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Clinical significance and pro-oncogenic function of DBF4 in clear cell renal cell carcinoma

BMC Urology volume 25, Article number: 8 (2025) Cite this article

Abstract

Background

Clear cell renal cell carcinoma (ccRCC) is the most common malignant urological tumor, and regrettably, and is insensitive to chemotherapy and radiotherapy, resulting in poor patient outcomes. DBF4 plays a critical role in DNA replication and participates in various biological functions, making it an attractive target for cancer treatment. However, its significance in ccRCC has not yet been explored.

Methods

We utilized external datasets and bioinformatics analyses to investigate the significance of DBF4 in ccRCC. We analysed its expression patterns, prognostic and diagnostic value, and potential mechanisms. We subsequently validated our findings through an immunohistochemistry (IHC) assay of ccRCC clinical samples. We further investigated the impact of DBF4 on the progression of ccRCC cells. Various assays, including assessments of cell proliferation, apoptosis, the cell cycle, cell migration and invasion, and colony formation, and xenograft tumor models were subsequently performed following to the knockdown of DBF4 expression via shRNA.

Results

Bioinformatics analyses revealed that DBF4 is significantly overexpressed in ccRCC tissues compared with adjacent normal tissues. This overexpression was confirmed by IHC analysis of 75 pairs of clinical ccRCC tumor and adjacent tissues. Kaplan-Meier analysis revealed that high DBF4 expression was associated with a significantly lower five-year overall survival rate. Moreover, DBF4 expression was identified as an independent risk factor in multivariate Cox regression analysis. GO and KEGG pathway enrichment analyses revealed a substantial enrichment of terms associated with cell division, whereas gene set enrichment analysis (GSEA) revealed correlations between increased DBF4 expression and the activation of cell cycle-related pathways. Subsequent in vitro and in vivo experiments demonstrated that DBF4 knockdown in ccRCC cells not only suppressed proliferation and migration in vitro but also significantly inhibited tumor growth in xenograft mice by arresting the cell cycle at the G1/G0 phase, which was mediated by the inhibition of MCM2 phosphorylation and cyclin D1 and CDK4 expression.

Conclusion

The current study revealed that DBF4 overexpression is a significant factor associated with malignant features and poor prognosis in patients with ccRCC. Therefore, it was proposed that DBF4 could serve as a novel potential prognostic biomarker and molecular target for ccRCC.

Clinical trial number

Not applicable.

Peer Review reports

Introduction

Renal cell carcinoma (RCC), which originates from epithelial tubular cells, is the most common cause of malignant urological tumors. More than 430,000 new cases and approximately 180,000 deaths occur worldwide each year [1]. Approximately 70–80% of the histological subtypes of RCC cases are clear cell renal cell carcinoma (ccRCC). Compared with other subtypes of RCC, ccRCC usually results in worse patient outcomes [2, 3]. Almost 30% of patients have metastases at the time of diagnosis of ccRCC because some ccRCC patients are asymptomatic [4]. Partial and radical nephrectomy are the main approaches for the treatment of RCC. However, approximately one-third of patients with ccRCC experience recurrence and metastasis after surgery. Moreover, ccRCC is insensitive to chemotherapy and radiotherapy [5]. Although current immunomodulatory therapies targeting immune checkpoints have improved overall patient survival, treatment failure is frequently caused by primary and secondary resistance, as well as therapeutic intolerance. The median survival time for patients with metastatic ccRCC is less than one year. However, no specific biomarkers have been reported for ccRCC to date. Therefore, identifying a novel biomarker for predicting ccRCC progression and treatment outcomes is desirable.

The initiation of DNA replication is critically dependent on the activation of DBF4-dependent protein kinase (DDK) [6, 7]. The activity of DDK is not only essential for the survival of cancer cells but also holds the key to a promising therapeutic target in oncology. Inhibition of DDK has been shown to induce tumor-specific cell death without compromising the viability of nontransformed cells, highlighting its significant value in cancer treatment strategies [8]. To this end, several small molecule inhibitors of DDK, such as PHA-767,491, XL413, and TAK-931, have been developed and have demonstrated notable antitumor activity in preclinical animal models [9,10,11]. Despite these advances, existing DDK inhibitors, which primarily target CDC7 kinase, have shown suboptimal performance and are plagued by issues such as endogenous competition, indicating a need for further refinement [12].

DBF4, also known as the DBF4 zinc finger, plays a pivotal role as a regulatory subunit; when it is complexed with CDC7 kinase, it forms the DDK kinase [13]. It is instrumental in the regulation of cell cycle progression, particularly during the G1/S transition and the mitotic checkpoint [14]. Given its integral role in the formation of the DDK kinase, targeting DBF4 specifically could unveil a novel therapeutic strategy for targeted cancer treatment. This approach has the potential to bypass the limitations associated with direct CDC7 kinase inhibition, including the challenges mentioned above, and may lead to the development of more selective and potent anticancer therapies.

