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. 2026 Jun 19;24:1144. doi: 10.1186/s12967-026-08468-5

ENO1 interacts with PKM to promote HBV-related hepatocellular carcinoma progression and HBV replication

Yan-chao Hu 1,2, Hui-juan Liu 1, Mei-ying Gu 4, Zi-min Ma 3, Li-Na Ma 1,2,5,✉, Xiang-Chun Ding 1,2,6,✉
PMCID: PMC13548708  PMID: 42321826

Abstract

Background

The progression of hepatitis B virus (HBV)-induced hepatocellular carcinoma (HCC) is driven by complex interactions between metabolic reprogramming and viral replication. Glycolytic enzymes, including α-enolase (ENO1) and pyruvate kinase M (PKM), play key roles in tumor progression, but their specific contribution to HBV-associated HCC remains inadequately defined. This study investigates the molecular mechanisms by which ENO1 and PKM promote HCC progression and HBV replication.

Methods

The expression levels of ENO1 and PKM in HBV-infected HCC cells were evaluated, and their interaction was explored using co-immunoprecipitation (Co-IP), quantitative reverse transcription polymerase chain reaction (qRT-PCR), and Western blot analysis. Additionally, an HCC mouse model was employed to assess the impact of this interaction on HCC progression and HBV replication.

Results

Both ENO1 and PKM were significantly upregulated in HBV-associated HCC cells and mouse models. Co-IP assays revealed a direct interaction between ENO1 and PKM. Functionally, ENO1 enhanced HCC cell proliferation, migration, invasion and in vivo tumor growth. Furthermore, elevated levels of ENO1 and PKM significantly increased HBV replication, thus exacerbating HCC progression.

Conclusions

This study indicated a novel mechanism in which ENO1 interacts with PKM to accelerate both HBV-related HCC progression and viral replication. These findings suggest that targeting the ENO1-PKM axis may provide a promising metabolic therapeutic approach for HBV-associated HCC.

Graphical abstract

graphic file with name 12967_2026_8468_Figa_HTML.webp

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Supplementary Information

The online version contains supplementary material available at https://doi.org/10.1186/s12967-026-08468-5.

Keywords: HBV-induced HCC, ENO1, Proliferation, Glycolysis, HBV replication

Background

Hepatitis B virus (HBV] infection is a major risk factor for the development of hepatocellular carcinoma (HCC) globally [1]. Despite the availability of effective vaccines and antiviral treatments, HBV infection remains a significant contributor to HCC-related morbidity and mortality [2]. The pathogenesis of HBV infection involves a series of pathological changes in the liver, including chronic hepatitis, liver fibrosis, cirrhosis, and ultimately, HCC [3, 4]. Chronic HBV infection is characterized by multiple pathogenic mechanisms, such as immune evasion and metabolic reprogramming, which together drive hepatocyte malignant transformation and tumorigenesis [5, 6]. HCC is an aggressive, drug-resistant malignancy closely associated with viral infection [7] and is influenced by both intrinsic and extrinsic factors, including genetic mutations, immune escape, chronic inflammation, and metabolic reprogramming [8, 9]. Metabolic reprogramming, a hallmark of cancer, is essential for sustaining tumor cell growth, proliferation, and survival [10]. Recent studies have highlighted glycolytic alterations as key drivers of HCC initiation and progression, providing valuable insights into metabolic regulation in HCC [11–13].

ENO1 and PKM, two key glycolytic enzymes, have been implicated in the pathogenesis of various cancers [14, 15]. ENO1 catalyzes the conversion of 2-phosphoglycerate [2-PG) to phosphoenolpyruvate (PEP), facilitating glycolytic flux [16]. In addition to its enzymatic activity, ENO1 also exerts non-enzymatic functions that promote tumor cell proliferation, survival, and metastasis [17, 18], such as modulating cell cycle progression and enhancing drug resistance via the regulation of multiple signaling pathways [19, 20]. Dysregulated ENO1 expression is commonly observed in several cancers, including HCC, lung cancer, and gastric cancer [17, 21, 22]. PKM, the terminal enzyme in glycolysis, catalyzes the conversion of PEP and adenosine diphosphate (ADP) into pyruvate and adenosine triphosphate (ATP) [23]. In normal cells, PKM predominantly exists as the PKM1 isoform, while the PKM2 isoform is more prevalent in tumor cells [24]. PKM2 plays regulatory roles, participating in cellular metabolic control, gene expression, and cell cycle regulation [25, 26]. Dysregulation of PKM2 expression or activity disrupts glycolytic dynamics, promoting energy metabolism adaptations that support tumorigenesis and cancer progression [27, 28]. Given the distinct yet interconnected roles of ENO1 and PKM in glycolysis and tumor biology, it is hypothesized that their interaction may significantly influence metabolic reprogramming in tumor cells. However, most studies have focused on their individual functions rather than their synergistic effects. Understanding the molecular interactions between ENO1 and PKM, particularly in the context of metabolic and immune regulation in HBV-associated HCC, is essential for elucidating their collective contribution to HCC progression and may reveal novel therapeutic targets.

Proliferating cell nuclear antigen (PCNA) is a key protein involved in DNA replication and repair [29]. Its overexpression is closely associated with tumor cell proliferation, metastasis, and chemoresistance [30, 31]. PCNA serves as a key component in several tumor-related signaling pathways, including DNA damage response, cell cycle regulation, and apoptosis modulation [32, 33]. In HCC, elevated PCNA levels are indicative of poor prognosis and are strongly correlated with malignant progression [34]. Despite extensive studies on PCNA’s role in tumors, its regulatory interaction with metabolic enzymes remains insufficiently explored. Emerging evidence suggests that glycolytic enzymes, such as pyruvate kinase M (PKM] and α-enolase (ENO1), promote tumor proliferation and chemoresistance through the modulation of PCNA expression [35, 36]. In the context of HBV-associated HCC, dysregulated expression or activation of these metabolic enzymes may accelerate HCC progression and enhance HBV replication by upregulating PCNA. Thus, this study researched the influence of ENO1 and PKM on PCNA expression.

This study aims to investigate the mechanistic interaction between ENO1 and PKM in promoting HBV-associated HCC progression and viral replication. By utilizing cellular experiments, animal models, and molecular pathway analyses, this research seeks to deepen the understanding of metabolic reprogramming in HCC and identify novel targets for precision therapy in HBV-related HCC. Through an in-depth examination of the mechanistic links between ENO1, PKM, and PCNA, this study uncovers the complex dynamics of metabolic regulation in HCC and provides a theoretical foundation for metabolism-based therapeutic strategies.

Methods

Bioinformatics analysis

The expression profiles of ENO1 and PKM across various cancers were analyzed using the Gene Expression Profiling Interactive Analysis (GEPIA) database. Expression data for ENO1 and PKM in HCC tissues and normal liver tissues, including paired and unpaired HCC and adjacent non-cancerous tissues, were retrieved and systematically evaluated. To assess the prognostic significance of ENO1 and PKM in HCC, overall survival (OS) and progression-free survival (PFS) data were extracted and analyzed from the GEPIA database. Additionally, immunohistochemical (IHC) images depicting ENO1 and PKM expression in HCC tissues and normal adjacent tissues were obtained from the Human Protein Atlas online database. To explore the correlation between ENO1, PKM, and PCNA, expression data from HCC tissues, normal liver tissues, and peripheral blood samples from patients with HCC were also collected from the GEPIA database. The correlations between ENO1/PKM and PCNA/Ki67 in HCC tissues were analyzed using the GEPIA database. Moreover, the STRING database was utilized to identify genes functionally related to ENO1, and the GeneMANIA website was employed to predict the common functions of ENO1 and PKM within the glycolytic pathway.

