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. 2025 Feb 6;17(1):mjaf001. doi: 10.1093/jmcb/mjaf001

ENO1–BACE2-mediated LDLR cleavage promotes liver cancer progression by remodelling cholesterol metabolism

Zhikun Li 1,#, Kaixiang Fan 2,3,#, Caixia Suo 4, Xuemei Gu 5, Chuxu Zhu 6, Haoran Wei 7, Liang Chen 8, Ping Gao 9,10,11,✉, Linchong Sun 12,13,✉
Editor: Weiwei Yang
PMCID: PMC12246778  PMID: 39914449

Abstract

Enolase 1 (ENO1) is a glycolytic enzyme involved in tumour progression that performs a variety of classical and nonclassical functions. However, the mechanism by which it promotes tumour progression is still not fully understood. Here, we found that ENO1 can bind to β-site amyloid precursor protein cleaving enzyme 2 (BACE2), a codependent gene of ENO1, in liver cancer cells. By suppressing lysosomal-dependent degradation, ENO1 stabilizes BACE2 protein level without affecting its messenger RNA level. Further analysis revealed that ENO1 and BACE2 promote low-density lipoprotein receptor (LDLR) cleavage, leading to decreased absorption of exogenous cholesterol. To maintain intracellular cholesterol levels, ENO1 and BACE2 upregulate the expression of genes involved in de novo cholesterol synthesis through a negative feedback mechanism. Both in vitro and in vivo, BACE2 mediates the tumour-promoting effect of ENO1 in liver cancer. Finally, high expression levels of ENO1 and BACE2 and low expression levels of LDLR were detected in clinical hepatocellular carcinoma samples, and abnormal expression of the ENO1–BACE2–LDLR axis was significantly associated with poor prognosis in patients with liver cancer. These data collectively demonstrated that ENO1 functions in protein cleavage by binding to BACE2 and promotes liver cancer progression by reprogramming cholesterol metabolism.

Keywords: ENO1, BACE2, LDLR, protein cleavage, liver cancer, cholesterol metabolism

Introduction

Aerobic glycolysis is a prominent feature of tumour development (Faubert et al., 2020; Pavlova et al., 2022). Enolase is a glycolytic enzyme that catalyzes the conversion of 2-phosphoglyceric acid to phosphoenolpyruvate. In mammals, there are three isoforms of enolase, encoded by the ENO1, ENO2, and ENO3 genes (Pancholi, 2001). Abnormal expression of these enzymes has been reported in various types of cancer, and more attention is given to Enolase 1 (ENO1), which is relatively abundant in tumours (Huang et al., 2022).

ENO1 is a multifunctional protein that not only promotes the Warburg effect as a classical glycolytic enzyme but also acts as a plasminogen receptor and DNA-binding protein (Miles et al., 1991; Ray and Miller, 1991). We recently reported that ENO1 can act as an RNA-binding protein to promote the messenger RNA (mRNA) degradation of iron-regulating protein (IRP1) or mRNA translation of yes-associated protein (YAP1), thereby suppressing ferroptosis and enhancing arachidonic acid metabolism, leading to tumour progression (Zhang et al., 2022a; Sun et al., 2023). Therefore, ENO1 plays an important role in the development of tumours, and its transcriptional, translational, and posttranslational modification activities and immunomodulatory effects on tumourigenesis have attracted increasing attention. Some functional studies have suggested that ENO1 is a potential target for cancer therapy (Cappello et al., 2013; Chen et al., 2022). Genetic codependence-based screening for functionally interacting genes has recently been applied to identify new vulnerabilities in cancer. The Cancer Dependency Map (DepMap) aims to use single-gene perturbations to reveal gene codependencies in hundreds of cancer cell lines through genome-wide clustered regularly interspaced short palindromic repeats (CRISPR) and short hairpin RNA (shRNA) screens (Tsherniak et al., 2017; Dwane et al., 2021). Perturbations in codependent genes (i.e. other genes whose effects on growth are strongly positively or negatively correlated with this gene) result in correlated phenotype changes in cell proliferation, signalling pathways, and metabolic transformation. Recently, PRC2 and monocytic leukemia zinc finger protein (MOZ) were identified as codependent genes of the mixed lineage leukemia (MLL)–MEN1 complex (Hemming et al., 2022; Chen et al., 2023). MEAF6, a component of the MOZ complex, was further found to bind to MLL1/2 to synergistically regulate gene transcription (Hemming et al., 2022). However, the codependent genes and interacting proteins of ENO1 and how they synergistically participate in the tumour process remain unclear.

Here, we found that β-site amyloid precursor protein (APP) cleaving enzyme 2 (BACE2) is a potential codependent gene of ENO1 in liver cancer. ENO1 interacts with BACE2 and stabilizes BACE2 protein level by suppressing its lysosomal degradation. ENO1 and BACE2 further promote low-density lipoprotein receptor (LDLR) cleavage and prevent the absorption of exogenous cholesterol but induce the accumulation of intracellular cholesterol by upregulating the mRNA levels of cholesterol synthetic genes, leading to increased cell proliferation and accelerated tumour growth. Moreover, we revealed abnormal expression of the ENO1–BACE2–LDLR axis in clinical liver cancer samples and its significant correlation with poor prognosis in patients with liver cancer. This study thus describes a previously unrecognized role of ENO1 in regulating protein cleavage, expanding our understanding of the relationship between glycolytic enzymes and cholesterol metabolism as well as the roles of the above signalling cascades in tumour progression.

