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The potential of MCM8 as a biomarker in esophageal carcinoma: a comprehensive analysis integrating m6a methylation and angiogenesis

European Journal of Medical Research volume 30, Article number: 373 (2025) Cite this article

Abstract

Background

Effective biomarkers for esophageal carcinoma (ESCA) are currently lacking. Here, we examined the role of minichromosome maintenance complex component 8 (MCM8) as a diagnostic and prognostic marker in ESCA and its association with m6a methylation and angiogenesis, and constructed a competing endogenous RNA (ceRNA) network.

Methods

Clinical data and gene expression profiles were obtained from The Cancer Genome Atlas and Gene Expression Omnibus datasets. Differential gene expression analysis was performed using DESeq2 and limma packages. The prognostic significance of MCM8 expression regarding overall survival (OS) was examined using the Cox proportional hazards model. Receiver Operating Characteristic (ROC) analysis was used to assess the diagnostic potential of MCM8. MCM8 expression in ESCA tissues was evaluated by immunohistochemical staining on a tissue microarray. Pearson correlation analysis identified co-expressed genes, followed by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses. The GEPIA online tool was used to examine the relationship between MCM8 and m6a methylation as well as angiogenesis-related genes. MicroRNA and long noncoding RNA predictions were made using miRWalk, MicroT-CDS, ENCORI, and miRNet tools to construct the ceRNA network.

Results

MCM8 was significantly overexpressed in tumor tissues and showed high diagnostic accuracy in the ROC analysis with an area under the curve of 0.920. Kaplan–Meier survival analysis revealed that high MCM8 expression correlated with poorer OS and disease-specific survival. Pearson correlation analysis identified a significant correlation between MCM8 and several m6a methylation-related genes such as HNRNPA2B1 and YTHDF1, as well as PTK2, an angiogenesis-related gene. A ceRNA network including MCM8, PURPL/hsa-miR- 135a- 5p/MCM8 was successfully predicted and constructed.

Conclusions

MCM8 is a promising biomarker in ESCA and it is associated with m6a methylation and angiogenesis, showing potential as a therapeutic target. The ceRNA network provided insight into the pathogenesis of ESCA.

Introduction

Esophageal carcinoma (ESCA) is a significant global health concern due to its high incidence and mortality rates. It is the seventh most common cancer worldwide and the sixth leading cause of cancer-related death, with an estimated 604,100 new cases and 544,100 deaths annually [1, 2]. Despite advances in diagnostic and therapeutic strategies, the prognosis of patients with ESCA remains poor, with a 5-year survival rate of < 20% [1, 3, 4]. Current treatment modalities, including surgery, chemotherapy, and radiotherapy, are often limited by late-stage diagnosis, treatment resistance, and severe side effects [5,6,7]. Therefore, there is an urgent need to identify novel diagnostic biomarkers and therapeutic targets to improve patient outcomes.

In recent years, the role of molecular markers in cancer diagnosis and prognosis has garnered considerable attention. Unlike study markers, such as TP53 mutations, EGFR overexpression, or PD-L1, which are common in ESCA, minichromosome maintenance complex component 8 (MCM8) is involved in DNA replication and repair processes, introducing a unique perspective on tumor progression and treatment resistance. This new perspective addresses a major gap in ESCA research [8,9,10]. In the context of ESCA, the diagnostic and prognostic implications of MCM8 expression remain largely unexplored. This study aims to fill this gap by investigating the expression patterns of MCM8 in ESCA and evaluating its potential as a diagnostic and prognostic marker. We used bioinformatics tools to analyze gene expression data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Additionally, we explored the relationship between MCM8 expression and overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI) in ESCA patients.

We examined the association between MCM8 and N6-methyladenosine (m6a) methylation, a common RNA modification that has been implicated in cancer development and progression [11, 12]. We also analyzed the association of MCM8 with genes related to angiogenesis, which plays an important role in the progression of esophageal cancer [13, 14]. Despite the known role of m6a methylation in cancer, there are few studies specifically linking this modification to ESCA and its interplay with angiogenesis. Finally, we constructed a competing endogenous RNA (ceRNA) network to elucidate the regulatory mechanisms underlying the role of MCM8 in ESCA. We performed a comprehensive analysis of these processes, thereby providing a more holistic understanding of the molecular mechanisms associated with ESCA progression and a fresh approach to biomarker development.

The prognostic value of MCM8 has been demonstrated in various cancers, including colorectal cancer and lung adenocarcinoma, in which high MCM8 expression is associated with poor survival outcomes [15, 16]. In cancer cells, MCM8 overexpression is associated with increased tumor aggressiveness and resistance to chemotherapeutic agents [17]. These findings suggest that MCM8 plays a critical role in cancer progression and could serve as a valuable biomarker for predicting patient prognosis. Although the diagnostic and prognostic value of MCM8 has been demonstrated in multiple solid tumors, previous studies have not examined its regulatory mechanisms involving m6a modification or angiogenic pathways, particularly in the context of ESCA. This comparison highlights the contribution of our study to expanding the scope of MCM8 research and its potential therapeutic implications.