Clinical tissues

Clinical samples, including HCC tissues (n = 30), adjacent normal liver tissues (n = 30), and HBV-infected HCC tissues (n = 30), were collected. The HBV-infected HCC samples were obtained from patients with serum HBV DNA levels exceeding 20,000 IU/mL [37]. All tissue specimens were preserved in liquid nitrogen. The study adhered to ethical approval from the General Hospital of Ningxia Medical University, with written informed consent obtained from all participants.

Cell culture

Human normal hepatocytes (THLE-2 cell line) (JK3784, Jingkang Biological Engineering, Shanghai, China), HCC cells (HepG2 cell line) (ZY-H108, Zeye Biotechnology, Shanghai, China), and HBV-stably expressing HCC cells (HepG2.2.15 cell line) (YB-CC0118, Yubo Biotechnology, Shanghai, China) were cultured in Dulbecco’s Modified Eagle Medium (DMEM) supplemented with 10% fetal bovine serum (FBS) at 37 °C in a humidified atmosphere with 5% CO2.

Cell transfection

HepG2.2.15 cells were seeded in 6-well plates at a density of 1 × 106 cells per well in serum-free DMEM. Transfection was carried out using Lipofectamine 3000 (Thermo Fisher Scientific, San Jose, CA, USA) according to the manufacturer’s instructions. The cells were transfected with ENO1 siRNA, siRNA negative control (NC), pcDNA3.1-ENO1 vectors, pcDNA3.1 empty vectors, and PKM siRNA (GeneChem, Shanghai, China). The transfected groups were designated as follows: siENO1 group, siNC group, ENO1 group, NC group, siPKM group, and a combined transfection group (ENO1 + siPKM) where pcDNA3.1-ENO1 vectors were co-transfected with PKM siRNA. Post-transfection, the cells were cultured in DMEM supplemented with 10% FBS for 48 h at 37 °C with 5% CO2. Transfection efficiency was validated via Western blot analysis. Non-transfected HepG2.2.15 cells served as the control group.

Cell counting kit-8 (CCK-8) assay

HepG2.2.15 cells were seeded into 96-well plates at a density of 5 × 103 cells per well in 100 µL of DMEM supplemented with 10% FBS. After incubation at 37 °C with 5% CO2 for 24, 48, and 72 h, cells were treated with 10 µL of CCK-8 reagent (Zeye Biotechnology, Shanghai, China) for 2 h at 37 °C. Absorbance at 450 nm was measured using a microplate reader (Biotek, Winooski, VT, USA) to quantify cell viability.

Colony formation assay

To assess colony formation, 1000 HepG2.2.15 cells were plated in 6-well plates and cultured in 1 mL of DMEM supplemented with 10% FBS at 37 °C with 5% CO2 for two weeks, with medium changes every three days. After the incubation period, cells were fixed with 4% paraformaldehyde for 10 min and stained with 0.1% crystal violet (Solarbio, Beijing, China). Colonies with a diameter greater than 50 μm were counted under a light microscope (BX51, Olympus, Tokyo, Japan).

EdU assay

Cell proliferation was evaluated using a 5-Ethynyl-2’-deoxyuridine (EdU) assay kit (RuiBo Biotechnology, Guangzhou, China) following the manufacturer’s protocol. Briefly, HepG2.2.15 cells were seeded at 1 × 105 cells per well in 6-well plates containing 1 mL of DMEM with 10% FBS. After 48 h of cultivation at 37 °C with 5% CO2, cells were treated with 50 µM EdU for 2 h. Cells were then fixed with 4% paraformaldehyde for 10 min, permeabilized with 0.1% Triton X-100 (Yubo Biotechnology, Shanghai, China) for 20 min, and examined for EdU-positive staining using a fluorescence microscope (Olympus, Tokyo, Japan).

TUNEL assay

Apoptosis was analyzed using a TUNEL assay. After 48 h of transfection, HepG2.2.15 cells were treated with 0.1% Triton X-100 (Yuanye Biotechnology, Guangzhou, China) for 20 min in the dark. Cells were then incubated with TUNEL working solution (Yeasen Biotechnology, Guangzhou, China) for 1 h, followed by staining with 4’,6-diamidino-2-phenylindole (DAPI) (Yuanye Biotechnology, Guangzhou, China) for 5 min at 37 °C. Apoptotic cells were visualized and quantified under a fluorescence microscope (Olympus, Tokyo, Japan).

Wound healing assay

Cell migration was assessed via a wound healing assay. HepG2.2.15 cells from each group were seeded into 6-well plates at a density of 1 × 105 cells per well in 1 mL of DMEM with 10% FBS. Once cells reached 90% confluence at 37 °C with 5% CO2, a straight scratch was made across the cell monolayer using a pipette tip. After removing the residual medium, cells were washed three times with serum-free DMEM. Subsequently, 1 mL of serum-free DMEM was added, and cells were incubated at 37 °C with 5% CO2 for 24 h. The initial and final wound widths were measured and imaged using an inverted microscope (Olympus, Tokyo, Japan). The migration rate was calculated as the wound healing rate, defined as 100% × [(original wound width – final wound width)/original wound width].

Transwell assay

HepG2.2.15 cells (1 × 105 cells/mL) were seeded into the upper chambers of transwell inserts pre-coated with Matrigel, with 100 µL of cell suspension per chamber. The lower chambers were filled with 500 µL of DMEM supplemented with 20% FBS. Cell invasion was performed at 37 °C with 5% CO2 for 24 h. After incubation, invasive cells were fixed with 4% paraformaldehyde for 10 min and stained with 0.1% crystal violet for an additional 10 min. Invasive cells were quantified by microscopic imaging using an inverted microscope (Olympus, Tokyo, Japan).

Glycolysis assay

HepG2.2.15 cells (100 µL, 1 × 106 cells/well) were seeded into 6-well plates and cultured in 1 mL of DMEM containing 10% FBS at 37 °C with 5% CO2 for 48 h. Culture media from different treatment groups were collected for subsequent analysis. Glucose consumption and lactate production were quantified using the Glucose Assay Kit and Lactate Assay Kit (AmyJet Scientific, Wuhan, China), respectively. Additionally, pyruvate production, glucose-6-phosphate dehydrogenase (G6PD) levels, 2-PG levels, and the NADPH/NADP+ ratio were measured using the Pyruvate Assay Kit (AmyJet Scientific, Wuhan, China), G6PD Assay Kit (Kanglang Biotechnology, Shanghai, China), 2-PG Assay Kit (ab174097, Abcam, Shanghai, China), and NADPH/NADP+ Assay Kit (Beyotime, Shanghai, China), respectively, following the manufacturers’ protocols.