DBF4 is an unstable protein whose expression level peaks at the G1/S transition and decreases at the end of mitosis. DBF4 is the regulatory subunit of the CDC7 kinase, and the DBF4 complex plays a key role in DNA replication and participates in biological functions [15]. Sustained high expression of DBF4 will lead to the abnormal activation of DDK kinase, causing uncontrolled cell proliferation, which in turn can lead to the occurrence and progtession of tumors. In recent years, increased expression of CDC7 and DBF4 has been reported in breast cancer [16], ovarian cancer [17], lung cancer [18], and oral cancer [19]. Increased DBF4 expression is considered related to tumor cell proliferation, which makes CDC7 and DBF4 attractive targets for cancer treatment [20]. However, the role of DBF4 in ccRCC remains unexplored. Whether DBF4 is involved in the progression of ccRCC is a question that warrants further investigation. In this study, we delve into the clinical significance and biological functions of DBF4 expression in ccRCC patients and cell lines, aiming to shed light on its potential role and therapeutic implications in this disease context.

Materials and methods

Sample datasets and clinical profiles

Standardized fragments per kilobase of transcript per million fragments mapped (FPKM) gene expression data profiles, genetic alteration landscapes, and clinical data were obtained from The Cancer Genome Atlas (TCGA) by using the R package TCGAbiolinks (v.2.24.3) [21]. A total of 613 samples from ccRCC patients, comprising 541 tumor samples and 72 normal samples, were collected. Only 72 of the ccRCC patients had paired samples of tumor and normal tissue. In addition, the expression profiles of GSE53757 (72 tumor tissues and 72 normal tissues) and GSE36895 (29 tumor tissues and 23 normal tissues) were obtained from the Gene Expression Omnibus (GEO) repository (https://www.ncbi.nlm.nih.gov/).

Analysis of DBF4 expression patterns

Using the TCGA cohort and two datasets from GEO, we compared DBF4 expression levels between cancer and normal samples. Additionally, to further investigate the relationships between DBF4 expression levels and clinical characteristics, we evaluated differences in DBF4 expression across clinical stages genders, and age groups. Wilcoxon signed-rank tests were used for comparisons between two groups, whereas paired Wilcoxon signed-rank tests were applied for paired sample comparisons. The results were visualised via the R packages ggplot2 (v.3.4.3) [22] and ggpubr (v.0.6.0) [23].

Prognostic and diagnostic analysis

Using the TCGA cohort, we evaluated the impact of DBF4 gene (ENSG00000006634) expression on the overall survival (OS) of ccRCC patients in different subgroups. Patients were divided into high- and low-expression groups according to whether their DBF4 expression levels were above or below the mdian. Univariate Cox regression analyses were carried out to assess the significance of DBF4, gender, age, clinical stage and AJCC stage in predicting OS in patients with ccRCC. The significant factors were further examined by multivariate analysis in accordance with the results of univariate regression analysis. The p values and HRs of statistically significant factors are shown in the forest plot. Both survival analysis and Cox analyses were carried out with the R packages survival (v.3.3-1) [24] and survminer (v.0.4.9). The diagnostic value of DBF4 was assessed through receiver operating characteristic (ROC) curves plotted via the R package pROC (v.1.18.0).

Similar gene detection and enrichment analysis

Genes that exhibited an expression pattern comparable to that of DBF4 in ccRCC were identified via GEPIA2 (http://gepia2.cancer-pku.cn/#index) [25]. Genes similar to DBF4 were defined as the top 100 genes with the highest Pearson correlation coefficient. Gene Ontology (GO) and KEGG pathway enrichment analyses of genes similar to DBF4 were subsequently conducted with the R package clusterProfiler (v.4.4.4; parameters: Padj ≤ 0.05) [26]. The enrichment analysis results were visualised via the R package circlize (v.0.4.15) [27]. The protein‒protein interaction (PPI) network of DBF4 was constructed through the Search Tool for the Retrieval of Interacting Genes (STRING) online database (https://string-db.org/) [28]. Afterwards, the PPI network data were further processed and displayed via Cytoscape software version 3.9.1 [29].

Gene set enrichment analysis

To elucidate the potential mechanisms by which DBF4 impacts the progression of ccRCC, gene set enrichment analysis (GSEA) was conducted via GSEA software (v.4.3.0) (v.4.3.0) [30]. Within the TCGA cohort, 613 ccRCC samples were divided into two groups: samples with DBF4 expression levels above the median were defined as high-expression samples, whereas those with DBF4 expression levels below the median were defined as low-expression samples. The gene expression data and grouping information for all samples were then used as inputs for analysis based on the database of hallmark gene sets (h.all.v2023.1.Hs.symbols.gmt). Gene sets with |NES| > 1, NOM p < 0.05, and FDR q < 0.25 were considered significantly enriched. The significantly enriched gene sets were visualised via the R package pplot2 (v.3.4.3).

