levels of UBE2C and cell proliferation antigen in gliomas.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A-D)\u003c/strong\u003eRepresents IHC images of UBE2C, Ki67, PCNA and MCM7 from The Human Protein Atlas database, respectively; \u003cstrong\u003e(E-G)\u003c/strong\u003e Represents the scatter plot of expression correlation between UBE2C and Ki67, PCNA and MCM7, respectively; \u003cstrong\u003e(H)\u003c/strong\u003eExpression levels of UBE2C mRNA in 48 brain cancer cell lines from the CCLE database; \u003cstrong\u003e(I)\u003c/strong\u003e The expression level of UBE2C mRNA in 5 cell lines was detected by qPCR; \u003cstrong\u003e(J)\u003c/strong\u003e The expression level of protein UBE2C in 5 cell lines was detected by Western blot.\u003c/p\u003e","description":"","filename":"OnlineFigure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4019424/v1/3eadf25088f38a02133c3ab9.png"},{"id":52255358,"identity":"007a5b22-8e46-4508-9c9a-e66277fab488","added_by":"auto","created_at":"2024-03-08 10:26:05","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":521286,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffect of UBE2C Expression on Malignant Phenotype for Glioma Cell.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A, B) \u003c/strong\u003eThe knockdown effect of three siRNA on LN229 and U251MG cells was evaluated by Western blot, respectively; \u003cstrong\u003e(C, D)\u003c/strong\u003e The healing ability of UBE2C - siRNA3 for LN229 and U251MG cells was evaluated by cell scratch assay, respectively; \u003cstrong\u003e(E, F)\u003c/strong\u003e The migration ability of UBE2C - siRNA3 for LN229 and U251MG cells was evaluated by trans-well assay, respectively; \u003cstrong\u003e(G, H)\u003c/strong\u003e The proliferation ability of UBE2C - siRNA3 for LN229 and U251MG cells was evaluated by cell clonal formation assay, respectively; \u003cstrong\u003e(I, J)\u003c/strong\u003e The proliferation ability of UBE2C - siRNA3 for LN229 and U251MG cells was evaluated by CCK8 assay (OD450), respectively. *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"OnlineFigure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4019424/v1/9564b4a8a3c28d16c336b968.png"},{"id":52255360,"identity":"e5164192-7794-482c-83ab-fe7550c9ca19","added_by":"auto","created_at":"2024-03-08 10:26:05","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":142916,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation analysis of UBE2C with immune cell infiltration and immune checkpoint based on TCGA- glioma.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003eCorrelation analysis between UBE2C and immune cell infiltration; \u003cstrong\u003e(B)\u003c/strong\u003e Co-expression analysis between UBE2C and secretory phenotypes from Th2 cells; \u003cstrong\u003e(C)\u003c/strong\u003e correlation analysis between UBE2C and secretory phenotype of Th2 cells; \u003cstrong\u003e(D)\u003c/strong\u003e Analysis of co-expression between UBE2C and immune checkpoints; \u003cstrong\u003e(E)\u003c/strong\u003e Correlation analysis between UBE2C and immune checkpoints; \u003cstrong\u003e(F)\u003c/strong\u003e Correlation analysis between immune checkpoint and Th2 cell secretion phenotype.\u003c/p\u003e","description":"","filename":"OnlineFigure6.png","url":"https://assets-eu.researchsquare.com/files/rs-4019424/v1/2ca5cd492f5bac665cf9e8de.png"},{"id":52255362,"identity":"99015d39-f3fc-431f-8e3b-4a0b407a98b8","added_by":"auto","created_at":"2024-03-08 10:26:05","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":976069,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExpression and prognostic survival analysis based on immune checkpoint and typical Th2 cell secretion phenotype.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003ePrognostic survival analysis based on immune checkpoint and typical Th2 cell secretion phenotype; \u003cstrong\u003e(B-G)\u003c/strong\u003e Represents IHC images of IL6, IL10, PDCD1LG2, PDCD1, HAVCR2 and CD80 from THPA database, respectively.\u003c/p\u003e","description":"","filename":"OnlineFigure7.png","url":"https://assets-eu.researchsquare.com/files/rs-4019424/v1/4fb65410c5245b144e5c0d2e.png"},{"id":52255364,"identity":"4b3dd736-f821-489f-aafc-cfa9cfec4bf1","added_by":"auto","created_at":"2024-03-08 10:26:05","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":448663,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrection of UBE2C-related risk factors with cell invasion in glioma.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A) \u003c/strong\u003eCorrelation analysis between immune checkpoint and MMP family expression; \u003cstrong\u003e(B)\u003c/strong\u003e Analysis of network correlation between immune checkpoint and MMP family by STRING online; \u003cstrong\u003e(C-E)\u003c/strong\u003e Analysis of the correlation between MMP2, MMP9 and MMP11 and immune cell infiltration, respectively; \u003cstrong\u003e(F-H)\u003c/strong\u003e Analysis of survival prognosis of MMP2, MMP9 and MMP11 in glioma patients, respectively; \u003cstrong\u003e(I, J)\u003c/strong\u003eThe prognostic nomogram and calibration curve of UBE2C, IL6, IL10, MMP2, MMP9, and MMP11 in glioma patients, respectively.\u003c/p\u003e","description":"","filename":"OnlineFigure8.png","url":"https://assets-eu.researchsquare.com/files/rs-4019424/v1/76ed26fa8db759ea39409147.png"},{"id":52412897,"identity":"3ee98902-2d43-42d3-93b3-74e330100037","added_by":"auto","created_at":"2024-03-11 10:27:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6058558,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4019424/v1/9025b4c3-e640-4f8e-ae44-0b65dd1de324.pdf"},{"id":52255368,"identity":"8a887680-907d-4426-a377-e3cb724e2755","added_by":"auto","created_at":"2024-03-08 10:26:06","extension":"tiff","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":15308686,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 1.\u003c/strong\u003e \u003cstrong\u003e(A, B) \u003c/strong\u003eKEGG and GO enrichment analysis bubble maps based on differential genes (TCGA-glioma, down-regulation), respectively; \u003cstrong\u003e(C, D)\u003c/strong\u003eRepresents Forest maps of univariate and multivariate Cox regression analyses based on 17 genes, respectively.\u003c/p\u003e","description":"","filename":"SupplementaryFigure1.tiff","url":"https://assets-eu.researchsquare.com/files/rs-4019424/v1/32c66b74ab141ff79227d3c9.tiff"},{"id":52255365,"identity":"24e77263-4c43-4c43-9665-361caddaef7a","added_by":"auto","created_at":"2024-03-08 10:26:05","extension":"tiff","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":8265302,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 2. (A, B) \u003c/strong\u003eRepresents the results of GSEA enrichment analysis based on dataset GSE12657, which includes biological processes of cell cycle and DNA behavior, respectively.