Detection of HBV DNA and Hepatitis B surface antigen (HBsAg)

HepG2.2.15 cells from each experimental group were cultured in 6-well plates (1 × 106 cells/well) for 48 h in DMEM containing 10% FBS. Culture supernatants were collected and analyzed for HBV DNA and HBsAg levels using the HBV DNA Assay Kit (JiangLai Industrial, Shanghai, China) and HBsAg Enzyme-linked immunosorbent assay (ELISA) Kit (JingKang Biological Engineering, Shanghai, China), respectively, according to the manufacturers’ instructions. According to the kit instructions, the method for detecting HBV DNA level employed real-time quantitative reverse transcription-polymerase chain reaction (qRT-PCR) technology.

Co-immunoprecipitation assay

HepG2.2.15 cells without treatment were harvested after 48 h of culture. Cells were lysed in radio-immunoprecipitation assay (RIPA) buffer (Beyotime, Shanghai, China) on ice for 30 min. The lysates were centrifuged at 12,000 rpm for 10 min at 4 °C to obtain the supernatant. A total of 800 µg of each protein sample was incubated with Protein A/G agarose (Zeye Biotechnology, Shanghai, China) for 30 min at 4 °C to remove non-specific proteins. Following centrifugation (12,000 rpm, 10 min, 4 °C), the supernatant was collected. For immunoprecipitation, 2 µg of rabbit anti-ENO1 primary antibody (1:50, HY-P87640, MCE, New Jersey, USA) was added and incubated at 4 °C for 12 h. Subsequently, 40 µL of Protein A/G agarose beads (Yubo Biotechnology, Shanghai, China) were added, and the mixture was rotated at 4 °C for 4 h. The beads were collected by centrifugation (3,000 rpm, 10 min, 4 °C), thoroughly washed with RIPA buffer, and eluted. Finally, loading buffer (Beyotime, Shanghai, China) was added to the eluted protein complexes, and protein levels were analyzed by Western blot. Rabbit anti-IgG (1:500, HY-P85688, New Jersey, USA) was used as an NC.

Glutathione S-transferase (GST) pull-down assay

GST-tagged ENO1 was cloned into pCR3.1 plasmids and expressed in BL21 E. coli. The ENO1 protein was purified using the T7 high-yield protein system (Promega, Madison, WI, USA). Similarly, His-tagged PKM, cloned into pcDNA3.1 plasmids, was expressed in HepG2.2.15 cells. The His-tagged protein was purified using the His•Bind Purification Kit (Yubo Biotechnology, Shanghai, China) according to the manufacturer’s instructions. GST or GST-ENO1 were then incubated with glutathione agarose (Yuanye Biotechnology, Shanghai, China) for 1 h at 4 °C. After washing, the glutathione agarose was incubated with His-PKM overnight at 4 °C. Following elution, the bound proteins were analyzed by Western blot. A reciprocal experiment was conducted where PKM was tagged with GST and ENO1 with His, following the same procedure.

Immunofluorescence staining

HepG2.2.15 cells, untreated, were seeded into 6-well plates with a coverslip placed at the bottom of each well. After 24 h of culture, the cells were fixed with 4% paraformaldehyde and permeabilized with 0.1% Triton X-100 (Yubo Biotechnology, Shanghai, China) for 10 min. Immunofluorescence staining was performed by incubating the cells overnight at 4 °C with mouse anti-ENO1 primary antibody (1:100, WH0002023M1, MERCK, Germany) and rabbit anti-PKM (1:200, HY-P86631, MCE, New Jersey, USA). The cells were then treated with Alexa Fluor488-conjugated goat anti-mouse secondary antibody (1:200, HY-P8005, New Jersey, USA) or Alexa Fluor555-conjugated goat anti-rabbit secondary antibody (1:200, HY-P81005, New Jersey, USA) for 30 min at room temperature. After nuclei were stained with DAPI for 5 min, the coverslips were examined under a fluorescence microscope (Olympus, Tokyo, Japan).

In vivo study

BALB/c nude mice (5-week-old, n = 60) were obtained from Kaixue Biotechnology (Shanghai, China). Mice were housed under controlled conditions (22 °C, 12-hour light/dark cycle) with ad libitum access to food and water. The animal protocol was approved by the Animal Ethics Committee of the General Hospital of Ningxia Medical University.

A subcutaneous tumor model was established by injecting HepG2.2.15 cells into the left flank of each mouse. In Experiment I, 24 mice were randomly assigned to four groups (n = 6 per group): Control, ENO1, siPKM, and ENO1 + siPKM. Mice in the Control group were injected with untreated HepG2.2.15 cells; the ENO1 group received HepG2.2.15 cells transfected with a lentiviral ENO1 overexpression vector; the siPKM group was injected with HepG2.2.15 cells transfected with lentiviral PKM siRNA; and the ENO1 + siPKM group received HepG2.2.15 cells co-transfected with the lentiviral ENO1 overexpression vector and PKM siRNA. In Experiment II, the remaining 36 mice were randomly divided into six groups (n = 6 per group): NC, NC + 2-DG, ENO1, PKM, ENO1 + 2-DG, and PKM + 2-DG. Mice in the NC group were injected with HepG2.2.15 cells transfected with lentiviral NC vectors; the NC + 2-DG group received HepG2.2.15 cells transfected with lentiviral NC vectors and were then treated with 500 mg/kg 2-DG (MCE, New Jersey, USA) via intraperitoneal injection; the ENO1 and PKM groups received HepG2.2.15 cells transfected with lentiviral ENO1 overexpression vectors and lentiviral PKM overexpression vectors, respectively; the ENO1 + 2-DG group received HepG2.2.15 cells transfected with lentiviral ENO1 overexpression vectors and were administered 500 mg/kg 2-DG via intraperitoneal injection; and the PKM + 2-DG group received HepG2.2.15 cells transfected with lentiviral PKM overexpression vectors and 500 mg/kg 2-DG via intraperitoneal injection. In all groups, 1 × 106 cells suspended in 100 µL of phosphate-buffered saline (PBS) were injected subcutaneously. Tumor volumes were recorded every 7 days, calculated as (long diameter × short diameter2)/2. After 28 days, mice were euthanized under deep anesthesia (5% isoflurane), and tumor tissues were harvested, weighed, and stored at -80 °C.

qRT-PCR

Total RNA was extracted from clinical tissues, cultured cells, and tumor tissues from nude mice using TRIzol reagent (Kemin Biotechnology, Shanghai, China) according to the manufacturer’s instructions. Cells were cultured for 48 h prior to RNA extraction. The isolated RNA was reverse-transcribed into cDNA using the PrimeScript™ RT reagent kit (TaKaRa, Tokyo, Japan). Quantitative PCR was performed on a 7500 Real-Time PCR System (Applied Biosystems, Foster City, CA, USA) with SYBR Premix Ex Taq II (TaKaRa, Shiga, Japan). The PCR conditions were as follows: initial denaturation at 94 °C for 30 s, followed by 40 cycles of 94 °C for 5 s, 58 °C for 15 s, and 72 °C for 15 s. The following primers were used: ENO1: forward 5′-GCCGGCTTTACGTTCACCTC-3′, reverse 5′-GTTGAAGCACCACTGGGCAC-3′; PKM: forward 5′-AGTACCATGCGGAGACCATC-3′, reverse 5′-GCGTTATCCAGCGTGATTTT-3′; PCNA: forward 5′-TGATGAGGTCCTTGAGTG-3′, reverse 5′-GAGTGGTCGTTGTCTTTC-3′; Ki67: forward 5′-GGGTCTGAATCGGCCTCATA-3′, reverse 5′-GCAAGGGTCACAGTTAAGGC-3′; GAPDH: forward 5′-CCACAGTCCATGCCATCACTG-3′, reverse 5′-GTCAGGTCCACCACTGACACG-3′. The relative mRNA expression levels of ENO1, PKM, and PCNA were calculated using the 2−ΔΔCt method, with GAPDH as the internal reference.

