The GSE41804 dataset (20 HCC, and 20 adjacent normal liver tissues), obtained from the Gene Expression Omnibus (GEO; https://www.ncbi.nlm.nih.gov/gds/) database. Additionally, we analyzed data from the Clinical Bioinformatics Assistant platform (https://www.aclbi.com/static/index.html#), which includes the TCGA-LIHC dataset containing 371 HCC and 50 normal samples. After normalization of the expression data, we performed comparative analysis using the Limma R package (version 3.56.2). Genes with |log₂FC| > 1 were considered differentially expressed, where log₂FC > 1 indicated upregulation and log₂FC < −1 indicated downregulation, with statistical significance defined as a false discovery rate (FDR) < 0.05.

We analyzed the differentially expressed genes from GSE41804 through WGCNA implementation in R (version 4.3.2). Prior to analysis, missing values were removed, and low-expression genes were filtered out to ensure data quality. Network construction employed a soft-threshold power of β = 14 to ensure scale-free topology. The choice of β was determined using the pickSoftThreshold function in the WGCNA R package (version 1.73), which evaluates a range of candidate powers based on the scale-free topology fit index (R²) and mean connectivity. β = 14 was selected as it was the lowest power at which the scale-free topology fit index first exceeded 0.85 while maintaining sufficient mean connectivity, ensuring that the constructed network approximates a scale-free topology, a key assumption of WGCNA. Gene connections were quantified using a topological overlap matrix (TOM), followed by hierarchical clustering to identify distinct modules. Each module, represented as a dendrogram branch with unique coloring, contained genes grouped based on their expression patterns and weighted correlation values. The biological significance of these modules was evaluated through correlation with clinical parameters.

We utilized the online Venn diagram analysis tool (https://bioinformatics.psb.ugent.be/webtools/Venn/) to identify overlapping genes among three groups: upregulated DEGs, downregulated DEGs, and genes in the turquoise module. Following that, batch survival analysis was done on 306 elevated overlapping genes using R software to investigate their influence on the overall survival (OS) prognosis of HCC patients. The prognostic significance was evaluated through univariate Cox regression, yielding hazard ratios (HR) and corresponding p-values with 95% confidence intervals (CIs). Among these, 106 genes showed significant prognostic differences. Finally, a nomogram was created to visually represent the top 20 genes having prognostic importance, which were selected based on having the most significant p-values in the survival analysis.

106 genes with prognostic potential were analyzed by Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression using the glmnet R package (version 4.1–2). Model parameters were optimized by ten-fold cross validation to determine the minimum criterion λ (λ.min = 0.0606) for selecting predictive signatures. The risk score for each patient was calculated using the following formula: Riskscore = (−0.0151)*CPEB3 + (−0.0186)*CYP2C9 + (−0.185)*NTF3 + (−0.0224)*SPP2 + (−0.0123)*LCAT + (−0.0002)*GGT5 + (−0.0719)*DNASE1L3 + (−0.0429)*CFHR3. The TCGA-LIHC cohort was separated into high- and low-risk groups based on expression patterns. Survival differences between groups were evaluated using Kaplan-Meier curves and log-rank tests in R (version 4.3.2), and median survival times and HR were calculated for risk assessment. Prediction accuracy was quantified using time-dependent receiver operating characteristic (timeROC) R package (version 0.4) to generate receiver operating characteristic (ROC) curves and calculate AUC values for 1-, 3-, and 5-year survival predictions, with higher values indicating stronger prognostic ability.

Following the identification of eight significant genes from the prognostic risk model, we analyzed their expression levels in the GSE41804 and TCGA-LIHC datasets. The Sangerbox website (http://vip.sangerbox.com/home.html) was used for data processing and box plot visualization to analyze their expression in the tumor and normal groups. A non-parametric test (rank-sum test) was applied for two-group comparisons, and p-values < 0.05 were considered statistically significant.

Patients at Shanghai Eastern Hepatobiliary Surgery Hospital, The Third Affiliated Hospital of Naval Medical University, provided a set of five liver cancer specimens and matching adjacent non-tumor liver tissues for this investigation after surgery, and informed agreement was obtained. Each HCC case was histopathologically confirmed by a qualified histopathologist to ensure diagnostic accuracy and consistency between samples. This study was approved by the Ethics Committee of the Third Affiliated Hospital of Second Military Medical University (Approval No.: BHBY2022-K020-B001). The study was conducted in accordance with the Declaration of Helsinki. Written informed consent was obtained from all participants, and institutional consent was obtained from Shanghai Eastern Hepatobiliary Surgery Hospital, The Third Affiliated Hospital of Naval Medical University.