\u003c/p\u003e","description":"","filename":"SupplementaryFigure2.tiff","url":"https://assets-eu.researchsquare.com/files/rs-4019424/v1/a6f66072360246c533bed0cf.tiff"},{"id":52255367,"identity":"53ce2ea3-3722-49f4-a1b2-6e10bbfcdc7a","added_by":"auto","created_at":"2024-03-08 10:26:05","extension":"tiff","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":13130626,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 3. (A) \u003c/strong\u003eUBE2C mRNA expression in 48 human glioma cell lines from the CCLE database; \u003cstrong\u003e(B)\u003c/strong\u003e The expression level of UBE2C gene in five cell lines was detected by qPCR; \u003cstrong\u003e(C)\u003c/strong\u003e The expression level of UBE2C gene in five cell lines was detected by Western blot. *\u003cem\u003eP\u003c/em\u003e\u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"SupplementaryFigure3.tiff","url":"https://assets-eu.researchsquare.com/files/rs-4019424/v1/9c609c7465a452b698e26ce9.tiff"},{"id":52255370,"identity":"df58f06e-8208-472e-bc61-6114ccf65ca7","added_by":"auto","created_at":"2024-03-08 10:26:06","extension":"tiff","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":22162378,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 4. (A-D) \u003c/strong\u003eIndicates the correlation analysis of MMP1, MMP7, MMP14 and MMP19 with immune cell infiltration in TCGA-glioma, respectively; \u003cstrong\u003e(E-H)\u003c/strong\u003eRepresents survival prognosis analysis for MMP1, MMP7, MMP14 and MMP19 in TCGA-glioma, respectively.\u003c/p\u003e","description":"","filename":"SupplementaryFigure4.tiff","url":"https://assets-eu.researchsquare.com/files/rs-4019424/v1/7d005d717121332ab7315085.tiff"},{"id":52255369,"identity":"341ddaf9-0791-4303-a0d6-6a78af9008a5","added_by":"auto","created_at":"2024-03-08 10:26:06","extension":"tiff","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":14545686,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 5. (A-E) \u003c/strong\u003eThe expression level of MMP2, MMP7, MMP9, MMP11 and MMP14 in glioma tissues was displayed by IHC images from THPA database, respectively (MMP1, MMP4 and MMP19 MMP1, MMP4 and MMP19 are not included in the THPA database).\u003c/p\u003e","description":"","filename":"SupplementaryFigure5.tiff","url":"https://assets-eu.researchsquare.com/files/rs-4019424/v1/8fd2d98579682b7f548befab.tiff"},{"id":52255489,"identity":"3f816dd9-941a-4b58-8ab2-ae9060a0da76","added_by":"auto","created_at":"2024-03-08 10:34:05","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":18179,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4019424/v1/01e0771fe1dfaec4ce12f240.docx"},{"id":52255490,"identity":"5d70e3db-5347-4061-9629-6128f8b3bbda","added_by":"auto","created_at":"2024-03-08 10:34:05","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":18651,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable2.docx","url":"https://assets-eu.researchsquare.com/files/rs-4019424/v1/0755118c1b0db9eaa5fffa38.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"UBE2C is associated with Prognosis of Immunosuppression and Cell Invasiveness in Glioma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAs the main type of central nervous system tumor, brain glioma is also one of the most prone to malignant progression of tumors, which seriously endangers people's nervous system health [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Currently, although this disease can be treated with surgery, radiotherapy and chemotherapy, the tumor is rapidly progressive, which prone to metastasis and immune checkpoints suppression [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. For example, the glioblastoma with a high degree of malignancy contains the above adverse characteristics [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Therefore, it is necessary to seek or explore relatively effective markers to effectively predict and evaluate the prognosis of patients with this tumor. Based on the malignant progression and high-risk characteristics of glioma, clarifying the correlation between this biomarker and the above characteristics becomes an important indicator of effective evaluation and prognosis [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], and it may even be used as a potential target for glioma intervention.\u003c/p\u003e \u003cp\u003eThe malignant phenotype of gliomas usually includes a highly aggressive and immunosuppressive phenotype, which is often a difficulty in clinical diagnosis and treatment [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Therefore, the search for valuable biomarkers has become an important task for the effective prediction and prognosis of the disease. Compared with normal tissues, high-risk genes in glioma samples may be important factors in the adaptive survival or malignancy process of tumor cells [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. UBE2C with relatively high expression in tumors may become typical risk factors. Studies have reported that UBE2C may play a role in promoting cancer, such as brain cancer, breast cancer, cervical cancer, pancreatic cancer and liver cancer [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Based on this characteristic and combined with the malignant phenotype of glioma, multifaceted or systematic analysis can reflect that the expression level of this gene and may be a key factor affecting tumor progression [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Such as immunosuppressive and highly aggressive phenotypes in glioma may be related to this gene, and thus become the key to the progression of tumor malignancy [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. These aspects mainly manifested by the systematic association of this gene with immune infiltrating cells, immune checkpoints and invasion-related genes [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Therefore, evaluating the intrinsic and extrinsic risk of these genes for glioma from a systemic perspective may be beneficial for obtaining valuable biomarkers.\u003c/p\u003e \u003cp\u003eAlthough there has been some progress in the study of biomarkers for glioma, systematic research on the relationship between risk genes and immunosuppressive or invasive phenotypes is rarely reported. Based on the TCGA database and GEO-sourced clinical glioma sample data (including glioblastoma multiforme and low-grade glioma), this study demonstrated that UBE2C gene could be used as a biomarker for glioma via systemic analysis, screening and evaluation. This gene has established a significant correlation with immunoinfiltrated Th2 cells, which may influence immunosuppressive phenotypes and tumor cell invasion via IL6 and IL10. In summary, based on these results, it can be reflected that the high expression of UBE2C in glioma may serve as a relatively typical risk factor and may become a valuable prognostic marker.