Results

ENO1 binds to BACE2 in liver cancer cells

Increasing attention has been given to the roles of metabolic enzymes in tumour development. We previously reported that ENO1, which is an RNA-binding protein, promotes liver cancer progression by activating arachidonic acid metabolism and suppressing ferroptosis. However, as a highly abundant metabolic enzyme similar to a housekeeping protein, its role in tumours is not fully understood. To discover the genetic vulnerabilities of cancer cells (Shimada et al., 2021), it is important to identify the codependent genes of ENO1. DepMap analysis revealed codependent genes of ENO1, and the top 10 genes were UNKL, TMEM176B, FAM181B, AGBL3, BACE2, MORC3, USP40, TRIM36, GAPDH, and SYT9 (Figure 1A). A comprehensive analysis of the correlation between expression levels of these genes and ENO1 in liver cancer (Figure 1B; Tang et al., 2017) as well as the association between high or low expression level of these genes and hepatocellular carcinoma (HCC) prognosis (Figure 1C; Menyhart et al., 2018) revealed that GAPDH, BACE2, and TRIM36 were the top 3 genes (Figure 1D; Supplementary Figure S1A). It has been known that glyceraldehyde-3-phosphate dehydrogenase (GAPDH) promotes whereas tripartite motif containing 36 (TRIM36) inhibits liver cancer progression (Liu et al., 2017; Tong et al., 2022). However, whether and how BACE2, a β-secretase that functions in Alzheimer's disease (AD), regulates liver cancer progression are largely unknown.

Figure 1.

Figure 1

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ENO1 binds to BACE2 in liver cancer cells. (A) The top 10 codependent genes of ENO1 via DepMap (Achilles + DRIVE + Marcotte DEMETER2). (B) Correlations between ENO1 and the indicated gene expression levels in liver cancer via GEPIA 2. (C) Kaplan–Meier curves with univariate analyses of HCC patients with low vs. high expression levels of the 10 indicated genes. (D) Venn diagram showing the overlapping codependent genes identified through survival analysis in liver cancer (P < 0.05) and ENO1 correlation analysis in liver cancer (R > 0.2) of the 10 indicated genes. (E) HEK293T cells were transfected with GFP-ENO1 plasmids together with Flag-EV or Flag-BACE2 plasmids. The cell lysates were immunoprecipitated with IgG or anti-Flag antibodies, followed by western blot analysis. (F) Huh7 and PLC cells stably overexpressing GFP-ENO1 were infected with viruses expressing Flag-EV or Flag-BACE2. The cell lysates were immunoprecipitated with IgG or anti-Flag antibodies, followed by western blot analysis. (G) Representative images of PLC and Huh7 cells in PLA. Evidence of proximity between ENO1 and BACE2 is indicated by the appearance of red puncta. Nuclei are counterstained with DAPI (blue). Scale bar, 10 μm or 5 μm.

Proteins encoded by codependent genes often interact with each other, such as the epigenetic codependent MLL and Menin (Fiskus et al., 2022, 2023) as well as MOZ and the Menin–MLL complex (Hemming et al., 2022). We next investigated whether ENO1 binds to BACE2 by performing co-immunoprecipitation (co-IP) assays. The results clearly demonstrated that Flag-tagged BACE2 interacted with GFP-tagged ENO1 in 293T cells (Figure 1E). Similar results were also observed in different liver cancer cell lines, including Huh7, PLC, Hep3B, and HepG2 (Figure 1F; Supplementary Figure S1B and C). To further confirm the interaction between ENO1 and BACE2, we performed the Duolink proximity ligation assay (PLA). This assay allows visualizing protein–protein interactions with high specificity and sensitivity (Fredriksson et al., 2002). Consistent with the co-IP results, a positive PLA signal was observed when both ENO1 and BACE2 antibodies, but not ENO1 or BACE2 antibody alone, were used in PLC and Huh7 cells (Figure 1G). These data support that ENO1 interacts with BACE2 at endogenous protein level. Collectively, we found that BACE2 is a codependent gene of ENO1 and binds to the ENO1 protein in liver cancer cells.

ENO1 stabilizes BACE2 protein level by suppressing its lysosomal degradation

Western blot analysis revealed that ENO1 knockdown significantly suppressed BACE2 protein levels (Figure 2A), while BACE2 knockdown had little effect on ENO1 protein levels (Figure 2B) in PLC and Huh7 cells. Further quantitative polymerase chain reaction (qPCR) analysis revealed that BACE2 mRNA levels remained consistent when ENO1 was knocked down, suggesting that ENO1 regulates BACE2 protein expression at the posttranscriptional level (Supplementary Figure S2A and B). The ubiquitin–proteasome system and autophagy–lysosome system are two major quality control systems responsible for protein degradation (Pohl and Dikic, 2019). To determine the pathway involved, we treated control and ENO1-knockdown PLC cells with 10 μM MG132 (Z-Leu-Leu-Leu-al, a potent proteasome inhibitor that effectively blocks the proteolytic activity of the 26S proteasome complex) for 8 h, 25 μM chloroquine (an autophagic inhibitor that blocks autophagosome fusion with lysosomes and slows lysosomal acidification) for 8 h, or 50 mM ammonium chloride (NH4Cl, a lysosomal inhibitor that increases the intralysosomal pH and prevents the activation of degradative enzymes inside lysosomes) for 12 h, followed by western blot analysis for BACE2 protein levels. As shown in Figure 2C–E, ENO1 knockdown-induced degradation of BACE2 was partially inhibited by the autophagy inhibitor chloroquine and the lysosomal inhibitor NH4Cl but not the proteasome inhibitor MG132. Similar results were observed when control and ENO1-knockdown Huh7 cells were treated accordingly (Supplementary Figure S2C–E). These results indicate that ENO1 promotes BACE2 protein stabilization by suppressing the autolysosome pathway.