The present study provides valuable information on the diagnostic and prognostic value of MCM8 in ESCA. We integrated gene expression analyses with survival data and explored the molecular mechanisms underlying the regulation of MCM8 with the aim of contributing to the development of novel therapeutic strategies for ESCA. The research process is shown in Fig. 1.

Fig. 1
figure 1

Experimental design. The figure was created by Figdraw (www.figdraw.com)

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Materials and methods

Public database

Clinical data and gene expression profiles were retrieved from The Cancer Genome Atlas (TCGA) database, which is accessible through the portal at http://portal.gdc.cancer.gov [18]. The dataset was expanded by collecting additional gene expression data from the Gene Expression Omnibus (GEO) database. Four distinct ESCA datasets were selected, namely GSE17351, GSE63941, GSE45168, and GSE45670, which are accessible via http://www.ncbi.nlm.nih.gov/geo [19]. These four GEO datasets contain human esophageal cancer samples and normal esophageal samples.

Gene expression analysis and survival outcome investigation in ESCA using bioinformatics tools

Differential gene expression analysis of TCGA ESCA dataset was performed using DESeq2 package within the R software environment [20], which identified genes with altered expression between normal and tumor groups. The thresholds for significance were set at a log-fold change (LogFC) > 1 and a p-value < 0.05. Differential gene expression analysis was performed on the four GEO datasets using the limma package [21], adhering to the same criteria for identifying differentially expressed genes (DEGs).

The prognostic significance of gene expression was examined using univariate Cox proportional hazards regression analysis with the survival package in R, based on the expression levels of each gene in TCGA ESCA dataset. Prognostic relevance was assessed for OS, with hazard ratios (HR) > 1 and p-values < 0.05 indicating significance [22]. To visually represent the overlap of positively identified genes, we constructed an UpSet plot using the UpSetR package [23], which illustrates the intersection of DEGs across various datasets.

The ggplot2 package [24] was used to depict the expression differences of MCM8 between normal and tumor groups in TCGA ESCA dataset, paired samples, and the four GEO datasets (GSE17351, GSE63941, GSE45168, and GSE45670), providing a comprehensive visual comparison of gene expression profiles.

Diagnostic and prognostic analysis of MCM8 expression in ESCA

The diagnostic potential of MCM8 expression in ESCA was examined by receiver operating characteristic (ROC) analysis using the pROC package in R. The sensitivity and specificity of MCM8 as a diagnostic biomarker were determined, and the results were visualized using the ggplot2 package.

The prognostic significance of MCM8 expression levels regarding different survival outcomes was assessed using the survival package. Proportional hazards assumption tests and fitted survival regression models were performed to explore the prognostic value of MCM8 for DSS, OS, and progression-free interval (PFI). The outcomes were presented using a combination of the survminer and ggplot2 packages to provide a comprehensive visual representation of the survival data.

The correlation between MCM8 expression levels and the clinical and pathological characteristics of ESCA patients was examined to assess the role of the gene in disease progression.

ESCA tissue microarray (TMA) and immunohistochemical staining

Human ESCA TMAs (48 tumor tissues, 48 paired paracancerous tissues) were purchased from Shanghai Zhuoli Biotechnology Co., Ltd. Immunohistochemical (IHC) staining was performed following an established protocol as previously described [25, 26]. Tissue samples were embedded in paraffin, sectioned, deparaffinized, and rehydrated, followed by IHC staining using rabbit anti-MCM8 polyclonal antibody. DAB was used for color development, followed by hematoxylin counterstaining. The stained sections were imaged under a light microscope to capture high-resolution images. Semi-quantitative analysis of the IHC staining images was performed using ImageJ software.

Correlation and functional enrichment analysis of MCM8 co-expressed genes in ESCA

The co-expression network associated with MCM8 in ESCA was examined using R software packages to analyze TCGA ESCA dataset. The correlation between MCM8 and other genes was validated using the Pearson correlation coefficient, and associations were expressed by generating a volcano plot using the ggplot2 package. Heatmaps were constructed to display the top 20 genes positively and negatively correlated with MCM8 [27]. Genes were selected for co-expression analysis based on a correlation threshold > 0.5 and a p-value < 0.05. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses of the identified co-expressed genes was performed using the clusterProfiler package [28]. The results of these analyses were visualized using the ggplot2 package, providing a comprehensive overview of the biological processes and pathways associated with MCM8.

Analysis of MCM8 in relation to m6a and angiogenesis

The correlation analysis module of the GEPIA online database (http://GEPIA.cancer-pku.cn/#correlation) [29] was used to explore the relationship between MCM8 expression and key biological processes in ESCA, specifically focusing on m6a methylation and angiogenesis. The gene lists for m6a and angiogenesis were curated based on previous studies [30, 31]. The correlation between MCM8 expression levels and genes related to ESCA m6a and angiogenesis from TCGA ESCA and GSE45670 datasets was analyzed using Pearson’s correlation. Potential associations between MCM8 expression and the indicated biological processes were visualized by constructing co-expression heatmaps using the ggplot2 package. Genes showing consistent correlations in both datasets were identified and their co-expression patterns were visualized using ggplot2. Samples were stratified according to the median expression level of MCM8 to investigate the differential expression of m6a and angiogenesis-related genes between high and low expression groups.