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eGlioma sample\u003c/h2\u003e \u003cp\u003eThe glioma samples involved in this study were taken from the TCGA database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cancer.gov/ccg/research/genome-sequencing/tcga\u003c/span\u003e\u003cspan address=\"https://www.cancer.gov/ccg/research/genome-sequencing/tcga\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), which contained 174 Glioblastoma multiforme, 532 low-grade gliomas, and 5 adjacent controls. In addition, there is a glioma sequencing dataset (GSE12657) from the GEO (Gene Expression Omnibus) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), which contains five adjacent controls and seven Glioblastoma patients.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eCell Culture and Transfection\u003c/h2\u003e \u003cp\u003eThe HBE, LN229, U87MG, A172 and U251MG cells involved in this experiment were all purchased from ATCC cell bank (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.atcc.org\u003c/span\u003e\u003cspan address=\"http://www.atcc.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). All the cell lines were cultured in medium: DMEM (90%, Gibco)\u0026thinsp;+\u0026thinsp;FBS (10% Fetal calf serum, Gibco)\u0026thinsp;+\u0026thinsp;1% PS (Penicillin streptomycin combination, Gibco), 37℃, 5% CO2. Cell transfection that transfection agents used for PolyPlus (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.polyplus-sartorius.com/\u003c/span\u003e\u003cspan address=\"https://www.polyplus-sartorius.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), siRNA was synthesized by Geneparma company (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.genepharma.com/\u003c/span\u003e\u003cspan address=\"https://www.genepharma.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Please refer to the instructions of transfection reagent for the procedure of transfection experiment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eWound Healing and Trans-Well Assays\u003c/h2\u003e \u003cp\u003eTumor cells were seeded into 12 - well plates and scratched with a 10 \u0026micro;L pipette tip after reaching confluency. The plates washed thrice with PBS, cultured in serum-free medium DMEM for 24 h, and photographed. Cell migration assays, 10\u003csup\u003e4\u003c/sup\u003e cells were spread into the filter of a 24 - well plate and cultured in serum-free medium DMEM for 48 h. Filters were then fixed with a neutral formaldehyde solution (4%) and stained with crystal violet.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eColony Formation Assays and Cell Proliferation\u003c/h2\u003e \u003cp\u003eColony formation assay, cells were plated at a density of 1000 cells/well into 12-well plates and cultured for 14 days, then fixed with neutral formaldehyde (4%) and stained with crystal violet. Cell proliferation assay was by seeding 5 \u0026times; 10\u003csup\u003e3\u003c/sup\u003e cells/well into a 96-well plate. Proliferation activity (Absorption in OD450 nm) was measured over four days using the CCK-8 method as per the manufacturer's instructions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eWestern Blot\u003c/h2\u003e \u003cp\u003eProtein samples extracted from cells were used for Western blotting, following a standard method. Reagents and consumables used in this process included PVDF membranes (Millipore, USA); anti-UBE2C (Rabbit, ab252940) (Abcam, USA); anti-GAPDH (Rabbit, ab181602) (Abcam, USA); TBST buffer (Biosharp, China); Running buffer (Biosharp, China); Transmembrane buffer (Biosharp, China); and Substrate luminescent liquid (Biosharp, China), Goat anti-Rabbit HRP (H\u0026thinsp;+\u0026thinsp;L, ab6721) (Abcam, USA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eRNA Isolation and Real-Time PCR\u003c/h2\u003e \u003cp\u003eTotal RNA from tumor cells was extracted using the Trizol reagent method following standard procedures. Reverse transcription of total RNA was performed using a two-step kit (TaKaRa, Japan), and real-time qPCR experiments were conducted using the SYBR fluorescent dye kit (TaKaRa, Japan). The primers used in this procedure included: UBE2C (F: 5'- CATCAGAACCAGCTCAACAGT \u0026minus;\u0026thinsp;3'; R: 5'- GTTGCAGAGTAAGCT\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eCCAGCA \u0026minus;\u0026thinsp;3'); GAPDH (F: 5\u0026prime;- ACAACTTTGGTATCGTGGAAGG \u0026minus;\u0026thinsp;3\u0026prime;; R: 5\u0026prime;- GCC\u003c/h2\u003e \u003cp\u003eATCACGCCACAGTTTC \u0026minus;\u0026thinsp;3\u0026prime;).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eImmunohistochemical (IHC)\u003c/h2\u003e \u003cp\u003eThe glioma IHC images involved in this study are all from The Human Protein Atlas database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.proteinatlas.org/\u003c/span\u003e\u003cspan address=\"https://www.proteinatlas.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), which contains the expression of existing related proteins and the IHC results of tumor pathological tissues (all images contain the sample number). Our conclusion is based on these image data for analysis and demonstration.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eGlioVis analysis\u003c/h2\u003e \u003cp\u003eGlioVis online analysis (Visualization Tools for Glioma Datasets, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://gliovis.bioinfo\u003c/span\u003e\u003cspan address=\"http://gliovis.bioinfo\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003cp\u003ecnio.es/), which is an important database that adopted for data visualization and analysis to explore glioma [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Meanwhile, the normalized gene expression, this database includes information on glioma molecular pathology and glioma subtypes, which are important tools for online analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eKEGG and GO enrichment analysis\u003c/h2\u003e \u003cp\u003eThe enrichment analyses involved in this study included KEGG (Kyoto Encyclopedia of Genes and Genomes) and GO (Gene Ontology). RNA-sequencing expression (level 3) profiles and corresponding clinical information for glioma were downloaded from the TCGA dataset (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.com\u003c/span\u003e\u003cspan address=\"https://portal.gdc.