Figure 2.

Figure 2

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ENO1 stabilizes BACE2 protein level by suppressing its lysosomal degradation. (A and B) Western blot analysis of ENO1 and BACE2 protein levels in PLC (left panel) and Huh7 (right panel) cells with ENO1 knockdown (A) or BACE2 knockdown (B). Actin served as a negative control. (C–E) Western blot analysis of ENO1 and BACE2 expression in control (NTC) and ENO1-knockdown (sh1 and sh2) cells treated with DMSO or 10 μM MG132 (C, for 8 h), 25 μM chloroquine (D, for 8 h), or 50 mM NH4Cl (E, for 12 h). Actin served as a negative control. Relative BACE2 expression was normalized to actin, and greyscale values were analysed by Image J. The experiments were independently repeated three times with similar results. The error bars denote mean ± SEM. *P < 0.05, unpaired Student's t-test.

ENO1 and BACE2 promote LDLR cleavage

BACE2 is an aspartic protease whose classic function is to cleave APP (Farzan et al., 2000). However, other substrates and the potential role of BACE2 in tumours through its cleavage function are largely unknown. In a previous study, Stützer et al. (2013) systematically investigated BACE2 substrates in pancreatic β-cells. By analysing their published data, we identified an interesting protein, LDLR, as a potential substrate of BACE2. We first tested whether BACE2 regulates LDLR protein levels in liver cancer cells. Indeed, overexpression of BACE2 significantly decreased whereas knockdown of BACE2 significantly increased the total protein level of endogenous LDLR in Huh7 and PLC cells (Figure 3A and B). To investigate whether BACE2 decreases LDLR protein level through its cleavage function, LDLR with an N-terminal Flag tag (Flag-N-LDLR) or C-terminal Flag tag (Flag-C-LDLR) was overexpressed in control or BACE2-overexpressing PLC cells. The shed N-terminal fragments of LDLR in the supernatant and the C-terminal fragments or full-length LDLR in cell lysates were subsequently detected. The results showed that in BACE2-overexpressing cells, the abundance of full-length LDLR was downregulated, whereas abundances of N-terminal fragments of LDLR in the supernatant and C-terminal fragments in cell lysates were both upregulated (Figure 3C–F). These data suggest that LDLR is indeed cleaved by BACE2 in liver cancer cells.

Figure 3.

Figure 3

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ENO1 and BACE2 promote LDLR cleavage. (A and B) Western blot analysis of LDLR protein levels in Huh7 (left panel) and PLC (right panel) cells with BACE2 overexpression (A) or BACE2 knockdown (B). Actin served as a negative control. (C) PLC cells overexpressing Flag-N-LDLR were infected with viruses expressing HA-EV or HA-BACE2. Full-length Flag-N-LDLR in cell lysates and the N-terminus of LDLR in the supernatant were detected via western blotting. Actin (left panel) or ponceau staining (right panel) served as negative controls. (D) Working model of BACE2 cleavage of Flag-N-LDLR. (E) PLC cells overexpressing Flag-C-LDLR were infected with viruses expressing HA-EV or HA-BACE2. Full-length Flag-C-LDLR and C-terminus of LDLR in cell lysates (left panel), as well as BACE2 and endogenous LDLR protein (right panel), were detected via western blotting. Actin served as a negative control. (F) Working model of BACE2 cleavage of Flag-C-LDLR. (G) Western blot analysis of BACE2 and LDLR protein levels in control and ENO1-knockdown PLC cells. Actin served as a negative control. (H) PLC cells overexpressing Flag-N-LDLR were infected with viruses expressing NTC or shENO1. Full-length Flag-N-LDLR in cell lysates and the N-terminus of LDLR in the supernatant were detected via western blotting. Actin (left panel) or ponceau staining (right panel) served as negative controls.

Next, we explored whether ENO1 regulates LDLR cleavage. At the endogenous level, ENO1 knockdown decreased BACE2 expression but increased LDLR expression (Figure 3G). Similarly, when ENO1 was knocked down in PLC cells overexpressing Flag-N-LDLR, the abundance of full-length LDLR in cell lysates increased, while that of N-terminal fragments in the supernatant decreased (Figure 3H). Collectively, these data suggest that both ENO1 and BACE2 inhibit LDLR expression through protein cleavage.

BACE2 promotes cholesterol synthesis and liver cancer cell proliferation

BACE2 has been studied primarily in the central nervous system, where it cleaves amyloid-β (Aβ) peptides, and the subsequent aggregation of the Aβ peptide drives AD. However, the function of BACE2 in cancer, especially liver cancer, is largely unknown. Cell growth assays performed in PLC, Huh7, and Hep3B cells with BACE2 knockdown or overexpression revealed that BACE2 knockdown significantly suppressed cell proliferation (Figure 4A; Supplementary Figure S3A), while BACE2 overexpression significantly facilitated cell proliferation in liver cancer cells (Figure 4B; Supplementary Figure S3B), indicating that BACE2 acts as an oncogene to promote liver cancer cell proliferation.