Predictive analysis and ceRNA network construction for MCM8 in ESCA

MicroRNAs (miRNAs) targeting MCM8 were identified using miRWalk (http://mirwalk.umm.uni-heidelberg.de/) and MicroT-CDS (https://dianalab.e-ce.uth.gr/html/dianauniverse/index.php?r=microT_CDS) [32]. We performed a systematic prediction followed by a differential expression analysis to confirm and refine the list of candidate miRNAs.

Long non-coding RNAs (lncRNAs) that may serve as targets for the identified miRNAs were predicted using ENCORI (https://rnasysu.com/encori/index.php) [33] and miRNet (https://www.mirnet.ca/miRNet/home.xhtml) [34]. Differential expression analysis was performed to confirm the final set of lncRNAs with significant regulatory potential.

Building upon the ceRNA hypothesis, which posits that mRNAs, miRNAs, and lncRNAs can interact through shared miRNA response elements, we constructed a comprehensive ceRNA network. The interactions within this network were verified using RNAhybrid online tool (https://bibiserv.cebitec.uni-bielefeld.de/rnahybrid?id=rnahybrid_view_submission) [35] to predict potential binding sites between mRNAs and miRNAs, as well as between lncRNAs and miRNAs, thereby providing a molecular basis for the observed ceRNA relationships.

Statistical analysis

Statistical analyses were performed using R software. All computations were performed with strict adherence to established statistical methods, thereby ensuring the reliability and validity of the results. The Xiantao online tool (https://www.xiantaozi.com/) was used for data visualization, which facilitated the generation of graphical representations of the statistical outcomes. Statistical significance was determined using a threshold of p < 0.05.

Results

Identification and analysis of differentially expressed genes in ESCA with a focus on MCM8

To identify deregulated genes in ESCA, we performed a comprehensive analysis of TCGA ESCA dataset and four datasets from GEO using stringent filtering criteria as previously described. The differential gene expression analysis identified 4912 dysregulated genes in the TCGA ESCA dataset, 1081 in GSE17351, 2875 in GSE63941, 1436 in GSE45168, and 1537 in GSE45670. These findings were refined by prognostic selection analysis, which identified 517 genes in the TCGA ESCA dataset that met the established criteria. An UpSet plot indicated that the MCM8 and E2 F7 genes consistently met the screening criteria across the six datasets (Fig. 2a). E2 F7 has been reported previously in the context of ESCA, whereas the involvement of MCM8 has not been reported extensively, leading us to focus on MCM8 for further investigation.

Fig. 2
figure 2

Identification and analysis of differentially expressed genes in ESCA with focus on MCM8. a UpSet Plot illustrating the overlap of genes meeting the screening criteria for differential expression and prognostic value across six datasets, including TCGA ESCA dataset and GEO datasets GSE17351, GSE63941, GSE45168, and GSE45670. b–g Plots illustrating the differential expression of MCM8 in tumor groups relative to normal groups in various datasets. h Receiver Operating Characteristic (ROC) curve for MCM8, highlighting its significant diagnostic value in ESCA with an AUC of 0.920 [95% confidence interval (CI), 0.850–0.989; p < 0.05]. i, j Kaplan–Meier survival curves demonstrating the association between elevated MCM8 expression and poorer overall survival (OS) and disease-specific survival (DSS) (p < 0.05). k Kaplan–Meier survival curve for the progression-free interval (PFI) (p > 0.05)

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Figure 2b–g shows the differential expression of MCM8, indicating that the expression of the gene was consistently higher in tumor groups than in normal groups in TCGA ESCA dataset, paired samples, and the four GEO datasets (GSE17351, GSE63941, GSE45168, and GSE45670). This supports the potential of MCM8 as a biomarker in ESCA (p < 0.05).

Diagnostic and prognostic value of MCM8 expression in ESCA

To further examine the diagnostic efficacy of MCM8 in ESCA, we constructed ROC curves and calculated the area under the curve (AUC). ROC analysis supported the diagnostic value of MCM8 in ESCA, with an AUC of 0.920 (confidence interval, 0.850–0.989; p < 0.05) (Fig. 2h).

A previous univariate Cox analysis indicated that MCM8 (HR = 1.77, p < 0.05) was statistically significant. The prognostic significance of MCM8 expression for different survival metrics was assessed using the Kaplan–Meier plotter. The findings indicated that elevated MCM8 expression was correlated with poorer OS and DSS (Fig. 2i, j, p < 0.05). However, no statistically significant association was observed between MCM8 expression and PFI (Fig. 2k, p > 0.05).