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Using the limma package in the R software to study the differentially expressed mRNA. \u0026ldquo;Adjusted \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and Log\u003csub\u003e2\u003c/sub\u003eFC (Fold Change)\u0026thinsp;\u0026gt;\u0026thinsp;1 or Log\u003csub\u003e2\u003c/sub\u003eFC\u0026thinsp;\u0026lt;\u0026thinsp;\u0026minus;\u0026thinsp;1\u0026rdquo; were defined as the threshold for the differential expression of mRNAs. In addition, online Metascape analysis (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://metascape.org/g\u003c/span\u003e\u003cspan address=\"https://metascape.org/g\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003ep/index.html#/main/step1) also serves as an important reference for enrichment analysis results [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eGene Set Enrichment Analyses (GSEA)\u003c/h2\u003e \u003cp\u003eDataset GSE12657 was downloaded from the GEO database and then GSEA (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://software.broadinstitute.org/gsea/index.jsp\u003c/span\u003e\u003cspan address=\"http://software.broadinstitute.org/gsea/index.jsp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) enrichment analysis was performed. The overall differential genes in this dataset were used as the data source for analysis without additional conditional screening. GSEA enrichments were estimated by normalized enrichment score (NES) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The significance of the results was assessed with FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.25 level, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, and FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.25 levels.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eNetwork system analysis\u003c/h2\u003e \u003cp\u003eOnline network analysis between multiple genes is performed using STRING (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cn.stringdb.org/cgi/input?sessionId=bsNTRVpzqx25\u0026amp;input_page_show_search=on\u003c/span\u003e\u003cspan address=\"https://cn.stringdb.org/cgi/input?sessionId=bsNTRVpzqx25\u0026amp;input_page_show_search=on\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and adjustments and layout optimization are made on this result. At the same time, the systematic analysis based on Gene Mania (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://genemania.org/\u003c/span\u003e\u003cspan address=\"https://genemania.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) is used as a reference for the above results.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eBioinformatics and statistical analysis\u003c/h2\u003e \u003cp\u003eGene expression differential analysis, clinical grading significance, survival prognosis analysis (K-M survival curve), immune infiltration analysis, gene expression correlation analysis, Cox risk regression analysis (Univariate and multivariate regression analysis) and nomogram analysis (Calibration curves) in gliomas were all implemented by R v4.0.3 software package. All the analysis results were represented by Spearman as the correlation coefficient (\u003cem\u003eR\u003c/em\u003e), and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was the significant result.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eFunction of Differentially Expressed Genes and Risk Genes in Glioma\u003c/h2\u003e \u003cp\u003eIdentifying the relatively typical biological processes in gliomas is the basis for seeking markers. Therefore, we first conducted differential gene enrichment analysis (KEGG and GO) based on glioma (containing the glioblastoma multiforme and low-grade glioma) sample data from TCGA database. Based on significantly upregulated genes, the enrichment results of both KEGG and GO reflected entries related to cell cycle regulation. We combined with the characteristics of tumor cell cycle, it was suggested that this process is the basis of malignant phenotype or progression of tumor cells. (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, B, \u003cb\u003ewhere the red arrow is pointing\u003c/b\u003e). Meanwhile, there were no typical characteristic items in the significantly down-regulated gene enrichment results (\u003cb\u003eSupplementary Fig.\u0026nbsp;1A, B\u003c/b\u003e). To further explore the commonality between the function of risk genes and up-regulated genes, 8344 risk genes (HR\u0026thinsp;\u0026gt;\u0026thinsp;1, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were obtained by analyzing TCGA-glioma data, and they were compared with 1091 up-regulated genes (Log\u003csub\u003e2\u003c/sub\u003eFC\u0026thinsp;\u0026gt;\u0026thinsp;1, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) for Venn diagram analysis. The results showed that there were 679 genes with common characteristics (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Enrichment analysis of these genes revealed four biological processes associated with the cell cycle in the top 5 entries (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD, \u003cb\u003ewhere the red arrow is pointing\u003c/b\u003e). These results suggest that upregulated risk genes in gliomas may also be involved in cell cycle processes.\u003c/p\u003e \u003cp\u003eBased on the above enrichment results, we conducted Venn diagram analysis for the genes from these four items, which found that 27 genes were the common part (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). Correlation analysis of these genes in glioma showed that 17 genes were strongly correlated with each other (\u003cem\u003eR\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.8, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and all showed high risk at high expression levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF, G). These results confirmed the high-risk characteristics for the selected genes. Meanwhile, Cox regression analysis showed that 17 genes were significant risk factors in univariate analysis, and the multivariate prognostic analysis shows that CHEK1, UBE2C and BUB1 were independent prognostic risk factors (\u003cb\u003eSupplementary Fig.