Figure 4.

Figure 4

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BACE2 promotes cholesterol synthesis and liver cancer cell proliferation. (A and B) Cell growth curves and crystal violet staining of PLC (left panel) and Huh7 (right panel) cells with BACE2 knockdown (A) or BACE2 overexpression (B). The western blots revealed the knockdown or overexpression efficiency of BACE2. (C–E) EV control and BACE2-overexpressing PLC cells were incubated with Dil-LDL for 6 h at 37°C. (C) Representative images and statistical analysis of Dil-fluorescence intensity. Scale bar, 50 μm. (D) Determination of the LDL-C concentration in the supernatant. (E) Determination of the intracellular total cholesterol level. (F and G) qPCR detection of the expression levels of cholesterol synthetic genes in PLC cells with BACE2 knockdown (F) or BACE2 overexpression (G). The error bars denote mean ± SD (A and B) or mean ± SEM (C–G). *P < 0.05, unpaired Student's t-test.

LDLR is a receptor normally localizd to the cell membrane, where it binds to cholesterol-rich LDLs and mediates their entry into cells (Catapano, 1989). To determine whether BACE2 regulates exogenous cholesterol uptake, we incubated empty vector (EV) control and BACE2-overexpressing liver cancer cells with Dil-LDL (an LDL complex with fluorescent labels). The fluorescence intensity in BACE2-overexpressing cells was lower than that in EV control cells (Figure 4C), indicating that BACE2 inhibits the uptake of exogenous cholesterol. Additionally, the low-density lipoprotein cholesterol (LDL-C) content in the cell culture medium of BACE2-overexpressing cells was greater than that in EV control cells (Figure 4D), indicating an impaired ability of BACE2-overexpressing cells to take up LDL-C. Interestingly, BACE2 overexpression promoted the accumulation of total cholesterol (Figure 4E). Previous studies have shown that LDLR plays an inhibitory role in liver cancer and blocking LDLR expression promoted liver cancer progression by reprogramming cholesterol synthesis (Chen et al., 2021a). Therefore, we speculated that BACE2 inhibits exogenous cholesterol absorption by promoting LDLR cleavage, which in turn activates cholesterol synthesis via negative feedback to promote cellular cholesterol accumulation. Indeed, qPCR analysis revealed that BACE2 knockdown significantly suppressed mRNA levels of genes related to cholesterol biosynthetic processes, including HMGCR, SQLE, NSDHL, DHCR7, MVK, and CYP51A1 (Figure 4F), while BACE2 overexpression significantly upregulated these gene levels (Figure 4G). Collectively, we concluded that BACE2 promotes cellular cholesterol synthesis through negative feedback caused by LDLR cleavage.

ENO1 promotes liver cancer progression through BACE2

To determine whether the glycolytic enzyme ENO1 regulates cholesterol synthesis, we measured cellular total cholesterol content and related cholesterol biosynthetic genes. The overexpression of ENO1 increased the cellular cholesterol level (Figure 5A). Consistently, ENO1 knockdown suppressed the expression of cholesterol biosynthetic genes (HMGCR, SQLE, DHCR7, MVK, and CYP51A1) (Figure 5B). These data indicate that ENO1 promotes the accumulation of cellular cholesterol by activating cholesterol synthesis. In addition, ENO1 knockdown decreased BACE2 levels, increased LDLR protein levels, and significantly suppressed cell proliferation (Figure 5C), which were all reversed when we further overexpressed BACE2 in ENO1-knockdown cells (Figure 5C). Furthermore, the overexpression of BACE2 dramatically reversed ENO1 knockdown-induced suppression of cholesterol biosynthetic gene expression (Figure 5D), indicating that ENO1 promotes cholesterol synthesis through BACE2.

Figure 5.

Figure 5

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ENO1 promotes liver cancer through BACE2. (A) Determination of the intracellular total cholesterol level in PLC (left panel) and Huh7 (right panel) cells with ENO1 overexpression. (B) qPCR detection of the expression levels of cholesterol synthetic genes in PLC cells with ENO1 knockdown. (C) The protein levels of ENO1, BACE2 and LDLR (left panel, actin served as a negative control), cell growth curves (middle panel), and crystal violet staining (right panel) of PLC cells stably expressing NTC or shENO1 with or without further overexpression of BACE2. (D) qPCR detection of the expression levels of cholesterol synthetic genes (HMGCR, SQLE, DHCR7, MVK, and CYP51A1) in the indicated cells. 18S served as a negative control. (E–I) PLC cells stably expressing NTC or shENO1 with or without further overexpression of BACE2 were injected subcutaneously into the flanks of BALB/c nude mice (n = 5 mice in each group). (E) Tumour sizes were measured every 3 days. (F) Representative xenograft tumours at the end of the experiment. (G) Tumour weight was determined. (H) Western blot analysis of ENO1, BACE2, and LDLR protein levels in tumour lysates. Actin served as a negative control. (I) qPCR detection of the expression levels of the indicated cholesterol synthetic genes in mouse tumours. 18S served as a negative control. The error bars denote mean ± SD (C) or mean ± SEM (A, B, D, E, G, I). *P < 0.05, unpaired Student's t-test (A, B, D, G, I) or two-way ANOVA with Tukey's multiple comparisons test (C and E).

