WDHD1 promotes hepatocellular carcinoma progression by affecting the cell cycle and immune evasion

Highlights

• •WDHD1 was upregulated in HCC cells and tissues, and correlated with poor prognosis.
• •MiRNAs and transcription factors potentially regulate the WDHD1 expression.
• •WDHD1 affectes proliferation, migration, and invasion of HCC in vitro and in vivo.
• •WDHD1 regulates cell cycle through its interaction with CGM complex.
• •WDHD1 affects the immune evasion of HCC.

Abstract

WD repeat and HMG-box DNA binding protein 1 (WDHD1) is dysregulated in various tumors; however, its role in hepatocellular carcinoma (HCC) remains unexplored. Herein, we observed that WDHD1 was significantly upregulated in HCC tissues and cell lines and correlated with poor prognosis. Regulatory analysis identified hsa-miR-22, hsa-miR-139, and the transcription factors EP300 and CREBBP as potential modulators of WDHD1. Functional assays revealed that WDHD1 knockdown suppressed cell proliferation, migration, and invasion, whereas its overexpression enhanced these oncogenic phenotypes both in vitro and in vivo. Furthermore, WDHD1 depletion promoted cellular apoptosis. Mechanistically, WDHD1 interacted with components of the CDC45-MCM-GINS (CMG) complex and maintained their structural integrity, thereby facilitating cell cycle progression. Drug sensitivity analysis indicated that elevated WDHD1 expression enhanced responsiveness to cell cycle-targeting agents. Additionally, high WDHD1 levels were associated with increased CD4 memory T cell infiltration, elevated tumor mutational burden (TMB), and enhanced expression of key immune checkpoint markers, suggesting a potential for improved response to immunotherapy in these patients. These findings suggest WDHD1 as a novel oncogenic driver and promising therapeutic target in HCC.

Introduction

Liver cancer is one of the most common malignancies worldwide and had the fastest increasing mortality for decades [1]. Hepatocellular carcinoma (HCC) is the most common pathological type of liver cancer, accounts for approximately 90% of cases [2]. HCC is a highly heterogeneous disease and exhibits clinical features of occult onset, high degree of invasiveness, and high rates of recurrence and metastasis [3]. Most of patients are diagnosed in advanced stages, when liver resection and transplantation are not feasible. Although many new effective ways have been applied for HCC in recent years, such as surgery, chemotherapy, radiotherapy, immunotherapy, natural chemicals and nanotechnology; however, the prognosis of HCC patients remains poor, with a 5-year overall survival rat [4]. Hence, it is important to uncover the molecular mechanisms of HCC progression and find the new therapeutic targets for improving the prognosis.

WDHD1 is a protein amalgamating WD repeat and HMG-box DNA-binding domains, and features multiple N-terminal WD40 domains and a C-terminal HMG box [5]. WD40 domains, prevalent in numerous eukaryotic proteins, likely serve as adaptor or regulatory modules in signal transduction, pre-mRNA processing, and cytoskeletal assembly. Studies have shown that WDHD1 is acknowledged as a pivotal contributor to DNA replication and DNA damage repair [6], and plays crucial roles in cell proliferation [7], embryonic development [8], and sister chromatid cohesion [9]. Moreover, the dysregulation of WDHD1 has been found to be associated with the development of multiple types of cancer [10,11]. However, the expression and role of WDHD1 in HCC remain to be fully investigated. Thus, the present study aimed to elucidate the role of WDHD1 in HCC and explore its potential molecular mechanisms.

Methods

Date retrieval

The RNA-seq profiles and clinical data of HCC patients along with normal samples, were obtained from two publicly available datasets: The Cancer Genome Atlas (TCGA-LIHC, https://cancergenome.nih.gov/) (n = 370) and the International Cancer Genome Consortium (ICGC, https://dcc.icgc.org/) Liver Cancer-RIKEN, Japan (LIRI-JP) cohorts (n = 243). HCC and paired paracancerous tissues (n = 15) were collected from the First Hospital of Wenzhou Medical University, the collection of these samples was reviewed and approved by the Human Research Ethics Committee of the First Hospital of Wenzhou Medical University. Protein expression levels of WDHD1 in HCC and normal liver tissues were retrieved from The Human Protein Atlas database (https://www.proteinatlas.org/). Kaplan-Meier Plotter (http://www.kmplot.com) [12] was used to explore the relationship between gene expression and patient prognosis.

Mutations, methylation, miRNAs and transcription factors analysis

Mutations, copy number alterations (CNAs), and DNA methylation patterns of WDHD1 were deeply analyzed using the cBioPortal database (http://cbioportal.org). To explore the relationship between promoter methylation and WDHD1 expression in HCC patients, we utilized the UALCAN [13] (http://ualcan.path.uab.edu). Differentially expressed miRNAs associated with WDHD1 in the TCGA-LIHC cohort were identified using the LinkFinder module within LinkedOmics [14].