HepaRG cells (YS2323C) were purchased from Shanghai Yaji Biotechnology (Shanghai, China), while liver cancer cell lines (HCCLM3, TCHu270; HepG2, SCSP-510; Huh7, SCSP-526; Hep3B, SCSP-5045) and THP-1 (SCSP-567) monocytes were obtained from the Cell Bank of the Chinese Academy of Sciences (Shanghai, China). All cell lines were authenticated using short tandem repeat (STR) profiling and confirmed to be free of mycoplasma contamination. HepaRG and HCC lines were maintained in DMEM (11965092, Gibco, Thermo Fisher Scientific, Waltham, MA, USA), while THP-1 cells were grown in RPMI 1640 medium (11875085, Gibco). Media were supplemented with 10% fetal bovine serum (FBS, A5669701, Gibco) and 1% penicillin-streptomycin (P1400, Solarbio, Beijing, China). Cells were incubated at 37°C with 5% CO₂.

To establish an in vitro co-culture system, THP-1 monocytes (1 × 10⁶ cells per well) were differentiated into macrophages using 10–100 nm Phorbol 12-myristate 13-acetate (PMA, 16561-29-8, Sigma, St. Louis, MO, USA) for 24–48 h, which promotes cell adherence and macrophage phenotype formation by activating the protein kinase C (PKC) signaling pathway. The resulting cells were polarized into M1 (baseline, pro-inflammatory) and M2 (anti-inflammatory) macrophages following established protocols [18]. Following differentiation and polarization, the THP-1 macrophages were cocultured with HepG2 and Huh7 cell lines. For co-culture experiments, a 24-mm Transwell system (3450, 0.4 μm pore polycarbonate membrane, Corning, Corning, NY, USA) was used. HCC cells (5 × 10⁵ cells per well) were seeded in the lower chamber, while differentiated macrophages (1 × 10⁵ cells per well) were placed in the upper chamber. Both cell types were pre-cultured separately for 24 h in complete medium. The assembled system was maintained at 37°C with 5% CO₂ for 12 h, after which cells were collected for subsequent analyses, including quantitative real-time polymerase chain reaction (qRT-PCR) and Western blotting (WB) to assess gene and protein expression related to macrophage–HCC interactions. All experiments were performed in at least three independent biological replicates, with technical replicates included as appropriate.

For transfection studies, HCC cells (HepG2 and Huh7) were seeded at 2 × 10⁵ cells per well in 24-well plates. The overexpression plasmid used to modulate DNASE1L3 expression was constructed using the pcDNA3.1 vector (pcDNA3.1, V79020, Invitrogen, Carlsbad, CA, USA), while DNASE1L3 knockdown was achieved using specific siRNAs (si-DNASE1L3-1 and si-DNASE1L3-2). The empty pcDNA3.1 vector served as the negative control for overexpression, and a non-targeting siRNA (si-NC) was used as the negative control for knockdown. Plasmid DNA (500 ng per well) or siRNAs were diluted in Opti-MEM and mixed with Lipofectamine 3000 (L3000150, Invitrogen) according to the manufacturer’s recommended ratios. The mixture was incubated at room temperature for 15–30 min to allow nucleic acid–lipid complex formation, followed by incubation with cells for 4–6 h at 37°C and 5% CO₂. The medium was then replaced with complete growth medium, and cells were cultured for an additional 24 h to allow sufficient overexpression or knockdown. The final siRNA concentration was 80 nm. The siRNA sequences were as follows: si-DNASE1L3-1 (sense: GCUUGGAAGAAACACAUAUAA; antisense: UUAUAUGUGUUUCUUCCAAGC), si-DNASE1L3-2 (sense: GACAUCAUACUCGUGAUGGAA; antisense: UUCCAUCACGAGUAUGAUGUC), and si-NC (sense: UUUUCCGAACGUGUCACGUTT; antisense: ACGUGACACGUUCGGAAAATT).

To examine proteasome activity and Wnt signaling in HCC, HepG2 and Huh7 cells were treated with MG132 (S2619, 20 μg/mL, Selleck Chemicals, Houston, TX, USA) or XAV939 (S1180, 10 μm, Selleck Chemicals) for 8 and 10 h, respectively. In addition, ICG-001 (S2662, 10 μm, Selleck Chemicals) was used to target the Wnt/β-catenin pathway for 24 h. All inhibitors were dissolved in dimethyl sulfoxide (DMSO), and the final solvent concentration was kept consistent across experimental groups. The treatment was applied to DNASE1L3 knockdown HepG2 cells and co-cultured with PMA-differentiated THP-1 macrophages at a 5:1 ratio (HCC cells: macrophages) for 24 h. All experiments were performed with at least three independent biological replicates and appropriate technical replicates.