Analysis of the correlation between MCM8 expression and various clinical characteristics showed that MCM8 expression was not associated with gender (Fig. 3a, p > 0.05), whereas it was significantly linked to age, histological subtype, histological grade, and the staging systems for both pathology and clinical presentation, encompassing T, N, and M categories (Fig. 3b–l, p < 0.05).

Fig. 3
figure 3

Correlation of MCM8 expression with clinical characteristics in ESCA. a There was no significant association between MCM8 expression and gender (p > 0.05). b–l Plots illustrating the significant correlation of MCM8 expression with age, histological subtype, histological grade, and staging systems for both pathology and clinical presentation, covering T, N, and M categories (p < 0.05)

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MCM8 is overexpressed in ESCA tissues

The expression of MCM8 in ESCA was further examined using a TMA and IHC staining (Fig. 4a). The results showed that the MCM8 protein is predominantly expressed in the cytoplasm. Statistical analysis demonstrated that MCM8 was significantly overexpressed in ESCA tissue samples compared with adjacent non-cancerous tissues (Fig. 4b, p < 0.05). However, MCM8 expression was not associated with age or gender (Fig. 4c, d, p  > 0.05).

Fig. 4
figure 4

Immunohistochemical analysis of MCM8 expression in ESCA. a Representative images of tissue microarray (TMA) sections showing MCM8 protein expression, which was predominantly localized to the cytoplasm. b MCM8 was significantly overexpressed in ESCA tissues compared with adjacent normal tissues (p < 0.05). c, d No significant association was observed between MCM8 expression and patient age or gender (p > 0.05)

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Correlation and functional enrichment analysis of MCM8 in ESCA reveals potential biological roles

To gain insight into the biological roles of MCM8 in ESCA, we examined the relationship between MCM8 and all genes in TCGA ESCA dataset using Pearson’s correlation. Protein-coding genes were analyzed using a correlation threshold |cor|> 0.3 and a p-value < 0.05, which led to the identification of 4341 genes that were positively correlated with MCM8 and 30 genes that were negatively correlated. Among these, POLR3 F showed the strongest positive correlation with MCM8, whereas PLEKHN1 had the most significant negative correlation (Fig. 5a).

Fig. 5
figure 5

Correlation and Functional Enrichment Analysis of MCM8 in ESCA. a Volcano plots with a Pearson correlation coefficient indicating the strength and significance of the relationship between MCM8 and other protein-coding genes within TCGA ESCA dataset. Highlighted are POLR3 F with the highest positive correlation and PLEKHN1 with the most significant negative correlation with MCM8. b, c Heatmaps depicting the top 20 genes with the highest positive and negative correlations with MCM8, respectively. These visualizations provide an overview of the gene expression patterns closely associated with MCM8 levels. d Enrichment analysis of MCM8 co-expressed genes was performed using Gene Ontology (GO) terms and the Kyoto Encyclopedia of Genes and Genomes (KEGG)

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A visual synopsis of these correlations was achieved by generating heatmaps to display the top 20 genes with the highest positive (Fig. 5b) and negative (Fig. 5c) correlations with MCM8. A higher correlation threshold of cor > 0.5 and p < 0.05 narrowed down the selection to 621 co-expressed genes. These genes, along with MCM8, underwent further functional enrichment analysis using the GO and KEGG databases. We used an adjusted p-value cutoff of p.adj < 0.05 and identified 649 pathways, including 453 biological processes, 107 cellular components, 75 molecular functions, and 14 KEGG pathways. The findings indicate that MCM8 and its co-expressed genes may participate in crucial biological processes such as DNA replication, kinetochore, ATP-dependent activity, acting on DNA, and the Fanconi anemia pathway (Fig. 5d).

Correlation between MCM8 expression and m6a methylation in ESCA

The potential association between MCM8 expression and the m6a methylation machinery in ESCA was examined by correlation analysis using the GEPIA online tool. The results showed a significant positive correlation between MCM8 expression and a panel of 20 m6a-associated genes (Fig. 6a, p < 0.05). Analysis of TCGA ESCA dataset confirmed this association, showing a positive correlation between MCM8 and the expression of these 20 m6a genes (Fig. 6b, p < 0.05). In the GSE45670 dataset, MCM8 was significantly positively correlated with the expression of HNRNPA2B1, RBMX, and YTHDF1 (Fig. 6c, p < 0.05). The significant positive correlation between MCM8 and the m6a genes HNRNPA2B1, RBMX, and YTHDF1 in the two datasets was visualized using scatter plots (Fig. 6d).