Protein interaction and functional enrichment analysis

Protein-protein interactions involving WDHD1 were identified using the STRING database [15]. Functional enrichment analysis of interacting proteins was performed using Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Additionally, FunRich tool [16] was used to analyze the biological processes, cellular components, molecular functions, and pathways enriched by negatively correlated miRNAs.

Cell culture and transfection

HCC cell lines (Huh-7, Bel-7405, LM3, JHH-7, Hep-3B, and Hepa 1-6) and normal liver cell lines (THLE-2), as well as 293T cells, were purchased from Shanghai Cell Bank (Shanghai Biological Sciences, Chinese Academy of Sciences, Shanghai, China). Huh-7, Bel-7405, LM3, JHH-7, Hep-3B, and Hepa 1-6 and 293T cells were maintained in Dulbecco's modified Eagle's medium (Biological Industries, Kibbutz Beit Haemek, Israel) and THLE-2 were cultured in BEGM (Biological Industries) basal medium containing 5 ng/ml EGF and 70 ng/ml phosphoethanolamine, supplemented with 10% fetal bovine serum and 1% penicillin/streptomycin (Solarbio, Beijing, China) in a humidified incubator at 37°C with 5% CO2. All cell lines were mycoplasma-free and authenticated by short tandem repeat (STR) profiling after >6 months of passage. Transfection was performed using either PolyJet (SignaGen, Ijamsville, MD, USA) or Lipofectamine 3000 (Life Technologies, Grand Island, NY), following the manufacturer’s protocols. The siRNA and plasmids used for cell transfection were provided and constructed by the laboratory, the target sequences of human WDHD1 as follow: 5′-GCATCCTACTTGTGGTCGAAT-3′ and 5′-CCCATTATAAAGCCTCTGATT-3′.

Lentiviral packaging and infection

293T cells (70% confluency) co-transfected with pLKO.1-shWDHD1 (shWDHD1#1: 5′-GCAATTTGTGGTCTGGCATGG-3′; shWDHD1#2: 5′-CGTTAAACACCAGACCGTACC-3′), psPAX2, and pMD2.G (ratio 4:3:1) using PolyJet. Lentiviral titers were determined at 48- and 72-hours post-transfection using filtered viral supernatants. Hepa 1-6 cells were infected with a viral concentrate (5 μL) for 48 h, and the stability of transfection was evaluated using puromycin. Cells were infected in the presence of polybrene (8 μg/mL).

Animal studies

Stable WDHD1-deficient Hepa 1-6 cells were subcutaneously injected into 6–8-week-old immunocompetent mice (C57BL/6, Vital River Experimental Animal Center, Beijing, China) to generate tumor xenografts. Tumors were measured with a caliper, and tumor volume was calculated as: V = L × W2 × 0.5 (L, long axis; W, short axis). The mice were then euthanized, and tumors were harvested and weighed. All mice kept under specific pathogen-free conditions. The animal experimental procedures were approved by the Laboratory Animal Ethics Committee of the First Affiliated Hospital of Wenzhou Medical University (WYYY-AEC-2025–085), in compliance with the principles and procedures of the National Institutes of Health Guide for the Care and Use of Laboratory Animals.

Real-time quantitative polymerase chain reaction (RT-qPCR)

Total RNA was extracted using the TRIzol reagent (Invitrogen, Carlsbad, CA) and subsequently reverse-transcribed using the Fast Quant RT Kit (with gDNase) (TOYOBO, Japan). qPCR was performed on an ABI 7500 Fast instrument (ThermoFisher Scientific, Waltham, MA) using SuperReal PreMix Plus (SYBR Green) (Tiangen Biotech, Beijing, China). The primer sequences are listed in Supplementary Table 1.

Western blotting

Cells and tissues were lysed using radioimmunoprecipitation assay buffer (Beyotime, Shanghai, China). Total cellular proteins were harvested with 5 × SDS sample buffer and denatured at 100°C for 10 min. Proteins were separated by 10% SDS-PAGE and transferred onto polyvinylidene fluoride (PVDF) membranes. After blocking with 5% skim milk, membranes were incubated with primary antibodies: MCM2 Rabbit mAb (A20699), MCM3 Rabbit mAb (A11475), MCM4 Rabbit mAb (A9251), MCM5 Rabbit mAb (A5008), MCM6 Rabbit pAb (A1955), MCM7 Rabbit mAb (A11325), Cyclin E1 Rabbit mAb (A22360), Cyclin B1 Rabbit mAb (A22435), CDC45 Rabbit pAb (A2047), WDHD1 Rabbit pAb (A15396), CDK2 Rabbit pAb (A0094), Phospho-CDK2-T160 Rabbit pAb (AP0325) were obtained from Abclonal, (WuhanChina). And FLAG-Tag (14793S), β-Actin (4967) were obtained from Cell Signaling Technology (Beverly, MA, USA). Subsequently, the membranes were incubated with rabbit secondary antibodies for a duration of 1.5 h. A highly sensitive substrate (Millipore, Billerica, MA, USA) was employed to detect specific bands on autoradiographic film.