PDOs were cultured from HCC tissue samples. The tissue was minced and enzymatically digested using Liberase TM (0.25 mg/mL, 5401119001, Sigma) and DNase I (0.1 mg/mL, AMPD1, Sigma) at 37°C until a homogeneous cell suspension was obtained. The suspension was then filtered and mixed with Advanced DMEM/F12 containing 10% FBS. Following a brief centrifugation (300× g, 5 min, 4°C), the cell pellet was resuspended in cold PBS after the supernatant was extracted. Cell clusters were suspended in a solubilized basement membrane and placed in 24-well plates. The cultivation medium consisted of Advanced DMEM/F12 added to with B-27 (2%, 17504044, Gibco), N-2 (1%, 17502048, Gibco), GlutaMAX (2 mm, 35050061, Gibco), HEPES (10 mm, 15630-080, Gibco), N-acetyl-L-cysteine (1 mm, A7250, Sigma), nicotinamide (10 mm, N0636, Sigma), and several growth factors including gastrin (10 nm, G9145, Sigma), EGF (50 ng/mL, 236-EG, R&D Systems, Minneapolis, MN, USA), FGF10 (100 ng/mL, 345-FG, R&D Systems), HGF (50 ng/mL, 294-HG, R&D Systems), forskolin (5 μm, F6886, Sigma), A8301 (500 nm, SML0788, Sigma), Y27632 (10 μm, Y0503, Sigma), and dexamethasone (100 nm, D4902, Sigma). The organoids were passaged weekly upon reaching confluence. Matrigel was then displaced, collected, and dissolved Matrigel and released the organoids by incubating for one hour at 4°C with a cell recovery solution (354253, Corning). The organoids were subsequently digested with TrypLE solution (12604021, Gibco) for 5–10 min at 37°C, resuspended in Matrigel, and replated to form new organoids. Organoid morphology and growth characteristics were evaluated using brightfield imaging with an Olympus CK30 microscope (Tokyo, Japan) and captured with cellSens Standard software (Olympus, Japan). Images were subsequently processed and analyzed using ImageJ (version 1.53, NIH, USA). Organoids were routinely passaged every 7–10 days using TrypLE Express, and experiments were conducted using organoids at passages 2–5 to ensure consistency. Each organoid line was derived from an independent patient sample and experiments were performed in triplicate to confirm reproducibility.

Following the manufacturer’s instructions, total RNA was extracted from THP-1 monocytes, HCC cells, and THP-1-derived M0, M1, and M2 macrophages using TRIzol reagent (15596018, Invitrogen). cDNA was synthesized using PrimeScript RT (RR047A, Takara, Kusatsu, Shiga, Japan) in a 20 μl reaction volume with 500 ng RNA input per reaction, incubated at 37°C for 15 min, followed by 85°C for 5 s. Using the SYBR Green PCR Master Mix (RR820A, Takara), qRT-PCR was carried out in a 20 μl reaction volume on a StepOnePlus Real-Time PCR System (4376600, Applied Biosystems, Thermo Fisher Scientific). Thermal cycling conditions were: 95°C for 30 s, followed by 40 cycles of 95°C for 5 s and 60°C for 30 s. The levels of gene expression were adjusted to GAPDH. All target levels were determined using the 2^−ΔΔCT method. Table 1 had a collection of primer sequences.

The protein levels of DNASE1L3 and β-catenin in the cytoplasm and nucleus of HepG2 cells (approximately 5 × 10⁶ cells per sample) overexpressing DNASE1L3 were measured by cellular fractionation using a Nuclear and Cytoplasmic Protein Extraction Kit (P0027, Beyotime, Shanghai, China) and a protease inhibitor (CW2200S, CoWin Biosciences, Nanjing, China). Following the manufacturer’s protocol, cells were lysed in 200 μl cytoplasmic extraction buffer and vortexed for 15 s, followed by incubation on ice. The cytoplasmic fraction was obtained after centrifugation at 16,000× g (4°C, 10 min). The remaining pellet underwent nuclear protein extraction with similar centrifugation conditions. DNASE1L3 (1:1000, 67041-1-Ig, Wuhan Sanying, Wuhan, China), β-catenin (1:5000, ab32572, Abcam, Cambridge, UK), H3 (1:1000, ab1791, Abcam), and GAPDH (1:1000, ab8245, Abcam) protein levels were measured in both the cytoplasmic and nuclear fractions using WB assay.

