Background

The minichromosome maintenance (MCM) protein family is a group of proteins closely related to DNA replication and genome stability [1]. Highly conserved MCM complex proteins may have helicase activity and are essential for the initiation of DNA replication. MCM complex proteins contain ATPase domains, and energy is harnessed to affect DNA unwinding [2]. There are ten characterized homologous MCM genes. MCM2–7 form a replicative helicase complex [3], and MCM8 and MCM9 form a dimer involved in homologous recombination repair [4]. The ninth gene that encodes an MCM domain is named MCM domain-containing 2 (MCMDC2) [5]. MCM10 is a dynamic scaffold at eukaryotic replication forks [6].

It has been reported that the expression of MCMs in the cell proliferation cycle is one of the important factors of DNA replication initiation and extension, and their positive expression is an important marker of cell proliferation [7]. Their expression levels are related to the proliferation and differentiation of tumor cells and can accurately reflect the proliferation activity of cells. MCMs have great reference value in the early diagnosis, classification and prognosis of clinical tumors [8, 9]. Therefore, it is necessary to strengthen the research on the basic theory and clinical application value of MCMs, including their mechanism of protein action, their expression characteristics and their related genes in cervical cancer tissue, as well as their value in clinical diagnosis and differential diagnosis. At present, there are few related studies in these areas. In this study, the roles of MCMs and related genes in cervical cancer were investigated by a variety of bioinformatics methods. By analyzing the mRNA and protein expression of MCM family members, their potential clinical value in cervical cancer was analyzed. The workflow of this study is shown in Fig. 1.

Fig. 1
figure 1

Flowchart of the integrated analysis

Full size image

Methods

Expression of different MCMs in cervical cancer patients

To explore the distinct prognostic and potential therapeutic values of different MCM members in cervical cancer patients, the mRNA expression of different MCMs was analyzed by the ONCOMINE database [10] (www.oncomine. org) and UALCAN [11] (http://ualcan.path.uab.edu). After analyzing the mRNA expression, the protein expression of different MCMs in cervical cancer was explored by the Human Protein Atlas [12,13,14] (HPA, http://www.proteinatlas.org) database, and the results of immunohistochemistry from HPA showed the expression of MCMs in normal tissues and tumor tissues. The data used for analysis were from databases, and the expression results of different MCMs can provide a reference for evaluating their potential clinical value.

Potential clinical value of MCMs

After mRNA and protein expression analyses, the relationship between the mRNA expression of MCMs and the clinicopathological parameters of patients, such as individual cancer stages, was assessed by performing The Cancer Genome Atlas (TCGA) database analysis via UALCAN. The data used for analysis were from the database, and the analysis results showed the potential value of different MCMs in clinical pathology. Moreover, the survival of patients was analyzed, and specific MCMs related to better prognosis were identified.

Furthermore, the Kaplan-Meier plotter database [15, 16] (http://kmplot.com/analysis/) was used to analyze the prognostic value of the mRNA expression of different MCMs in cancer patients. The correlation between the mRNA expression of MCMs and the prognosis of patients with cervical cancer was explored and analysed. MCMs useful for predicting the survival of patients with cervical cancer were identified.

After mRNA expression of specific MCMs was found to be significantly associated with patient prognosis, genetic alterations in MCMs and their associations with overall survival (OS) and disease-free survival (DFS) of cervical squamous cell carcinoma (CESC) patients were analyzed by the cBioPortal database [17, 18] (www.cbioportal.org). Analysis of genetic alterations promoted the exploration and understanding of different MCMs in CESC, identified MCMs that are prone to alteration and provided information support for genetic alterations of MCMs in cervical cancer. In addition, key coexpressed genes of MCMs in CESC (TCGA, PanCancer Atlas) were screened and analyzed via a Venn diagram (http://bioinformatics.psb.ugent.be/webtools/Venn/).