Cell proliferation, colony formation, migration, invasion and apoptosis assays

For cell proliferation assays, 2 × 10³ cells were seeded in the wells of 96-well plates. Cell viability was subsequently assessed using the CCK-8 assay (Vazyme, Jiangsu, China), absorbance at 450 nm was measured with a SynergyMx M5 microplate reader, with a reference wavelength of 630 nm. For colony formation assay, cells were inoculated into a twelve-well plate with 400 cells/well, after 12 days, the cells were fixed with absolute methanol for 20 min, stained with 0.5% crystal violet at room temperature for 20 min and the sample was rinsed and images captured for counting. For wound-healing assays, cells were seeded into six-well plates at a density of 1 × 10⁵ cells per well, after scratching, the medium was replaced with DMEM high glucose containing 100x penicillin-streptomycin solution. For the transwell assay, cells were seeded into transwell inserts of 8 µm-pore plain (migration) or Matrigel-coated (invasion; Costar; Corning, Inc, USA). The upper chamber was filled with DMEM medium containing 0.1% bovine serum albumin, while the lower chamber contained DMEM medium supplemented with 10% fetal bovine serum, after 12 h of incubation, The cells adherent to the bottom surface were fixing it in 4% paraformaldehyde, staining with 1% crystal violet (Sigma), and counting the migrated cells by selecting five fields at random under a light microscope. Cell apoptosis was detected using the Annexin V-FITC/PI Apoptosis Detection Kit (Beyotime) according to manufacturer's instructions.

Flow cytometry analysis

Cells were frozen in 70% ethanol and then incubated at 4°C for 12 h. Following washing, the cells were incubated in propidium iodide staining solution at 37°C in the dark for 30 min. Subsequently, the cellular distribution was detected and analyzed using the NovoCyte flow cytometer (ACEA).

IP and immunoblotting analysis

Proteins were extracted from cultured cells using a modified buffer, followed by immunoblotting with corresponding antibodies using immunoblotting techniques. Briefly, cells were collected and washed three times with cold PBS. The cells were then resuspended and lysed in a lysis buffer (25 mM Tris-HCl (pH 7.4), 150 mM NaCl, 1% NP-40, 5 mM EDTA, 10% glycerol, 1 mM NaVO3, 50 mM NaF, and protease inhibitor mixture) at 4°C for 30 min. The lysate was centrifuged at 15,000 g, and the supernatant was transferred to a pre-chilled microcentrifuge tube. Protein concentration was determined using a BCA protein assay kit (Pierce) according to the manufacturer's instructions. Approximately 10% of the supernatant was collected as input for western blot analysis. Protein was incubated overnight with the specified antibody, followed by mixing with protein A or protein G agarose beads for 2 h. The beads were washed five times with lysis buffer before immunoblotting analysis. The blots were blocked with 3% bovine serum albumin and then incubated with primary and enzyme-labeled secondary antibodies.

Therapeutic drug sensitivity, immune infiltration and immune checkpoint response analysis

We used R package “pRRophetic” to compute the semi-inhibitory concentration (IC50) values of chemotherapeutic drugs. The expression of immune cells was evaluated by CIBERSORT algorithm. Additionally, we calculated the tumor mutation burden (TMB) score based on somatic mutation data and compared the differences in TMB between the low and high WDHD1 expression groups. Pearson correlation coefficients were used to assess the correlation between WDHD1 and several common immune checkpoint markers.

Statistical analyses

Data are presented as mean ± standard deviation (SD). All experiments were independently repeated at least three times. Categorical variables are expressed as absolute and relative frequencies. For normality testing, the Kolmogorov-Smirnov (K-S) test method is used for large samples, and the Shapiro-Wilk (S-W) test is used for small samples. Differences between continuous variables were analyzed using Student’s t-test, while categorical variables were compared using the Mann–Whitney U test. The relationship between two variables was evaluated by Spearman’s correlation analysis. Median survival time and cumulative survival curves were determined by the Kaplan–Meier method and differences between/among the groups were analyzed using the log-rank test. All tests were two-sided, and a P-value of < 0.05 was considered statistically significant. The sample size was not predetermined, and no data points were excluded.