Western blot analysis

Protein extraction from clinical tissues and tumor tissues from nude mice was performed by homogenizing the samples in RIPA lysis buffer for 30 min on ice. Additionally, cells cultured for 48 h were collected and lysed with RIPA buffer for total protein extraction. After 48 h of culture, the supernatant from HepG2.2.15 cell cultures was also subjected to protein extraction using RIPA buffer. Protein concentration was measured using the BCA assay kit (Beyotime, Shanghai, China) according to the manufacturer’s protocol. Protein samples were separated by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and transferred to polyvinylidene fluoride (PVDF) membranes. After blocking with 5% non-fat milk, the membranes were incubated overnight at 4 °C with the following primary antibodies: rabbit anti-ENO1 (1:1000, HY-P87640, MCE, New Jersey, USA), mouse anti-HBV core protein (HBc) (1:1000, MA1-7606, Thermo Fisher Scientific, China), mouse anti-HBx (1:1000, ZMS1083, MERCK, Germany), rabbit anti-PKM (1:1000, HY-P86631, MCE, New Jersey, USA), rabbit anti-PCNA (1:1000, HY-P86534, MCE, New Jersey, USA), rabbit anti-Ki67 (1:1000, HY-P86608, MCE, New Jersey, USA), rabbit anti-β-actin (1:5000, HY-P87821, MCE, New Jersey, USA) and rabbit anti-GAPDH (1:10000, HY-P80137, MCE, New Jersey, USA). After primary antibody incubation, membranes were treated with horseradish peroxidase-conjugated secondary antibodies for 2 h at room temperature: goat anti-rabbit (1:5000, HY-P8001, New Jersey, USA) or goat anti-mouse (1:5000, HY-P8004, New Jersey, USA). Protein bands were visualized using enhanced chemiluminescence reagent (Absin Biotechnology, Shanghai, China). Densitometric analysis of the protein bands was performed using ImageJ software (version 1.46r, NIH, Bethesda, MD, USA).

Immunohistochemistry

Clinical tissues and tumor tissues from nude mice were fixed in 4% paraformaldehyde (Solarbio, Beijing, China) for 48 h. After fixation, the tissues were embedded in paraffin and sectioned into 4 μm thick slices. The sections were mounted on glass slides and baked at 60 °C for 2 h. Deparaffinization was carried out using xylene, followed by rehydration through a graded series of ethanol. Endogenous peroxidase activity was blocked by incubating the sections with 0.3% H2O2 (Yubo Biotechnology, Shanghai, China) for 20 min. After blocking with normal goat serum (Beyotime, Shanghai, China), the sections were incubated overnight at 4 °C with primary antibodies: rabbit anti-ENO1 (1:100, HY-P87640, MCE, New Jersey, USA), rabbit anti-PKM (1:200, HY-P86631, MCE, New Jersey, USA), rabbit anti-PCNA (1:1000, HY-P86534, MCE, New Jersey, USA), and rabbit anti-Ki67 (1:500, HY-P86608, MCE, New Jersey, USA). Following primary antibody incubation, sections were incubated for 30 min at 37 °C with horseradish peroxidase (HRP)-conjugated goat anti-rabbit secondary antibody (1:500, HY-P8001, New Jersey, USA). Immunoreactivity was visualized using 3,3′-diaminobenzidine (DAB) (Solarbio, Beijing, China) for 5 min at room temperature, followed by counterstaining with hematoxylin. Sections were dehydrated through graded ethanol, cleared with xylene, and mounted with neutral resin. ENO1-positive staining was examined under a light microscope (BX51, Olympus, Tokyo, Japan).

Statistical analysis

Data are presented as mean ± standard deviation (SD) and analyzed using GraphPad Prism 6.0 software (GraphPad Software, San Diego, CA, USA). Statistical comparisons between two groups were performed using a two-tailed paired Student’s t-test, while comparisons among multiple groups were conducted using one-way analysis of variance (ANOVA) followed by Tukey’s post hoc test. Pearson’s correlation analysis was used to assess the correlations between ENO1 and HBc, as well as between PKM and HBc in clinical HBV-infected HCC tissues. A P-value < 0.05 was considered statistically significant.

Results

Overexpression of ENO1 and PKM in HBV-related HCC is associated with patient survival

Analysis using the GEPIA database revealed significantly elevated expression levels of ENO1 and PKM in HCC tissues compared to adjacent normal tissues (Fig. 1A and B; P < 0.001). Survival analysis indicated that high expression of ENO1 and PKM was significantly associated with reduced OS and PFS in patients with HCC (Fig. 1C and D), suggesting that both genes may serve as adverse prognostic markers in HCC. To investigate the potential involvement of ENO1 and PKM in tumor immunity, the TCGA database was integrated with the TIDE analytical tool to assess correlations between gene expression and immune checkpoint markers, immune evasion scores, and immune cell infiltration. Elevated expression of ENO1 and PKM showed significant positive correlations with several immune checkpoint genes, including PD1 (CD274), CTLA4, HAVCR2, TIGIT, and CD40 (Fig. 1E and F; P < 0.05), indicating a potential association with enhanced immune evasion. Furthermore, the high-expression group exhibited significantly higher TIDE scores compared to the low-expression group (Fig. 1G; P < 0.0001), suggesting reduced responsiveness to immunotherapy. Further analysis using the CIBERSORT algorithm demonstrated that ENO1 and PKM expression levels were closely linked to immune cell infiltration patterns within the tumor microenvironment (Fig. 1H and I). High ENO1 expression was inversely correlated with the infiltration of Th17 cells, CD8+ T cells, and eosinophils, while positively correlated with Th2 cells, macrophages, activated dendritic cells (aDC), and helper T cells. Similarly, elevated PKM expression was negatively correlated with Th17 cell infiltration and positively correlated with Th2 cells, macrophages, dendritic cells, and CD56bright NK cells. These results suggest that ENO1 and PKM may contribute to immune evasion and impact the therapeutic response in HBV-related HCC by modulating the tumor immune microenvironment.

Fig. 1.

Fig. 1

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Overexpression of ENO1 and PKM in HBV-Related HCC is associated with patient survival. (A-B) Based on GEPIA database, ENO1 (A) and PKM (B) were highly expressed in HCC tissues than that in adjacent normal tissues. * P < 0.05. ** P < 0.01. *** P < 0.001. (C-D) Analysis of GEPIA database indicated that, high expression of ENO1 (C) and PKM (D) in patients with HCC was associated with shorter overall survival and progression-free survival. (E-F) By analysis of TCGA database with the TIDE analytical tool, high expression of ENO1 (E) and PKM (F) was positively correlated with multiple immune checkpoint genes, including PD1 (CD274), CTLA4, HAVCR2, TIGIT, and CD40. * P < 0.05. ** P < 0.01. *** P < 0.001. **** P < 0.0001. (G) High expression of ENO1 and PKM indicated high TIDE score. **** P < 0.0001. (H-I) By CIBERSORT algorithm, ENO1 (H) and PKM (I) were associated with immune cell infiltration patterns in the tumor microenvironment. * P < 0.05. ** P < 0.01. *** P < 0.001. The symbol “ns” represented differences that were not statistically significant

Experimental validation of ENO1 and PKM expression changes in HBV-related HCC

To validate the differential expression of ENO1 and PKM in HBV-infected HCC, experimental analyses were conducted on clinical tissue samples and cellular models. IHC analysis demonstrated significantly increased protein expression of ENO1 and PKM in HBV-infected HCC tissues (Fig. 2A and B). Consistent with these findings, Western blot analysis confirmed significantly higher protein levels of ENO1 and PKM in HBV-infected HCC tissues compared to normal liver and non-infected HCC tissues (Fig. 2C and D; P < 0.001). qRT-PCR analysis further corroborated these results, showing parallel increases in mRNA expression (Fig. 2E).

