MCM4 is a novel prognostic biomarker and promotes cancer cell growth in glioma

Background Gliomas account for 75% of all primary malignant brain tumors in adults and result in high mortality. Accumulated evidence has declared the minichromosome maintenance protein complex (MCM) gene family plays a critical role in modulating the cell cycle and DNA replication stress. However, the biological function and clinic characterization of nine MCM members in low-grade glioma are not yet clarified. Methods In this study, we utilized diverse public databases, including The Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA), Rembrandt, Human Protein Atlas (HPA), Linkedomics, cbioportal, Tumor and Immune System Interaction Database (TISIDB), single-sample GSEA (ssGSEA), Tumor Immune Estimation Resource (TIMER), Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Therapeutics Response Portal databases to explore the mRNA and protein expression profiles, gene mutation, clinical features, diagnosis, prognosis, signaling pathway, tumor mutational burden (TMB), immune subtype, immune cell infiltration, immune modulator and drug sensitivity of nine MCMs. Afterward, qRT-PCR was utilized to detect the expression of the MCM family in glioblastoma multiforme (GBM) cell lines. The one-, three-, or five-year survival rate was predicted by utilizing a nomogram established by cox proportional hazard regression. Results In this study, we found that nine MCMs were consistently up-regulated in glioma tissues and glioma cell lines. Elevated nine MCMs expressions were significantly correlated with a higher tumor stage, isocitrate dehydrogenase (IDH) mutates, 1p/19q codeletion, histological type, and primary therapy outcome. Survival analyses showed that higher expression of MCM2-MCM8 (minichromosome maintenance protein2-8) and MCM10 (minichromosome maintenance protein 10) were linked with poor overall survival (OS) and progression-free survival (PFS) in glioma patients. On the other hand, up-regulated MCM2-MCM8 and MCM10 were significantly associated with shorter disease-specific survival (DSS) in glioma patients. Univariate and multivariate analyses revealed that MCM2 (minichromosome maintenance protein2), MCM4 (minichromosome maintenance protein 4), MCM6 (minichromosome maintenance protein 6), MCM7 (minichromosome maintenance protein 7) expression and tumor grade, 1p/19q codeletion, age, and primary therapy outcome were independent factors correlated with the clinical outcome of glioma patients. More importantly, a prognostic MCMs model constructed using the above five prognostic genes could predict the overall survival of glioma patients with medium-to-high accuracy. Furthermore, functional enrichment analysis indicated that MCMs principal participated in regulating cell cycle and DNA replication. DNA copy number variation (CNV) and DNA methylation significantly affect the expression of MCMs. Finally, we uncover that MCMs expression is highly correlated with immune cell infiltration, immune modulator, TMB, and drug sensitivity. Conclusions In summary, this finding confirmed that MCM4 is a potential target of precision therapy for patients with glioma.