Results

Elevated WDHD1 expression correlates with poor prognosis in HCC

Bioinformatic analysis based on the public mRNA expression data of TCGA-LIHC and LIRI-JP revealed a significant upregulation of WDHD1 expression in HCC samples compared to normal tissue (Fig. 1A, B). To validate this finding, we performed RT-qPCR and western blot analyses to measure WDHD1 expression in normal and HCC cancer cell line and tissues. Consistently, WDHD1 expression was elevated in HCC cells and tissues (Fig. 1C–F). Furthermore, immunohistochemical data from The Human Protein Atlas confirmed higher WDHD1 protein levels in HCC tissues relative to normal liver tissues (Fig. 1G). We then investigated the prognostic relevance of WDHD1 using data from TCGA-LIHC, LIRI-JP, and the Kaplan–Meier Plotter database. Elevated WDHD1 expression was significantly associated with poor patient prognosis (Fig. 1H–J). Collectively, these findings indicate that WDHD1 is upregulated in HCC and is strongly associated with unfavorable clinical outcomes.

Fig. 1.

High expression of WDHD1 in HCC is associated with poor prognosis. (A, B) Boxplots showed WDHD1 expression of HCC patients and normal patients in TCGA-LIHC (A) and LIRI-JP (B). (C-F) mRNA and protein expression of WDHD1 were detected by qRT-PCR (C, E) and western blot (D, F) in HCC cells and tissues. (G) The protein levels of WDHD1 in HCC tissue compared with normal liver tissue in The Human Protein Atlas database. Normal liver tissue (patient ID: 1720), HCC sample (patient ID: 929). Blue, neoplastic cells; red, lymphocytic infiltration. (H-J) Overall survival of WDHD1 in TCGA-LIHC (H), LIRI-JP (I), and Kaplan-Meier Plotter (J). *P < 0.05, **P < 0.01.

MicroRNAs and transcription factors potentially regulate the expression of WDHD1 in HCC

To investigate the mechanism of WDHD1 dysregulation in HCC, we first analyzed its mutation patterns and DNA methylation levels of WDHD1 in TCGA-LIHC. Among the 366 HCC samples, genetic alterations in WDHD1 were observed in only 5 samples (1.4%) (Supplementary Fig. S1A), and no correlation was found between CNAs, DNA methylation and WDHD1 expression (Supplementary Fig. S1B). Regression analysis also indicated no significant correlation between WDHD1 expression and its methylation status (Supplementary Fig. S1C). Additionally, the methylation level of the WDHD1 promoter in HCC tissue was similar to that in normal tissue (Supplementary Fig. S1D). These findings suggest that mutations and DNA methylation do not play a major role in regulating WDHD1 expression.

We next hypothesized that WDHD1 might be regulated by miRNAs and transcription factors in HCC. Using LinkedOmics, we identified miRNAs correlated with WDHD1 expression, including 456 positively and 42 negatively correlated miRNAs (Fig. 2A). Given that miRNAs typically repress their target genes, we focused on the 42 miRNAs negatively correlated with WDHD1 expression (Fig. 2B). Enrichment analysis of the target genes of these 42 miRNAs using the FunRich tool revealed several enriched pathways, including Beta1 integrin cell surface interactions, integrin family interactions, and the TRAIL signaling pathway (Supplementary Fig. S2A, Supplementary Table 2). The associated biological processes included the regulation of nucleobase, nucleoside, nucleotide, and nucleic acid metabolism, as well as signal transduction (Supplementary Fig. S2B, Supplementary Table 2). Enriched molecular functions included transcription factor activity, GTPase activity, and protein serine/threonine kinase activity (Supplementary Fig. S2C, Supplementary Table 2). These target genes were primarily localized in the nucleus, cytoplasm, and lysosome (Supplementary Fig. S2D, Supplementary Table 2), aligning with the known functions of WDHD1. We further assessed the expression and prognostic significance of two representative miRNAs, hsa-miR-22 and hsa-miR-139, in HCC. Low expression levels of these miRNAs were associated with poorer patient prognosis (Fig. 2C). Moreover, transfection of miR-22 and miR-139 mimics into HCC cells suppressed WDHD1 protein expression (Fig. 2D, E), suggesting that these miRNAs may influence HCC prognosis by regulating WDHD1.