Protease and phosphatase inhibitors (CW2200S, CoWin Biosciences) were added to RIPA buffer (R0010, Solarbio) before cells and tissues were lysed. Protein concentrations were determined using the Bicinchoninic Acid (BCA) assay (P0012, Beyotime). Equal amounts of protein (20 μg per lane) were separated by SDS-PAGE (10% gel for most targets; 12% gel for low-molecular-weight proteins) and transferred onto PVDF membranes. The membranes were blocked with 5% non-fat milk in TBST (Tris-buffered saline with 0.1% Tween 20) for 1 h at room temperature and incubated with primary antibodies overnight at 4°C. The primary antibodies were as follows: DNASE1L3 (1:1000, 67041-1-Ig, Wuhan Sanying), E-cadherin (1:1000, ab40772), vimentin (1:1000, ab92547), N-cadherin (1:5000, ab76011), c-Myc (1:1000, ab32072), P21 (1:1000, ab109520), P27 (1:5000, ab32034), CD206 (1:1000, ab300621), ARG1 (1:1000, ab133543), β-catenin (1:5000, ab32572), PD-L1 (1:1000, ab228415), and Axin2 (1:1000, ab109307), H3 (1:1000, ab1791), Cyclin D1 (10,000, ab134175), with GAPDH (1:1000, ab8245) used as the internal control. All antibodies, except for DNASE1L3, were purchased from Abcam. After washing three times with TBST, membranes were incubated with species-specific HRP-conjugated secondary antibodies (anti-mouse IgG, 1:10000, ab6728, Abcam; anti-rabbit IgG, 1:5000, ab6721, Abcam) for 1 h at room temperature. Protein bands were identified using enhanced chemiluminescence (ECL) detection reagents (P0018AS, Beyotime) and recorded using the ChemiDoc System (1708280, Bio-Rad, Hercules, CA, USA) following incubation with secondary antibodies. All primary antibodies were validated by the manufacturers for specificity via knockout/knockdown controls or by referencing previous publications. Secondary antibodies were species-specific and verified for minimal cross-reactivity.

The CCK-8 test was used to evaluate the cells’ vitality (KGA1606-1000, KeyGEN, Nanjing, China). In 96-well plates, HCC cells were cultivated at a density of 5 × 10³ cells per well in a total culture volume of 100 µL per well. After overexpression of DNASE1L3, 10 µL of was added to each well, and cells were incubated for 2 h at 37°C. Absorbance at 450 nm was measured using a microplate reader (ST-960, Kehua Technologies, Inc., Shanghai, China), with values from blank wells subtracted. Measurements were taken at 450 nm at one, two, three, and four days post-treatment. Experiments were performed with at least three biological replicates to ensure reproducibility.

Transwell tests were used to assess the cells’ capacity for migration and invasion. HCC cells were resuspended in serum-free media to produce a single-cell suspension for the migration test. The lower chamber of the Transwell system (3422, Corning) received 500 μL of complete medium containing 10% FBS, whereas the top chamber received 200 μL of the cell suspension, which contained around 5 × 10⁴ cells. For the invasion assay, the upper surface of the membrane was pre-coated with Matrigel (356234, Corning) at a concentration of 1 mg/mL and a coating thickness of ~100 μm to simulate the extracellular matrix. The cells were maintained in a humidified incubator at 37°C for twenty-four hours. A cotton swab was used to remove the non-migrated cells from the top chamber, and DAPI (C1005, Beyotime) at 1 μg/mL was used to stain the migrated cells on the lower surface for 15 min at room temperature. Following staining, the membrane was washed with PBS and air-dried. Cell migration was quantified by counting five random fields at ×200 magnification using an Olympus CKX53 microscope (Olympus, Tokyo, Japan), with representative images captured. For the invasion assay, a layer of Matrigel was pre-coated onto the membrane of the Transwell insert to simulate the extracellular matrix. The remaining procedures were identical to the migration assay. Invasive cells were quantified using the same counting method.

HCC cells were planted (1 × 10⁵ cells per well) on 6-well plates and cultivated for 24 h. After 48-h treatment, cells were trypsinized (0.25%), centrifuged (300× g, 3 min, 4°C), and treated in 100 μL of with RNase A (100 μg/mL, 30 min). Following overnight fixation in 70% alcohol at 4°C, cells were stained with 50 μL of propidium iodide (PI, 50 μg/mL, 30 min). Analysis was performed using a flow cytometer (CyFlow Cube8, Jiyuan, Guangzhou, China) equipped with a 488 nm laser and detection in the FL2 channel. Data were analyzed with FlowJo software (version 10.8.1, Becton Dickinson, USA).