In vivo xenograft experiments demonstrated that ENO1 knockdown suppressed tumour growth, whereas BACE2 overexpression promoted tumour growth and increased tumour weight (Figure 5E–G). Moreover, BACE2 overexpression obviously rescued ENO1 knockdown-induced defects in tumour growth and tumour incidence (Figure 5E–G). Western blot analysis further confirmed protein levels of ENO1 and BACE2, as well as the regulation pattern of LDLR protein level by ENO1 and BACE2, in tumour lysates (Figure 5H). qPCR analysis of mRNA levels of cholesterol synthetic genes in these tumours showed that overexpression of BACE2 could partially reverse ENO1 knockdown-induced suppression of cholesterol-related gene expression (Figure 5I). Collectively, these data suggest that ENO1 promotes cholesterol synthesis and liver cancer progression through BACE2 in vivo.

Activation of the ENO1–BACE2–LDLR axis predicts poor prognosis in HCC patients

Finally, we explored the clinical relevance of our findings. By analysing ENO1, BACE2, and LDLR mRNA levels in paired clinical HCC lesions and adjacent noncancerous tissue samples, we found that ENO1 and BACE2 mRNA levels were upregulated, whereas LDLR mRNA levels were downregulated in liver cancer tissues compared with paracarcinoma tissues (Figure 6A). Western blot analysis of ENO1, BACE2, and LDLR protein levels showed consistent results that ENO1 and BACE2 protein levels markedly increased, while LDLR protein levels decreased in liver cancer tissues compared with paracarcinoma tissues (Figure 6B). ENO1 and BACE2 protein levels were also analysed by immunohistochemistry (IHC) in a cohort of 73 pairs of clinicopathologically characterized liver cancer cases. Quantitative analysis revealed the elevated expressions of ENO1 and BACE2 (Figure 6C and D) and a strong positive correlation between ENO1 and BACE2 protein levels in clinicopathologically characterized liver cancer samples (Figure 6E). Survival curves generated via Kaplan–Meier Plotter (liver cancer) revealed that HCC patients with low levels of BACE2 or ENO1 or high levels of LDLR in their liver cancer lesions survived longer (Figure 1C; Supplementary Figure S4A and B), suggesting that the ENO1–BACE2–LDLR axis could represent promising prognostic biomarkers in patients with liver cancer.

Figure 6.

Figure 6

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Activation of the ENO1–BACE2–LDLR axis predicts poor prognosis in HCC patients. (A) qPCR detection of ENO1, BACE2, and LDLR mRNA levels in 16 pairs of clinically matched tumour-adjacent noncancerous liver tissues (nontumour) and human HCC tissues (tumour). 18S served as a negative control. (B) Western blot analysis of ENO1, BACE2, and LDLR protein levels in paired paracarcinoma tissues (N) and HCC tissues (T) (n = 7). Ponceau staining served as a loading control. (C–E) IHC-based analysis of ENO1 and BACE2 expression in normal liver tissues (nontumour) and HCC specimens (tumour) with serial sectioning (n = 73). (C) Representative IHC staining. (D) Statistical quantification of the percentage of positive signal of ENO1 or BACE2 staining. (E) Correlation analysis with the protein levels of ENO1 and BACE2 in HCC specimens. (F and G) Kaplan–Meier curve analyses of patients with the indicated expression levels of BACE2, ENO1, and/or LDLR. The error bars denote mean ± SEM. *P < 0.05, unpaired Student's t-test.

Furthermore, BACE2high/ENO1high patients had poorer prognoses than BACE2low/ENO1high patients (Figure 6F left panel), but there was no difference in survival probability between BACE2high/ENO1low and BACE2low/ENO1low patients (Figure 6F right panel). Moreover, BACE2high/LDLRhigh patients had poorer prognoses than BACE2low/LDLRhigh patients (Figure 6G left panel), but there was no difference in survival probability between BACE2high/LDLRlow and BACE2low/LDLRlow patients (Figure 6G right panel). These results suggest that the effects of ENO1, BACE2, and LDLR on the prognosis of patients with liver cancer should be comprehensively analysed.

Overall, we found that ENO1 and BACE2 are codependent genes that bind to each other at the protein level in liver cancer cells. ENO1 stabilizes BACE2 protein level by suppressing its lysosomal-dependent protein degradation. ENO1 and BACE2 further promote the cleavage of LDLR and suppress the absorption of exogenous cholesterol. As a negative feedback mechanism, ENO1 then activates cholesterol biosynthesis through BACE2, ultimately promoting intracellular cholesterol accumulation and liver cancer progression (Figure 7).

Figure 7.

Figure 7

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ENO1–BACE2-mediated LDLR cleavage promotes liver cancer progression by remodelling cholesterol metabolism. In liver cancer cells, ENO1 binds to BACE2 and inhibits its lysosomal pathway-dependent protein degradation. ENO1 and BACE2 then promote the cleavage of LDLR, leading to the reduced uptake of extracellular LDL-C. As a negative feedback mechanism, the ENO1–BACE2 axis promotes the accumulation of intracellular cholesterol by upregulating the expression of genes involved in intracellular cholesterol synthesis, ultimately promoting the progression of liver cancer.