Fig. 2.

miRNAs and transcription factors that regulate the expression of WDHD1 in HCC. (A)Pearson test was useed to analyze correlations between WDHD1 and miRNAs differentially expressed in HCC. (B) Heatmap shows miRNAs negatively correlated with WDHD1 in HCC. (C) Overall survival of hsa-miR-22 (top) and hsa-miR-139 (bottom) in HCC patients. (D, E) WDHD1 expression were determined by RT-PCR (left) and western blot (right) after transfection with miR-22 and miR-139 mimic in Hep-3B and Huh-7 cells. (F) Venn diagram for identifying potential transcription factors regulating WDHD1 using Cristrome DB, hTFtarget, and ChIPBase v3.0. (G) Heatmap showing the correlation between the expression of transcription factors and WDHD1. (H) Scatterplot of the correlation between WDHD1 and representative transcription factors EP300 (top), and CREBBP (bottom). (I, J) WDHD1 expression were determined by RT-PCR (left) and western blot (right) after knockdown EP300 and CREBBP in Hep-3B and Huh-7 cells. **P < 0.01.

In addition, we investigated potential transcription factors regulating WDHD1 using Cistrome DB, hTFtarget, and ChIPBase v3.0. Eleven transcription factors were identified across all three databases (Fig. 2F). Correlation analysis in TCGA-LIHC revealed significant associations between WDHD1 expression and the transcription factors EP300, CREBBP, EHF, FOXA1, ATF1, BRD4, and BRD2 (Fig. 2G, H). Notably, knockdown of EP300 and CREBBP in HCC cells led to reduced WDHD1 expression (Fig. 2I, J), indicating that these transcription factors may contribute to the regulation of WDHD1 in HCC.

WDHD1 affected the proliferation, migration, invasion and apoptosis of HCC cells

To elucidate the role of WDHD1 in HCC, we investigated its impact on malignant cell phenotypes including proliferation, migration, invasion and apoptosis. Specifically, CCK-8 assay and colony formation revealed that WDHD1 markedly suppressed cell proliferation (Fig. 3A–D). Wound healing and transwell assays demonstrated that WDHD1 knockdown impaired cell migration and invasion, respectively (Fig. 3E–G). Annexin V/PI staining showed that WDHD1 depletion enhanced HCC cell apoptosis (Fig. 3H). Consistently, inhibition of WDHD1 also suppressed xenograft tumor growth in vivo (Fig. 3I–K). In contrast, WDHD1 overexpression enhanced cell proliferation, migration, and invasion in HCC cells (Supplementary Fig. S3A–G). Collectively, these findings indicate that WDHD1 promotes HCC progression both in vitro and in vivo.

Fig. 3.

Knockdown WDHD1 suppressed cell proliferation, migration, invasion and apoptosis, and decreased tumor growth. (A, B) WDHD1 expression were determined by western blot (A) and RT-PCR (B) after transfection with siRNAs against WDHD1 in Hep-3B and Huh-7 cells. (C) Representative images of colony formation assay (left) and the number of colonies (right). (D) The cell proliferation assay was performed at the indicated time points. (E, F) Representative micrographs and quantitative analysis of cell migration by the transwell (E) and wound healing (F) assays. (G) Representative micrographs of cell invasion assays (left) and quantification results (right). Data are expressed as the mean ± SD of the values from three independent experiments. (H) The apoptosis was assayed using Annexin V FITC/PI staining. (I-K) Representative images, and weights of subcutaneous xenografts of Hep1-6 cells with WDHD1 knockdown or control. Data represent means ± SD for 5 mice per group. *P < 0.05, **P < 0.01.

Proteins interacting with WDHD1 and their functional enrichment analysis

To investigate the mechanism of WDHD1 in HCC, we analyzed its interacting proteins and associated functional pathways. Proteomic analysis identified 30 WDHD1-interacting proteins, which were visualized as a physical interaction network using the STRING database. Notably, WDHD1 interacts with members of the MCM family (MCM2, MCM3, MCM4, MCM5, MCM6, MCM7, MCM9, MCM10) and the CDC family (CDC6, CDC7, CDC45) (Fig. 4A). To explore the functional implications of these interactions, we conducted GO and KEGG pathway enrichment analyses on the 30 proteins identified to interact with WDHD1. These proteins were enriched in biological processes such as DNA replication, DNA metabolic processes, and DNA replication initiation (Fig. 4B). Significant enrichment was also observed in molecular functions, including single-stranded DNA binding and DNA replication origin binding (Fig. 4C). Subcellular localization analysis indicated that these proteins are primarily localized to nuclear chromosomes, the CDC45-MCM-GINS (CMG) complex, and the nucleoplasm (Fig. 4D). The enriched pathways included cell cycle, DNA replication, homologous recombination, and nucleotide excision repair (Fig. 4E). Overall, our analysis of WDHD1-interacting proteins sheds light on its functional network, highlighting the complex regulatory mechanisms through which WDHD1 may promote HCC progression.