\u0026nbsp;1C, D\u003c/b\u003e, HR\u0026thinsp;\u0026gt;\u0026thinsp;1, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In addition, according to the expression level of 17 genes in glioma, UBE2C had the most significant upregulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eH, ***\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In summary, UBE2C may be a risk factor and significantly upregulated in gliomas, which may be the internal cause of malignant phenotype of tumors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eClinical Classification and Prognostic Significance of UBE2C in Glioma\u003c/h2\u003e \u003cp\u003eUBE2C was selected as a typical representative gene. To further verify its clinical significance in glioma, these samples from the TCGA database were clinically classified (\u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e). The results showed that the expression level of UBE2C was significantly up regulated with the progression of the disease in both WHO classification (G2, G3, G4) and primary therapy outcome (CR, Complete response; PR, Partial Response; SD, Stable disease; PD, Progressive disease) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, B, **\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Among the pathological grades of glioma, the malignant degree of glioblastoma was the highest [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], and the expression level of UBE2C was significantly higher than that of the other three types (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC, ***\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In terms of age and survival status, glioma patients over 60 years of age and those who had died showed significant UBE2C upregulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD, E, ***\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). These features can also be demonstrated by the results of Cox regression analysis of clinical grading (\u003cb\u003eSupplementary Table\u0026nbsp;2\u003c/b\u003e). These results suggest that the expression level of UBE2C is significantly different in the clinical grades of glioma, which can significantly affect the prognosis of these clinical grades.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn addition, the ROC Curve (Receiver Operating Characteristic Curve) analysis of UBE2C in glioma samples showed that the AUC (Area Under ROC Curve, CI: 0.935\u0026ndash;0.993) value was 0.964. This result indicates that UBE2C gene has good diagnostic value for glioma (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF). To assess the prognostic value of UBE2C in glioma, survival curves (K-M) with OS (Overall Survival), DSS (Disease Specific Survival), and PFI (Progress Free Interval) were performed on these clinical samples, respectively. The results showed that the three types of K-M curves showed that the survival rate of the group with high expression of UBE2C was relatively low, that is, the expression level of this gene was significantly negatively correlated with the survival rate of patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG-I). In conclusion, the differences in the expression of UBE2C in clinical classification and survival prognosis can reflect the related characteristics of disease progression, and thus show certain clinical diagnosis and prognostic value.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eThe Intrinsic Effect of UBE2C in Glioma\u003c/h2\u003e \u003cp\u003eBased on the clinical significance of UBE2C in gliomas, we need to further explore the internal influence of this gene in gliomas, which can reflect the value of UBE2C as a marker of endogenous adverse factors. We need to further explore the major biological processes involved in UBE2C by enrichment analyzing the genes that were significantly positively correlated with UBE2C (\u003cem\u003eR\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.6, adj. \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The results show that the TOP 20 items presented mainly include two biological processes, which are cell cycle regulation (red arrow) and DNA behavior (blue arrow) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Meanwhile, GSEA analysis of differential genes based on TCGA verified the significance of cell cycle and DNA behavior, respectively (NES\u0026thinsp;\u0026gt;\u0026thinsp;1, adj. \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, FDR q\u0026thinsp;\u0026lt;\u0026thinsp;0.25) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, C). The analysis results of the GEO-sourced dataset (GSE12657) can also fully verify this conclusion (\u003cb\u003eSupplementary Fig.\u0026nbsp;2A, B\u003c/b\u003e, NES\u0026thinsp;\u0026gt;\u0026thinsp;1, adj. \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, FDR q\u0026thinsp;\u0026lt;\u0026thinsp;0.25). Therefore, the above results indicate that UBE2C may satisfy the adaptive survival of tumor cells in glioma mainly via the regulation of cell cycle and DNA behavior.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSummarizing the above results, it can be inferred that UBE2C gene may be related to malignant proliferation of glioma cells. To verify this association, we separately display the IHC (Immunohistochemistry) images from The Human Protein Atlas (THPA) database. Since Ki67, PCNA and MCM7 are common markers reflecting tumor cell proliferation [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], they are related to the replication behavior of nuclear DNA. Therefore, according to the IHC of these three proliferative markers, the nuclear coloring degree of tumor region is deeper than adjacent region (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB-D). Meanwhile, the staining trend of UBE2C cells in tumor region was consistent with these three proliferative markers (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). In addition, the scatter plot of correlation analysis shows that UBE2C had a significant positive correlation with Ki67, PCNA and MCM7, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE-G). In summary, the expression level of UBE2C is related to the proliferation of tumor cells, and it may be an internal factor affecting the adaptive survival of tumor cells.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eEffect of UBE2C Expression on Malignant Phenotype for Glioma Cell\u003c/h2\u003e \u003cp\u003eTo verify the expression of UBE2C in glioma cell lines, our analysis based on the mRNA data collected in the CCLE database (Cancer Cell Line Encyclopedia), which showed that there was little difference in the expression of UBE2C in 48 brain cancer cell lines (\u003cb\u003eSupplementary Fig.\u0026nbsp;3A\u003c/b\u003e). However, the differences between the common glioma cell lines LN229 and U251MG were relatively obvious (\u003cb\u003eSupplementary Fig.\u0026nbsp;3A\u003c/b\u003e, where the red arrow is pointing). The expression of UBE2C in 4 glioma cell lines (LN229, U87MG, A172 and U251MG) was detected by qPCR and Western blot, respectively. The results showed that the mRNA expression level of UBEC in these four cell lines was significantly higher than that of normal glioma cells (HEB), but there was no difference among the four types (\u003cb\u003eSupplementary Fig.\u0026nbsp;3B\u003c/b\u003e, **\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In addition, the expression of UBE2C protein in the four types of glioma cell was similar to mRNA levels (\u003cb\u003eSupplementary Fig.\u0026nbsp;3C\u003c/b\u003e, *\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In conclusion, the relatively high expression level of UBE2C in glioma cell lines was consistent with TCGA data and IHC results.