Discussion

In the present study, we demonstrated that, in addition to regulating the Warburg effect, the glycolytic enzyme ENO1 can reprogramme cholesterol metabolism through an unrecognized role of protein cleavage. Specifically, we found that ENO1 interacts with BACE2, a potential codependent gene of ENO1, and promotes BACE2 protein stabilization without affecting its mRNA level. In addition to its nonclassical functions such as binding to DNA or RNA, ENO1 can promote LDLR protein cleavage, resulting in decreased uptake of exogenous cholesterol. As a negative feedback mechanism to reduce LDLR expression and decrease cholesterol absorption, ENO1 activates genes involved in de novo cholesterol synthesis through BACE2, accelerating cell proliferation by promoting intracellular cholesterol accumulation. Our findings provide the first evidence of the protein cleavage function of ENO1 and demonstrate the role of the ENO1–BACE2–LDLR axis in liver cancer progression.

Aberrant expression of ENO1 has been reported in a variety of cancers and is associated with poor tumour prognosis (Cappello et al., 2013; Fu et al., 2015; Zhan et al., 2017; Jiang et al., 2020; Zang et al., 2020; Zhang et al., 2022b). In addition to its known role in catalysing glucose metabolism, ENO1, as a moonlighting protein, performs multiple biological functions in cancer development, such as gene regulation, protein synthesis, cascade signalling pathway activation, immune responses, host–pathogen interactions, chemotherapy resistance, and cellular stress responses (Feo et al., 2000; Gao et al., 2013; Fu et al., 2015; Gemta et al., 2019; Wang et al., 2019). Recently, we reported that ENO1 binds to and degrades IRP1 mRNA, thereby regulating the metabolic homeostasis of iron ions in cells, inhibiting ferroptosis, and promoting the occurrence and development of liver cancer (Zhang et al., 2022a). Additionally, ENO1 binds to and promotes the translation of YAP1 mRNA, promoting liver carcinogenesis by activating arachidonic acid metabolism and PGE2 accumulation (Sun et al., 2023). In this study, we found that ENO1 can also bind to its protein partner BACE2 to further participate in regulating protein cleavage in liver cancer cells. Mechanistically, BACE2 is a codependent gene of ENO1 and interacts with ENO1 at the protein level. ENO1 promotes protein stabilization of BACE2, a membrane protease, and further cleaves the membrane protein LDLR. Notably, ENO1 has been identified as a plasminogen receptor; thus, whether these two proteins cleave LDLR on the membrane is worthy of attention and further investigation.

BACE2, a membrane protease with aspartic protease activity, hydrolyses APP and processes it to produce the Aβ peptide, which is a trigger pathologic effector of AD. Therefore, BACE2 is a promising neuroprotective candidate for AD. In addition, BACE2 can perform other biological functions by cleaving other proteins. For example, in neurons, BACE2 can cleave the potassium-channel protein Kv2.1 to prevent the outflow of potassium ions and inhibit neuronal apoptosis (Liu et al., 2018). In pancreatic cells, BACE2 cleaves the transmembrane protein TMEM27, thereby inhibiting pancreatic cell function (Esterhazy et al., 2011). In recent years, an increasing number of studies have shown that BACE2 is abnormally expressed in a variety of cancers and plays a role in promoting tumour progression. However, BACE2 regulates signalling pathways including Yap (Matafora et al., 2020), STAT3 (Chen et al., 2021b), NF-κB (Wang et al., 2020), and Ca2+ influx (He et al., 2021), rather than performing its classical cleavage enzyme function, in these studies, and its role in liver cancer has not been reported. Here, we found that BACE2, a codependent gene and protein partner of ENO1, functions as an oncogene by promoting liver cancer cell proliferation and tumour progression through its cleavage enzyme function. Specifically, BACE2 overexpression reduces the absorption of exogenous cholesterol by cleaving LDLR but promotes endogenous cholesterol synthesis, leading to intracellular cholesterol accumulation and cancer cell proliferation. The LDLR structure from the N-terminus to the C-terminus includes seven ligand-binding domain repeats, an epidermal growth factor (EGF) precursor homology domain, an O-linked sugar domain, a transmembrane domain, and a cytoplasmic domain (Brown and Goldstein, 1986). The mature LDLR protein contains 839 amino acids (Sudhof et al., 1985). Our results revealed that the N-terminal fragment of LDLR is ∼95 kDa; thus, we predict that the LDLR cleavage site by BACE2 may be the EGF precursor homology domain C. Proprotein convertase subtilisin/kexin type 9 (PCSK9) is known to bind to the EGF precursor homology domain A (EGF-A) of LDLR and promote its lysosomal degradation, and monoclonal antibodies block the interaction between PCSK9 and EGF-A, significantly reducing plasma LDL-C levels (Zhang et al., 2007; Stein and Raal, 2014). More importantly, in liver cancer and colorectal cancer, PCSK9 inhibitors suppress metabolism and growth of cancer cells, suggesting that PCSK9 is a therapeutic target for cancer treatment (Alannan et al., 2022; Wong et al., 2022). Therefore, it is worth exploring whether ENO1 can be another pharmaceutical target similar to PCSK9 by targeting LDLR. Additionally, further analysis of the BACE2 cleavage site on LDLR and clinical drugs that target BACE2 for the treatment of AD will provide potential therapeutic strategies for targeting ENO1-expressing tumours.