Fig. 4.

Proteins interacting with WDHD1 and their functional enrichment analysis. (A) Protein-protein interaction network of WDHD1 via STRING. (B–E) Functional enrichment analysis of the proteins interacted with WDHD1. Biological processes (B), molecular functions (C), cellular components (D), and KEGG Pathway (E).

WDHD1 promotes HCC progression by regulating the G1/S transition of the cell cycle

Given the strong correlation between WDHD1-interacting proteins and the cell cycle, we further investigated the impact of WDHD1 on the cell cycle of HCC cells. WDHD1 Knockdown reduced Cyclin E1 and p-CDK2 expression, the crucial regulator of the G1/S checkpoint, in Huh-7, Hep-3B, while Cyclin B1, a regulator of the M phase, remained unchanged (Fig. 5A). Flow cytometry analysis revealed an elevated proportion of cells in the G1 phase and decreased proportions of cells in the S and G2 phases in siRNA-treated Huh-7 and Hep-3B cells (Fig. 5B). In contrast, WDHD1 overexpression resulted in the opposite trend (Supplementary Fig. S4A, B). These findings suggest that WDHD1 promotes HCC progression by modulating the G1/S transition of the cell cycle.

Fig. 5.

WDHD1 regulates the G1/S transition of the cell cycle by interacting with the CMG complex.Hep-3B and Huh-7 cells transfected with siRNAs against WDHD1 for 48 h. (A) Western blot were performed with the indicated antibodies. (B) Flow cytometry analysis of the cell cycle distribution in the G1, S, and G2 phases (left) and quantification results of the cell cycle (right) (C) Western blot analyses of anti-Flag immunoprecipitates in Hep-3B and Huh-7 cells were transfected with overexpressing Flag-WDHD1 plasmid for 48 h. (D, E) Western blot analyses of anti-CDC45 immunoprecipitates in Hep-3B and Huh-7 cells were transfected with siRNAs against WDHD1 (D) or overexpressing Flag-WDHD1 plasmid (E) for 48 h. (F) Chemotherapeutic drugs with significant IC50 differences between low and high WDHD1 expression groups in TCGA-LIHC. (G) Cell viability was assessed in WDHD1 depletion Huh-7 cells treated as indicated. Data are expressed as the mean ± SD of the values from three independent experiments. *P < 0.05, **P < 0.01.

WDHD1 influences the cell cycle by interacting with the CMG complex

During the G1/S phase of the cell cycle, CDC45 and GINS interact with the MCM2–7 complex to form the CMG complex replicative helicase complex, which plays a crucial role in DNA replication [17]. Immunoprecipitation assays revealed that WDHD1 interacts with CDC45, MCM2, MCM5, and MCM7, key components of the CMG complex, in Huh7 and Hep3B cells (Fig. 5C). Furthermore, WDHD1 knockdown disrupted the binding between CDC45 and MCM2, MCM5, and MCM7 (Fig. 5D), whereas WDHD1 overexpression enhanced these interactions (Fig. 5E). Cycloheximide chase assays showed that WDHD1 depletion did not alter the half-lives of individual CMG components (Supplementary Fig. S4C), indicating that WDHD1 promotes CMG complex integrity rather than individual protein stability. To further map the interaction domain, we performed co-immunoprecipitation assays using a series of truncated WDHD1 constructs. Our results indicate that neither the WD40 domain nor the HMG-box alone is essential for binding to the CMG complex, suggesting that the interaction likely involves a multi-domain interface or depends on the overall conformation of WDHD1 (Supplementary Fig. S4D). These findings suggest that WDHD1 functions as a factor promoting the integrity of the CMG complex, thereby influencing the cell cycle. Notably, patients with high WDHD1 expression exhibited increased sensitivity to anti-cancer drugs targeting the cell cycle (Fig. 5F). We next evaluated the therapeutic implications of targeting WDHD1 in combination with cell cycle inhibitors. In vitro treatment with the CDK1 inhibitor RO-3306 synergized with WDHD1 knockdown, leading to enhanced suppression of HCC cell proliferation (Fig. 5G).