Introduction
Glioma is one of the most common tumors in the central nervous system that mainly includes brain lower-grade glioma (LGG) and glioblastoma multiforme (GBM) (1). Increasing evidence has demonstrated that the molecular characteristics of gliomas include mutated isocitrate dehydrogenase 1 and 2 genes (IDH1/2) and co-deletion of 1p/19q (2). With various advances in diagnosis and therapies for a low-grade glioma, the mortality rate for glioma remains higher. Recently, molecular biomarkers have been indicated to be helpful in the diagnosis and prognosis of various cancers. Therefore, uncovering the molecular mechanisms underlying the initiation and progression of glioma and identifying highly reliable biomarkers is crucial to improving the diagnosis and treatment of glioma patients.
Mounting evidence has demonstrated that MCMs play a central role in the cell cycle and DNA synthesis (3). Members of the MCM family including MCM2-MCM10, these members are evolutionally and functionally conserved throughout eukaryotes (4). It has been shown that abnormal expression of MCMs was correlated to diverse cancer initiation and progression by regulating the cell cycle and DNA replication stress (5). Overexpression of MCM7 was reported to facilitate cell proliferation and invasion of GBM cells (6). Additionally, MCM5 was highly expressed in renal cell carcinoma (RCC) tissues and the ablation of MCM5 inhibited RCC cell line proliferation and repressed tumor growth (7). A recent study proved that MCM2 and MCM5 might take the prognostic markers for colon cancer (8). In our previous study, we developed a new method called CVAA (Cross-Value Association Analysis), which functions without a normalization and distribution assumption. We applied it to large-scale glioma transcriptome data generated by The Cancer Genome Atlas (TCGA) project, and successfully discovered numerous new differentially expressed genes (DEGs) (9). MCM4 is one of these DEGs. However, the expression profiles, genetic alterations, clinicopathological parameters, diagnosis values, prognostic values, and immune functions of MCM4 glioma remain to be further elucidated.
In the current study, we performed a comprehensive analysis of the expression profiles, genetic alterations, clinicalpathological parameters, diagnosis values, prognostic values, and immune functions of the MCMs family in glioma. Furthermore, we explored CNV, DNA methylation, Kyoto Encyclopedia of Genes and Genomes (KEGG) expression of the MCMs family. Finally, we examined the correlation between MCMs expression and immune cell infiltration, immune modulator, TMB, and drug sensitivity. We also applied qRT-PCR to validate the expression of MCMs in glioma cell lines.

Materials and methods
Data collection and bioinformatic analysis TCGA (https://www.cancer.gov/about-nci/organization/ ccg/research/structural-genomics/tcga) is a public data platform containing 30 different cancer types and the clinical information of 11,000 patients. The LGG and GBM sequencing data and the corresponding clinical data of the samples were obtained from the TCGA database. After excluding data with missing clinical information, we obtained the 529 LGG and 174 GBM samples for further analysis. Subsequently, we download GTEx normal brain tissue gene expression data from UCSC Xena (https://xena.ucsc.edu/). Based on this GTEx data, we analyzed the MCMs expression level in normal brain tissues using the R language "ggpubr" package. The expression of MCMs in 703 glioma samples from TCGA and 1152 nontumor brain tissues from GTEx was analyzed. Correlation between the expression level of MCMs and clinical data in glioma, Kaplan-Meier survival curve, and ROC curve analysis in glioma were verified with TCGA data.
The CGGA database contains the bioinformatics data of more than 2,000 glioma samples from China, depicting the genomic and molecular genetic characteristics of glioma patients in China, and has guiding significance on the molecular typing and drug target development of glioma. We downloaded mRNA sequencing data and the corresponding clinical data of glioma patients from the CGGA (http://www. cgga.org.cn). After excluding data with missing clinical information, we obtained 807 glioma samples for further analysis, including 472 males and 335 females. According to the median expression level of MCMs, the expression level of MCMs in glioma samples was divided into the low-expression group and the high-expression group. Kaplan-Meier survival curve was used to analyze the influence of MCMs expression on the survival of glioma patients. Correlation between MCMs expression level and clinical data in glioma patients was detected; the accuracy of the prognosis model for glioma was then assessed using a receiver operating characteristic (ROC) curve. Pearson coefficient analysis was used to screen other genes related to MCMs expression in the TCGA databases. For the intersecting genes, the Database for Linkedomics (http://www.linkedomics.org/login.php) was used to perform KEGG analysis on the top 200 genes positively related to MCMs expression (Pearson r > 0.7, P < 0.001). Rembrandt (http://gliovis.bioinfo.cnio.es/) (10) to examine the expression, clinical information and prognosis of MCMs in glioma. The Human Protein Atlas (HPA) (https://www. proteinatlas.org/) database was utilized to examine the protein of MCMs in glioma (11).