In conclusion, the findings of the codependency and interaction between ENO1 and BACE2 shed new light on their coordinated and versatile role in cholesterol metabolism and liver cancer progression. We reveal a potential mechanism by which the ENO1–BACE2 interaction promotes liver cancer progression and expect that future studies of the function of ENO1 in LDLR cleavage will further expand our understanding of the molecular mechanism of cholesterol reprogramming. Ectopic expression of the ENO1–BACE2–LDLR axis in liver cancer tissues and its significant correlation with prognosis indicate that targeting this axis may be an effective approach for treating liver cancer.

Materials and methods

Cell lines

HEK293T, Hep3B, Huh7, HepG2, and PLC cell lines were cultured in Dulbecco's modified Eagle's medium containing 10% foetal bovine serum. The medium was supplemented with 1% penicillin–streptomycin solution. The cells were maintained at 37°C in a humidified incubator with 5% CO2.

Plasmids and stable cell lines

BACE2 shRNA sequences were cloned and inserted into pLKO.1 vector. shENO1 vectors were purchased from Sigma–Aldrich. ENO1, BACE2, and LDLR sequences were subcloned and inserted into the pSin-3×Flag, pSin-HA, or pSin-EGFP vector. Detailed primer and shRNA targeting sequences are listed in Supplementary Tables S1 and S2. All the plasmids were co-transfected with pMD2.G and psPAX into HEK293T cells via PEI (Polysciences). Lentivirus-infected cells were treated with polybrene and selected with 2 μg/ml puromycin to establish stable cell lines.

qPCR

Total RNA was isolated using TRIzol, treated with DNase (Ambion), and reverse-transcribed into cDNA using a cDNA synthesis kit (Vazyme). qPCR was performed with SYBR Green master mix (Vazyme) on a LightCycler 96 system (Roche). All primer sequences are listed in Supplementary Table S3. All samples were normalized to 18S rRNA.

Western blotting

The cells were collected, washed with precooled phosphate-buffered saline (PBS), and then lysed with radioimmunoprecipitation assay buffer (50 mM Tris-Cl, pH 8.0, 150 mM NaCl, 5 mM ethylenediamine tetraacetic acid (EDTA), 0.1% sodium dodecyl sulfate (SDS), and 1% NP-40) supplemented with protease-inhibitor cocktails (Roche). For supernatant analysis, the cells were cultured in serum-free medium for 48 h, and the cell supernatant was collected and centrifuged to remove debris. The supernatant was concentrated using Amicon Ultra-4 Centrifugal Filter Units with a 30000-molecular-weight cutoff (Merck Millipore). Equal amounts of protein were fractionated via SDS–polyacrylamide gel electrophoresis. Primary antibodies against ENO1, BACE2, LDLR, actin, Flag, HA, and GFP. Horseradish peroxidase-conjugated anti-rabbit and anti-mouse secondary antibodies (Bio-Rad) were used. Detailed information of the antibodies and reagents used is provided in Supplementary Table S4.

IP assay

HEK293T, Hep3B, Huh7, HepG2, and PLC cells were lysed with IP buffer (1% NP-40, 20 mM HEPES, pH 7.5, 150 mM NaCl, 2 mM EDTA, and 1.5 mM MgCl2) supplemented with protease inhibitor cocktail for 2 h on ice and centrifuged at 16000× g for 10 min at 4°C. After protein quantification, the supernatant was incubated with primary antibody at 4°C overnight, followed by the addition of protein A/G-conjugated beads and incubation for another 1 h. The immunoprecipitates were washed three times with 0.5% NP-40 IP buffer, then boiled with SDS buffer, and analysed by western blotting.

Cell growth assay

A total of 5 × 104 cells were seeded into a 12-well cell culture plate (NEST) and cultured at 37°C in a humidified incubator. At 24, 48, and 72 h, cells were counted, and cell proliferation curves were plotted. At 72 h, the cells were washed three times with PBS and stained with 0.5% crystal violet. Images were obtained with an Epson Perfection V330 Photo scanner.

Intracellular total cholesterol and extracellular LDL-C detection

The cells were collected and washed with PBS, followed by the addition of an extraction solution (isopropanol). An ultrasonic homogenizer was then used to lyse the cells. After centrifugation (12000× g, 10 min), the supernatant was collected for the determination of total cholesterol in the cells using the Total Cholesterol Content Assay Kit (Solarbio). The cell supernatant was collected and centrifuged to remove cell debris, and an LDL-C content assay kit (Solarbio) was used to detect LDL-C in the supernatant.

Dil-LDL uptake

A total of 5 × 104 cells were seeded into glass-bottom cell culture dishes (NEST) and cultured in a 37°C humidified incubator overnight. Then, the medium was removed, the cells were washed with PBS, and serum-free medium containing Dil-LDL (10 μg/ml, YEASEN) was added for 6 h. Excess Dil probes were washed away, and the cells were fixed with 2% paraformaldehyde/2.5% glutaraldehyde. The cell nuclei were stained with 4′,6-diamidino-2-phenylindole (DAPI; Beyotime Biotech). Random fields were captured using multiphoton confocal microscopes (Nikon, A1RMP). The fluorescence intensity was analysed with Image J.