Similar gene detection and enrichment analysis of related genes in CESC tumors

After the analysis of the ONCOMINE, UALCAN, HPA, Kaplan-Meier plotter and cBioPortal databases, the functions of the MCMs with potential value and their related genes in CESC tumors were further enriched and explored. Genes that had a similar expression pattern to MCMs in CESC tumors were analyzed by GEPIA2 [19] (http://gepia2.cancer-pku.cn/#similar). Then, the key coexpressed genes and related genes of MCMs in CESC tumors were analyzed by Metascape [20] (https://metascape.org/gp/index.html). The pathways and process enrichment of these genes were determined [21]. Database for Annotation, Visualization and Integrated Discovery (DAVID, https://david.ncifcrf.gov/) was also used to verify the biological processes, cellular components, molecular functions and KEGG pathways of these genes [22,23,24].

Protein-protein interaction (PPI) network construction and screening of hub genes

Next, the network of enriched terms and the PPI network were also analyzed by Metascape [25,26,27]. Then, key subnetworks and related hub genes were obtained, and the Kaplan-Meier plotter database was used for survival analysis of cervical cancer patients to evaluate and predict the potential clinical value of the hub genes [28]. Moreover, multiple gene comparisons of the expression of MCMs and related genes in different cancer types were performed to show the clinical significance of these potential targets by GEPIA2. In particular, MCMs and their related genes that are involved in the progression of cervical cancer might provide potential targets for the clinical prevention, treatment, and effective prognostication of cervical cancer.

Results

Expression of different MCMs in cervical cancer patients

As shown in Fig. 2 and Table 1, the mRNA expression levels of MCM2/3/4/5/6/7/8/9/10 in cervical cancer tissues and normal tissues were compared using the ONCOMINE database [29,30,31,32], and MCM2/3/4/5/6/7/8/10 expression was significantly increased in tumor tissues. Then, the mRNA expression of MCM2/3/4/5/6/7/8/9/10 was further analyzed by the UALCAN database. As shown in Fig. 3, the mRNA expression of MCM2/3/4/5/6/7/8/10 in tumor tissues was significantly higher than that in normal tissues (p < 0.05), while the expression of MCM8/9/10 in tumor tissues was lower than that of other MCMs (MCM2/3/4/5/6/7). However, the expression of MCM9 in tumor tissues was not significant. In addition to the above analysis, immunohistochemical information was obtained from the HPA database to analyze the protein expression of MCMs in normal and tumor tissues (Fig. 4). MCM2 and MCM5 were not detected in normal tissues, but their high expression was observed in tumor tissues. The expression of MCM6/9/10 was low in normal tissues, but medium and high protein expression was observed in tumor tissues. In addition, medium protein expression of MCM3/4/7 was observed in normal tissues, and high protein expression was observed in tumor tissues. Moreover, high expression of MCM2/3/5/7 was associated with good survival and prognosis (Fig. 5).

Fig. 2
figure 2

Transcriptional expression of MCMs in different types of cancer (ONCOMINE). Differences in transcriptional expression were compared by t-tests. The cutoff p-value, fold change, and other settings were as follows: p-value: 0.01, fold change: 1.5, gene rank: top 10%, data type: mRNA

Full size image
Table 1 Significant changes in MCM expression at the transcription level between cervical cancer tumor and normal tissues (ONCOMINE)
Full size table
Fig. 3
figure 3

mRNA expression of MCMs in CESC tissues and normal tissues (UALCAN). *p < 0.05, **p < 0.01, ***p < 0.001

Full size image
Fig. 4
figure 4

Differential expression of MCMs in cervical cancer tissues and normal tissues (Human Protein Atlas). MCM8: No data in HPA database

Full size image
Fig. 5
figure 5

Prognostic markers of cervical cancer (favorable) in the Human Protein Atlas database (p < 0.001)

Full size image

Potential clinical value of MCMs and screening of coexpressed genes

As shown in Fig. 6, the results showed that the mRNA expression of MCMs was significantly correlated with individual cancer stages in the UALCAN database, and cancer patients (stage 1-stage 4) had higher mRNA expression of MCMs than normal controls. The effects of MCM2 and MCM5 expression are shown in Fig. 7. High expression of MCM2 and MCM5 had a significant impact on patients, and they might be better targets to promote the good survival and prognosis of patients.