Function analysis for MCMs in glioma
In the present research, we utilized the linkedomics database (http://www.linkedomics.org/login.php) to obtain the coexpression genes of MCMs in glioma. We took the cluster profile package to examine the function of MCMS in glioma (12,13). GeneMANIA database (http://genemania.org/) used to constructed gene-gene interaction network of MCMs (14).
Immune Infiltration and tumor mutation burden (TMB) analysis TIMER (https://cistrome.shinyapps.io/timer/) (15), an interactive web portal, could perform a comprehensive analysis of the infiltration levels of different immune cells. In this study, we use the TIMER database to explore the correlation between MCMs and diverse immune cell infiltration in glioma. The correlation of MCMs and immune cell infiltration in glioma was analyzed in TIMER. The "Gene" module can investigate the relationship between MCMs expression and immune cell infiltration levels (B cells, CD8+ T cells, CD4+ T cells, neutrophils, macrophages, and dendritic cells) using the TCGA database. We also use the GSVA R package to quantify the glioma immune infiltration of 24 tumor-infiltrating immune cells in tumor samples via ssGSEA (16). The TISIDB (http://cis. hku.hk/TISIDB/) database utilized analysis of the expression of MCMs in a different immune subtype of glioma (17). In tumor mutation burden (TMB) analysis, Spearman's correlation analysis was performed to calculate the correlation between gene expression and TMB and MSI score. A p-value of less than 0.05 was considered statistically significant.

Analysis of the correlation between MCMs expression and drug sensitivity
We utilized the Genomics of Drug Sensitivity in Cancer (GDSC) (https://www.cancerRxgene.org) and the Cancer Therapeutics Response Portal (http://www.broadinstitute.org/ ctrp) databases to analyze the correlation between MCMs expression and drug sensitivity (18,19).

Constructs, lenti-viral preparation, and establishment of different cell lines
For shRNA knockdown experiments, independent shRNAs targeting a different region of MCM4 RNA were constructed using a pLKO.1 vector (Addgene), and the oligo sequences were provided in follow. Lenti-viruses were generated according to the manufacture protocol as previously documented (23) and indicated cells were infected by viruses twice with 48 h and 72 h viral supernatants containing 4 mg/mL polybrene, and stable cell lines were established by appropriate puromycin selection. The two independent MCM4 targeting sequences are: shRNA#1, 5'-GGGTGGAGATGGACCGCGGCC-3'; shRNA#2, 5'-GCCTTGATGAAGAAGCAGAAC-3'.
Cell culture conditions GBM cells lines (including A172, U87, and U251 cells) were purchased from the cell bank of Kunming Institute of Zoology, and cultured in DMEM medium (Corning) including 10% fetal bovine serum (FBS) and 1% penicillin/streptomycin at 37°C an atmosphere containing 95% air and 5% CO2.

Colony formation and flow cytometry assays
For colony formation assay, indicated cells were seeded in a 6-well plate (China, NEST, Cat. 703001) with 600 cells per well supplemented with 2 mL cell culture medium, and the cell culture medium was changed every 3 days for 2~3 weeks, and then indicated cells were fixed with 4% PFA and stained with 0.5% crystal violet. Annexin V FITC Apoptosis Detection Kit I (556547, BD, China) was used to evaluate the cellular apoptosis according to the manufacturer's instructions. For cell cycle analysis experiments, indicated cells were digested and washed with PBS twice and then fixed in 75% alcohol overnight at -20°C. The fixed cells were washed three times and then stained with propidium iodide (PI) staining buffer at room temperature for 30 min in the dark, and then the cell cycle was analyzed by the FACSAria SORP machine (BD, USA).

CCK8 assay
Cell viability and growth were determined using CCK8 assays in 96-well plates. Cells were transfected with the relevant plasmids culturing for 48 h, followed by incubation with 8 mL CCK8 for 4 h. Absorbance was read at 450 nm using a spectrophotometer.

Statistical Analysis
For the datasets from the TCGA database, statistical analyses were performed using R (v.3.6.3). The Wilcoxon rank sum test and Chi-square test were used to estimate the association between MCMs and clinical pathologic characteristics. The Kaplan-Meier method was used to calculate glioma patient survival rates. Univariate and multivariate cox analyses were performed to assess the correlation between clinical features and OS, DSS, and PFS. For the data regarding the function of MCMs, GraphPad Prism 7.0 was used for statistical analyses. The Student's t-test evaluated the statistical significance between groups. The significance of the data between the two experimental groups was determined by Student's t-test, and multiple group comparisons were analyzed by one-way ANOVA. P < 0.05 (*), P < 0.01 (**) and P < 0.001 (***), were significant.

