GRAPHICAL ABSTRACT

INTRODUCTION

Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer, ranking sixth in incidence and third in cancer-related mortality worldwide [1]. Both its incidence and mortality continue to rise globally, with annual mortality increasing by approximately 2% to 3% [2]. Conventional treatment modalities such as surgery, radiotherapy, and chemotherapy have reached a developmental plateau, while newer approaches—including targeted therapy and immunotherapy—face limitations due to low efficacy and high recurrence rates [3,4]. Therefore, gaining a deeper understanding of the biological characteristics and key molecular pathways underlying HCC is essential for developing effective therapeutic strategies.

Metabolic reprogramming is a major driver of tumorigenesis, resulting from both direct and indirect effects of oncogenic mutations [5]. This metabolic shift, which supports homeostasis and rapid cell proliferation, is now recognized as a hallmark of cancer [6]. Numerous studies investigating metabolic reprogramming in HCC have identified tumor metabolism as a promising therapeutic target [7–9]. However, the identification of critical enzymes and intermediates involved in these pathways, as well as a comprehensive understanding of their roles in HCC progression, remains incomplete.

Glucose metabolism plays a crucial role in cellular function, and its dysregulation has been shown to significantly contribute to cancer development [10,11]. Among metabolic pathways, the Warburg effect—characterized by a preference for glycolysis over oxidative phosphorylation even in the presence of oxygen—is a hallmark of tumor metabolic reprogramming [12]. Conversely, gluconeogenesis, the reverse process of glycolysis, is vital for maintaining normal blood glucose levels but is frequently suppressed in HCC [13]. Key gluconeogenic enzymes such as glucose-6-phosphatase (G6PC) and phosphoenolpyruvate carboxykinase 1 (PCK1) are downregulated in HCC [14–16]. The loss of these enzymes has been linked to hepatocarcinogenesis; for instance, G6PC deficiency impairs autophagy and mitochondrial function, enhances glycolysis and the pentose phosphate pathway, and ultimately promotes HCC progression [17,18]. While PCK1 is upregulated in certain cancers, it is downregulated in HCC, reflecting the unique metabolic characteristics of this malignancy [19]. Thus, elucidating the mechanisms that regulate gluconeogenesis in HCC may yield novel therapeutic insights.

In addition to metabolic regulation, signaling pathways such as Hippo play a pivotal role in tumor progression [20,21]. The Hippo pathway governs cell and tissue growth by integrating multiple upstream signals [22,23]. The core kinases large tumor suppressor kinase 1 (LATS1) and LATS2 modulate the activity of key transcriptional coactivators Yes-associated protein (YAP) and transcriptional co-activator with PDZ-binding motif (TAZ) [22]. When phosphorylated, YAP and TAZ are retained in the cytoplasm and degraded; when dephosphorylated, they translocate to the nucleus to activate growth-promoting genes via TEA domain family member (TEAD) transcription factors [24,25]. Both coactivators are essential in various cancers, including HCC, and share high sequence similarity in functional domains, enabling interactions with diverse proteins for transcriptional regulation [23,26–30]. Although targeting the YAP/TAZ-TEAD axis represents a promising therapeutic approach, accumulating evidence indicates that YAP and TAZ can also regulate tumor metabolism independently of TEAD [31–33]. For example, YAP enhances glucose uptake and glycolysis to support cancer progression [34]. Previous studies have shown that YAP suppresses gluconeogenesis in primary hepatocytes by inhibiting transcription of gluconeogenic genes through repression of peroxisome proliferator-activated receptor-gamma coactivator 1-alpha (PGC1α) [35]. Despite the structural similarity between YAP and TAZ, their downstream gene regulation differs due to interactions with distinct transcription factors in specific biological contexts [36–39]. Our previous work demonstrated that TAZ is stably expressed in adult mouse hepatocytes and significantly suppresses key gluconeogenic enzymes such as G6PC and PCK1 in primary hepatocytes and normal liver tissue [40]. However, the precise role of TAZ in glucose metabolism within HCC remains poorly understood. Whether TAZ promotes HCC cell proliferation and tumor progression by inhibiting gluconeogenesis remains an important question.

This study aimed to explore the role of TAZ in regulating gluconeogenesis and promoting tumorigenesis in HCC. We investigate TAZ expression in HCC tissues and cell lines, its effects on gluconeogenic gene expression, and its interaction with the glucocorticoid receptor (GR). Understanding the regulatory role of TAZ may provide new insights into HCC pathogenesis and suggest potential therapeutic targets.

METHODS

Bioinformatics analysis using public databases

Differential expression and survival analyses were performed using data from The Cancer Genome Atlas (TCGA) database. The correlations between TAZ, PCK1, and G6PC expression levels in HCC were evaluated using the Gene Expression Omnibus (GEO) dataset (GSE15765) via the R2 database.