Total protein was extracted from HepG2 cells according to manufacturer directions, and the protein content was determined using a protein quantification assay. Five milligrams of total protein were used as the starting material for immunoprecipitation. Specific antibodies were used to incubate protein samples against the target proteins at 4°C. Depending on the experimental requirements, antibodies such as β-catenin (1:30, ab32572), GSK-3β (1:30, ab280376), Axin2 (1:150, ab109307), CK1 α (1:30, ab206652), APC (1:70, ab40778), or anti-Flag (1:30, ab205606) were added. Target proteins were immunoprecipitated with 4 μg specific antibody or IgG control using Protein A/G beads overnight at 4°C. The eluted complexes were analyzed by WB to assess protein interactions.

HCC PDO was preserved in 10% neutral buffered formalin and 4% paraformaldehyde, then sequentially dehydrated in graded ethanol (70%, 80%, 95%, and 100%, 5 min each), cleared in xylene, and paraffin embedded. Sectioning was performed to obtain 4 µm thick tissue sections and set up on glass slides. Hematoxylin and eosin (H&E) staining was performed using the H&E Staining Kit (C0105, Beyotime) to assess histopathological features under a Nikon Eclipse E200 light microscopy (Nikon, Japan).

For tissue staining, antigen retrieval was performed in citrate buffer (pH 6.0) by heating in a microwave at 95°C for 15 min. Endogenous peroxidase activity was blocked with 0.3% NaBH4 (CAS:16940-66-2, Sinopharm Chemical Reagent, Shanghai, China) at room temperature for 10 min. The tissue sections were then processed using the Vectastain Elite ABC Kit (PK-6200, Vector Laboratories, USA) according to the manufacturer’s instructions. Primary antibody incubation was conducted overnight at 4°C using DNASE1L3 (1:200, 67041-1-Ig, Wuhan Sanying). Secondary antibody (anti-mouse IgG, 1:10000, ab205719, Abcam) was applied at room temperature for 1 h. Nuclei were visualized with hematoxylin counterstain (H8070, Solarbio) and immunoreactivity with DAB staining solution (DA1010, Solarbio) for 5 min. The growth of HCC organoids overexpressing DNASE1L3 was monitored daily using a Nikon Eclipse Ti-S phase-contrast microscopy (Nikon, Japan). Organoid size and morphology were assessed to analyze the impacts of overexpression DNASE1L3 on HCC organoid growth dynamics.

R software was used for data analysis, with results expressed as mean ± SD (n = 3). Group comparisons employed ANOVA with Tukey’s test. Survival outcomes were evaluated using log-rank tests, and Cox regression identified prognostic indicators. Significance was defined at p < 0.05.

Transcriptome data of HCC were obtained from the GSE41804 dataset, which includes 20 HCC tissue samples and 20 matched adjacent normal liver tissues. Using this dataset, we first identified 1235 DEGs, comprising 365 upregulated and 870 downregulated genes (Fig. 1A). We then performed WGCNA on the GSE41804 dataset. As shown in Fig. 1B, the ideal soft threshold power to ensure scale-free topological model fitting was determined to be 14, R² = 0.85 without scale. Afterwards, we conducted a thorough analysis of the clustering of 40 samples in the GSE41804 dataset (Fig. 1C). The WGCNA method then divides the genes into many modules, each of which is represented by a different hue, based on the patterns of gene co-expression in different samples (Fig. 1D,E). We examined the adjacencies of signature genes to evaluate the relationships between the identified modules. There was a significant association between the sample and the turquoise module, as indicated by the correlation coefficient of 0.773 (Fig. 1F). In summary, the analysis of differentially expressed genes and co-expression modules laid the foundation for identifying potential key genes in this study.

To validate and prioritize candidate genes, transcriptome data from the TCGA-LIHC dataset, which includes 371 HCC samples and 50 normal liver tissues, were analyzed. Differential expression analysis identified 2451 upregulated and 446 downregulated genes (Fig. 2A). Cross-over analysis by a bioinformatics platform revealed 306 overlapping genes between the turquoise module and down-regulated DEGs of TCGA (Fig. 2B). A batch survival analysis highlighted 106 overlapping genes with significant prognostic value, and a forest plot was utilized to depict the top 20 genes with their corresponding p-values and hazard ratios (Fig. 2C). Through survival analysis and overlap assessment, candidate genes with significant prognostic value were identified, providing a basis for further key gene selection.