MCMs were highly expressed in glioma
We employed TCGA and GTEx databases to examine the mRNA expressions of MCMs in glioma, the results showed nine MCMs were significantly elevated in glioma ( Figures 1A, B). Furthermore, we uncovered that the protein of nine MCMs was highly expressed in glioma tissue compared with the control group based on the Human Protein Atlas datasets ( Figure 1C). Finally, we used qRT-PCR assay to verify that nine MCMs were significantly overexpressed in glioma cell lines (Figures 2A-C). Collectively, these results suggested that MCMs were highexpressed in glioma tissues and glioma cell lines.

Relationship between MCMs expression and glioma clinical characteristics
We further explored the correlation between MCMs and clinical characteristics of glioma and uncovered that upregulated MCM2-MCM8 and MCM10 were significantly associated with the higher tumor stage, IDH mutation status, 1p/19q chromosome co-deletion, histological type, and primary therapy outcome based on the TCGA dataset ( Given that MCMs were high-expressed in glioma and their higher expression related to poor clinical characteristics. Therefore, we further explored the prognostic values of MCMs in glioma. glioma patients were divided into high or low-expression groups based on the median expression value. We found that higher expressions of MCM2-MCM8 and MCM10 were linked with poor overall survival (OS) and progression-free survival (PFS) in glioma patients ( Figures 4B, 5). On the other hand, up-regulated MCM3-MCM8 and MCM10 were significantly associated with shorter disease-specific survival (DSS) in glioma patients ( Figure 6). These results were verified by CGGA and Rembrandt datasets (Supplementary Figures 6, 7). Given that MCMs differentially expression and are associated with poor clinic-pathologic features and prognosis in glioma. Therefore, we then investigated the diagnosis value of MCMs in glioma. Receiver operator characteristic (ROC) curve analysis results confirmed that MCM2-MCM7, MCM9, and MCM10 had high accuracy (AUC > 0.80) in predicting glioma ( Figures 7A-C). Univariate and multivariate analyses revealed that MCM2, MCM4, MCM6, MCM7 expression and tumor grade, 1p/19q codeletion, primary therapy outcome, and age were independent factors that influenced the clinical outcome for glioma patients ( Table 1). The above results confirm that MCMs is a highly sensitive and specific markers with the potential to be used in glioma diagnosis.  Figure 8E). Given that six prognostic MCMs were correlated with the tumor stage in glioma, our next construction nomogram was used to forecast the overall survival. Univariate and multivariate analyses uncovered that MCM2, MCM4, MCM6, MCM7 expression, tumor grade, and age were independent factors that influence the clinical outcome of glioma patients ( Figures 9A, B). The predictive nomogram confirmed that the one, three, and five-year OS rates could be precise predictions ( Figures 9C, D).

Validation of the Prognostic Significance of MCMs in diverse glioma cohorts
Next, to assess the differences in survival time between lowand high-risk glioma patients, the Kaplan-Meier method was performed. Meanwhile, the log-rank test was also used to determine the statistical significance between groups. Compared with those in the low-risk group, we illustrated that the glioma patients in the high-risk group had shorter overall survival (OS) in the CGGA, Gravendeel, and Rembrandt cohorts, respectively ( Figure 10A). Finally, the time-dependent ROC curve was also used to assess the predictive power of the nomogram in predicting 1-, 3-and 5-years OS, and the AUC for one-, threeand five-year survival rate of glioma patients was 0.864, 0.859, and 0.8747 in the CGGA cohort, 0.725, 0.743, and 0.678 in the Gravendeel cohort, 0.751, 0.788, and 0.698 in the Rembrandt cohort, respectively ( Figure 10B). It is noteworthy, compared with the clinical common indicators including WHO grade, IDH mutation, age, and histopathology. This nomogram had a higher predictive power in the CGGA, Gravendeel, and Rembrandt cohorts, respectively ( Figure 10C). Taken together, these results suggest that this nomogram is a moderately sensitive index for predicting the prognosis of glioma patients, and can act as an effective prognostic biomarker in glioma.