Fig. 2.

Fig. 2

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Experimental validation of ENO1 and PKM expression changes in HBV-related HCC. (A-B) Immunohistochemistry of clinical tissues implied the enhanced ENO1 (A) and PKM (B) expression in HCC tissues of patients, especially in HBV-infected HCC tissues. (C-D) Western blot demonstrated the up-regulated proteins for ENO1 and PKM proteins in HCC tissues of patients, especially in HBV-infected HCC tissues. *** P < 0.001 vs. Normal liver tissues. ### P < 0.001 vs. HCC tissues. (E) qRT-PCR revealed the increased expression of ENO1 (D) and PKM (E) mRNAs in HCC tissues of patients, especially in HBV-infected HCC tissues. *** P < 0.001 vs. Normal liver tissues. ### P < 0.001 vs. HCC tissues. (F) Western blot showed the elevated expression of HBc protein in HCC tissues of patients, especially in HBV-infected HCC tissues. *** P < 0.001 vs. Normal liver tissues. ### P < 0.001 vs. HCC tissues. (G-H) Pearson’s correlation analysis displayed positive correlations between ENO1 and HBc (G), as well as between PKM and HBc (H) in HBV-infected HCC tissues. (I-K) qRT-PCR (I) along with Western blot (J, K) indicated the abnormally increased expression of ENO1 and PKM in HCC cells (HepG2), especially in HBV-infected HCC cells (HepG2.2.15). *** P < 0.001 vs. human normal hepatocytes (THLE-2). ### P < 0.001 vs. HCC cells (HepG2)

The expression of HBc protein in normal liver tissues, non-HBV-infected HCC tissues, and HBV-infected HCC tissues was examined by Western blot. Higher HBc protein expression was observed in HBV-infected HCC tissues compared to normal liver and non-HBV-infected HCC tissues (Fig. 2F; P < 0.001). Pearson’s correlation analysis revealed positive correlations between ENO1 and HBc, as well as between PKM and HBc in HBV-infected HCC tissues (Fig. 2G-H; P < 0.001).

At the cellular level, further validation was performed using normal hepatocytes (THLE-2), HCC cells (HepG2), and HBV-stably expressing HCC cells (HepG2.2.15). Both mRNA and protein levels of ENO1 and PKM were significantly upregulated in HepG2.2.15 cells compared to THLE-2 and HepG2 cells (Fig. 2I-K; P < 0.001).

Interaction between ENO1 and PKM promotes PCNA upregulation

To investigate the functional interaction between ENO1 and PKM and its impact on PCNA expression, binding assays were conducted using co-immunoprecipitation (Co-IP), immunofluorescence co-localization (IF), and GST pull-down techniques. Co-IP assays confirmed a direct interaction between ENO1 and PKM, forming a protein complex within cells (Fig. 3A). IF staining revealed significant cytoplasmic co-localization of ENO1 and PKM (Fig. 3B). GST pull-down assays further validated the direct physical association of these proteins (Fig. 3C). These findings establish the ENO1-PKM interaction as a foundation for subsequent functional studies.

Fig. 3.

Fig. 3

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Interaction Between ENO1 and PKM promotes PCNA upregulation. (A) Co-immunoprecipitation experiment indicated the binding between ENO1 and PKM. (B) Immunofluorescence staining showed the co-localization of ENO1 and PKM in the cytoplasm. (C) GST-pull down assay verified the binding between ENO1 and PKM. (D-E) Data from GEPIA database suggested positive correlations between ENO1 and PCNA, between ENO1 and Ki67 (D), between PKM and PCNA and between PKM and Ki67 (E) in HCC tissues. (F-G) Transfection efficiency of pcDNA3.1-ENO1 vectors (F) and PKM siRNA (G) in HepG2.2.15 cells was examined by Western blot. *** P < 0.001 vs. the Control group. ### P < 0.001 vs. the NC or the siNC group. (H-I) By Western blot and qRT-PCR, PKM silencing reduced PCNA and Ki67 mRNA and protein expression in HepG2.2.15 cells, but this effect was reversed by ENO1 overexpression. *** P < 0.001 vs. the Control group. ## P < 0.01 and ### P < 0.001 vs. the ENO1 group. ^^ P < 0.01 and ^^^ P < 0.001 vs. the siPKM group

PCNA and Ki67 are key proteins associated with the malignant proliferation of cancer cells, including those in HCC. To assess the relationship between ENO1/PKM and PCNA/Ki67 in HCC, data from the GEPIA database were analyzed. Positive correlations were observed between ENO1 and both PCNA and Ki67 in HCC tissues (Fig. 3D), as well as between PKM and both PCNA and Ki67 (Fig. 3E). To further explore the regulatory effect of the ENO1-PKM interaction on PCNA expression, HepG2.2.15 cells were transfected with pcDNA3.1-ENO1 vectors and PKM siRNA. As shown in Fig. 3F-G, pcDNA3.1-ENO1 vector transfection significantly increased ENO1 protein levels in HepG2.2.15 cells, while PKM siRNA transfection effectively reduced PKM protein. qRT-PCR and Western blot analysis revealed that ENO1 overexpression in HepG2.2.15 cells significantly elevated PCNA and Ki67 mRNA and protein levels (P < 0.001). Conversely, siRNA-mediated knockdown of PKM substantially reduced PCNA and Ki67 expression (P < 0.001) and effectively counteracted the upregulatory effect of ENO1 on PCNA and Ki67 (Fig. 3H-I). These results indicate that ENO1 may promote PCNA upregulation through its interaction with PKM, with PKM playing a critical synergistic role in this regulatory process.

In summary, these data suggest that the ENO1-PKM interaction may enhance PCNA expression in HBV-related HCC.

Cooperative effects of ENO1 and PKM on HCC cell proliferation, apoptosis, migration, and invasion

To investigate the biological implications of the ENO1-PKM interaction in HCC, a comprehensive series of experiments were conducted to assess their combined effects on cell proliferation, apoptosis, migration, and invasion.

Cell proliferation and apoptosis analysis

The impact of ENO1 and PKM on HepG2.2.15 cell viability, proliferation, and colony formation was evaluated using CCK-8 assay (Fig. 4A), EdU incorporation assay (Fig. 4B), and colony formation assay (Fig. 4C), respectively. ENO1 overexpression significantly enhanced cell viability (P < 0.001), while PKM silencing notably diminished this pro-viability effect (P < 0.01) (Fig. 4A). In the EdU assay, a marked increase in the proportion of EdU-positive cells was observed in the ENO1 overexpression group, indicating enhanced DNA synthesis (Fig. 4B; P < 0.001). In contrast, PKM suppression reduced proliferative capacity, highlighting its critical role in ENO1-driven proliferation (Fig. 4B; P < 0.01). The colony formation assay confirmed these findings, showing an increase in colony numbers following ENO1 overexpression, with PKM silencing reversing this effect (Fig. 4C; P < 0.01).