Relationship between DBF4 expression and immunity

The analysis of whether DBF4 is related to immunomodulators, including immunoinhibitors and immunostimulators, was carried out using the TISIDB database (http://cis.hku.hk/TISIDB/index.php) [31]. The top five immunoinhibitors and immunostimulators most highly correlated with DBF4 in ccRCC were identified, and a correlation bubble heatmap was subsequently generated via the R package ggplot2 (v.3.4.3).

Clinical patient specimens

Seventy-five paired ccRCC and adjacent nontumor renal tissues were used to construct a tissue microarray (HKid-CRCC150CS-02; Shanghai Outdo Biotech Co. Ltd., China). The study was approved by the ethics committee of the Second Affiliated Hospital of Hainan Medical University, and informed consent was obtained from the patients for the use of their clinical information and specimens.

Cell lines and cell culture

The RCC cell lines A498, and 786-O were purchased from IMMOCELL (Xiamen, China) and were subjected to validation via short tandem repeat (STR) analysis. A498 cells were grown in MEM (Gibco, C12571500BT) supplemented with 10% fetal bovine serum (FBS) (Gibco, 10099141 C) and RPMI 1640 (Cytiva HyClone, SH30809.01) supplemented with 10% FBS was used for the culture of 786-O cells. All the cells were maintained at 37 °C, in a 5% CO2 incubator.

Specific knockdown of DBF4 expression

The shRNA targeting the human DBF4 lentiviral expression vector was constructed as described previously [32]. Specifically, three DNA templates were created to target various locations of the shRNAs (Table S1). These templates were annealed and subcloned and inserted into the sites between Age І and EcoR І to construct pLKO.1-DBF4-shRNA (named DBF4-sh1, DBF4-sh2, and DBF4-sh3). The constructed vectors were verified via PCR and direct DNA sequencing. The primers used are shown in Table S2. The constructed pLKO.1-DBF4-shRNA lentivirus was subsequently used to transfect A498 and 786-O ccRCC cells via Lipofectamine™ 2000 (Invitrogen) with polybrene (8 mg/mL). The infected cells were selected and harvested for further verification and experiments.

Cell apoptosis and cell cycle analysis

RCC 786-O or A498 cells seeded in 6-well plates were infected with pLKO.1-shDBF4 lentivirus containing 10 µg of polybrene per mL upon reaching 80% confluence. After an additional 48 h of culture, an annexin V-FITC/PI apoptosis detection kit (BD) was used to analyse the degree of cell apoptosis according to the manufacturer’s instructions. For cell cycle detection, the cells were fixed with 70% precooled alcohol at 4 °C overnight, washed and then resuspended in 0.2% Triton X-100 PBS supplemented with 10 µL of RNase A (100 mg/mL) for 30 min at 37 °C and then incubated with 7-AAD (Beyotime, C1053S) for 30 min at 4 °C. Cell apoptosis and the cell cycle were analysed via flow cytometry (BD).

Cell proliferation

The proliferation of ccRCC cells subjected to DBF4 knockdown was assessed via a cell counting kit 8 (CCK-8) (Monmouth Junction, NJ, USA). After DBF4 was knocked down with pLKO.1-DBF4-shRNA, 2,000 786-O or A498 RCC cells were seeded in 96-well plates and cultured in complete medium for 3 days. 10 µL of CCK-8 reagent was added at intervals of 24, 48, and 72 h, after which the mixture was incubated at 37 °C for 2‒3 h. The OD value at 450 nm was measured with a Multiskan (Thermo Fisher, USA).

Cell migration and invasion assays

Tumor cell migration ability was determined by scratch wound healing assays. Confluent cells were starved for 18 h, and then a sterile pipette tip was used to create a scratch in the monolayer. Wound closure was monitored by imaging each well every 6 h. Tumor migration ability was assessed by measuring the scratch closure percentages in phase-contrast images. For the Transwell invasion assay, 100 µL of serum-free medium containing 5 × 104 starved ccRCC cells was cultured in the upper chamber (Corning, USA) with Matrigel (BD Biosciences, USA). In the lower chamber, 500 µL of medium supplemented with 10% FBS was used as the attractant. Following a 24-h period of cultivation, the invading cells on the bottom were fixed with 4% paraformaldehyde and then stained with 0.1% crystal violet (Beyotime, China). Finally, the stained cells were counted under a microscope.

Colony formation assay

Colony formation assays were also conducted in vitro to assess tumor cell proliferation. Briefly, 1,000 cells in 500 µL of medium were seeded in the wells of a 6-well plate. After approximately 14 days of subculturing, the colonies were stained with 0.1% crystal violet for 10–30 min at room temperature. Finally, the colonies were photographed and counted. The experiment was carried out three times independently.