Gene mutation analysis
We utilized cBioPortal analysis of the genetic alteration of differentially expressed MCMs families. Results confirmed that nine MCMs were all altered, with 0.4, 0.8, 0.4, 1.6, 0.6, 1, 0.6, 0.6 and 1.6 alterations in the glioma samples, respectively (Supplementary Figure 8A). We also summarized the incidence of CNV and somatic mutations of nine MCMs in glioma. The results confirmed that missense mutation and C > T were the highest variant type. The findings as well as showed that MCM10 had the highest mutation rate, followed by MCM6 and MCM7 (Supplementary Figure 8B). CNV analysis results confirmed that copy number variations of MCM7, MCM9, and MCM3 were significantly positively correlated with its expression in glioma (Supplementary Figure 8C). Furthermore, MCM5, MCM8, and MCM10 CNV affected the prognosis of glioma patients (Supplementary Figure 8D). Finally, we uncovered that the DNA methylation level of MCM2, MCM5, MCM6, and MCM7 is negatively associated with its expression (Supplementary Figure 8E). More importantly, the higher DNA methylation level of MCM2, MCM5, MCM6, and MCM10 were correlated with poor disease-free survival (DSS), OS, and PFS (Supplementary Figure 8F).

Functional analysis of MCMs in glioma
To explore the functional roles of MCMs in glioma progression, we took linkedomics tools to obtain the coexpression genes that positively correlated with that of MCMs in glioma ( Figures 11A, B; Supplementary Figures 9A, B). Furthermore, we utilized these genes to perform KEGG enrichment analysis. The results confirmed that increased MCMs principal participated in the cell cycle, and cellular senescence ( Figure 11C; Supplementary Figure 9C). Additionally, we uncovered that MCMs showed a high level of activation in the apoptosis, EMT, PI3K/AKT, cell cycle, and DNA damage response (Supplementary Figures 10A, B). Finally, we constructed gene interaction networks by the GeneMania database for MCMs. The result confirmed that the most closely related to the MCM genes, including minichromosome maintenance domain containing 2 (MCMDC2), cell division cycle 5-like (CDC5), MCMBP minichromosome maintenance complex binding protein (MCMBP), origin recognition complex, subunit 2 (ORC2), origin recognition complex, subunit 6 (ORC6), GINS complex subunit 2 (Psf2 homolog) (GINS2), TIMELESS interacting protein (TIPIN), claspin (CLSPN), origin recognition complex, subunit 3 (ORC3),  Figure 10C). Functional exploration results demonstrated that these genes were correlated with cell cycle regulation. Collectively, these data suggest that MCMs result in glioma cancer initiation and progression by regulating the cell cycle and DNA replication.

Correlation between MCMs and TMB, immune subtypes of glioma
Emerging evidence has demonstrated that TMB could be a potential biomarker for predicting the efficacy of immunotherapy for cancer (25). To examine the relationships between MCMs expression and TMB in gliomas. We conducted the related correlation analysis. The findings confirmed that MCMs were positively related to the TMB in glioma (Figures 12A-C). Furthermore, we also examined the expression of MCMs in diverse immune subtypes of glioma. The results confirmed that MCMs mainly highly expressed in the C4 subtype ( Supplementary Figures 11A-C). Collectively, these results indicate that MCMs had different expression patterns in glioma.