Cell culture

Human liver cancer cell lines (HepG2, Hep3B, Huh7, SK-Hep1, LM3, and 97H), the human normal liver epithelial cell line LO2, and the human embryonic kidney 293A cell line were obtained from the American Type Culture Collection and the Shanghai Institute of Cell Biology (Shanghai, China). The cells were cultured in Roswell Park Memorial Institute-1640 or Dulbecco’s Modified Eagle Medium (DMEM) (both from Gibco, Grand Island, NY, USA), supplemented with 100 units/mL penicillin, 100 units/mL streptomycin, and 10% fetal bovine serum (FBS; Biological Industries, Beit Haemek, Israel). All cells were maintained in a humidified incubator at 37°C with 5% CO_2.

Collection of clinical HCC tissue samples

A total of 20 pairs of HCC and adjacent noncancerous tissue samples were collected from patients who underwent surgical resection at the First Affiliated Hospital of Nanchang University between March and December 2023. All tissue samples were transferred to liquid nitrogen within 30 minutes for preservation. The study protocol was approved by the Medical Research Ethics Committee of the First Affiliated Hospital of Nanchang University (Approval No. 2023CDYFYYLK05-028), and written informed consent was obtained from all participants. Only patients who had not received neoadjuvant therapy were included in the study.

Cell transfection and lentiviral infection

The TAZ overexpression (OE) plasmid, short-hairpin TAZ (shTAZ) knockdown plasmid, TAZ-TEAD binding domain deletion plasmid, and TAZ WW domain deletion plasmid used in this study were previously constructed by our research group [40]. For plasmid transfection, cells at approximately 70% confluence in 6-well plates were transfected using Lipofectamine^TM 3000 (Cat#L3000015; Invitrogen, Carlsbad, CA, USA) according to the manufacturer’s instructions. Lentiviral vectors were constructed and packaged by GeneChem (Shanghai, China). Stable cell lines were generated by infecting cells with lentivirus in the presence of 25× HitransG A or HitransG P, followed by selection with the optimal puromycin concentration. In addition, small interfering RNA (siRNA) was used to knock down GR expression. The siRNA targeting GR (nuclear receptor subfamily 3 group C member 1 [NR3C1]) had the sequence 5′-GCACCAACCTTAACAAGAAdTdT-3′, and a scrambled siRNA served as the negative control (NC). Transfections were performed at approximately 50% to 60% confluence using Lipofectamine^TM RNAiMAX (Invitrogen, Carlsbad, CA, USA) at a final siRNA concentration of 50 nM, following the manufacturer’s instructions.

RNA extraction and quantitative real-time polymerase chain reaction

Total RNA was extracted using TRIzol reagent (Cat#15596018; Invitrogen, Carlsbad, CA, USA) according to the manufacturer’s instructions. For tissue samples, 1 mL of TRIzol was used per 50–100 mg of tissue, while for cell samples, 1 mL was used per 5–10×10⁶ cells. RNA purity and concentration were determined using a NanoDrop spectrophotometer. RNA was reverse transcribed into cDNA using the cDNA Synthesis SuperMix kit (Cat#11141ES60; Yeasen, Shanghai, China). The reaction conditions were 25°C for 10 minutes, 60°C for 15 minutes, and 85°C for 5 minutes. Quantitative real-time polymerase chain reaction (qRT-PCR) was performed using TB Green Premix Ex Taq II (Cat#11184ES08; Yeasen, Shanghai, China) in a 10 μL reaction mixture containing 0.2 μL of each primer (forward and reverse), 5 μL of TB Green Premix, and 3.6 μL of diethyl pyrocarbonate (DEPC)-treated water. PCR amplification was conducted on Thermo Scientific instruments (Waltham, MA, USA). Primers were synthesized by Sangon Biotech Co., Ltd. (Shanghai, China), and their sequences are listed in Supplemental Table S1.

Western blotting

Proteins were extracted from cells using NP-40 lysis buffer supplemented with protease and phosphatase inhibitors. Protein concentration was determined using a bicinchoninic acid (BCA) assay kit (Cat#GK10009; GLPBIO, Montclair, CA, USA) with a Thermo Scientific microplate reader. Equal amounts of protein were loaded onto sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) gels and separated by electrophoresis. Following electrophoresis, proteins were transferred onto NC membranes (Cat#N66485; PALL, Annandale, VA, USA). The membranes were blocked with 5% non-fat milk in Tris-buffered saline (TBS) with Tween-20 for 1 hour at room temperature, followed by overnight incubation at 4°C with the following primary antibodies: TAZ (1:1,000; Cat#48835; CST, Danvers, MA, USA), PCK1 (1:1,000; Cat#BS6870; Bioworld, Irving, TX, USA), G6PC (1:1,000; Cat#BS67798; Bioworld), Vinculin (1:500; Cat#A2752; ABclonal, Wuhan, China), GR (1:500; Cat#A2164; ABclonal), hepatocyte nuclear factor 4 alpha (HNF-4α) (1:1,000; Cat#A20865; ABclonal), Forkhead box O1 (FoxO1) (1:1,000; Cat#A2934; ABclonal), YAP (1:1,000; Cat#4912; CST), PGC1α (1:1,000; Cat#A12348; ABclonal), and β-actin (1:80,000; Cat#AC026; ABclonal). After incubation, membranes were washed and incubated with horseradish peroxidase (HRP)-conjugated secondary antibodies (Proteintech, Rosemont, IL, USA) for 1 hour at room temperature. After final washes, the membranes were developed using an enhanced chemiluminescence kit (Cat#S6010100; Suzhou Yuheng, Suzhou, China) and visualized using a Bio-Rad imaging system (Hercules, CA, USA).