Quantitative real-time polymerase chain reaction (qRT‒PCR)

Total RNA was extracted with a total RNA kit II (Omega Bio-Tek, Norcross, GA, USA). The RNA was reverse transcribed to cDNA by RT Master mix for qPCR (MCE, HY-K0510). qRT‒PCR was performed on an ABI 7500 Real-Time PCR System with SYBR Green qPCR master mix (MCE, HY-K0501) to quantify DBF4 expression with the following primer sequences: DBF4 left primer, 5’-TGCAGTCCATTTGATGTAGACAAG-3’ and right primer, 5’-GAGGTTCCACCATACTTATCGCC-3’; and 18 S left primer, 5’- CGACGACCCATTCGAACGTCT-3’ and right primer, 5’- CTCTCCGGAATCGAACCCTGA-3’. After 40 cycles at 94 °C for 30 s and 58 °C for 60 s, the result was normalised to 18 S rRNA (ΔCt). The relative DBF4 expression level was calculated and is presented as the fold change (fold change = 2−ΔΔCt) compared with that of the control group.

Western blot

Western blotting was conducted following the protocol previously described by [33]. In brief, proteins were separated and transferred to PVDF membranes and then incubated with primary antibodies against DBF4 (abcam, ab124707, 1:10,000 dilution), CDC7 (Proteintech, 17980-1-AP, 1:3000 dilution), Cylclin D1 (Proteintech, 26939-1-AP, 1:4000 dilution), CDK4 (Proteintech, 11026-1-AP, 1:3000 dilution), phospho-MCM2(Ser139)(cell signaling, #12958, 1:1000 dilution)or beta actin (Proteintech, 20536-1-AP, 1:5,000 dilution) in TBST at 4 °C overnight. After being washed three times, the membranes were incubated with goat anti-rabbit IgG secondary antibodies (Proteintech, SA00001-2, 1:10000 dilution) at room temperature for 1 h. Finally, enhanced chemiluminescence reagents (ECL, Thermo Fisher, Rockford, IL, USA) were used to visualise the signals.

Immunohistochemical staining

The tumor and adjacent tissues were collected to confirm DBF4 expression in ccRCC. The protocol was approved by the ethics committee of the Second Affiliated Hospital of Hainan Medical University (LW2023058). The paraffin-embedded tissues were deparaffinised via the standard xylene-deparaffinisation procedure, stained with anti-DBF4 (BIOSS antibodies, bs-7895R) at 4 °C overnight, and then incubated with an HRP-conjugated secondary antibody (Cell Signalling Technology, #7074S, 1:1,000 dilution) for 1 h at 37 °C. Then, haematoxylin and eosin were used to counterstain the sections. The slides and the intensity of immunohistochemical staining were analyzed under light microscopy.

Animal experiments

The animal experiments were conducted in compliance with a protocol endorsed by the Ethics Committee of Hainan Medical University (HYLL-2022-410). Briefly, male BALB/c nude mice (6 weeks old) were procured from Charles River Laboratories (Beijing, China) and housed under specific pathogen-free conditions at a temperature ranging from 21 to 25 °C and humidity ranging from 50 to 60%. Six mice were housed in a single cage and provided free access to sufficient food and water in their enclosures. Twelve mice were then subcutaneously injected with 4 × 106 786-O/shNC cells or 786-O/shDBF4 cells (n = 6) in the lower back region. The total number of animals was calculated via the E value method, as reported [34]. The tumors were measured with callipers every 4 days, starting 21 days post inoculation. The tumor volume was calculated using the following formula: length × width2/2. On the 41st day after inoculation, the mice were euthanised through the administration of 100% CO2 gas, with a flow rate of 30–70% of the chamber volume per minute. Additionally, the tumors were weighed and photographed. For further analysis, the tumor tissue sections were stained for Ki-67 to enhance the validity of the vivo experiments.

Statistical analyses

Statistical analyses were conducted using GraphPad Prism version 8.0 and R version 4.3.1. Quantitative data are presented as means ± SDs. The Wilcoxon test was used to compare expression differences of DBF4 between tumor and normal samples, as well as among samples with varying clinical characteristics, based on external datasets. Differences between the DBF4 knockdown and control groups in ccRCC were assessed using Student’s t-test. P value of less than 0.05 was regarded as statistically significant.

Results

Expression pattern and clinical significance of DBF4 in ccRCC

Dysregulation of gene expression may result in uncontrolled cell division and cancer. To investigate the potential correlation between DBF4 expression and cancer, the expression of DBF4 was evaluated in the tissues of various tumors and their adjacent tissues. According to data from the TCGA database, DBF4 mRNA expression is significantly greater in multiple types of cancer than in their corresponding normal tissues (Fig. 1A). DBF4 expression was notably higher in ccRCC tumor tissues (median: 1.11) than in normal tissues (median: 0.75) (Fig. 1B). The expression levels of DBF4 were compared in paired tumor and normal samples. DBF4 was also upregulated in the ccRCC samples compared with the matched normal samples (Fig. 1C). The datasets GSE53757 and GSE36895 in the GEO database provided additional evidence of DBF4 overexpression in ccRCC tumor tissues (Fig. 1D, E). In addition, it was determined whether DBF4 expression levels are correlated with different characteristics. The findings revealed a significant correlation between DBF4 and several characteristics, such as stage and gender (Fig. 1F-H). Notably, higher expression of DBF4 was observed in later clinical stages. In conclusion, DBF4 may function as an oncogenic molecule in ccRCC.