Immune infiltration analysis of the MCMs in glioma
TIMER database analysis was used to explore that somatic copy number alterations of MCMs were significantly related to diverse immune cell infiltration levels in glioma (Supplementary Figure 12). Furthermore, we utilized the TIMER database analysis of the correlation between MCMs and diverse immune cells.  Figures 13, 14). We utilized the cox proportional hazard model and found that macrophages, neutrophils, MCM8, and MCM9 expression were associated with a poor prognosis of glioma.
Finally, we utilized the single sample gene set enrichment analysis (ssGSEA) tools analysis the correlation between MCMs expression and various immune cells. The results confirmed that MCM2 is positively related to the immune infiltration of Th2 cells, T helper cells, and Macrophages, and negatively related to the immune infiltration of NK CD56 bright cells and Tem. MCM3 is positively related to the immune infiltration of Th2  Figures 15, 16). Taken together, these results confirm that MCMs play a pivotal role in immune regulation.

Correlation between MCMs expression and diverse drug sensitivity
Temozolomide (TMZ) is a first-choice alkylating agent inducted as a gold standard therapy for glioblastoma multiforme (GBM) and astrocytoma. Exploring the potential therapeutic targets is extremely crucial to examine the relationship between these MCMs' expression and diverse drugs in pan-cancer. In the present study, we utilized the GSCA tools to explore the association between MCMs expression and various drug sensitivity. We found that MCMs were positively correlated with the sensitivity of 17-AAG, RDEA119, selumetinib, and Trametinib, negative correlated with the sensitivity of Genentech Cpd 10, GSK690693, Temozolomide, FK866, CP466722, BMS345541, Vorinostat, NavitoclaxNPK76−II−72−1, Methotrexate, KIN001−102 and AR-42 (Supplementary Figure 18).

Knockdown of MCM4 inhibits the cell proliferation of glioma
Given that MCM4 expression was an independent factor affecting the prognosis of glioma patients and there is no previous study that reported the function of MCM4 in glioma. Therefore, we decided to explore the function of MCM4 in glioma. We firstly we utilized shRNA knockdown of MCM4 in U251 and A172 cells, the knockdown efficiency was verified by qRT-PCR assay (Figures 13A, B). The CCK8 assay showed that depletion of MCM4 significantly inhibits glioma cells' proliferation ability (Figures 13C, D). Furthermore, to validate whether MCM4 is critical for cell cycle transition, we performed flow cytometry analysis and revealed that MCM4 knock-down led to increased G0/G1 phase arrested cells (Figures 13E, F). We also validated the expression of non-coding RNA that targets MCM4 and found that EXTL3-AS1was highly expressed, and miR-24-3p was down-regulated in glioma cell lines, respectively ( Figure 13G, H). Collectively, these results demonstrate that MCM4 was highly expressed in glioma cell lines and significantly affected their proliferation and cell cycle.