For nuclear and cytoplasmic protein extraction, cells were fractionated using a hypotonic lysis–detergent method. Briefly, cells were resuspended in hypotonic buffer and incubated on ice, followed by addition of NP-40. After centrifugation, the supernatant was collected as the cytoplasmic fraction. Nuclear pellets were washed and lysed in a high-salt buffer. Both fractions were clarified by centrifugation and analyzed by Western blotting as described above.

Cell counting kit-8 assay

Cell viability was evaluated using a cell counting kit-8 (CCK-8) assay kit (Cat#GK10001; GLPBIO). Cells were trypsinized, counted, and seeded into 96-well plates at a density of 1,000–2,000 cells per well in 100 μL of medium, with five replicates per condition. Cells were incubated at 37°C in a 5% CO₂ atmosphere, and viability was assessed at 24, 48, 72, and 96 hours. At each time point, the culture medium was replaced with 100 μL of CCK-8 working solution (1:9 dilution with medium), followed by incubation at 37°C for 2 hours. Absorbance was measured at 450 nm using a microplate reader.

Cell cycle analysis

Cells (2×10⁶) were harvested and centrifuged at 1,000 g for 3 minutes. After washing with 1 mL of cold phosphate-buffered saline (PBS), the cell pellet was fixed in 1 mL of pre-chilled 70% ethanol at −20°C for at least 48 hours. Fixed cells were washed with PBS and stained with propidium iodide staining solution (Cat#C1052; Beyotime, Shanghai, China) containing RNase A, followed by incubation at 37°C for 30 minutes in the dark. Cell cycle distribution was analyzed using a BD FACS Canto II flow cytometer (Franklin Lakes, NJ, USA).

Apoptosis assay

Apoptosis was detected using an Annexin V/7-aminoactinomycin D (7-AAD) apoptosis detection kit (Cat#AP104; Multiscience, Hoya, Germany). Cells were washed with PBS, centrifuged, and resuspended in binding buffer. The cell suspension was incubated with Annexin V and 7-AAD at room temperature for 10 minutes in the dark. After staining, samples were analyzed using a BD FACS Canto II flow cytometer. All analyses were completed within 2 hours to ensure accurate quantification of apoptotic cells.

Migration and invasion assays

Cells were collected, washed with serum-free DMEM, and resuspended in DMEM containing 0.1% FBS. For the migration assay, 4×10⁴ cells in 200 μL were seeded into the upper chamber of an 8-μm pore Transwell insert (24-well format). For the invasion assay, the upper chambers were pre-coated with 50 μL of Matrigel (Cat#40191ES08; Yeasen), diluted 1:8 in serum-free DMEM, and incubated at 37°C for 2 hours to allow gel formation before cell seeding. The lower chambers were filled with 500 μL (migration) or 600 μL (invasion) of DMEM containing 20% FBS as a chemoattractant. Cells were incubated at 37°C in 5% CO₂ for 24 hours (migration) or 48 hours (invasion). After incubation, non-migrated or non-invaded cells on the upper surface of the membrane were removed. Cells that migrated or invaded to the lower surface were fixed with 4% paraformaldehyde, stained with 1% crystal violet, and counted under a microscope.

Glucose production assay

Glucose production was measured using a glucose assay kit (Cat#ab65333; Abcam, Cambridge, UK). Cells were cultured in 6-well plates to 70%–80% confluence, washed with PBS, and incubated overnight in serum-free, low-glucose DMEM. The following day, cells were washed twice with PBS and incubated with 800 μL of glucose production buffer (DMEM without glucose and phenol red, supplemented with 2 mM sodium lactate, 20 mM sodium pyruvate, 100 units/mL penicillin, 100 μg/mL streptomycin, and 20 nM glucagon) at 37°C in a 5% CO₂ incubator for 1 hour. Supernatants were collected every 10 minutes and stored in Eppendorf tubes. Glucose concentrations in the supernatants were determined fluorometrically following the manufacturer’s protocol. A glucose standard curve was generated by serial dilution, and fluorescence intensity was measured at an excitation/emission wavelength of 535/587 nm using a microplate reader.