Fig. 6
figure 6

Correlation analysis of MCM8 expression with m6a methylation genes in ESCA. a Correlation plot from the GEPIA online tool demonstrating a significant positive correlation between MCM8 expression and a panel of 20 m6a-associated genes in ESCA (p < 0.05). b Heat map illustrating the consistent positive correlation between MCM8 and the 20 m6a genes in TCGA ESCA dataset (p < 0.05). c Heat plot highlighting the significant positive correlation of MCM8 expression with HNRNPA2B1, RBMX, and YTHDF1 in the GSE45670 dataset (p < 0.05). d Scatter plots displaying the significant positive correlation between MCM8 and the m6a genes HNRNPA2B1, RBMX, and YTHDF1 in TCGA and GSE45670 datasets. e Bar chart showing the significant differential expression of 18 m6a genes in TCGA ESCA dataset between high and low MCM8 expression groups. f Bar chart indicating significant expression differences for two m6a genes, HNRNPA2B1 and YTHDF1, between high and low MCM8 expression groups in the GSE45670 dataset. g Venn diagram illustrating the positive association of HNRNPA2B1 and YTHDF1 with MCM8 expression levels in the analyzed datasets

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Further exploration of TCGA ESCA dataset identified 18 m6a genes with significantly different expression between high and low MCM8 expression groups, including ALKBH5, FTO, HNRNPA2B1, HNRNPC, IGF2BP1, IGF2BP2, IGF2BP3, METTL14, METTL3, RBM15, RBM15B, RBMX, VIRMA, WTAP, YTHDC1, YTHDF1, YTHDF3, and ZC3H13 (Fig. 6e). However, the GSE45670 dataset showed significant expression differences between the high and low MCM8 expression groups for only two m6a genes, HNRNPA2B1 and YTHDF1 (Fig. 6f). A Venn diagram was drawn to illustrate the positive association of HNRNPA2B1 and YTHDF1 with MCM8 expression (Fig. 6g).

Association between MCM8 expression and angiogenesis in ESCA

The association between MCM8 expression and angiogenesis in ESCA was examined by correlation analysis using the GEPIA online tool. This analysis was designed to encompass a broad spectrum of genes known to participate in angiogenesis, a critical hallmark of cancer progression. The results showed a significant positive correlation between MCM8 and the expression patterns of 36 angiogenesis-related genes, suggesting a potential regulatory role of MCM8 in this process (Fig. 7a, p < 0.05). We expanded the analysis to TCGA ESCA dataset and observed a consistent positive correlation between MCM8 and the expression of 15 angiogenesis-related genes, including APOH, APP, CXCL6, FGFR1, FSTL1, ITGAV, LRPAP1, PDGFA, PGLYRP1, PRG2, PTK2, SERPINA5, VAV2, VCAN, and VTN (Fig. 7b, p < 0.05). The correlation was more pronounced in TCGA dataset, supporting the involvement of MCM8 in angiogenesis in ESCA. By contrast, the GSE45670 dataset revealed a significant positive correlation predominantly between MCM8 and PTK2 (Fig. 7b, p < 0.05). Scatter plots were utilized to visually represent the significant positive correlation between MCM8 and PTK2 in both datasets and to confirm the differential expression of these genes between high and low MCM8 expression groups (Fig. 7c, d).

Fig. 7
figure 7

Analysis of the correlation between MCM8 expression and angiogenesis-related genes in ESCA. a Correlation plot illustrating the significant positive association between MCM8 expression and a panel of 36 angiogenesis-related genes in ESCA samples, as determined by the GEPIA online tool (p < 0.05). b Heat map and associated statistical analysis from TCGA ESCA dataset showing a consistent positive correlation between MCM8 and the expression of 15 angiogenesis-related genes (p < 0.05). Heat plot highlighting the predominant significant positive correlation between MCM8 and PTK2 expression in the GSE45670 dataset (p < 0.05). c, d Scatter plots show angiogenesis-related genes (PTK2) significantly positively correlated with MCM8 expression levels in TCGA and GSE45670 datasets and confirm that the expression of PTK2 is also significantly different between the high and low MCM8 expression groups

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MCM8-miRNA regulatory axis in ESCA

Evidence suggests that the aberrant regulation of ceRNA networks contributes to tumorigenesis and cancer progression. In this study, we aimed to predict and construct an MCM8-centered ceRNA network in ESCA. Analysis of the miRWalk and MicroT-CDS databases identified 2179 and 60 miRNAs, respectively, predicted to interact with MCM8, and a Venn diagram identified a subset of 37 miRNAs present in both databases that could be involved in the regulation of MCM8 (Fig. 8a). Expression analysis of these miRNAs in TCGA ESCA dataset showed that seven miRNAs were upregulated and two were downregulated in tumor samples compared with normal controls (Fig. 8b). The upregulated miRNAs included hsa-miR- 105 - 5p (LogFC = 6.87, p = 6.134e− 14), hsa-miR- 335 - 3p (LogFC = 1.73, p = 1.196e− 07), hsa-miR- 940 (LogFC = 1.63, p = 3.577e− 05), hsa-miR- 942 - 3p (LogFC = 1.48, p = 0.003), hsa-miR- 135b- 5p (LogFC = 2.13, p = 1.360e− 07), hsa-miR- 1304 - 3p (LogFC = 3.58, p = 4.606e− 05), and hsa-miR- 6783 - 3p (LogFC = 1.27, p = 0.048), whereas hsa-miR- 135a- 5p (LogFC = − 3.13, p = 6.317e− 07) and hsa-miR- 202 - 5p (LogFC = − 2.92, p = 2.539e− 05) were downregulated, suggesting a potential reciprocal relationship with the overexpression of MCM8 mRNA in tumor tissues. Guided by the ceRNA hypothesis, which posits that mRNAs overexpressed in tumors correspond to the downregulation of targeting miRNAs, we selected hsa-miR- 135a- 5p and hsa-miR- 202 - 5p as candidate miRNAs for targeting MCM8. Their reduced expression in tumor samples was further confirmed (Fig. 8c, e). Bioinformatics analysis using RNAhybrid identified potential binding sites between MCM8 and the two miRNAs, providing molecular evidence for their interaction (Fig. 8d, f).