Prognostic analysis was conducted on the 306 overlapping genes, revealing 106 genes with significant prognostic value. LASSO Cox regression analysis (λ = 0.0606) identified 8 signature genes (Fig. 3A,B). Risk score was calculated as: Riskscore = (−0.0151)*CPEB3 + (−0.0186)*CYP2C9 + (−0.185)*NTF3 + (−0.0224)*SPP2 + (−0.0123)*LCAT + (−0.0002)*GGT5 + (−0.0719)*DNASE1L3 + (−0.0429)*CFHR3. The high-risk group had lower survival rates than low-risk individuals, with median survival durations of 1.1 and 3.9 years, respectively (HR = 2.464; Fig. 3C,D). The model demonstrated strong predictive performance with a five-year AUC of 0.786 (Fig. 3E), highlighting these genes’ prognostic significance. By integrating immune-related analyses, candidate genes closely associated with the tumor microenvironment and macrophage polarization were further prioritized, laying the groundwork for subsequent studies on DNASE1L3.

Expression analysis revealed significant downregulation of the eight genes (CPEB3, CYP2C9, NTF3, SPP2, LCAT, GGT5, DNASE1L3, CFHR3) in tumor samples from both the GSE41804 dataset and the TCGA-LIHC dataset (Fig. 4A,B). This downregulation may implicate these genes in crucial metabolic and regulatory pathways that are altered during hepatocarcinogenesis. Notably, existing research has demonstrated that DNASE1L3 can suppress glycolysis in hepatocellular cancer cells, promoting the tricarboxylic acid cycle involving the pathways of CEBPβ-p53-PFK1 and PTPN2-HK2, thereby inhibiting the progression of HCC [12]. Based on these integrative analyses and functional relevance, DNASE1L3 was selected as the key gene for further experimental investigation.

In HepG2 and Huh7 cells, DNASE1L3 expression was substantially lower than in normal controls (Fig. 5A–C). Subsequent qRT-PCR and WB analyses demonstrated efficient overexpression of DNASE1L3 (Fig. 5D–F). After successful overexpression validation (Fig. 5D–F), CCK-8 assays showed reduced cell viability in DNASE1L3-overexpressing HepG2 and Huh7 cells (Fig. 5G,H). Transwell assays further revealed that overexpression of DNASE1L3 attenuated the invasive and migrating properties of HCC cells (Fig. 5I,J).

Flow cytometry revealed that overexpressed DNASE1L3 induced G1 phase arrest (Fig. 6A,B). Concurrently, WB analysis observed a downregulation of EMT markers, including a decrease in c-Myc, N-cadherin, and vimentin levels, coupled with an upregulation of the epithelial marker E-cadherin (Fig. 6C–E). Additionally, the WB analysis indicated a rise in the expression of the G1 phase-associated proteins P21 and P27, suggesting a potential mechanism by which DNASE1L3 overexpression influences cell cycle progression and EMT processes in the cellular context.

Although DNASE1L3 has traditionally been recognized for its role in extracellular DNA clearance, emerging evidence suggests that it also participates in immune regulation and inflammatory processes [19]. Together with the substantial signaling alterations we observed in HCC cells, we hypothesized that DNASE1L3 may not only modulate the intrinsic biological behaviors of tumor cells but also influence the tumor immune microenvironment by regulating tumor-derived secretory factors-particularly those capable of shaping macrophage polarization. To test this hypothesis, we further employed a co-culture system to assess the impact of DNASE1L3 dysregulation on macrophage phenotypes. DNASE1L3 silencing efficiency was confirmed in HepG2 and Huh7 cells using two siRNAs, with si-DNASE1L3-2 showing greater effectivenes (Fig. 7A–E). To further understand the influence of DNASE1L3 on macrophages, we created a co-culture system that monitored HCC cell-macrophage interactions. The levels of M1 markers (NOS2, CXCL10, CXCL9, and TNF-α) and M2 markers (CD206, ARG1, CD163, IL10, and TGF-β1) were assessed by co-culturing THP-1 differentiated macrophages with DNASE1L3 knockdown/overexpression HCC cells. According to the findings, overexpression of DNASE1L3 markedly increased M1 marker expression while decreasing M2 marker expression. On the other hand, induction with si-DNASE1L3-2 had the opposite effect, leading to a notable rise in M2 marker expression and a fall in M1 marker levels (Fig. 7F). According to these findings, DNASE1L3 controls the polarization of macrophages in hepatocellular carcinoma by encouraging M1 polarization when overexpressed and M2 polarization when downregulated.