Subcutaneous tumor xenograft model

Five-week-old male BALB/c nude mice were purchased from Southern Model Organisms (Shanghai, China) and maintained in a specific pathogen-free facility at Nanchang University. All animal experiments were approved by the Ethics Committee of the First Affiliated Hospital of Nanchang University (Approval No. CDYFY-IACUC-202304QR026). The study strictly adhered to the 3R principles (Replacement, Reduction, and Refinement). Huh7 liver cancer cells stably infected with lentivirus were collected during the logarithmic growth phase and suspended in serum-free medium at a concentration of 2×10⁷ cells/mL. A total of 2×10⁶ cells in 100 μL were subcutaneously injected into the right flank of each mouse. Each group contained six mice. Tumor growth was monitored every other day by measuring tumor length and width with calipers. Tumor volume was calculated using the formula V=(length×width²)/2. After 3 weeks, mice were sacrificed, and tumors were excised, weighed, and processed for RNA and protein extraction for subsequent analyses.

Molecular docking analysis

TAZ and glucose metabolism-related transcription factors were analyzed using the Search Tool for the Retrieval of Interacting Genes/Proteins, previously Search Tool for Recurring Instances of Neighbouring Genes (STRING) database. Protein-protein interactions between TAZ (WW domain containing transcription regulator 1 [WWTR1]) and the glucocorticoid receptor (NR3C1) were predicted through molecular docking. The protein structures of WWTR1 (Protein Data Bank [PDB]: AF-Q9GZV5-F1) and NR3C1 (PDB: AF-P04150-F1) were downloaded from the PDB database. Flexible docking was performed using the High Ambiguity Driven protein-protein DOCKing (HDOCK) Server, generating 1,000 conformations and orientations. Electrostatic, hydrogen bonding, and van der Waals interactions were calculated to determine docking scores. The conformation with the highest docking score was selected for further analysis. Visualization and distance measurements between molecular residues (distances <5 Å indicating significant interactions) were performed using Python version 3.7.7 and Py-MOL version 2.4.0. Final figures were generated after removing water molecules and labeling protein subunits.

Immunoprecipitation

Protein samples were prepared as described previously, with one-fifth of each sample reserved as input and stored at −20°C. The remaining protein was used for immunoprecipitation (IP). Protein G magnetic beads (Cat#P2106; Beyotime) were washed three times with TBS and resuspended in TBS. Antibodies against TAZ (Cat#A8202; ABclonal) and GR (Cat#SC-393232; Santa Cruz, Santa Cruz, CA, USA) were added to TBS and incubated with pretreated magnetic beads at room temperature for 1 hour with rotation. The bead-antibody complexes were washed and resuspended in TBS. Protein samples were pre-cleared with immunoglobulin G (IgG)-conjugated beads at 4°C for 1 hour. After magnetic separation, the supernatant was incubated with antibody-conjugated beads overnight at 4°C with rotation. The beads were washed several times with TBS and resuspended in 1×SDS loading buffer. The protein–bead complexes were heated at 95°C for 5–10 minutes to elute the bound proteins, and the supernatant was collected for SDS-PAGE analysis.

Immunofluorescence

Cells were cultured on sterilized coverslips in 12-well plates until they reached 50% to 70% confluence. Cells were fixed with 4% paraformaldehyde at room temperature for 30 minutes and washed three times with PBS. Blocking was performed in PBS containing 0.05% bovine serum albumin (BSA) and 0.03% Triton X-100 for 2 hours at room temperature. Primary antibodies—TAZ (1:100; Cat#A8202; ABclonal) and GR (1:50; Cat#SC-393232; Santa Cruz)—were diluted in PBS containing 1% BSA and 0.03% Triton X-100, then incubated with the cells overnight at 4°C. The following day, samples were washed three times with PBS and incubated with fluorescent secondary antibodies diluted in PBS containing 1% BSA for 1 hour at 37°C in the dark. After final PBS washes, the coverslips were mounted with 4′,6-diamidino-2-phenylindole (DAPI) mounting medium (Cat#P0131; Beyotime) and sealed with nail polish. Stained samples were stored at 4°C in the dark and imaged using a Leica confocal microscope (Leica, Wetzlar, Germany).

Chromatin immunoprecipitation

The promoter regions of PCK1 and G6PC were analyzed using the Profiler of Multi-Omics data (PROMO) tool (https://alggen.lsi.upc.es). Primers were designed around predicted GR binding sites using National Center for Biotechnology Information (NCBI) Primer-BLAST. Cells were fixed with 1% formaldehyde, quenched with glycine, and washed with PBS. Cells were collected, lysed, and subjected to sonication using a non-contact ultrasonic disruptor (Xiaomei Ultrasonic, Kunshan, China) to generate chromatin fragments ranging from 200 to 1,000 bp. Chromatin was immunoprecipitated using a chromatin immunoprecipitation (ChIP) kit (Cat#56383; CST) with antibodies against GR (Cat#3660; CST), Ac-Histone4 (Cat#A15233; ABclonal), and IgG as a NC. Immunoprecipitated DNA was purified using the kit’s purification system and analyzed by qRT-PCR. Primer sequences are provided in Supplemental Table S1.