Fig. 8
figure 8

MCM8-miRNA regulatory axis in ESCA. a Venn diagram displaying the intersection of 37 miRNAs predicted to interact with MCM8, as identified by the miRWalk and MicroT-CDS databases. b Volcano chart of the differential expression of the 37 intersecting miRNAs in TCGA ESCA dataset showing the seven upregulated (hsa-miR- 105 - 5p, hsa-miR- 335 - 3p, hsa-miR- 940, hsa-miR- 942 - 3p, hsa-miR- 135b- 5p, hsa-miR- 1304 - 3p, hsa-miR- 6783 - 3p) and two downregulated (hsa-miR- 135a- 5p, hsa-miR- 202 - 5p) miRNAs in tumor samples compared with normal controls. c Expression of hsa-miR- 135a- 5p in tumor versus normal samples from TCGA ESCA dataset confirming its downregulation in tumor tissues. d Bioinformatics analysis using RNAhybrid depicting the potential binding site between MCM8 and the downregulated miRNA hsa-miR- 135a- 5p. e Expression of hsa-miR- 202 - 5p corresponding to the downregulation of hsa-miR- 135a- 5p in tumor samples. f RNAhybrid analysis identified the potential binding site between MCM8 and the downregulated miRNA hsa-miR- 202 - 5p

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LncRNA-miRNA interactions in the MCM8 ceRNA network

To further examine the network of ceRNAs associated with MCM8 in ESCA, we analyzed lncRNAs that may interact with hsa-miR- 135a- 5p. A search of the ENCORI and miRNet databases identified 27 and 37 lncRNAs, respectively, predicted to interact with hsa-miR- 135a- 5p. Eight lncRNAs (GAS5, DANCR, PURPL, RPARP-AS1, NEAT1, MALAT1, OIP5-AS1, and XIST) were common to the two databases (Fig. 9a). Differential expression analysis in TCGA ESCA dataset showed that PURPL (log2 FC = 1.61, p = 0.041) was the only lncRNA showing a significant difference in expression (Fig. 9b), and its upregulation in tumor samples compared with normal samples was confirmed (Fig. 9c). Bioinformatics analysis using RNAhybrid identified potential binding sites between PURPL and hsa-miR- 135a- 5p, suggesting a direct molecular interaction (Fig. 9d).

Fig. 9
figure 9

Comprehensive analysis of lncRNAs in the MCM8 ceRNA network in ESCA. a Venn diagram showing the intersection of lncRNAs predicted to interact with hsa-miR- 135a- 5p, as identified by the ENCORI and miRNet databases. b Volcano chart from TCGA ESCA dataset showing the differential expression of the intersecting lncRNAs; PURPL was identified as the only lncRNA significantly upregulated in tumor samples. c Validation plot confirming the specific upregulation of PURPL in tumor versus normal samples in TCGA ESCA dataset. d Bioinformatics analysis using RNAhybrid shows the potential binding sites between PURPL and hsa-miR- 135a- 5p, suggesting direct molecular interaction. e Venn diagram representing the overlap of lncRNAs predicted to interact with hsa-miR- 202 - 5p, as identified by the ENCORI and miRNet databases. f Expression analysis of the lncRNAs targeting hsa-miR- 202 - 5p shows no significant differences in expression between tumor and normal samples, resulting in their exclusion from further analysis

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Parallel analysis of lncRNAs targeting hsa-miR- 202 - 5p yielded 14 and 13 lncRNAs from the ENCORI and miRNet databases, respectively, with 3 lncRNAs (HCG11, EBLN3P, and NORAD) common to both databases (Fig. 9e). However, the expression of these lncRNAs did not differ significantly between tumor and normal samples, leading to their exclusion from further analysis (Fig. 9f).

Taken together, the findings indicate that PURPL may act as a competitive ceRNA for hsa-miR- 135a- 5p, potentially promoting the expression of MCM8. These data may contribute to our understanding of the role of MCM8 and its ceRNA network in the pathogenesis of ESCA.