Fig. 4.

Fig. 4

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Cooperative effects of ENO1 and PKM on HCC cell proliferation, apoptosis, migration, and invasion. (A-C) From CCK-8 (A), EdU incorporation (B) and colony formation (C) assays, PKM silencing suppressed the viability, proliferation and clone formation abilities of HepG2.2.15 cells, which was counteracted by ENO1 overexpression. (D) By TUNEL staining, PKM silencing enhanced the apoptosis of HepG2.2.15 cells, but this influence was abolished by ENO1 overexpression. (E-F) Based on wound healing (E) and Transwell invasion (F) assays, the suppression of PKM silencing on the migration and invasion of HepG2.2.15 cells was reversed by ENO1 overexpression. *** P < 0.001 vs. the Control group. ## P < 0.01 and ### P < 0.001 vs. the ENO1 group. ^^ P < 0.01 vs. the siPKM group

For apoptosis evaluation, TUNEL staining (Fig. 4D) revealed that ENO1 overexpression significantly inhibited apoptosis in HepG2.2.15 cells, whereas PKM silencing counteracted this anti-apoptotic effect, resulting in a significant increase in apoptotic rates (P < 0.01). These results suggest that ENO1 promotes HCC cell proliferation and survival through PKM-mediated mechanisms.

Cell migration and invasion analysis

Wound healing (Fig. 4E) and Transwell invasion assays (Fig. 4F) were performed to assess the roles of ENO1 and PKM in cell migration and invasion. ENO1 overexpression significantly enhanced migratory and invasive capacities (P < 0.001), while PKM silencing reduced these phenotypic traits (P < 0.01). These results indicate that the ENO1-PKM axis contributes to tumor malignancy by not only promoting proliferation but also enhancing migratory and invasive potential.

In conclusion, these results demonstrate that the ENO1-PKM interaction plays a critical role in HCC progression by synergistically regulating cell proliferation, apoptosis, migration, and invasion. ENO1 overexpression amplifies these processes, whereas PKM silencing mitigates their effects, underscoring the pivotal role of the ENO1-PKM axis in hepatocarcinogenesis.

ENO1 and PKM promote HBV replication in HCC by modulating glycolysis

To explore the roles of ENO1 and PKM in metabolic reprogramming and HBV replication in HCC, their regulation of glycolysis and associated downstream effects were systematically analyzed. Key glycolytic markers revealed that ENO1 overexpression significantly enhanced glycolytic activity in HepG2.2.15 cells, while PKM silencing notably attenuated this effect.

In particular, the ENO1 overexpression group exhibited a significant increase in glucose consumption and lactate production (Fig. 5A and B; P < 0.001), indicative of robust glycolytic activation. Conversely, PKM silencing reduced these parameters, effectively neutralizing the pro-glycolytic effect of ENO1 overexpression (P < 0.001). Metabolic profiling showed elevated levels of pyruvate and G6PD in the ENO1 overexpression group (Fig. 5C and D; P < 0.001), along with a marked increase in 2-PG (Fig. 5E; P < 0.001), reflecting enhanced glycolytic flux and key enzymatic activity. Additionally, the NADPH/NADP+ ratio was significantly elevated in ENO1-overexpressing cells (Fig. 5F; P < 0.001), suggesting that glycolytic activation not only meets energy demands but also maintains cellular redox balance, thus bolstering antioxidant defenses in tumor cells.

Fig. 5.

Fig. 5

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ENO1 and PKM promote HCC progression and HBV replication by modulating glycolysis. (A-B) According to glycolysis assay, PKM silencing suppressed glucose consumption (A) and lactate production (B) of HepG2.2.15 cells, whereas this influence was reversed by ENO1 overexpression. (C-E) Analysis of metabolic intermediates implied the inhibition of PKM silencing on the production of pyruvate (C), G6PD (D), and 2-PG (E) of HepG2.2.15 cells. ENO1 overexpression counteracted this effect of PKM silencing. (F) PKM silencing in HepG2.2.15 cells decreased NADPH/NADP+ ratio, which was reversed by ENO1 overexpression. (G) qRT-PCR indicated that the inhibition of PKM silencing on HBV DNA replication in HepG2.2.15 cells was abrogated by ENO1 overexpression. (H) According to ELISA, PKM silencing reduced HBsAg level in HepG2.2.15 cells, but ENO1 overexpression eliminated this effect. (I) Western blot implied that, the repression of PKM silencing on HBx protein expression was counteracted by ENO1 overexpression. *** P < 0.001 vs. the Control group. ## P < 0.01 vs. the ENO1 group. ^^ P < 0.01 vs. the siPKM group

To assess HBV replication, qRT-PCR and ELISA were performed to quantify HBV DNA and HBsAg levels (Fig. 5G-H). ENO1 overexpression significantly increased HBV DNA and HBsAg levels (P < 0.001), whereas PKM silencing markedly decreased HBV replication and transcription (P < 0.001). By Western blot analysis, ENO1 overexpression increased HBx protein in HepG2.2.15 cells (P < 0.001), whereas PKM silencing reduced it (P < 0.001). The suppression of PKM silencing on HBx protein in HepG2.2.15 cells was abrogated by ENO1 overexpression (Fig. 5I; P < 0.001). This reinforced the critical role of the ENO1-PKM axis in HBV replication.

In summary, these results demonstrate that ENO1 and PKM drive HCC progression by activating glycolysis, thereby enhancing metabolic flexibility to sustain energy production and provide biosynthetic precursors for rapid tumor proliferation. Additionally, glycolytic upregulation creates a metabolic environment conducive to HBV replication. The regulatory role of PKM is further emphasized by the fact that its silencing significantly reduces ENO1-induced metabolic reprogramming. These insights highlight the critical role of glycolysis in both HCC progression and HBV replication, suggesting potential therapeutic strategies targeting both metabolic pathways and viral infection simultaneously.

Validation of ENO1 and PKM effects on HCC progression and HBV replication in a murine tumor model

To investigate the roles of ENO1 and PKM in HCC progression and HBV replication, a tumor-bearing nude mouse model was established using HBV-stably expressing HepG2.2.15 cells. The experimental groups included a control group (Control), an ENO1 overexpression group (ENO1-OE), a PKM knockdown group (siPKM), and a combination group with ENO1 overexpression and PKM knockdown (ENO1-OE + siPKM). Tumor growth and HBV replication were systematically assessed across these groups.

Tumor Growth Analysis: Tumor progression was significantly accelerated in the ENO1-OE group compared to the control group (Fig. 6A; P < 0.001), with notable increases in both tumor volume and weight (Fig. 6B and C; P < 0.001). In contrast, the siPKM group exhibited a significantly slower tumor growth rate, accompanied by reduced tumor volume and weight (P < 0.01). In the ENO1-OE + siPKM group, tumor volume and weight were higher than in the siPKM group but significantly lower than those in the ENO1-OE group (P < 0.01), indicating that ENO1 promotes rapid tumor growth via PKM, with PKM playing a pivotal regulatory role in this process.