Fig. 1
figure 1

The expression pattern of DBF4. (A) DBF4 expression across diverse cancer types. (B) DBF4 mRNA expression was upregulated in ccRCC tissues (n = 541) compared with normal tissues (n = 72). (C) Comparison of DBF4 expression in 72 paired ccRCC tissues and adjacent normal tissues. (D) DBF4 expression in the GSE53757 dataset (72 cancerous tissues and 72 paracancerous tissues) and (E) GSE36895 dataset (29 cancerous tissues and 23 paracancerous tissues) (F-H) Increased expression of DBF4 was detected in the subgroups of higher individual cancer stage, gender, and age. *, p < 0.05; **, p < 0.01; ***, p < 0.001

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DBF4 is highly expressed in ccRCC clinical samples

To confirm the expression of DBF4 in ccRCC, 75 pairs of ccRCC tumor and adjacent tissues were collected. DBF4 expression was detected via immunohistochemical (IHC) analysis. A total of 69 pairs of ccRCC tissues (6 tissues detached from the slides) were analysed. Compared with that in adjacent tissues, DBF4 expression was upregulated in most ccRCC tumor tissues (Fig. 2A, B).

Fig. 2
figure 2

DBF4 expression in ccRCC clinical samples. (A) Representative immunohistochemistry results for DBF4 expression in ccRCC clinical tumor tissues and adjacent tissues. (B) Box plot depicting the IHC staining scores for DBF4 in 69 paired ccRCC samples. *, p < 0.05; **, p < 0.01; ***, p < 0.001

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Prognostic and diagnostic value of DBF4 in ccRCC

To better understand the association between DBF4 expression levels and patient prognosis, survival curves were analysed by comparing ccRCC patients with higher DBF4 expression levels with ccRCC patients with lower DBF4 expression levels. Compared with high DBF4 expression, low DBF4 expression significantly increased the five-year overall survival rate by ~ 15% (Fig. 3A). Survival analyses were also conducted on subgroups with distinct characteristics to elucidate the prognostic value of DBF4. DBF4 expression was found to be an evident risk factor in ten subgroups, including clinical stage I (HR = 2.17), stage IV (HR = 1.81), T stage T1 (HR = 2.07), T stage T3 plus T4 (HR = 1.57), M stage M0 (HR = 1.54), M stage M1 (HR = 1.70), race: white (HR = 1.87), age ≤ 60 (HR = 2.77), Gender: female (HR = 1.90), Gender: male (HR = 1.77) (Fig. 3B-K). Univariate Cox regression analyses revealed that DBF4 expression was a risk factor for patients with ccRCC, as were age, clinical stage, T stage, N stage and M stage (Table 1). However, only age, M stage and DBF4 were independent predictors according to multivariate Cox regression (Fig. 3O; Table 1). Furthermore, the diagnostic receiver operating characteristic (ROC) curve yielded area under the curve (AUC) values of 0.871, 0.845, and 0.835 on the basis of the TCGA cohort and two independent GEO datasets, respectively (Fig. 3L-N). Taken together, these findings suggest that DBF4 has the potential to serve as a diagnostic and prognostic biomarker in ccRCC.

Fig. 3
figure 3

Analysis of the prognostic and diagnostic value of DBF4. (A) ccRCC patients with high DBF4 expression exhibit poorer overall survival rates. (B-K) Survival analysis of DBF4 in different subgroups. (L-N) Diagnostic ROC analysis with the AUC of DBF4 in ccRCC. (O) Forrest plot of the multivariate Cox regression analysis for ccRCC. *, p < 0.05; **, p < 0.01; ***, p < 0.001

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Table 1 Cox regression analysis for OS in ccRCC patients
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Preliminary study on the mechanism of DBF4

A total of 100 genes similar to DBF4 (Additional file 1) were analysed via GO and KEGG pathway enrichment analysis (Pearson correlation coefficient > 0.56) (Fig. 4A). Through GO enrichment analysis, numerous GO terms related to cell division were significantly enriched, including nuclear division (biological process, GO:0000280), organelle fission (biological process, GO:0048285), microtubule motor activity (molecular function, GO:0003777), ATPase activity (molecular function, GO:0016887), spindle (cellular component, GO:0005819), and kinetochore (cellular component, GO:0000776). Similarly, the results of the KEGG enrichment analysis revealed that multiple pathways significantly enriched were related to cell division, such as the cell cycle (hsa04110) and homologous recombination (hsa03440). Eleven nodes and fifty-five edges make up the PPI network of DBF4 (Fig. 4B). The proteins CHEK2, CDC7, CDC45, and ATR and the MCM family are all part of the PPI network of DBF4 and are involved in controlling the cell cycle. Moreover, the gene set enrichment analysis (GSEA) was also performed to identify significantly different gene sets between the high and low DBF4 expression groups. The hallmark gene set enrichment results revealed that high DBF4 expression is associated with the activation of pathways, including E2F targets, the G2M checkpoint, the inflammatory response, IL6/JAK/STAT3 signalling, and apoptosis, and the inhibition of pathways, including the oestrogen response early and adipogenesis (Fig. 4C).