\u003c/p\u003e \u003cp\u003eBased on the difference of UBE2C expression in glioma (tissue and cellular level) and its relevance to the clinical prognosis of this tumor, we need to further verify the effects of UBE2C on the malignant phenotype for the glioma at the cellular level. UBE2C was highly expressed in glioma cell lines LN229 and U251MG (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eH, J). Therefore, we achieved knockdown of UBE2C protein expression via transfecting these two cells with siRNA. The results showed that siRNA3 had the most significant knockdown effect on these two types of cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, B, *\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Since the knockdown effects of siRNA3 above, cell scratch experiments showed that the knockdown of UBE2C in both LN229 and U251MG cells could significantly inhibit the healing ability of these two cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC, D, **\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In addition, when UBE2C is knocked down in LN229 and U251MG cells, it can significantly inhibit the migration ability of these two cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE, F, **\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). These results indicate that the expression level of UBE2C is positively correlated with the migration ability of tumor cell lines LN229 and U251MG.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn addition, the cell cloning assay of LN229 and U251MG showed that when UBE2C was knocked down, it could significantly inhibit the ability of clonal formation for the two tumor cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eG, H, **\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Meanwhile, the cell proliferation curve assay (OD450, CCK8) showed that the knockdown of UBE2C could significantly inhibit the proliferation ability of these two types of cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eI, J, *\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Therefore, these results indicate that the expression level of UBE2C can significantly affect the malignant phenotype of LN229 and U251MG cells.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eThe External Effects of UBE2C in Glioma\u003c/h2\u003e \u003cp\u003eAs an intrinsic risk factor affecting the progression of glioma, UBE2C may be involved in the regulation of cell cycle to positively link the proliferative activity of tumor cells. However, we also need to understand its correlation with external factors. Based on TCGA data, we analyzed the correlation between UBE2C and 24 types of immune cell infiltration in glioma. The results showed that the expression level of UBE2C was most significantly correlated with the immune infiltration of Th2 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA, R\u0026thinsp;=\u0026thinsp;0.871, ***\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Therefore, we are concerned that this feature may be the focus as UBE2C is linked to external factors of the tumor. According to the secretory phenotype of Th2 cells [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] and the trend of UBE2C expression, and combined with their correlation analysis, we can see that IL6 (R\u0026thinsp;=\u0026thinsp;0.396, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and IL10 (R\u0026thinsp;=\u0026thinsp;0.338, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) are the most significant (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB, C). In conclusion, UBE2C may be closely related to the immune infiltration of Th2 cells.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIt has been reported that IL6 or IL10 can induce immunosuppressive phenotypes in the tumor microenvironment, which leads to the immune escape mechanism of tumor cells [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Therefore, for malignant progression and poor prognosis of glioma, the correlation between UBE2C and external adverse factors may reflect the essence behind this phenomenon. We analyzed the correlation between the expression of UBE2C and 14 immune checkpoints in glioma, and the results showed that eight immune checkpoints (CD80, SIGLEC7, LAG3, CD28, PDCD1LG2, PDCD1, HAVCR2, SIGLEC15) were significantly positively correlated with UBE2C (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD, E, R\u0026thinsp;\u0026gt;\u0026thinsp;0.3, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Based on the correlation analysis of Th2 cells secretion phenotype and immune checkpoints, we found that IL6 and IL10 were most significantly correlated with various immune checkpoints, among which six immune checkpoints were more typical (where the red arrow is pointing) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF, R\u0026thinsp;\u0026gt;\u0026thinsp;0.5, *\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). These results suggest that secretory phenotypes IL6 and IL10 from Th2 cells may be important factors affecting immunosuppressive phenotypes in gliomas.\u003c/p\u003e \u003cp\u003eBased on the systematic correlation characteristics of UBE2C - IL6/IL10 - immune checkpoint axis, we further analyzed the prognostic value of glioma. The results showed that the expression levels of 13 genes, such as IL6/IL10 and immune checkpoint, were significantly negatively correlated with the survival rate of patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). Meanwhile, IHC images from the THPA database displayed relatively typical expressions of six proteins, which indicated that IL6, IL10, PDCD1LG2, PDCD1, HAVCR2 and CD80 were upregulated in glioma tissues, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB-G). Taking together, the expression levels of UBE2C-related immune infiltrating Th2 cells and immune checkpoints can significantly affect the survival and prognosis of glioma, which suggesting that UBE2C may be an external risk factor to predict tumor progression.