Statistical analysis

Data are expressed as mean±standard deviation. Statistical significance between two groups was assessed using the Student’s t test. For multiple-group comparisons, one-way analysis of variance was performed. All statistical tests were two-sided, and a P value <0.05 was considered statistically significant; ‘NS’ denotes no significance. All analyses and graph generation were performed using GraphPad Prism version 9.0 software (La Jolla, CA, USA).

RESULTS

Expression and validation of TAZ in liver cancer and cell lines

To explore the role of TAZ in tumorigenesis, we analyzed its expression across 33 cancer types using the TCGA database. TAZ was found to be highly expressed in five tumor types, including HCC, and downregulated in 11 others (Fig. 1A). In 371 HCC samples, TAZ expression was significantly higher compared with 50 adjacent noncancerous tissues (Fig. 1B). A paired analysis of 50 matched HCC and adjacent tissues further confirmed elevated TAZ levels in HCC (Fig. 1C).

To validate these findings, we collected paired cancerous and adjacent noncancerous tissue samples from 20 newly diagnosed, untreated HCC patients. Both qRT-PCR and Western blot analyses revealed markedly higher TAZ expression in cancerous tissues, consistent with the TCGA data (Fig. 1D–F).

At the cellular level, TAZ expression was compared between the normal human liver cell line and six liver cancer cell lines. qRT-PCR and Western blot analyses demonstrated increased TAZ expression in all liver cancer cell lines, with the highest levels observed in HepG2 and lower expression in SK-Hep1 and Huh7 cells (Fig. 1G, H). To further assess TAZ localization, cytoplasmic and nuclear protein fractions were isolated from liver cancer and normal liver cells. Western blotting showed that TAZ levels were elevated in both cytoplasmic and nuclear fractions of liver cancer cells compared to normal hepatocytes, suggesting that a portion of TAZ translocates into the nucleus where it may participate in transcriptional regulation (Supplemental Fig. S1). Based on these expression patterns, HepG2 and Huh7 cells were selected for further functional analyses. We employed TAZ-OE and knockdown vectors to modulate TAZ levels in these cells. qRT-PCR and Western blot assays confirmed successful OE and silencing, showing significant differences in TAZ levels compared with controls (Fig. 1I–L).

Biological effects of TAZ on proliferation, apoptosis, and migration in HCC cells

Using the transfected cell lines, we investigated the effects of TAZ on cell proliferation, apoptosis, and migration. The CCK-8 assay demonstrated that TAZ-OE significantly enhanced proliferation, whereas knockdown markedly inhibited it (Fig. 2A, B). Flow cytometric cell cycle analysis further revealed that TAZ-OE increased the proportion of S-phase cells, indicating enhanced proliferation, while TAZ-knockdown reduced this proportion (Fig. 2C–F).

Apoptosis analysis via flow cytometry showed that TAZ-OE significantly decreased apoptotic cell populations at 72 hours, while TAZ-knockdown increased apoptosis. Notably, changes were observed at 24 hours in Huh7 cells and at 48 hours in HepG2 cells compared with controls (Supplemental Fig. S2), confirming that TAZ suppresses apoptosis.

To examine cell migration and invasion, Transwell migration and Matrigel invasion assays were conducted. TAZ-OE significantly promoted both migration and invasion in Huh7 cells, whereas knockdown attenuated these abilities (Fig. 2G, H). These findings indicate that TAZ enhances the migratory and invasive potential of HCC cells.

Incomplete reversal of TAZ-induced proliferation and anti-apoptosis by blocking TAZ-TEAD interaction

Previous studies have demonstrated that TAZ exerts tumor-promoting effects primarily through interaction with the transcription factor TEAD. To investigate how disrupting this interaction influences HCC cell behavior, HepG2 and Huh7 cells were transfected with a TAZ-OE plasmid, a TAZ-TEAD Del plasmid (lacking the TEAD-binding domain), or a control plasmid. CCK-8 assays revealed that cells transfected with the TAZ-TEAD Del plasmid exhibited reduced proliferation compared to the TAZ-OE group, although proliferation was not fully suppressed (Fig. 3A, B).

Similarly, flow cytometry indicated that the apoptosis rates were lowest in the TAZ-OE group and highest in the NC group, whereas the TAZ-TEAD Del group exhibited intermediate apoptosis levels (Fig. 3C, D). These results suggest that disrupting the TAZ-TEAD interaction partially mitigates the anti-apoptotic effects of TAZ but does not completely restore apoptosis to baseline levels.