Fig. 6.

Fig. 6

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Validation of ENO1 and PKM effects on HCC progression and HBV replication in a Murine tumor model. (A) Images of the xenograft tumors in nude mice and the stripped tumor tissues. (B-C) PKM silencing reduced the volume (B) and weight(C) of the xenograft tumors, but was counteracted by ENO1 overexpression. (D-F) By qRT-PCR (D) and Western blot (E, F), PKM silencing suppressed the expression of ENO1, PKM2, PCNA and Ki67 in xenograft tumors, whereas ENO1 overexpression reversed this effect of PKM silencing. (G) Based on immunohistochemistry, the suppression of PKM silencing on the staining of ENO1, PKM2, PCNA and Ki67 proteins in xenograft tumors was abolished by ENO1 overexpression. (H-I) The inhibition of PKM silencing on HBV DNA replication (H) and HBsAg secretion (I) in xenograft tumors was eliminated by ENO1 overexpression. *** P < 0.001 vs. the Control group. ## P < 0.01 vs. the ENO1 group. ^^ P < 0.01 vs. the siPKM group

Molecular Validation in Tumor Tissues: qRT-PCR and Western blot analyses of tumor tissues confirmed the regulatory roles of ENO1 and PKM in HCC. In the ENO1-OE group, mRNA and protein levels of PCNA, Ki67 (cellular proliferation markers), and HBx (a key HBV protein) were significantly upregulated (Fig. 6D–F; P < 0.001). Conversely, PKM silencing led to a marked reduction in these markers (P < 0.01), demonstrating that PKM knockdown effectively inhibited ENO1-mediated tumor proliferation and HBV replication. IHC staining further corroborated these findings, revealing increased expression of ENO1, PKM, PCNA, and Ki67 in tumor tissues (Fig. 6G).

HBV Replication Analysis: HBV replication was assessed via qRT-PCR to measure HBV DNA levels and ELISA for HBsAg secretion. ENO1 overexpression significantly enhanced HBV DNA replication and HBsAg secretion (Fig. 6H and I; P < 0.001), whereas PKM silencing significantly suppressed HBV replication and transcription, neutralizing the pro-replicative effects of ENO1 (P < 0.01).

The murine tumor model demonstrates that ENO1 and PKM synergistically facilitate HCC progression by regulating tumor metabolism and HBV replication. ENO1 overexpression enhances tumor growth through PKM activation, creating a metabolic environment conducive to HBV replication. Conversely, PKM silencing significantly disrupts these processes, underscoring its essential role in tumor metabolic regulation and viral replication.

Verification of ENO1 and PKM influences on HBV replication in HCC by regulating glycolysis in a mouse tumor model

To investigate whether ENO1 and PKM influence HBV replication in HCC through the regulation of glycolysis, HepG2.2.15 cells stably transfected with lentiviral ENO1 and PKM overexpression vectors were injected into nude mice to establish a tumor-bearing model. Subsequently, the model was treated with 500 mg/kg 2-DG via intraperitoneal injection.

Cell transfection efficiency assay

Western blot analysis confirmed successful transfection of the ENO1 and PKM overexpression vectors in HepG2.2.15 cells, leading to significantly elevated protein levels of ENO1 and PKM (Fig. 7A-B; P < 0.001), validating the successful modulation of these proteins for subsequent in vivo studies.

Fig. 7.

Fig. 7

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Verification of ENO1 and PKM influences on HBV replication in HCC by regulating glycolysis in a mouse tumor model. (A-B) Western blot showed the effectively increased expression of ENO1 (A) and PKM (B) proteins in HepG2.2.15 cells by transfecting with lentiviral ENO1 overexpression vectors and lentiviral PKM overexpression vectors. (C) Images of the xenograft tumors in nude mice and the stripped tumor tissues. (D-E) ENO1 and PKM overexpression increased the volume (D) and weight (E) of the xenograft tumors, which was abrogated by 2-DG treatment. (F-G) By qRT-PCR and ELISA, 2-DG treatment reversed the promotion of ENO1 and PKM on HBV DNA replication (F) and HBsAg secretion (G) in xenograft tumors. *** P < 0.001 vs. the NC group. ### P < 0.001 vs. the ENO1 group. ^^^ P < 0.001 vs. the PKM group

Tumor growth analysis

Tumor growth in nude mice was significantly reduced following 2-DG treatment, as evidenced by lower tumor volume and weight in the NC + 2-DG group than the NC group (P < 0.001). In contrast, the ENO1 and PKM overexpression groups exhibited increased tumor growth, with larger tumor volumes and weights (P < 0.001). However, 2-DG administration attenuated tumor growth in these groups, as indicated by smaller tumor volumes and weights in the ENO1 + 2-DG and PKM + 2-DG groups compared to the ENO1 and PKM groups, respectively (Fig. 7C-E; P < 0.001).

HBV replication analysis

The levels of HBV DNA were assessed by qRT-PCR (Fig. 7F) and HBsAg secretion by ELISA (Fig. 7G). The NC + 2-DG group showed significantly lower HBV DNA and HBsAg levels compared to the NC group (P < 0.001). Conversely, the ENO1 and PKM overexpression groups exhibited elevated HBV DNA replication and HBsAg secretion (P < 0.001). Importantly, 2-DG administration effectively reduced HBV replication and secretion in both the ENO1 + 2-DG and PKM + 2-DG groups compared to the ENO1 and PKM groups (P < 0.001).

In conclusion, the in vivo data suggest that ENO1 and PKM promote HBV replication in HCC by enhancing glycolytic activity, highlighting their potential as therapeutic targets for HBV-infected HCC.

Discussion

Role of ENO1 and PKM in HBV-associated HCC

This study identified the critical roles of ENO1 and PKM in HBV-associated HCC, demonstrating that their interaction in promoting both HCC cell proliferation and HBV replication. These findings provide novel insights into how metabolic reprogramming influences HBV-associated HCC. As a key enzyme in glycolysis, ENO1 acts not only as a catalyst in energy metabolism but also influences tumor cell proliferation, migration, and immune evasion through non-enzymatic functions [17, 18, 38]. This study explored the role of ENO1 in HCC, especially in HBV-related HCC. Results suggested the abundantly expressed ENO1 in HCC patients, which was markedly linked to the worse overall survival and progression-free survival of HCC patients. Importantly, ENO1 upregulation was more significantly in HBV-related HCC tissues and cells. Functionally, ENO1 overexpression could promote the replication and transcription of HBV, and inhibit the proliferation, migration, invasion and glycolysis of HBV-related HCC cells. PKM, particularly its PKM2 isoform, supports tumor growth and metastasis by modulating glycolytic flux and enhancing the energy acquisition capacity of tumor cells [28, 39]. As suggested by this work, PKM silencing showed an opposite effect to that of ENO1 overexpression, which exerted anti-tumor effect on HBV-related HCC. Importantly, this study further elucidated the interaction between ENO1 and PKM, suggesting that their synergistic mechanism drives HCC progression primarily. Specifically, in vitro mechanistic studies suggested that ENO1 might exacerbate the advancement of HBV-related HCC via interacting with PKM. Similarly, through in vivo study, ENO1 was suggested to enhance the growth of HBV-related HCC in nude mice, but was reversed by PKM silencing. Previous research has explored the individual roles of ENO1 and PKM in various malignancies. ENO1 overexpression in breast, gastric, and lung cancers has been linked to increased tumor aggressiveness and metastatic potential [17, 40, 41]. Likewise, PKM’s regulatory role in glycolysis and metabolic reprogramming is widely recognized as essential for tumor cell survival and proliferation [15, 42–44]. However, these previous studies have mainly focused on the independent oncogenic effects of ENO1 or PKM. In contrast, this study uniquely identifies the synergistic interaction between ENO1 and PKM in HBV-associated HCC, offering an innovative perspective.