WDHD1 affects the immune evasion of HCC

To investigate the impact of WDHD1 on the immune microenvironment of HCC, we compared tumor-infiltrating immune cell profiles,TMB and immune checkpoint molecule expression between high and low WDHD1 expression groups. Patients with high WDHD1 expression consistently exhibited significantly increased levels of activated CD4 memory T cells (1.31% vs. 0.23% in TCGA-LIHC, 1.14% vs. 0.35% in LIRI-JP) and M0 macrophages (15.43% vs. 11.08% in TCGA-LIHC, 3.39% vs. 1.71% in LIRI-JP), along with a marked reduction in resting mast cell infiltration (4.54% vs. 7.12% in TCGA-LIHC, 2.18% vs. 5.03% in LIRI-JP) (Fig. 6A and Supplementary Fig. S5A). Moreover, patients with high WDHD1 expression showed elevated TMB in the TCGA-LIHC cohort (Fig. 6B, C). Further analysis revealed that WDHD1 mRNA levels were significantly positively correlated with the expression of several key immune checkpoint markers in both datasets, suggesting a potential role for WDHD1 in promoting immune evasion in HCC (Fig. 6D and Supplementary Fig. S5B). Functional validation in HCC cell lines further confirmed that WDHD1 expression positively regulates the transcription of immune checkpoint molecules, including PD‑L1 and CTLA‑4 (Supplementary Fig. S6A). To explore a direct mechanism by which WDHD1 may influence immune cell recruitment, we examined chemokine expression. Knockdown of WDHD1 markedly reduced the secretion of T‑cell chemoattractants such as CCL5 and CXCL9, whereas its overexpression enhanced their production (Supplementary Fig. S6B). Additionally, immunofluorescence (IF) staining of tumor tissues indicated that WDHD1 depletion reduced the intratumoral accumulation of CD4⁺ T cells. Collectively, these findings suggest that WDHD1 contributes to the regulation of immune evasion in HCC.

Fig. 6.

WDHD1 affects the immune evasion of HCC in TCGA-LIHC. (A) Discrepancy analysis of tumor-infiltrating immune cells between low and high WDHD1 expression groups in TCGA-LIHC. (B, C) OncoPrint of frequently mutated genes in low and high WDHD1 expression groups. (D) Correlation analysis between WDHD1 expression and key immune checkpoint markers in TCGA-LIHC. *P < 0.05, **P < 0.01.

Discussion

WDHD1, an evolutionarily conserved protein, has homologs ranging from fungi to vertebrates, with its budding yeast counterpart designated as Chromosome Transmission of Fidelity 4 (Ctf4) [18]. Previous studies have shown that WDHD1 suppression leads to reduced replication in cells expressing human papillomavirus E7 [19] and promotes cancer development in cholangiocarcinoma, lung cancer, and esophageal cancer [20,21]. However, its role in HCC remains unexplored. In this study, we revealed that WDHD1 is significantly upregulated in both human HCC cells and clinical tumor tissue, and elevated WDHD1 expression levels are closely correlated with poor overall survival in HCC patients. These suggest that WDHD1 holds potential as a novel prognostic biomarker and therapeutic target for HCC.

Previous research has implicated miRNAs as crucial regulators of HCC growth [22]. To explore the upstream regulatory mechanisms of WDHD1 in HCC, we analyzed miRNAs potentially associated with WDHD1 and found that hsa-miR-22 and hsa-miR-139 were significantly downregulated as WDHD1 expression increased. Consistent with the prognostic trend of WDHD1, low expression of hsa-miR-22 and hsa-miR-139 was also significantly associated with poor prognosis in HCC patients. These findings align with prior studies highlighting the tumor-suppressive roles of these miRNAs. miR-22 has been reported to suppress IL-17 signaling and modulate tumor growth by enhancing cytotoxic T cells, reducing regulatory T cells, and regulating the cell cycle [23]. miR-139, meanwhile, can suppress HCC progression by targeting RH2 and SPOCK1 [24,25]. Additionally, our bioinformatic predictions indicated that transcription factors such as EP300 and CREBBP — known to promote HCC progression [26,27] — may regulate WDHD1 expression, providing a direction for further exploration of WDHD1’s transcriptional regulatory network..

To validate the functional role of WDHD1 in HCC, we performed in vivo and in vitro experiments, which confirmed that WDHD1 promotes HCC cell proliferation, migration, and invasion in vitro, and accelerates tumor growth in nude mouse xenograft models. To further elucidate the underlying molecular mechanism, we identified proteins interacting with WDHD1 and found strong associations with MCM and CDC family proteins. The MCM family proteins (MCM2–7), as members of the ATPase AAA+ family, form a heterohexameric ring that binds to single-stranded DNA [28]. Together with CDC family members and GINS, they assemble into the CMG, which is essential for parental DNA unwinding in eukaryotic cells [29], and plays crucial roles in the cell cycle. Our pathway enrichment analysis further confirmed that WDHD1-interacting proteins are primarily enriched in cell cycle pathways. Mechanistically, we demonstrated that WDHD1 could interact with the CMG complex in HCC cells, and promote its integrity. These findings clarify that WDHD1 promotes HCC progression by interacting with the CMG complex to maintain its stability and thereby influences the cell cycle. Notably, drug sensitivity analysis revealed that patients with high WDHD1 expression were more responsive to cell cycle-targeting therapeutic agents, and in vitro experiments demonstrated synergy between WDHD1 knockdown and the CDK1 inhibitor RO-3306, which directly supports our mechanistic findings and provides a rationale for personalized chemotherapy targeting the cell cycle in WDHD1-high HCC.