Discussion

ESCA is a highly aggressive malignancy characterized by high morbidity and mortality rates, which poses a substantial threat to global health. It is the sixth leading cause of cancer-related death worldwide, and its incidence is particularly high in certain regions such as East Asia [1, 2]. The prognosis of patients with ESCA remains poor, which is primarily due to late-stage diagnosis and limited effective treatment options. The aggressive nature of this cancer and its tendency towards early metastasis further complicate treatment efforts, underscoring the need to identify effective diagnostic and prognostic biomarkers to improve early detection and treatment efficacy [36,37,38,39,40].

ESCA is characterized by rapid progression and a high rate of recurrence, which contribute to its poor survival rates [2, 41]. Advances in molecular biology and bioinformatics techniques have opened new avenues for understanding the complex genetic and epigenetic landscape of ESCA. For instance, studies show that the expression of specific genes and non-coding RNAs can significantly impact the progression of ESCA [42, 43]. The integration of bioinformatics tools for analyzing gene expression and the identification of key regulatory networks can uncover novel therapeutic targets. This research direction is promising for developing personalized treatment strategies and improving patient outcomes [26, 44]. Comprehensive analysis of the molecular mechanisms underlying ESCA, such as the role of MCM8 and its interaction with m6a methylation and angiogenesis, may provide insight into the pathogenesis of the disease and suggest potential interventions.

The MCM8 gene encodes a member of the mini-chromosome maintenance (MCM) protein family, which plays a crucial role in the initiation of DNA replication. Although significant progress has been made in identifying molecular markers such as TP53 mutations, EGFR overexpression, and PD-L1 expression, these markers often lack specificity or are not entirely applicable to clinical practice for early diagnosis or the design of personalized treatments. The involvement of MCM8 in DNA replication and repair provides a new perspective, particularly because of its association with epigenetic mechanisms such as m6a methylation and angiogenic pathways, which have not been analyzed in detail in ESCA. In this study, we showed that MCM8 was significantly overexpressed in ESCA tissues compared with normal tissues, and this overexpression was associated with poorer OS and DSS. The high diagnostic potential of MCM8, as evidenced by an AUC of 0.920, underscores its value as a biomarker for ESCA. Pearson correlation analysis revealed that MCM8 is co-expressed with multiple genes involved in critical biological processes such as DNA replication and cell cycle regulation. The significant positive correlation between MCM8 and m6a methylation-related genes suggests that MCM8 may influence ESCA progression through epigenetic modifications. Additionally, ceRNA network analysis indicated that miRNAs such as hsa-miR- 135a- 5p and hsa-miR- 202 - 5p, which target MCM8, are downregulated in tumor samples, potentially leading to MCM8 overexpression. The overexpression of MCM8 and its prognostic value have been demonstrated in several malignancies such as bladder, cervical, colorectal, hepatocellular, gastric, and pancreatic cancers [15, 45,46,47,48,49]. The consistency of these findings across various cancer types supports the involvement of MCM8 in cancer progression and its potential as a target for therapeutic intervention.

In this study, we identified several DEGs in ESCA in multiple datasets, and MCM8 was identified as a key gene of interest due to its elevated expression in tumor tissues compared with normal tissues. Enrichment analysis indicated that MCM8 is involved in several crucial biological processes and pathways such as DNA replication, kinetochore, ATP-dependent activity, acting on DNA, and the Fanconi anemia pathway. These processes are essential for maintaining genomic stability and proper cell division, and they play an important role in the etiology and progression of oncogenic processes [50,51,52]. The interaction between MCM8 and the cellular machinery that regulates genomic stability and cell division suggests a complex regulatory network in which MCM8 dysregulation could have a significant impact on cancer cell behavior.

We found a positive correlation between MCM8 and genes related to the m6a methylation machinery, including HNRNPA2B1 and YTHDF1. m6a methylation is an important post-transcriptional modification involved in the regulation of mRNA stability, alternative splicing, and translational efficiency, thereby influencing a multitude of cellular functions. In the context of ESCA, Guo et al. described the role of HNRNPA2B1 as an oncogenic driver in ESCA, promoting tumor progression by upregulating the lipogenic enzymes ACLY and ACC1 [53]. In previous work, we identified YTHDF1 as a prognostic biomarker in ESCA and demonstrated its involvement in immune cell infiltration, glycolytic regulation, ferroptosis, and the ceRNA network [54]. The observed association of MCM8 with these m6a-related genes suggests that its role in ESCA involves the modulation of m6a methylation, thereby impacting gene expression patterns and contributing to the interplay of molecular mechanisms underlying ESCA pathogenesis. This interconnection underscores the intricate regulatory networks within the cancer cell and the potential for MCM8 as a node in these pathways, influencing both the epigenetic and transcriptional profiles in the disease context.