Fig. 4
figure 4

Potential mechanism of DBF4. (A) GO terms and KEGG pathways of genes similar to DBF4. (B) PPI network of DBF4. (C) GSEA analysis based on hallmarks gene sets. (D) Correlation analysis between DBF4 and immunomodulatory genes

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Numerous studies have shown that the host immune response determines the progression of cancers. Therefore, the correlation between DBF4 expression and immune modulators was analysed. In ccRCC, the top five immunoinhibitors that are most strongly correlated with DBF4 are KDR, LAG3, IL10, BTLA, and TIGIT, whereas the top five immunostimulators that are most strongly correlated with DBF4 are IL6R, CD80, MIC8, TNFSF4, and IL6 (Fig. 4D). The correlation between these immunomodulatory genes and DBF4 varies among different types of cancers.

These results suggest that DBF4 may primarily affect the occurrence and development of ccRCC by regulating cell division and is closely associated with immune regulation in the body.

Knockdown of DBF4 expression inhibited ccRCC cell proliferation

To further confirm that DBF4 is involved in cell cycle control in ccRCC, the ccRCC cell lines 786-O and A498, which highly express DBF4, were selected for further investigation of the pro-oncogenic function of DBF4 in ccRCC. Three constructed shRNA sequences targeting DBF4 were used to silence DBF4 expression in ccRCC. The Western blot and qRT‒PCR results verified that DBF4 expression was knocked down by the constructed shRNAs (Fig. 5A, B). The two shRNAs with the highest silencing efficiency (DBF4-sh1 and DBF4-sh3) were selected for the subsequent experiments. Compared with that of the shNC control cells, the proliferation of 786-O and A498 ccRCC cells was inhibited (Fig. 6A), and the apoptosis rate was increased (Fig. 6B).The colony formation assay results also confirmed that the number of colonies decreased markedly in ccRCC cells (Fig. 6C).The cell cycle in ccRCC cells was blocked at G0/G1 (Fig. 7A). The Western blot results revealed that MCM2 phosphorylation was inhibited and the cell cycle protein cyclin D1 was downregulated after DBF4 was knocked down, while CDK4 levels appeared to decrease, but statistical analysis did not find significant differences (Fig. 7B).

Fig. 5
figure 5

Knockdown of DBF4 expression in ccRCC cells via shRNAs. (A) The knockdown efficiency of DBF4-shRNAs in ccRCC cell lines was evaluated by qRT‒PCR, (B) Western blot. *, p < 0.05; **, p < 0.01; ***, p < 0.001

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Fig. 6
figure 6

Knockdown of DBF4 expression inhibited cell proliferation and increase apoptosis of ccRCC cells. (A) Knockdown of DBF4 significantly reduced the proliferation of 786-O and A498 cells. (B) Apoptosis of 786-O and A498 cells increased significantly after DBF4 expression was knocked down. (C) Colony formation assay for 786-O and A498 cells after DBF4 expression was knocked down. All experiments were repeated three times.*, p < 0.05; **, p < 0.01; ***, p < 0.001

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Fig. 7
figure 7

Knockdown of DBF4 expression blocked the cell cycle in ccRCC cells. (A) Knockdown of DBF4 induced cell cycle arrest at the G1/G0 phase transition in 786-O and A498 cells. (B) Knockdown of DBF4 expression suppressed cell cycle proteins. *, p < 0.05; **, p < 0.01; ***, p < 0.001

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The migration of ccRCC cells was attenuated after DBF4 knockdown

Migration is an important characteristic of tumor progression. Transwell invasion (Fig. 8A, B) and wound scratch assays (Fig. 8C) were carried out to determine whether DBF4 affects the migration of ccRCC cells. These results confirmed that the migration of ccRCC cells was attenuated after DBF4 knockdown.

Fig. 8
figure 8

DBF4 knockdown suppressed ccRCC cell migration in vitro. (A) Representative images of migration assays of 786-O and A498 cells subjected to DBF4 knockdown. (B) Quantification of the relative number of migrated cells. (C) Wound healing assays for 786-O and A498 cells after DBF4 was knocked down. *, p < 0.05; **, p < 0.01; ***, p < 0.001

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ccRCC cell growth in vivo is mitigated by DBF4 deficiency

To further confirm the cell growth results in vivo, the DBF4-silenced 786-O cells were injected into nude mice subcutaneously, and wild-type 786-O cells were injected as a control group. Upon DBF4 knockdown, the tumor volume and weight were dramatically decreased in a xenograft model (Fig. 9A-C, n = 6), indicating that the tumor growth capability of ccRCC cells was reduced.