Discussion
Glioma is the most common primary tumor of the central nervous system, accounting for 15% of all brain tumors (27). Grades I and II are grouped as low-grade gliomas and grades III and IV as high-grade gliomas, low-grade gliomas have a 10-to 15-year survival. With various advances in diagnosis and therapies for low-grade glioma, the mortality rate for glioma remains higher (28). Therefore, more sensitive and specific diagnostic biomarkers and potential therapeutic targets for this cancer type need to be identified.
Many studies have found that MCM members play an indispensable role in DNA replication (29), embryogenesis (30), maintaining genome instability (31), enhancing cell proliferation (31), and cancer progression (32). For example, overexpression of MCM5 significantly promotes the proliferation and invasion of NSCLC cell lines (33). In particular, MCM2 and MCM6 were reported as potential biomarkers to predict overall survival for LIHC patients (34). Gene mutation of MCM2 was associated with the tumor status, lymph node status, metastatic status, pathologic stage, histologic grade, and prognosis for ESCC patients (35). MCM7 has been reported to be a potential therapeutic target and prognostic biomarker for ESCC patients (36). In this study, we found, for the first time, that MCMs expression was highly upregulated in glioma tissues compared with normal brain tissues. We also showed that MCMs were significantly up-regulated in glioma cell lines, including U251, U87, and A172 cell lines. Meanwhile, elevated MCMs expression was associated with poor clinical characteristics, including higher tumor grades, histological type, IDH mutation status, 1p/19q chromosome co-deletion, and primary therapy outcome. More importantly, we uncover that higher expression of MCM2-MCM8 and MCM10 were linked with poor OS and PFS in glioma patients. On the other hand, upregulated MCM3-MCM8 and MCM10 were significantly associated with shorter disease-specific survival (DSS) in glioma patients. These results were verified by CGGA and Rembrandt datasets. Receiver operator characteristic (ROC) curve analysis results confirmed that MCM2-MCM7, MCM9, and MCM10 had high accuracy (AUC > 0.80) in predicting glioma. Univariate and multivariate analyses revealed that MCM2, MCM4, MCM6, MCM7 expression and tumor grade, 1p/19q codeletion, primary therapy outcome, and age were independent factors correlated with poor clinical outcomes of glioma patients. These results implied that MCMs overexpression may have played a key role in the malignant phenotypes of gliomas, and is a potentially unfavorable prognostic biomarker for glioma patients. Given that MCMs expression was correlated with terrible clinical outcomes in glioma. We constructed a prognostic gene model based on 9 MCMs (Figures 8A, B). The risk score= To verify the predictive value of the nomogram, we used diverse glioma cohorts, including the CGGA, Gravendeel, and the Rembrandt cohorts, to assess the differences in survival time between lowand high-risk glioma patients, the Kaplan-Meier method was performed. We found that compared with those in the low-risk group and illustrated that the glioma patients in the high-risk group had shorter OS, respectively. Finally, the time-dependent ROC curve was also used to assess the predictive power of the nomogram in predicting 1-, 3-and 5-years OS, and the AUC for 1-, 3-and 5-year survival rate of glioma patients was 0.864, 0.859, and 0.8747 in the CGGA cohort, 0.725, 0.743, and 0.678 in the Gravendeel cohort, 0.751, 0.788, and 0.698 in the Rembrandt cohort, respectively. It is noteworthy that, compared with the clinical common indicators including WHO grade, IDH mutation, age, and histopathology, this nomogram had a higher predictive power in the CGGA, Gravendeel, and Rembrandt cohorts, respectively. Taken together, these results suggest that this nomogram is a moderately sensitive index for predicting the prognosis of glioma patients, and can act as an effective prognostic biomarker in glioma.
A growing number of studies have reported that CNV and DNA methylation plays an important role in gene expression regulation (37). In this research, we uncovered that copy number variations of MCM7, MCM9 and MCM3 were significantly positively correlated with its expression in glioma. Furthermore, MCM5, MCM8, and MCM10 CNV affect the prognosis of glioma patients. Finally, we uncovered that the DNA methylation level of MCM2, MCM5, MCM6, and MCM7 is negatively associated with its expression. More importantly, the higher DNA methylation level of MCM2, MCM5, MCM6, and MCM10 were correlated with poor DSS, OS, and PFS.
Previous studies reported that MCMs are necessary for cell cycle and DNA synthesis (3). In our study, we found that MCMs principal participated in the cell cycle, DNA replication, p53 signaling pathway, and cellular senescence. Furthermore, we uncovered that MCMs showed a high level of activation in the apoptosis, EMT, PI3K/AKT, cell cycle, and DNA damage response. Emerging evidence has demonstrated that TMB could be a potential biomarker for predicting the efficacy of immunotherapy for cancer (25). In this study, we found that MCMs were positively correlated with TMB in glioma. More importantly, we confirmed that MCMs mainly highly expressed in the C4 subtype of glioma.