Relationship between TAZ and gluconeogenesis-related genes in liver cancer

Given the central role of metabolism in cancer, we analyzed gene expression profiles from the TCGA database in liver cancer patients. Gluconeogenesis-related genes PCK1 and G6PC were markedly downregulated in liver cancer tissues compared with normal tissues (Fig. 4A, B). Analysis of paired cancerous and adjacent tissues confirmed lower expression of PCK1 and G6PC in HCC (Fig. 4C, D). Importantly, among HCC patients, those with relatively higher PCK1 or G6PC expression exhibited improved overall survival (Fig. 4E, F), suggesting that suppression of gluconeogenic genes is a frequent event in HCC, and preservation of their expression correlates with favorable prognosis.

Next, we examined the relationship between TAZ and gluconeogenic gene expression using the R2 database. A negative correlation was identified between TAZ and PCK1/G6PC expression in liver cancer (Fig. 4G). This finding was validated in clinical samples through qRT-PCR and Western blot analyses, showing that tissues with elevated TAZ expression exhibited reduced PCK1 and G6PC levels (Fig. 4H–J). Collectively, these data suggest that TAZ negatively regulates gluconeogenesis in HCC.

Gluconeogenesis inhibition by TAZ in liver cancer cells

To investigate the role of TAZ in regulating gluconeogenesis in liver cancer cells, we used TAZ-OE and knockdown cell lines. qRT-PCR and Western blot analyses showed that PCK1 and G6PC expression levels were significantly downregulated in TAZ-OE cells and upregulated in TAZ-knockdown cells (Fig. 4K–R).

To validate these findings functionally, glucose production assays were performed. TAZ-OE markedly reduced glucose production, whereas TAZ-knockdown increased it (Fig. 4S). These results are consistent with the gene expression data, confirming that TAZ negatively regulates gluconeogenesis in liver cancer cells.

In vivo effects of TAZ modulation on tumor growth and glucose metabolism in liver cancer

To examine the in vivo effects of TAZ, lentiviral vectors were used to establish TAZ-OE and knockdown (shTAZ) Huh7 cells, which were then injected into nude mice. Tumors derived from TAZ-OE cells grew faster, larger, and heavier than those in the NC group (Fig. 5A–D), whereas tumors from shTAZ cells were smaller, lighter, and grew more slowly (Fig. 5E–H).

Tumor tissues were further analyzed by qRT-PCR and Western blot. In the TAZ-OE group, TAZ expression was elevated while PCK1 and G6PC levels were reduced compared to the NC group (Fig. 5I, J). Conversely, in the shTAZ group, TAZ expression was decreased and PCK1 and G6PC expression was elevated relative to the short-hairpin NC (shNC) group (Fig. 5K, L). These results confirm that TAZ promotes tumor growth and concurrently suppresses gluconeogenic gene expression in liver cancer.

Relationship between TAZ and key transcription factors in glucose metabolism

We next hypothesized that TAZ may influence gluconeogenesis by interacting with transcription factors involved in glucose metabolism. STRING database analysis identified four transcription factors (FoxO1, HNF4A, GR, and PPARG coactivator 1 alpha [PPARGC1α]) associated with the Hippo signaling pathway (Supplemental Fig. S3A).

However, Western blot analyses revealed no significant changes in the expression of these transcription factors or YAP in TAZ-OE or TAZ-knockdown cells (Supplemental Fig. S3B, C). These findings suggest that TAZ does not regulate gluconeogenesis by altering transcription factor abundance directly.

TAZ-GR interaction and its role in regulating gluconeogenesis genes in liver cancer

Previous research demonstrated that TAZ can interact with GR in hepatocytes to repress gluconeogenic gene expression [40]. Based on this, we hypothesized that in liver cancer cells, TAZ similarly regulates downstream gene expression by modulating GR transcriptional activity rather than changing GR protein levels. To test this hypothesis, molecular docking using the Z-Dock tool predicted a strong interaction potential between TAZ and GR (Fig. 6A). Reciprocal co-immunoprecipitation (Co-IP) in 293A cells co-transfected with DYKDDDDK (FLAG)-TAZ and Myc-GR plasmids confirmed the TAZ-GR interaction (Supplemental Fig. S3D, E).

Endogenous Co-IP in HepG2 and Huh7 cells further validated this finding, showing clear binding between TAZ and GR (Fig. 6B, C). Immunofluorescence (IF) analysis demonstrated colocalization of TAZ and GR in both the cytoplasm and nucleus (Fig. 6D).

To explore the functional implications of this interaction, ChIP assays were conducted in TAZ-OE and knockdown Huh7 cells. GR enrichment at the PCK1 and G6PC promoters was reduced in TAZ-OE cells but increased in knockdown cells (Fig. 6E). ChIP-qPCR using acetylated histone antibodies further revealed decreased histone acetylation at these promoter regions in the TAZ-OE group, indicating that TAZ may repress gene expression by limiting GR promoter binding (Fig. 6F).