Fig. 9
figure 9

DBF4 knockdown suppressed ccRCC cell xenograft growth in vivo. (A) Representative images of tumors formed by 786-O cells upon DBF4 knockdown (n = 6). (B) Tumor volume. (C) Tumor weight. (D) Immunohistochemical staining for Ki-67.*, p < 0.05; **, p < 0.01; ***, p < 0.001

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Discussion/Conclusion

Since ccRCC lacks typical symptoms and early biomarkers, most patients have distant metastases at initial diagnosis. Distant metastasis is the leading cause of mortality among ccRCC patients, and there is no ideal curative treatment [35]. In recent decades, since the investigation of the molecular mechanism of ccRCC, molecular targeted therapy has been applied in clinical practice as the main treatment option for metastatic ccRCC patients [36]. However, after a median of 6–15 months, drug resistance and cancer progression occur. Therefore, new biomarkers to diagnose ccRCC in its early stages and develop new potential molecular therapeutic targets are urgently needed. In this study, it was found that DBF4 expression was significantly higher in ccRCC tissue than in normal tissue. Patients with higher DBF4 expression were diagnosed at later clinical stages and had a worse prognosis and shorter survival.

To further explore the significance of DBF4 in ccRCC patients, the STRING online database was used to construct the PPI network of DBF4. MCM2 ~ 7, CDC7, CDC45, ATR, and CHEK2 interact with DBF4 and are involved mainly in the regulation of DNA replication initiation, checkpoint regulation and DNA damage repair in the cell cycle. After activation by the DBF4 subunit, CDC7 directly phosphorylates the MCM 2–7 complex [14, 37]. The MCM complex remains inactive until DDK and cyclin-dependent kinase (CDK) recruitment and activates the CDC45 and GINS initiation factors, leading to the formation of the CMG complex and DNA replication [38]. The MCM proteins were phosphorylated by DBF4/CDC7 kinase, and then GINS and CDC45 bind to the phosphorylated proteins [39]. Several studies have suggested that DBF4 degradation during mitosis is the major regulator of the ability of DDK to activate DNA replication. Thus, when DBF4 levels are high, DBF4 may bind to CDC7 and activate it to initiate DNA replication [40]. The results demonstrated that following the knockdown of DBF4, there was a noticeable inhibition of MCM2 phosphorylation. Concurrently, the expression levels of Cyclin D1 were significantly reduced, leading to the blockade of DNA replication.

In tumors, abnormal cell cycle activity drives tumor growth. Targeting a specific cell cycle protein seems to be an effective anticancer strategy [41]. The most successful inhibitors of the cell cycle are CDK4/6-palbociclib [41]. Breast cancer treatment may be revolutionized by using these compounds in clinical practice, and many other types of tumors may also benefit. A similar study reported that the CDK4/6 inhibitor abemaciclib significantly prolonged progression-free survival after treatment in metastatic or advanced breast cancer patients [42]. Although CDK4/6 inhibitors have achieved success to date, tumor treatment with cyclins is still in its infancy. Other cyclins-inhibiting compounds will be tested in preclinical studies and eventually in patient therapy.

The DBF4-CDC7 complex initiates DNA replication by sequentially phosphorylating and promoting CDC45 binding to MCM2-7, subsequently activating DNA helicase at the beginning of S-phase [43]. Therefore, specific inhibition of DBF4 expression may inhibit the replication and proliferation of tumor cells, thus inhibiting the growth of tumors. DBF4 may serve as a molecular target for tumor therapy in the context of ccRCC. In this study, it was confirmed that after DBF4 knockdown, the growth of ccRCC cells was inhibited both in vitro and in vivo, the migration of ccRCC cells was attenuated, and the cell cycle was arrested at the G1 phase in ccRCC cells.

The analysis of the correlation between DBF4 and immune modulators suggests that DBF4 may play a role in shaping the immune microenvironment of ccRCC. However, the regulatory mechanisms and functional roles of DBF4 in the tumor immune microenvironment (TIME) remain largely unexplored. Willemsen et al. reported a case of severe congenital neutropenia [44], revealing that the DBF4 variant impaired CDC7 binding, leading to reduced DDK-mediated phosphorylation, disrupted S-phase entry and progression, and impaired granulocyte differentiation associated with activation of the p53-p21 pathway. Notably, introducing wild-type DBF4 into CD34 + cells from the patient rescued the differentiation blockade at the promyelocyte stage. Another study conducted by Zhang et al. demonstrated that DBF4 promotes STAT3 signaling activation in hepatocellular carcinoma cells via a CDC7-mediated and XPO1-dependent mechanism [45]. Furthermore, the combination of the DDK inhibitor XL413 with anti-PD-1 immunotherapy significantly suppressed tumor growth and prolonged survival in tumor-bearing mice. These studies suggest that DBF4 may contribute to the remodeling of the TIME by regulating immune cell differentiation and activation, thereby playing a role in the development and progression of ccRCC. However, the specific mechanisms by which DBF4 functions within the ccRCC immune microenvironment, as well as the potential therapeutic benefits of targeting DBF4 in combination with immunotherapy, require further investigation.