It has been shown that CD8+ tumor-infiltrating lymphocytes are related to glioma prognosis (38). Another  Table 2). The abundance of immune cells affects the outcomes of patients with head and neck squamous cell carcinoma (HNSCC) and lung adenocarcinoma (LUAD) (39), which is in agreement with the results of this study. As studies have reported, on the one hand, DCs are professional antigenpresenting cells, which can capture and process antigens to present antigenic peptides on MHC Class I and Class II to activate CD8+ and CD4+ cells respectively (40). On the other hand, DCs could promote breast cancer bone metastasis via A B C FIGURE 12 Analysis of the correlation between MCMs and TMB. (A-C) Analysis of the correlation between MCMs expression and TMB in glioma. TMB, tumor mutational burden.
increasing Treg cells and reducing CD8+ cytotoxic T cells and play a crucial role in cell proliferation, invasion, and intercellular communication (41).
In recent years, the study of immune checkpoints has made a breakthrough in the field of tumor immunotherapy, and immune checkpoint blockade has also been approved for the treatment of melanoma and lung cancer (42). Although PD-L1 expression could predict the immunotherapy response of some patients, PD-L1 expression is not enough to predict which patients should be treated with immunotherapy (43). Therefore, an accurate precision biomarker is of great importance to individualized immunotherapy. Interestingly, our results confirmed that MMD2, MCM3, MCM5, and MCM8 expression was significantly positively associated with the CD274, CTLA4, HAVCR2, LAG3, PDCD1, PDCD1LG2, TIGIT, and SIGLEC15, while, MCM4, MCM6, and MCM9 were significantly negatively correlated with CD274, CTLA4, HAVCR2, PDCD1, PDCD1LG12, TIGIT, and SIGLEC15, which suggested that MCMs has the potential to act as a predictive biomarker for the effectors of immune checkpoint blockade in glioma. These findings confirmed that MCMs expression was significantly related to immune modulator-related genes in glioma.
Accumulating evidence suggests vital roles for lncRNA/ miRNA in multiple cellular processes and various cancers. Mechanistically, some lncRNA with specific miRNA target sites are capable of regulating gene expression via acting as ceRNAs (26). In this study, we constructed the lncRNA -miRNA-mRNA regulatory networks to show the relationships of the MCMs-related lncRNA along with their binding miRNAs and target genes. We uncovered that MCMs were positively or negatively correlated with the diverse drug sensitivity in the cancer therapeutic response portal database.
Minichromosomal maintenance 4 (MCM4) belongs to the family of MCM proteins (MCMs), a group of family proteins strongly linked with DNA replication and cell proliferation, participates in the regulation of DNA replication initiation and can be used as an effective marker for tumor diagnosis (44). In recent years, many studies have proved that MCMs have the potential as prognostic markers in different tumors. However, it has been found that MCM4 expression is related to other tumors. Kikuchi  MCM4 in esophageal cancer was positively correlated with the pathological grade (46). A study reported that some melanoma patients with increased expression of MCM4 have a poor prognosis (47). In this finding, we found that MCM4 was highly expressed in glioma cell lines and significantly affected their proliferation and cell cycle. We also uncovered that MCMs related ceRNA network in glioma progression. These ceRNA plays a critical role in MCMs expression.
To the best of our knowledge, this is the first study to explore the correlation between MCM4 and glioma. However, there are some limitations to our research. First, our study was based on expression data extracted from TCGA but may be more convincing if supported by a prospective clinical study. Furthermore, the biological functions of MCM4 need to be further explored in vivo experiments. In the future, we will pay more attention to the function of MCM4 in tumor progression and tumor microenvironment regulation of glioma. Furthermore, we will perform more in vivo and in vitro experiments to explore the function and the potential molecular mechanisms of MCM4 in tumor progression and tumor microenvironment regulation of glioma.

Conclusions
In this study, we comprehensively explored the expression pattern, gene alteration, diagnosis, and prognosis of the MCMs family in glioma, by combining the five MCMs, a risk signature was established and validated to be competent to predict the 1-, 3-, and 5-year survival of glioma patients. Furthermore, we also uncovered a correlation between MCMs expression and TMB, immune subtype, immune modulator, immune cell infiltration, and drug sensitivity. Finally, we revealed that MCMs related ceRNA network in glioma progression. In summary, this finding confirmed that MCM4 is a potential target of precision therapy for patients with glioma.

Data availability statement
The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding authors.

Author contributions
SY, YY and WR designed this work and performed a related assay. HW, ZZ, HZ, QZ and XC analyzed the data. LZ and XJ supervised and wrote the manuscript. All authors have read and approved the final version of the manuscript.