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eCorrection of UBE2C-Related Risk Factors with Cell Invasion in Glioma\u003c/h2\u003e \u003cp\u003eSince the high aggressiveness of glioma cells has been a major difficulty in the treatment of this disease [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], we should also consider its association with cell aggressiveness phenotype when exploring the internal and external correlation of UBE2C. It has been reported that the invasiveness of tumor cells is correlated with soluble immune checkpoints, which reflects that immune checkpoints in the immune microenvironment can significantly affects the invasive phenotype of tumor cells [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Therefore, we performed network and correlation analyses of between MMPs (Matrix Metalloproteinase) family genes and immune checkpoints, which due to the MMP family plays an important role in the invasion of tumor cells [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Based on these backgrounds and analysis, we found that UBE2C-related risk genes (IL6, IL10 and immune checkpoints) were significantly positively correlated with MMP1, -2, -3, -7, -8, -9, -10, -11, -12, -13, -14 and \u0026minus;\u0026thinsp;19, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA, *\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In addition, online analysis by STRING showed that UBE2C, IL6, IL10 and MMP9 acted as a \u0026ldquo;linker\u0026rdquo; that between immune checkpoints and the MMP family (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB). Subsequently, after verifying the correlation between seven MMPs (The most typical seven MMPs, which including MMP1, -2, -7, -9, -11, -14, -19) and immune infiltration in glioma, it was found that MMP2, MMP9 and MMP11 had the most significant correlation with Th2 cells in glioma (\u003cem\u003eR\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.5, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC-E, \u003cb\u003eSupplementary Fig.\u0026nbsp;4A-D\u003c/b\u003e). In conclusion, UBE2C, IL6 and IL10 may be the link between immune checkpoint and MMP family. This result also confirmed that the immune invasive phenotype (IL6, IL10 and immune checkpoints) of Th2 may be related to the aggressiveness of tumor cells.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBased on these findings, we need to further clarify the prognosis of these seven MMPs for the glioma patients. K-M survival analysis (OS) showed that the expression levels of seven MMPs were significantly negatively correlated with the survival rate of glioma patients, and the MMP2, MMP9 and MMP11 were more typical (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eF-H, \u003cb\u003eSupplementary Fig.\u0026nbsp;4E-H\u003c/b\u003e). IHC images from the THPA database showed that MMP2 and MMP14 expression differences were most significant in gliomas (MMP1 and MMP19 data were not included) (\u003cb\u003eSupplementary Fig.\u0026nbsp;5A-E\u003c/b\u003e). Finally, multivariate Cox regression models of UBE2C, IL6, IL10, MMP2, MMP9 and MMP11 were analyzed by nomogram to predict 1-, 3-, and 5-year survival probabilities. The results showed that the contribution value of UBE2C, IL10 and MMP2 was the largest (total score ratio: 250/280\u0026thinsp;=\u0026thinsp;89.3%), which may be used as a more accurate multi-factor prediction model for glioma patients, and the prediction model was consistent with the conclusion, that is, UBE2C was correlated with immune invasion and cell invasion (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eI). Meanwhile, the calibration curve verifies that the 1-, 3-, and 5-year survival probability curves fit well with the ideal line based on the same conditions, which indicates that the multi-factor prediction model is reliable (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eJ). In conclusion, among the risk factors for glioma, UBE2C may be systematically associated with IL10 and MMP2, which become a better predictive model. Therefore, UBE2C may be an ideal biomarker for the prognosis of glial patients.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eGlioma is a major tumor that seriously threatens the health of the nervous system, which includes a variety of diseases with different degrees of malignancy [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. At present, the difficulty of clinical treatment of glioma is the malignant progression and poor prognosis from the tumor [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. However, there has been some progress in the study of gliomas, especially in the molecular mechanisms of the tumor microenvironment and cell invasion [\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. However, the search for effective diagnosis and treatment of clinical tumors and prognosis assessment is still one of the tasks that need to be solved [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The clinical application of biomarkers for glioma is mainly reflected in pathological diagnosis, prediction and prognosis assessment, which is also based on the gene expression profile, mutant and the expression difference of risk genes between tumor cells and normal cells to screen [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The purpose of this study was to obtain candidate markers via the differential expression of risk genes in the overall sample of gliomas (including glioblastoma and low-grade gliomas) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cb\u003eSupplementary Fig.\u0026nbsp;1\u003c/b\u003e). It is characterized by the possibility of considering the range of gliomas with different degrees of malignancy, which should be an important angle to search for potential biomarkers of gliomas. Currently, non-coding RNA, circulating exosome factors and cerebrospinal fluid can be the scope of seeking glioma biomarkers [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. However, this study suggests that in addition to circulatory source factor expression differences and prognostic analysis, systematic analysis and evaluation based on markers are also key to validate their reliability [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], such as assessing the intrinsic and extrinsic association and risk of marker genes, which are also needed. Therefore, a relatively typical marker - UBE2C was selected based on systemic intrinsic factors such as differential expression genes, risk genes, cell cycle and DNA replication in glioma. However, in terms of the reliability of prognostic markers, validation of external associations is still necessary.\u003c/p\u003e \u003cp\u003eStudies have reported that UBE2C, as an E2 ubiquitin binding enzyme, is involved in the regulation of glioma autophagy, chemotherapy resistance, aggressiveness, and poor prognosis [\u003cspan additionalcitationids=\"CR42\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. However, systematic analysis to evaluate this gene as a potential marker has not been reported. Based on this background, this study not only analyzed the prognostic effect of UBE2C in glioma from multiple perspectives (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), but also found that this gene is related to cell cycle and DNA behavior (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). These biological processes may be the intrinsic reasons for glioma adaptive survival, and the correlation between UBE2C and malignant phenotypes of glioma cell also reflects this inference (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). In addition, since the immunosuppression and cell invasion phenotypes of glioma are difficult