To determine whether the Wnt/β-catenin pathway participates in macrophage polarization, the mRNA expression levels of Wnt receptors and downstream signaling genes were analyzed in M0, M1, and M2 THP-1 macrophages using qRT-PCR (Fig. 8A). M1 macrophages showed consistently low expression of Fzd4, Fzd7, and Fzd9, whereas M2 macrophages displayed markedly elevated levels of these receptors, suggesting a higher responsiveness to Wnt signals. Consistently, β-catenin, Axin2, and c-Myc mRNA levels were significantly increased in M2 macrophages compared with M0 and M1 cells (Fig. 8B–D), and Western blotting confirmed substantially higher β-catenin protein levels in M2 macrophages (Fig. 8E). When combined, these results suggest that Wnt/β-catenin signaling is significantly active in M2 macrophages and may play a role in monocyte differentiation.

Wnt ligands produced from macrophages have been demonstrated in several studies to stimulate Wnt signaling in tumor cells [20,21]. Through canonical Wnt/β-catenin signaling, tumor cell-derived Wnt ligands induce M2-like polarization of TAMs, which results in tumor growth, migration, metastasis, as well as immunosuppression in HCC [22,23]. The function of DNASE1L3 in Wnt/β-catenin signaling and macrophage polarization was investigated in HCC. Distinct Wnt ligand patterns emerged across macrophage subtypes (Fig. 9A), with M1 macrophages showing elevated Wnt2 and Wnt16 expression, while M2 macrophages exhibited higher Wnt3, Wnt3a, and Wnt4 levels compared to other subtypes. However, comparison with HepG2 cells revealed that HCC cells expressed significantly higher levels of Wnt2, Wnt3, Wnt3a, Wnt4, Wnt10b, and Wnt16 (Fig. 9B), indicating that HCC cells may serve as the major paracrine source of Wnt ligands in the tumor microenvironment. We knocked down DNASE1L3 in HepG2 cells using siRNA and co-cultured them with THP-1 macrophages to study its influence on Wnt/β-catenin signaling. Silencing of DNASE1L3 led to up-regulation of the expression of M2 macrophage markers (CD206 and ARG1) and Wnt/β-catenin pathway proteins (β-catenin and c-Myc) in THP-1 cells according to WB analysis. Treatment with ICG001, a Wnt/β-catenin inhibitor, reversed these effects, demonstrating that DNASE1L3 controls macrophage polarization via the Wnt/β-catenin signaling pathway (Fig. 9C,D). These results demonstrate that DNASE1L3 restrains tumor-derived Wnt/β-catenin signaling and inhibits the M2-like polarization of macrophages.

According to our study, DNASE1L3 knockdown encourages M2-like polarization of macrophages in HCC, maybe by triggering the macrophages’ Wnt/β-catenin signaling pathway. Therefore, we sought to investigate whether DNASE1L3 is associated with the Wnt/β-catenin signaling pathway and enhances HCC cell proliferation. To this end, we performed co-IP assays in HepG2 and Huh7 cell lines (Fig. 10A,B). The results showed that DNASE1L3 and β-catenin physically interact in both cell lines. Furthermore, overexpression of DNASE1L3 in HCC cells led to a significant reduction in β-catenin protein levels, as determined by WB analysis (Fig. 10C,D). These results demonstrate that DNASE1L3 physically interacts with β-catenin.

DNASE1L3 has been shown to bind to β-catenin and prevent its activation. We hypothesized that DNASE1L3 might attract additional β-catenin destruction complexes to interact with β-catenin protein and subsequently control the protein level of β-catenin in order to further validate this regulatory mechanism. Co-IP assays in HepG2 cells showed that DNASE1L3 interacted with key components of β-catenin and its degradation complex (Fig. 11A). Analysis of β-catenin stability demonstrated that treatment with MG132 markedly increased the accumulation of ubiquitinated β-catenin. Notably, compared with the vector control, DNASE1L3 overexpression further enhanced the ubiquitination of β-catenin (Fig. 11B), indicating that DNASE1L3 promotes β-catenin ubiquitination in HepG2 cells. Subcellular fractionation experiments in HepG2 cells showed that DNASE1L3 overexpression reduced β-catenin levels in the cytoplasmic and nuclear fractions (Fig. 11C,D). This reduction was more obvious in the nuclear fraction, indicating that DNASE1L3 may preferentially target nuclear β-catenin for degradation or inhibit its nuclear translocation. These findings indicate that DNASE1L3 interacts with the β-catenin degradation complex, increases β-catenin ubiquitination, and lowers β-catenin levels in the cytoplasm and nucleus.