Interaction between ENO1/PKM and PCNA

The ENO1-PKM interaction was shown to enhance PCNA expression. PCNA, a key player in DNA replication and transcription, supports tumor cell survival and proliferation, while also participating in glycolytic processes [45]. Moreover, PCNA has been identified as a key prognostic marker for predicting HCC survival due to its strong correlation with tumor cell proliferation [46]. Recent studies have suggested that PCNA is essential for HBV DNA synthesis, further emphasizing its relevance in HBV-associated HCC [47]. The present study demonstrates that the ENO1-PKM interaction promotes PCNA expression in HBV-associated HCC. While PCNA’s role in tumors has been extensively studied, its interactions with metabolic enzymes, particularly the synergistic regulation of HCC progression by ENO1 and PKM through PCNA modulation, remain underexplored. The data presented here offer new avenues for understanding the interplay between metabolic enzymes and cell proliferation regulators.

Significance of metabolic reprogramming in HBV-associated HCC

Metabolic reprogramming is a crucial process in the development and progression of HCC [48, 49]. Tumor cells often reconfigure metabolic pathways to enhance energy acquisition, supporting rapid proliferation and growth [50, 51]. Glycolysis, particularly the activation of PKM2, plays a pivotal role in metabolic reprogramming within HCC cells, not only promoting glycolytic activity but also driving cell proliferation and cycle progression [27, 52, 53]. As a key glycolytic enzyme, ENO1 overexpression has been implicated in enhancing tumor cell proliferation and metastasis [17, 54, 55]. This study demonstrates that the interaction between ENO1 and PKM promotes HCC cell proliferation and HBV replication, thereby clarifying the role of metabolic reprogramming in HBV-associated HCC. Increasing evidence highlights the relationship between metabolic reprogramming and viral replication [56, 57]. While prior studies have predominantly examined the impact of metabolic alterations on tumor biology [58, 59], the contribution of viral infection to tumorigenesis has often been underexplored. The current findings suggest that metabolic pathway modifications not only accelerate tumor growth but also create a microenvironment conducive to viral replication, offering a novel perspective on the pathogenesis of HBV-associated HCC. Regulating the expression of ENO1 and PKM may potentially modulate HBV replication and impede HCC progression, presenting a promising clinical intervention approach.

Clinical significance and future perspectives

This study identified ENO1 and PKM as potential therapeutic targets in HBV-associated HCC. Aberrant overexpression or activation of ENO1 and PKM is a critical aspect of metabolic reprogramming in HCC cells, facilitating tumor proliferation and HBV replication. Targeting ENO1 and PKM could thus provide innovative therapeutic strategies for managing HBV-associated HCC. Developing pharmacological inhibitors of ENO1 and PKM, especially when combined with antiviral and anticancer therapies, may open new clinical avenues.

Furthermore, the study emphasizes the interaction between metabolic enzymes and immunomodulatory factors. Despite advancements in tumor immunotherapy, integrating metabolic regulation to enhance immune efficacy remains a challenge. Modulating ENO1 and PKM expression may influence immune cell infiltration and immune escape mechanisms within the tumor microenvironment, potentially impacting the success of immunotherapeutic interventions. Future research should focus on clarifying the roles of ENO1 and PKM in modulating the immune microenvironment and assessing their therapeutic potential in combination with immunotherapy.

This study has limitations. This study established an immunodeficient xenograft model by injecting HepG2.2.15 cells into immunodeficient xenograft nude mouse. However, immunodeficient xenograft nude mouse did not fully recapitulate immune-mediated HBV-host interactions observed in patients. Establishing an immunocompetent xenograft animal model would be more helpful in elucidating the immune-mediated HBV-host interactions observed in patients. However, current laboratory conditions do not allow for the completion of this part of the experiment, and this issue will be a priority for our future research.

Conclusions

This study uncovers a novel mechanism through which ENO1 interacts with PKM to promote the progression of HBV-associated HCC and HBV replication. Intuitively, ENO1 may promote HBV replication and transcription, as well as proliferation, migration, invasion and glycolysis in HBV-infected HCC cells via interacting with PKM. Indirectly, ENO1 may interact with PKM to promote metabolic reprogramming, thereby enhancing HBV replication in HCC. The interaction between ENO1 and PKM is integral to metabolic reprogramming in HCC, offering new therapeutic targets for clinical application. Further investigation into the roles of ENO1 and PKM in tumor immunomodulation and their potential as clinical therapeutic targets is essential.

In HBV-infected hepatocellular carcinoma (HCC) cells, the ENO1 protein promotes PCNA transcription through interaction with the PKM protein, thereby promoting HBV replication and HCC progression.

Supplementary Information

Below is the link to the electronic supplementary material.

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Supplementary Material 2 (200.4KB, docx)
Supplementary Material 3 (1.4MB, tif)
Supplementary Material 4 (1.4MB, tif)
Supplementary Material 5 (2.8MB, tif)
Supplementary Material 6 (1.4MB, tif)
Supplementary Material 7 (1.4MB, tif)
Supplementary Material 8 (1.4MB, tif)
Supplementary Material 9 (1.4MB, tif)
Supplementary Material 10 (1.4MB, tif)
Supplementary Material 11 (1.4MB, tif)

Acknowledgements

n/a.

Authors’ contributions

Yan-chao Hu and Hui-juan Liu: Methodology, Investigation, Data curation, original draft. Mei-ying Gu, Zi-min Ma: Methodology, Investigation, Data curation, original draft. Li-Na Ma and Xiang-Chun Ding: Writing, review and editing.

Funding

Ningxia Natural Science Foundation (2022AAC02065,2024AAC03701, 2025AAC030940).

Data availability

The datasets used during the present study are available from the corresponding author upon reasonable request. All the data obtained in the current study were available from the corresponding authors on reasonable request.

Declarations

Ethics approval and consent to participate

The study was conducted following ethical approval from the General Hospital of Ningxia Medical University.

Consent for publication

Written informed consent was obtained from all participants.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Li-Na Ma, Email: 13619511758@163.com.

Xiang-Chun Ding, Email: 13619511768@163.com.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Material 1 (1.4MB, tif)
Supplementary Material 2 (200.4KB, docx)
Supplementary Material 3 (1.4MB, tif)
Supplementary Material 4 (1.4MB, tif)
Supplementary Material 5 (2.8MB, tif)
Supplementary Material 6 (1.4MB, tif)
Supplementary Material 7 (1.4MB, tif)
Supplementary Material 8 (1.4MB, tif)
Supplementary Material 9 (1.4MB, tif)
Supplementary Material 10 (1.4MB, tif)
Supplementary Material 11 (1.4MB, tif)

Data Availability Statement

The datasets used during the present study are available from the corresponding author upon reasonable request. All the data obtained in the current study were available from the corresponding authors on reasonable request.

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