Fig. 6
figure 6

Relationship between mRNA expression of MCMs and individual cancer stages in CESC, *p < 0.05, **p < 0.01, ***p < 0.001 (UALCAN)

Full size image
Fig. 7
figure 7

Effect of MCM2 and MCM5 expression levels on CESC patient survival (UALCAN)

Full size image

Furthermore, the prognostic value of the mRNA expression of MCMs was evaluated using the Kaplan-Meier plotter database (Fig. 8). The mRNA expression of MCM2/3/4/5/6/7/8 was significantly associated with CESC patient prognosis, and the results showed that higher mRNA expression of MCM2/3/4/5/6/7/8 was associated with favorable OS in CESC patients. Moreover, higher mRNA expression of MCM3/5/6/7/8 was significantly associated with longer OS, but the mRNA expression of MCM9, MCM10 and MCMDC2 had no significant effect on the prognosis of CESC patients (Fig. 9). These results indicated that MCM2/3/4/5/6/7/8 may serve as useful biomarkers for CESC patients.

Fig. 8
figure 8

Prognostic value of MCM2/3/4/5/6/7/8 mRNA expression in CESC patients (Kaplan-Meier plotter database)

Full size image
Fig. 9
figure 9

Prognostic value of the mRNA expression of MCM9/10 and MCMDC2 in CESC patients (Kaplan-Meier plotter database)

Full size image

In particular, genetic mutations in MCMs and their associations with OS and DFS were explored by the cBioPortal database. The cBioPortal analysis showed a high mutation rate (71%) of MCMs in CESC patients (TCGA, PanCancer Atlas). MCM2, MCM4, MCM8, MCM3 and MCM7 were the top five genes with the highest genetic alterations, and the mutation rates were 35, 20, 19, 15 and 14%, respectively (Fig. 10). Moreover, the results showed that genetic alterations in MCMs were not significantly associated with longer OS or DFS in CESC patients.

Fig. 10
figure 10

Analysis of MCMs in CESC patients (cBioPortal). A. Genetic mutations; B. Relationships between MCM gene mutations and OS and DFS in CESC patients (cBioPortal)

Full size image

Furthermore, to deeply explore the potential clinical value of MCMs, their coexpressed genes were analyzed using the cBioPortal database. The coexpressed genes of MCM2/3/4/5/6/7/8 (top 25 correlated genes) were screened, and the results of intersection analysis are shown in Table 2. A total of 9 intersections and 16 key coexpressed genes (MCM5 [33], CHAF1B, MCM2, GINS2, MCM6, RFC5, FANCC, TIMELESS, CLSPN, BRIP1, RBL1, CDT1, RFC2, CDC45, ORC1, and TOP2A) were obtained for further study.

Table 2 Intersection analysis of the coexpressed genes of MCMs in CESC (cBioPortal)
Full size table

Detection and enrichment analysis of related genes

Next, the top 50 genes related to MCMs were identified by GEPIA2. The 57 selected genes (as shown in Table 3) and MCM7/8 were analyzed by Metascape. The pathway and process enrichment analysis results are shown in Fig. 11. The results showed that MCMs and related genes (59 total) were mainly enriched in DNA replication, DNA repair, the cell cycle, cell division and expression regulation. These genes were also analyzed by the DAVID database, and the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment results (p-value< 0.05, false discovery rate < 0.05) are shown in Table 4, Table 5, Table 6 and Table 7. The MCM-related genes were significantly enriched in 21 biological processes, 5 cellular components, 14 molecular functions and 5 important KEGG pathways.

Table 3 Intersection analysis of the related genes and key coexpressed genes of MCMs in CESC
Full size table
Fig. 11
figure 11

Enrichment results of the hub genes. A. Bar graph of the enriched terms (the top 20 clusters) and the top-ranked Gene Ontology biological processes; B. Network of the enriched terms colored by cluster ID. Nodes that share the same cluster ID are typically close to each other (Metascape)

Full size image
Table 4 GO enrichment analysis and the significant terms (biological processes)
Full size table
Table 5 GO enrichment analysis and the significant terms (cellular components)
Full size table
Table 6 GO enrichment analysis and the significant terms (molecular functions)
Full size table
Table 7 Significant KEGG signaling pathways
Full size table