in clinical diagnosis and treatment [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], the evaluation of the application value of biomarkers should also focus on these two aspects [\u003cspan additionalcitationids=\"CR47\" citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. In this study, UBE2C was found to be significantly positively correlated with immune infiltration of Th2 cells in gliomas (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA-C). Therefore, according to the secretory phenotype of Th2 cells, it is speculated that Th2 cells may be an important factor leading to immunosuppressive phenotype in the glioma microenvironment, which has been reported in relevant studies [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Our correlation analysis results confirmed the conjecture that IL6 and IL10 were significantly positively correlated with most immune checkpoints (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF). This conclusion confirms the extrinsic association between UBE2C and glioma, which may be a risk factor for the adaptive progression of tumor cells.\u003c/p\u003e \u003cp\u003eThe aggressiveness of tumor cells is also one of the main malignant phenotypes of glioma, which may be regulated and influenced by the tumor microenvironment [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. It has been reported that immune checkpoints can affect the expression of MMPs in tumor cells, which promoting the aggressiveness of tumor cells [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Therefore, exploring the correlation between UBE2C and MMP gene family is also an important aspect of the external correlation between UBE2C and glioma. In this study, the network or association analysis of between immune checkpoints and MMP families showed that IL6, IL10 and MMP9 correlated with UBE2C may be a \u0026ldquo;linker\u0026rdquo; of two extrinsic phenotypes, which is reflected that a significant correlation (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA, B). This feature highlights the potential value of UBE2C as a biomarker for glioma. In summary, we identified UBE2C as a potential prognostic biomarker for glioma, and these results demonstrated the internal or external correlation and effects of UBE2C in glioma, but this causal relationship need to be further verified and explored by experiments. Based on the prognostic value and correlation of UBE2C, it may systematically reveal the factors of glioma cells' adaptive survival in the tumor microenvironment, and further reflect that UBE2C may be a potential target for the treatment of this disease.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStudy concept and design: HF and FQ; Acquisition of data: HF, AF and RY; Analysis and interpretation of data: HF, AF; Statistical analysis: HF; Drafting of the manuscript: HF and FQ; Manuscript check: HF, RY and FQ; Critical revision and final approval of the manuscript: FQ. All authors contributed to the article and approved the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe studies involving data and platform were reviewed and approved by The Institutional Research Ethics Committee of Nanchong Central Hospital.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll personal data and samples involved in this study have been obtained with their knowledge and permission for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll datasets generated and analyzed during the current study are available from the corresponding authors on request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was not supported by any funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are especially grateful for the relevant personnel of the Nanchong Central Hospital/Department of Neurosurgery for their platform and help.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLapointe S, Perry A, Butowski NA: Primary brain tumours in adults. 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Genome Med 2020, 12:83.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Glioma, Biomarker, UBE2C, Th2 cells, Immune checkpoint, MMP family","lastPublishedDoi":"10.21203/rs.3.rs-4019424/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4019424/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eGlioma is one of the common tumors of the central nervous system, which presents difficulties in clinical diagnosis and treatment due to its characteristics of immunosuppression and cell invasion phenotypes. The condition and prognosis of glioma may be predicted during the process of diagnosis and treatment, it will be more conducive to timely intervention or evaluation of glioma.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eDifferential or risk genes were analyzed based on TCGA (The Cancer Genome Atlas) - glioma samples, selecting relative typical biological processes based on enrichment analysis of their common genes. Target gene UBE2C were obtained by the expression correlation and differential expression analysis for the enrichment results. UBE2C were evaluated by clinical grading, survival prognosis and cell experiments. GSEA (Gene Set Enrichment Analysis) analysis based on GEO (Gene Expression Omnibus) data sets and the above conclusions were verified. The correlation of UBE2C with immune invasion, immune checkpoint and cell invasiveness of gliomas was analyzed by TCGA-glioma data and STRING, respectively.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOur results suggests that the high expression and risk of UBE2C in gliomas may be a factor that promotes malignant phenotype of tumor cells. The immune phenotype shows that IL6 and IL10 may be the key nodes affecting the immunosuppressive phenotype of glioma. Further, the invasion genes from the MMP family can be correlated with immunosuppressive phenotypes via UBE2C-IL6/IL10 axis, especially displayed by MMP2/MMP9.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe UBE2C may systemic effects the malignant phenotype, immunosuppression and cell invasiveness of tumors systematically, which reflects UBE2C as a potential biomarker and therapeutic target for glioma.\u003c/p\u003e","manuscriptTitle":"UBE2C is associated with Prognosis of Immunosuppression and Cell Invasiveness in Glioma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-08 10:26:00","doi":"10.21203/rs.3.rs-4019424/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"53f9ab92-f98b-4241-8193-0f8493db4fba","owner":[],"postedDate":"March 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-03-11T10:24:58+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-08 10:26:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4019424","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4019424","identity":"rs-4019424","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}