Immune evasion is a hallmark of HCC progression and a key determinant of immune checkpoint inhibitor (ICI) efficacy [30]. ICI therapy has become a first-line treatment for advanced HCC, and two factors closely associated with ICI response are: (1) tumor-infiltrating lymphocytes (TILs), where higher TIL abundance typically correlates with better ICI outcomes; and (2) TMB, where high TMB is associated with more neoantigens and enhanced ICI efficacy [31,32]. In this study, we observed that HCC patients with high WDHD1 expression exhibited significantly increased CD4+ memory T lymphocyte infiltration and elevated TMB, suggesting that these patients may derive greater benefit from ICI therapy. Additionally, WDHD1 expression was significantly positively correlated with the expression of key immune checkpoints markers (e.g., CD274, CTLA-4) commonly involved in HCC, and we functionally validated that WDHD1 regulates the transcription of these markers. Furthermore, we identified that WDHD1 promotes the secretion of the T-cell chemoattractant, providing a direct mechanism for its influence on immune cell recruitment. These findings further indicating its pivotal role in immune evasion and its potential as a therapeutic target for HCC.

Our findings also point to a potential interplay between the core cell-cycle function of WDHD1 and immune modulation. While this study establishes that WDHD1 regulates CMG complex integrity and promotes CCL5 expression, we hypothesize that its role in DNA replication may indirectly shape the immune microenvironment. For example, by ensuring efficient CMG complex function, WDHD1 could alleviate replication stress and associated genomic instability, processes known to generate immunogenic neoantigens and activate innate immune sensors such as the cGAS-STING pathway. This connection offers a plausible mechanistic bridge linking DNA replication fidelity to immune recognition, a valuable direction for future research.

Despite the valuable findings of this study, several limitations remain. First, the prognostic signature was validated using retrospective data from public databases. Large-scale prospective clinical studies are needed to further assess its effectiveness and clinical utility. Second, while we demonstrated that WDHD1 promotes CMG complex integrity, the precise domain of WDHD1 that mediates its interaction with CMG complexes and its broader impact on cell cycle regulation remain unclear and require further in vitro and in vivo experiments. Third, the mechanisms by which WDHD1 influences tumor immunity, including its potential crosstalk with established HCC pathways such as PI3K/AKT and Wnt/β-catenin, remain unexplored. Fourth, while our data suggest a role in immune evasion, functional validation through in vivo blocking experiments (e.g., combining WDHD1 inhibition with anti-PD-1 therapy) is warranted. Future studies should aim to: 1) delineate the structural determinants of WDHD1-CMG interaction; 2) investigate the interplay between WDHD1-mediated DNA replication fidelity and immune modulation, such as through replication stress and genomic instability; and 3) evaluate the therapeutic potential of targeting WDHD1 in combination with immunotherapy in immunocompetent models.

In conclusion, our findings indicate that WDHD1 can serve as a novel oncogenic driver and therapeutic target for HCC, with translational potential to improve clinical chemotherapy and immunotherapy outcomes by regulating cell cycle and immune evasion.

Ethics approval and consent to participate

This study involved human participants and was approved by the Research Ethics Committee of the First Affiliated Hospital of Wenzhou Medical University (KY2023–198). The animal experimental procedures were approved by the Research Ethics Committee and complied with the ethical regulations of Wenzhou Medical University (WYYY-AEC-2025–085), in compliance with the principles and procedures of the National Institutes of Health Guide for the Care and Use of Laboratory Animals.

Data availability

Data will be made available on request. The data supporting this study's findings are available from the corresponding author upon reasonable request.

CRediT authorship contribution statement

Zheng Xiang: Writing – original draft, Formal analysis, Data curation. Xianfeng Huang: Writing – original draft, Investigation, Data curation. Shimao Zhu: Writing – original draft, Investigation, Data curation. Xian Zhang: Formal analysis, Data curation. Jiejie Guo: Formal analysis, Data curation. Yujie Chen: Formal analysis, Data curation. Zhiming Huang: Writing – original draft, Supervision, Project administration, Formal analysis, Data curation, Conceptualization. Shanshan Hu: Writing – original draft, Supervision, Project administration, Funding acquisition, Formal analysis, Data curation, Conceptualization.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.