Given that DNASE1L3 inhibits the Wnt/β-catenin pathway and that this pathway is known to modulate immune checkpoints like PD-L1 in tumor microenvironments [24], we next investigated whether DNASE1L3 influences PD-L1 expression in HCC. We studied the correlation between DNASE1L3 and PD-L1 expression in HCC cells. qRT-PCR research revealed that DNASE1L3 overexpression dramatically decreased PD-L1 mRNA levels in HepG2 cells, whereas DNASE1L3 knockdown boosted PD-L1 mRNA expression in Huh7 cells (Fig. 12A). WB analysis confirmed these findings at the protein level in HepG2 and Huh7 cells (Fig. 12B,C). We investigated the impact of DNASE1L3 knockdown on PD-L1 expression in relation to Wnt/β-catenin signaling in order to investigate the possible mechanisms behind this regulation. DNASE1L3 knockdown markedly raised PD-L1 mRNA levels in HepG2 and Huh7 cells. Treatment with the Wnt/β-catenin pathway inhibitor XAV939 partially reversed the increased PD-L1 expression caused by DNASE1L3 knockdown in both cell lines (Fig. 12D,E). Further analysis showed DNASE1L3 knockdown enhanced Wnt pathway components (Axin2, c-Myc, β-catenin) alongside PD-L1 in both cell lines (Fig. 12F,H), effects that were attenuated by XAV939, suggesting DNASE1L3’s regulatory role in PD-L1 expression via Wnt/β-catenin pathway. Quantitative analysis of these WB results confirmed that after DNASE1L3 knockdown in HepG2 and Huh7 cells, the protein levels of Axin2, c-Myc, β-catenin, and PD-L1 were all significantly increased (Fig. 12G,I).

We investigated the role of DNASE1L3 in regulating Wnt/β-catenin signaling pathway in HCC cells, both with and without co-culture with M2-like macrophages. As shown in Fig. 13A,B, in HepG2 cells, DNASE1L3 knockdown led to increased protein expression of Wnt/β-catenin pathway components Axin2, c-Myc, and β-catenmin when there was no co-culture. It is important to note that co-culturing with M2-like macrophages increased this impact. Consistent observations were made in Huh7 cells. Compared to the si-NC group under no co-culture conditions, DNASE1L3 knockdown resulted in increased protein levels of Axin2, c-Myc, and β-catenin (Fig. 13C,D). This impact was enhanced by co-cultivation with M2-like macrophages, as seen by a significant rise in protein expression as compared to the conditions without co-cultivation. These findings imply that DNASE1L3 could act in HCC cells as a negative regulator of the Wnt/β-catenin signaling pathway. Furthermore, the enhanced effect observed under co-culture conditions with M2-like macrophages implies that DNASE1L3 may play a crucial role in regulating Wnt/β-catenin signaling pathway within the tumor microenvironment by interacting with M2-polarized tumor-associated macrophages.

We investigated the effects of DNASE1L3 overexpression on liver organoid formation and growth. Liver organoids were successfully generated and observed at different magnifications. Fig. 14A,B shows images of organoids at 40× and 100× magnifications under an optical microscope. Multiple organoids were observed at 40×, and each organoid presented a compact spherical structure with clear boundaries. 100x magnification provided a closer view, showing dense cell arrangement and integrity of the outer layer of the organoid, indicating that the organoids formed a solid histology. Subsequently, paraffin-embedded sections were subjected to histological examination, and DNASE1L3 expression was increased in organoids overexpressing DNASE1L3 compared with vector controls (Fig. 14C). To explore the biological function of DNASE1L3 in HCC organoids, we constructed a human HCC organoid model from resected primary HCC tissue and overexpressed DNASE1L3. The growth of the DNASE1L3 overexpression organoid model was evaluated at 1, 5, and 11 days, and the diameter of the organoid was measured (Fig. 14D). The findings revealed that the development of DNASE1L3 overexpression organoids was severely suppressed. Overexpression of DNASE1L3 lowered the expression of Wnt/β-catenin pathway proteints (Axin2, c-Myc, β-catenin) and cell cycle regulator Cyclin D1 in HCC organoids (Fig. 14E,F). In contrast, the expression of the cell cycle inhibitors p27 and p21 increased. This shows that DNASE1L3 suppresses Wnt/β-catenin signaling and cell cycle progression in liver organoids.