In conclusion, DBF4 is significantly overexpressed in the tissues of ccRCC patients and is closely related to a poor prognosis. Silencing DBF4, which is associated with the cell cycle, inhibited the proliferation and migration of ccRCC cells. Our study predominantly examined the expression levels of DBF4 and its effects on ccRCC. However, several areas require further investigation to comprehensively elucidate the role of DBF4 in ccRCC. First, the fundamental molecular mechanisms by which DBF4 mediates ccRCC development remain unclear. Future studies should focus on identifying th specific signaling pathways and molecular interactions associated with DBF4, particularly those involved in regulating the cell cycle, DNA replication, and tumor progression. Second, while this study assessed the effects of DBF4 knockdown on the cell cycle of renal cancer cells, further research is needed to explore the overexpression of DBF4 in renal epithelial cells with inherently low DBF4 expression, to determine its potential role in cellular transformation and tumor initiation. Third, the impact of DBF4 on the TIME represents a critical area for future research. Although our study and existing evidence suggest that DBF4 may play a role in remodeling the TIME, further investigation is needed to determine whether it influences immune cell infiltration, cytokine signaling, and immune checkpoint regulation in ccRCC. This could provide valuable insights into its potential implications for immunotherapy. Fourth, the potential of DBF4 as a prognostic or therapeutic stratification biomarker for ccRCC patients warrants further validation. Future studies should evaluate DBF4 in larger cohorts and assess the performance of predictive models incorporating DBF4 alongside other clinical and molecular markers. Finally, the translational potential of DBF4 as a therapeutic target necessitates further exploration, particularly in the development of specific inhibitors and the evaluation of their efficacy and safety in both preclinical and clinical trials. Despite many aspects of DBF4 remain unexplored, the current study establishes a foundation for future research into DBF4 as a prospective biomarker and therapeutic target in ccRCC.

Data availability

The datasets analyzed during the current study are available in The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/), Gene Expression Omnibus (GEO) repository (https://www.ncbi.nlm.nih.gov/), GEPIA2 (http://gepia2.cancer-pku.cn), and the Search Tool for the Retrieval of Interacting Genes (STRING) online database (https://string-db.org/), and TISIDB database (http://cis.hku.hk/TISIDB/). Data associated with this study are available from the corresponding author upon request.

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Acknowledgements

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Funding

This work was supported by the Key Research and Development Projects of Hainan Province of China (ZDYF2022SHFZ133, To Dr Zhu), the Natural Science Foundation of Fujian Province of China (2024J011256, To Dr Zhu), and the Natural Science Foundation of Hainan Province of China (822MS179, To Dr Chen). The project was also supported by Hainan Provincial Clinical Medical Center.

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  1. Liuyan Chen, Lvying Wu and Minying Tang contributed equally to this work.

Authors and Affiliations

  1. Institute of Clinical Medicine, The Second affiliated Hospital of Hainan Medical University, 368th Yehai Avenue, Haikou, Hainan, 570311, China

    Liuyan Chen, Lvying Wu, Jianan Zhang, Wenyi Deng, Lingfeng Zhu & Jin Chen

  2. Fujian Provincial Key Laboratory of Transplant Biology, 900th Hospital, Fuzhou, Fujian, 350025, China

    Minying Tang & Yuanhang Cheng

  3. Minimally Invasive Urology and Translational Medicine Center, Fuzhou First General Hospital Affiliated with Fujian Medical University, 190th Dadao Road, Fuzhou, Fujian, 350009, China

    Kuanyin Wang, Lingfeng Zhu & Jin Chen

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Contributions

Conceptualization, J.C. and L.Z.; methodology, L.C., Y.C.; software, L.W. and Y.C.; validation, L.C., M.T., W.D. and J.Z.; formal analysis, J.C. and L.W.; investigation, L.C., M.T., W.D. and J.Z.; resources, K.W.; data curation, K.W.; writing—original draft preparation, J.C. and L.W.; writing—review and editing, J.C. and L.Z.; visualization, J.C. and L.W.; supervision, L.Z.; project administration, J.C; funding acquisition, J.C. and L.Z. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Lingfeng Zhu or Jin Chen.

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The study was approved by the Animal Ethics Committee of Hainan Medical University (HYLL-2022-410) and the ethics committee of the Second Affiliated Hospital of Hainan Medical University (LW2023058). Informed consent was obtained from the patients for the use of their clinical information and specimens.

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Chen, L., Wu, L., Tang, M. et al. Clinical significance and pro-oncogenic function of DBF4 in clear cell renal cell carcinoma. BMC Urol 25, 8 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12894-025-01694-x

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BMC Urology

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