PLA

PLA was performed using the Duolink® In Situ Red Starter Kit Mouse/Rabbit (Sigma–Aldrich, DUO92101) following the manufacturer's instructions. Briefly, cells were seeded into glass-bottom cell culture dishes and cultured in a 37°C humidified incubator overnight. The next day, cells were fixed with 2% paraformaldehyde/2.5% glutaraldehyde, permeabilized with 0.2% Triton X-100, and blocked with Duolink blocking solution. Then, cells were incubated with rabbit anti-ENO1 antibody (PTG, 11204–1-AP) and mouse anti-BACE2 antibody (Santa Cruz Biotechnology, sc-271212). Negative controls were prepared using only rabbit anti-ENO1 or mouse anti-BACE2 antibodies. After washing, the cells were further incubated with Plus and Minus oligonucleotide probe-conjugated secondary antibodies (PLA probes). Further ligation and amplification of the PLA probes were performed according to the manufacturer's instructions. The cell nuclei were stained with DAPI. Finally, PLA signals were captured using confocal microscopes.

IHC

Tumour tissue microarrays, purchased from Shanghai Outdo Biotech Company, contained 73 pairs of hepatocellular carcinomas along with matched adjacent normal hepatocellular tissues (HLivH180Su31). The IHC procedure for ENO1 and BACE2 was performed as previously reported (Zhang et al., 2022a). Images of IHC staining were acquired with Leica AperioCS2. Quantitative analysis of IHC staining was performed using Image J (IHC Profiler), and a t-test was conducted to compare the percentage of positive differences between groups.

Animal studies

All animal experiments and procedures were performed in compliance with ethical regulations and approved by the Animal Research Ethics Committee of South China University of Technology (AEC number 2024060). All the mice were housed under pathogen-free conditions at an ambient temperature of 20°C–26°C, relative humidity of 30%–70%, and a 12-h light/dark cycle. For xenograft experiments, 8 × 106 PLC cells stably expressing different levels of ENO1 and BACE2 were injected subcutaneously into 4-week-old male nude mice (SJA Laboratory Animal Company). The tumour volumes were measured using digital callipers every 3 days and calculated with the following equation: length (mm) × width2 (mm) × 0.52.

Clinical human HCC specimens

Snap-frozen HCC lesion tissues and corresponding adjacent noncancerous tissues, located at least 2 cm away from the tumour margins, were taken from patients with HCC via radical HCC resection at the First Affiliated Hospital of the University of Science and Technology of China in Hefei. To use these clinical materials for research purposes, written informed consent and approval from each participant were obtained from the Institutional Research Ethics Committee of the First Affiliated Hospital of the University of Science and Technology of China. Total RNA and protein were extracted from the paired liver tissues and analysed using qPCR and western blotting, respectively. The studies were conducted in accordance with the ethical guidelines of the Declaration of Helsinki and the Declaration of Istanbul.

Bioinformatics analysis

The ENO1 codependent genes were analysed using DepMap (https://depmap.org/portal/). The correlations between ENO1 and UNKL, TMEM176B, FAM181B, AGBL3, BACE2, MORC3, USP40, TRIM36, GAPDH, or SYT9 expression levels in liver cancer were assessed via GEPIA 2 (http://gepia2.cancer-pku.cn/#index). The 5-year overall survival of liver cancer patients was analysed via Kaplan–Meier Plotter (https://kmplot.com/analysis/index.php?p=service&cancer=liver_rnaseq).

Statistical analysis

Two-tailed unpaired Student's t-test and two-way analysis of variance (ANOVA) were used to calculate P-values unless otherwise specified in the figure legends. Statistical analysis was performed using GraphPad Prism software (version 8.0).

Supplementary Material

mjaf001_Supplemental_File
mjaf001_supplemental_file.pdf (449.7KB, pdf)

Contributor Information

Zhikun Li, School of Medicine, South China University of Technology, Guangzhou 510006, China.

Kaixiang Fan, School of Medicine, South China University of Technology, Guangzhou 510006, China; Medical Research Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou 510080, China.

Caixia Suo, Department of Colorectal Surgery, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou 510180, China.

Xuemei Gu, School of Medicine, South China University of Technology, Guangzhou 510006, China.

Chuxu Zhu, School of Medicine, South China University of Technology, Guangzhou 510006, China.

Haoran Wei, Medical Research Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou 510080, China.

Liang Chen, School of Medicine, South China University of Technology, Guangzhou 510006, China.

Ping Gao, School of Medicine, South China University of Technology, Guangzhou 510006, China; Medical Research Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou 510080, China; School of Basic Medical Sciences, Division of Life Science and Medicine, University of Science and Technology of China, Hefei 230026, China.

Linchong Sun, School of Medicine, South China University of Technology, Guangzhou 510006, China; Medical Research Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou 510080, China.

Funding

This work was supported in part by grants from the National Natural Science Foundation of China (82422057, 82273221, and 82341013), the Basic Funding of Guangzhou Municipal Science and Technology Bureau (2024A04J6493), and the Special Program for Young Scholars of Southern Medical University (G623281133).

Conflict of interest: none declared.

Author contributions: L.S. and P.G. conceived and supervised the study. Z.L., L.S., and P.G. designed the experiments. Z.L., K.F., C.S., X.G., C.Z., H.W., and L.C. performed and analysed the experiments. L.S., Z.L., K.F., and P.G. wrote the manuscript. All the authors read and approved the manuscript.

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Supplementary Materials

mjaf001_Supplemental_File
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