TAZ-GR interaction as the main mechanism of TAZ-regulated gluconeogenesis in liver cancer

To determine whether TAZ-GR interaction represents the main mechanism underlying TAZ-mediated regulation of gluconeogenesis, we used the GR inhibitor mifepristone (RU486), which disrupts GR regulatory activity. Co-IP results showed that RU486 markedly weakened the TAZ-GR interaction (Supplemental Fig. S3F).

In RU486-treated liver cancer cells, qRT-PCR results demonstrated that the inhibitor abolished the TAZ-induced suppression of PCK1 and G6PC expression (Fig. 6G, H). Glucose production assays showed that RU486 restored glucose production in TAZ-OE cells to control levels (Fig. 6I). These findings indicate that TAZ regulates gluconeogenesis primarily through its interaction with GR.

In addition to pharmacological inhibition, we further validated the dependence of TAZ-mediated regulation on GR using siRNA knockdown. The upregulation of PCK1 and G6PC observed in shTAZ cells was markedly reversed upon GR silencing (Supplemental Fig. S4A–E). Functionally, CCK-8 assays showed that GR knockdown restored the proliferative capacity of shTAZ cells to levels comparable with controls (Supplemental Fig. S4F, G). Collectively, these results provide direct evidence that GR is essential for mediating both the regulatory and tumor-promoting effects of TAZ in liver cancer cells.

The WW domain of TAZ mediates interaction with GR

Previous studies have shown that proteins containing WW domains often interact with proline-rich motifs such as PPxY, PPLP, PPR/KP, (pS/pT)P, and PPxF, thereby activating downstream signaling pathways both in vitro and in vivo [41]. To determine whether the WW domain of TAZ mediates its interaction with the I/LPxY motif in GR, we performed Co-IP experiments in 293A cells. The results showed that the interaction between TAZ and GR was markedly reduced in TAZ mutants lacking the WW domain (TAZ ww del group) (Supplemental Fig. S4H). These findings indicate that the WW domain of TAZ is essential for mediating the TAZ-GR interaction.

No inhibition of gluconeogenesis in liver cancer by TAZ mutants lacking the WW domain

To investigate the role of the WW domain in TAZ-mediated regulation of gluconeogenesis, we introduced TAZ mutants lacking this domain into HepG2 and Huh7 cells via lentiviral transduction. qRT-PCR and Western blot analyses demonstrated that the expression levels of PCK1 and G6PC were significantly restored in cells expressing the WW domain-deleted TAZ mutant (Fig. 7A–D). This indicates that deletion of the WW domain attenuates or abolishes the inhibitory effects of TAZ on these gluconeogenic genes, restoring their expression to near control levels.

Glucose production assays further confirmed this result. Liver cancer cells expressing the WW domain-deleted TAZ mutant exhibited glucose production comparable to that of the normal control group, in contrast to the markedly reduced glucose output observed in TAZ-OE cells (Fig. 7E, F). These findings underscore the critical role of the WW domain in TAZ-mediated suppression of gluconeogenesis in liver cancer cells.

Reduced proliferation of liver cancer cells by TAZ WW domain deletion

Building upon these findings, we next assessed the impact of WW domain deletion on the proliferative capacity of liver cancer cells. Cell cycle analysis revealed that, relative to the TAZ-OE group, the proportion of S-phase cells decreased and the proportion of G2/M-phase cells increased in the WW domain deletion mutants. In Huh7 cells, the proportion of S-phase cells remained higher and G2/M-phase cells lower than those of the NC group (Fig. 7G, H). CCK-8 proliferation assays showed that cells expressing the WW domain deletion mutant displayed significantly reduced proliferation compared to the TAZ-OE group but slightly higher proliferation than the NC group (Fig. 7I). These results suggest that removal of the WW domain partially diminishes the pro-proliferative effects of TAZ in liver cancer cells.

Promotion of apoptosis in liver cancer cells by TAZ WW domain deletion

To examine the effect of WW domain deletion on apoptosis, we performed flow cytometric analysis. The WW domain deletion group exhibited a significant increase in apoptosis compared with the TAZ-OE group. In HepG2 cells, apoptosis in the deletion group was slightly lower than in the NC group, but the difference was not statistically significant (Supplemental Fig. S5). In contrast, Huh7 cells expressing the WW domain deletion mutant showed significantly higher apoptosis rates than the NC group (Supplemental Fig. S5). These findings indicate that WW domain deletion enhances apoptosis in liver cancer cells, counteracting the anti-apoptotic effect associated with TAZ-OE.