PPI network construction and screening of hub genes

As shown in Fig. 12, the PPI network was successfully constructed. There were 4 Molecular Complex Detection (MCODE) components identified from the PPI network. MCODE 1 (MCM2/3/4/5/6/7/8, CLSPN, CDC45, ORC1, RPA1, and CDT1) played an important role in the activation of the prereplicative complex (R-HSA-68962), the activation of ATR in response to replication stress (R-HSA-176187) and DNA replication preinitiation (R-HSA-69002). MCODE 2 (TARDBP, RBMX, U2AF2, HNRNPM, SRSF3, and SRSF1) was important in the regulation of mRNA metabolic processes (GO:1903311) and spliceosomes (ko03040, hsa03040). MCODE 3 (USP1, RFC5, and RFC2) was important in the recognition of DNA damage by the PCNA-containing replication complex (R-HSA-110314), DNA damage response (GO:0042769) and PID Fanconi pathway (M1). MCODE 4 (MSH6, DTL, and MSH2) was important in the response to UV (GO:0009411), the response to light stimulus (GO:0009416) and DNA repair (R-HSA-73894). The survival analysis results of the hub genes from the 4 MCODE components are shown in Fig. 13 (the prognostic value of MCMs is shown in Fig. 8 and Fig. 9). The expression of HNRNPM, U2AF2, USP1 and CLSPN showed no correlation with the prognosis of CESC patients, while the high expression of the other 13 genes was significantly related to a better prognosis. Moreover, higher mRNA expression of RFC5, RFC2, DTL, RBMX, ORC1 and MSH2 was significantly associated with longer OS in CESC patients. These results indicated that the hub genes might play an important role in cervical cancer and provide potential molecular targets. In addition, GEPIA2 was used to generate an interactive heat map of the expression of MCMs and related genes in different cancer types (Fig. 14). The hub genes and MCM2/3/4/5/6/7/8 might have potential clinical value for the survival and prognosis of cervical cancer patients.

Fig. 12
figure 12

Protein-protein interaction network and MCODE components (Metascape)

Full size image
Fig. 13
figure 13

Prognostic value of the mRNA expression of the hub genes related to MCMs in CESC patients (Kaplan-Meier plotter database)

Full size image
Fig. 14
figure 14

Interactive heat map analysis of tissue-wise expression in different cancer types (GEPIA2)

Full size image

Discussion

Cervical cancer is the most common cancer among women worldwide. High-risk human papillomavirus (HPV) infection causes high morbidity and mortality. Therefore, the development of cervical cancer vaccines and screening technology and the exploration of clinical targets with good application prospects are still important. MCMs are implicated in the development of multiple cancers, including cervical cancer. Thus, MCM proteins have emerged as exceptionally promising markers for cervical cancer screening and early diagnosis [34]. Mitali Das et al. [35] explored the role of MCM4/5/6/10 in cervical cancer and their correlation with the clinical parameters of cervical cancer, and further study indicated that cervical cancer cells may use excess MCMs as a backup for replicative stress [36]. V N Saritha et al. [37] showed that MCM2/5 expression was upregulated in low-grade lesions, high-grade lesions and malignancies to a greater extent than p16 and p63. Gurjeet Kaur et al. [38] evaluated MCM gene expression profiles and MCM2 protein in HPV-associated cervical carcinogenesis. There is growing evidence that MCMs may be used as biomarkers to predict the malignant potential of cervical lesions. However, as cervical cancer is a complex disease involving many molecular interactions and complex signaling pathways, reports of MCMs and their related genes in cervical cancer are few and insufficient, and more research is still needed for the prevention and treatment of cervical cancer. In this study, bioinformatics was used to mine expression data and perform subsequent comprehensive analyses, which were based on a large amount of public data of patients with cervical cancer, for the in-depth study of the related molecular mechanisms of MCMs and protein molecular interactions, the prediction of related biomarkers, and the exploration of factors related to the good survival and prognosis of patients.