Angiogenesis, the process of new blood vessel formation, is a cardinal mechanism involved in tumor growth and metastasis, as it provides cancer cells with the blood supply essential for growth and dissemination [13, 14]. We found a significant positive correlation between MCM8 and the angiogenesis-associated gene PTK2. Feng et al. showed that HMGB1-induced overexpression of KLF7 promotes liver cancer progression and metastasis through the upregulation of TLR4 and PTK2. This observation together with the present findings suggests a mechanistic convergence in which MCM8 may be implicated in angiogenesis-related pathways. MCM8 could contribute to tumor progression by fostering the development of new vasculature, thereby providing a conducive environment for the nourishment and spread of neoplastic cells.

ceRNA networks play an important role in the development of ESCA [55,56,57]. We identified several miRNAs, such as hsa-miR- 135a- 5p and hsa-miR- 202 - 5p, that were downregulated in tumor samples and negatively correlated with MCM8 expression. This finding aligns with the ceRNA hypothesis by which downregulation of these miRNAs leads to the upregulation of their target gene, MCM8. We also showed that the lncRNA PURPL increases MCM8 expression by sequestering hsa-miR- 135a- 5p, thereby reducing its inhibitory effect on MCM8. Wang et al. found that the lncRNA RP11 - 197 K6.1 is significantly upregulated in colorectal cancer tissues and cell lines and promotes cancer cell migration and metastasis by interacting with miR- 135a- 5p, thereby promoting tumor growth by suppressing the inhibition of DLX5 expression [58]. Berhane et al. found that knockout of PURPL inhibits the proliferation of liver cancer cells, induces apoptosis, and sensitizes tumor cells to treatment with the chemotherapy agent doxorubicin [59]. This study supports that ceRNA networks involving MCM8 may play an important role in the progression of ESCA.

In summary, this study highlights the multifaceted role of MCM8 in ESCA including its involvement in m6a methylation and angiogenesis. The construction of the ceRNA network provides further insight into the regulatory mechanisms underlying MCM8 expression. These findings underscore the potential of MCM8 as a diagnostic and prognostic biomarker and as a therapeutic target in ESCA.

Conclusion

In this study, we performed comprehensive bioinformatics analyses and elucidated the role of MCM8 in ESCA. We showed that MCM8 is expressed at high levels in ESCA and its overexpression is associated with poor OS. ROC analysis supported the potential of MCM8 as a diagnostic biomarker. We found that MCM8 is correlated with genes related to m6a methylation and angiogenesis, providing insight into its possible regulatory mechanisms. The construction of a ceRNA network involving MCM8 offers a new perspective on its role in ESCA. Future studies should focus on validating these findings through experimental studies and clinical trials to fully elucidate the diagnostic and prognostic potential of MCM8 in ESCA.

Availability of data and materials

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

Not applicable.

Funding

This work was supported by the Hubei Provincial Natural Science Foundation of China (2024AFD284), Hubei Provincial Clinical Research Center for precision Diagnosis and Treatment of Liver Cancer Open Fund (2024LCOF08), Innovative Research Program for Graduates of Hubei University of Medicine (grant no. YC2024042), the Shiyan Taihe Hospital hospital-level project (2024JJXM087), and the Key Discipline Project of Hubei University of Medicine.

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  1. Xu-Sheng Liu, Yong Xu and Li-Song Dai have contributed equally.

Authors and Affiliations

  1. Department of Nuclear Medicine, The Second Affiliated Hospital of Soochow University, Suzhou, 215004, Jiangsu, China

    Zhi-Jun Pei

  2. Department of Nuclear Medicine, Hubei Provincial Clinical Research Center for Precision Diagnosis and Treatment of Liver Cancer, Taihe Hospital, Hubei University of Medicine, Shiyan, 442000, Hubei, China

    Xu-Sheng Liu & Yu Zhang

  3. Hubei Provincial Clinical Research Center for Umbilical Cord Blood Hematopoietic Stem Cells, Taihe Hospital, Hubei University of Medicine, Shiyan, 442000, Hubei, China

    Xu-Sheng Liu

  4. Department of Neurological Rehabilitation Area 3, Taihe Hospital, Hubei University of Medicine, Shiyan, 442000, Hubei, China

    Yong Xu

  5. Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, 430060, Hubei, China

    Li-Song Dai

  6. Department of Outpatient, Taihe Hospital, Hubei University of Medicine, Shiyan, 442000, Hubei, China

    Ao Li

  7. Department of Gastroenterology, Taihe Hospital, Hubei University of Medicine, Shiyan, 442000, Hubei, China

    Jie Liu

  8. Department of Rehabilitation Medicine Center, Taihe Hospital, Hubei University of Medicine, Shiyan, 442000, Hubei, China

    Jin Xie

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Contributions

X.-S.L.: Conceptualization, Methodology, Writing—Original Draft. Y.X.: Software, Validation. L.-S.D.: Software, Validation. A.L.: Validation. J.L.: Validation. Y.Z.: Validation. J.X.: Supervision. Z.-J.P.: Supervision, Writing—Review & Editing.

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Correspondence to Jin Xie or Zhi-Jun Pei.

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Liu, XS., Xu, Y., Dai, LS. et al. The potential of MCM8 as a biomarker in esophageal carcinoma: a comprehensive analysis integrating m6a methylation and angiogenesis. Eur J Med Res 30, 373 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s40001-025-02541-3

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European Journal of Medical Research

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