The expression of different MCMs in cervical cancer patients obtained from professional databases (ONCOMINE, UALCAN and HPA) showed that the mRNA and protein expression of MCMs increased in tumor tissue. These findings promoted our understanding of the expression of different MCMs in cancer patients. In particular, to evaluate and predict the potential clinical value of MCMs, analyses of individual cancer stages, the survival of cancer patients and the genetic alterations of different MCMs were performed using the UALCAN, Kaplan-Meier plotter and cBioPortal databases. The multidatabase analysis revealed that MCMs had great potential clinical significance. MCM2/3/4/5/6/7/8 might be used as potential indicators for survival in patients with cervical cancer, which needs more research and verification. Moreover, a high mutation rate (71%) of MCMs was observed in cervical cancer patients. MCM2, MCM4, MCM8, MCM3 and MCM7 ranked as the top five genes with the highest number of genetic alterations, but genetic alterations in MCMs were not significantly associated with longer OS or DFS in CESC patients. According to these results, the intervention strategy of mutating MCM genes to achieve longer survival times in patients might not be effective. However, controlling the gene transcription and protein expression of MCMs might be an effective intervention method for CESC patients.

After the expression analysis and clinical value evaluation of MCMs, the coexpressed genes and related genes of MCMs were screened by cBioPortal and GEPIA2 analysis, and then the systematic enrichment analysis of related genes was performed by Metascape and the DAVID database to deepen the understanding of the role of MCMs and related genes in cervical cancer. A total of 59 genes were involved in the enrichment analysis. The enrichment results revealed that these genes played an important role in DNA replication, the cell cycle, DNA repair, the DNA damage response, the regulation of signal transduction by p53 class mediators and other important biological processes. Moreover, these genes were significantly enriched in some important cellular components, such as the nucleoplasm, nucleus, MCM complex, nuclear chromosome, telomeric region and nuclear speck, which were also involved in DNA binding, DNA helicase activity, protein binding, ATP binding, nucleoside binding, DNA replication origin binding and RNA binding. We found that MCMs and their related genes were significantly enriched in some important pathways, such as the DNA replication (RFC5, MCM7, RFC2, PRIM1, RPA1, MCM3, MCM4, MCM5, MCM6, and MCM2), cell cycle (RBL1, CDC45, MCM7, ORC1, MCM3, MCM4, MCM5, MCM6, and MCM2), mismatch repair (RFC5, MSH6, MSH2, RFC2, and RPA1), spliceosome (HNRNPM, U2AF2, SRSF1, HNRNPU, SRSF3, and RBMX) and Fanconi anemia (BRIP1, RPA1, USP1, and FANCC) pathways. Further study of these pathways can deepen the understanding of the molecular mechanisms related to the occurrence and development of cervical cancer.

In the current study, genes coexpressed with MCMs and their related genes were successfully screened for PPI network construction. A total of 4 key subnetworks were screened. Survival analysis showed that 13 hub genes (CDC45, ORC1, RPA1, CDT1, TARDBP, RBMX, SRSF3, SRSF1, RFC5, RFC2, MSH6, DTL, and MSH2) from the key subnetworks might have potential clinical value. These genes and MCM2/3/4/5/6/7/8 were significantly related to the survival and prognosis of cervical cancer patients. Thus, our results might provide bioinformatics support for MCMs and their related genes in the prevention and clinical treatment of cervical cancer.

However, this study had three limitations. First, the data analyzed in this study were from public databases, and the analysis results might be affected by the quantity and quality of the data. Second, we did not evaluate the potential therapeutic or diagnostic effects of MCMs in detail. Finally, we did not explore the potential mechanisms of MCMs and hub genes in cervical cancer in detail, and the effect on prognosis requires follow-up data. Therefore, further research is needed to verify our findings and to explore the clinical application of MCMs and their related genes for the treatment of cervical cancer.

Conclusion

In conclusion, this study used a variety of bioinformatics methods to explore the transcriptional expression of MCMs as potential indicators of survival in patients with cervical cancer, obtained target genes with potential application value, and deepened the understanding of the influence of MCMs and their related genes in cervical cancer. These genes can be used to diagnose the progression of the disease before it leads to cancer. Moreover, our findings promoted the understanding of the MCM protein family and clinically related molecular targets for cervical epithelial neoplasia and cervical cancer, which provided new insight into the biological functions of MCMs in cervical cancer.