Reduced migration and invasion of liver cancer cells by TAZ WW domain deletion

To evaluate how the WW domain affects cell motility, we performed Transwell migration and Matrigel invasion assays in Huh7 cells. The number of migrating cells in the WW domain deletion group was markedly lower than in the TAZ-OE group, although still higher than in the NC group (Fig. 7J, K). A similar pattern was observed in invasion assays, with partial reduction of invasive capacity in the deletion group relative to the OE group (Fig. 7J, K). These findings indicate that deletion of the WW domain partially suppresses the migratory and invasive abilities of liver cancer cells.

TAZ WW domain deletion inhibits tumorigenic ability in nude mice

To explore the in vivo effects of WW domain deletion on tumor growth, subcutaneous xenograft models were established in nude mice using Huh7 cells infected with lentiviral vectors expressing the control, TAZ-OE, or WW domain deletion constructs. Tumor growth in the WW domain deletion group was significantly slower, and the resulting tumors were smaller and lighter than those in the TAZ-OE group. However, tumor volume and weight in the deletion group remained higher than in the control group (Fig. 8A–D).

To further validate the relationship between TAZ and glucose metabolism, tumor samples from the experimental mice were subjected to qRT-PCR and Western blot analyses. Expression levels of PCK1 and G6PC were significantly restored to control levels in the WW domain deletion group (Fig. 8E, F). These results reinforce the essential role of the TAZ WW domain in regulating glucose metabolism and tumor growth in liver cancer.

DISCUSSION

The Hippo signaling pathway and its core transcriptional coactivators, YAP and TAZ, play crucial roles in the progression of human cancers, particularly HCC. This study examined the upregulation of TAZ in HCC and its involvement in regulating gluconeogenesis-related genes. Using TCGA and GEO database analyses alongside clinical validation, we found that TAZ expression was significantly elevated in HCC tissues compared with adjacent noncancerous tissues. Functional experiments in vitro and in nude mouse xenograft models demonstrated that TAZ promotes proliferation, inhibits apoptosis, and enhances migration and invasion in liver cancer cells, corroborating previous findings on its oncogenic role in HCC (Fig. 8G).

Numerous studies have shown that TAZ activates downstream oncogenic signaling primarily through interaction with the transcription factor TEAD, driving tumor cell proliferation, migration, and metastasis [42–44]. The YAP/TAZ-TEAD complex can also interact with cell cycle-related transcription factors such as activator protein-1 (AP-1) to promote tumor growth [45]. Although several inhibitors targeting the YAP/TAZ-TEAD axis have demonstrated tumor-suppressive effects, their clinical utility remains limited due to off-target toxicity and poor specificity [46–48]. To explore alternative mechanisms beyond TEAD-dependent pathways, we constructed a TEAD-binding domain deletion mutant and assessed its effects on HCC cell proliferation and apoptosis. Interestingly, TAZ retained partial oncogenic activity even in the absence of the TEAD-binding domain, suggesting that additional signaling pathways or protein interactions contribute to TAZ-driven tumorigenesis.

The liver is the central organ for metabolism and detoxification, endowed with exceptional regenerative capacity [49,50]. Metabolic reprogramming in tumors promotes glycolysis while suppressing gluconeogenesis, thereby ensuring sufficient energy supply for rapid cell proliferation [51,52]. Recent studies have revealed that glucose availability directly influences YAP/TAZ activity. For example, YAP enhances glucose uptake by transcriptionally activating glucose transporter 1 (GLUT1) through TEAD, facilitating aerobic glycolysis and tumor progression [34]. Conversely, glycolytic inhibition induces phosphorylation and inactivation of YAP/TAZ, reducing their oncogenic potential [53]. Furthermore, as glucose levels decline, YAP/TAZ phosphorylation increases, resulting in cytoplasmic retention and decreased transcriptional activity [53–55]. Thus, glucose availability modulates YAP/TAZ function through both extracellular uptake and intracellular gluconeogenesis. Glucagon further promotes gluconeogenesis by activating protein kinase A (PKA), which inhibits YAP/TAZ activity via a cyclic adenosine monophosphate-dependent mechanism, revealing a negative regulatory relationship between TAZ and gluconeogenesis in liver cancer [56,57].

Previous research has shown that YAP regulates glucose metabolism in hepatocytes by upregulating PGC1α, whereas TAZ inhibits peroxisome proliferator-activated receptor γ (PPARγ) activity in adipocytes, thereby reducing insulin sensitivity [35,58]. Despite their shared and distinct pathways in glucose metabolism, we focused on the role of TAZ-mediated metabolic regulation in promoting HCC growth [59]. Analysis of TCGA datasets, clinical samples, and cell line experiments revealed a negative correlation between TAZ expression and the gluconeogenic genes PCK1 and G6PC. Functional assays confirmed that TAZ-OE suppressed PCK1 and G6PC expression and reduced glucose production in liver cancer cells, whereas TAZ-knockdown exerted the opposite effects—findings further validated in xenograft models.

Gluconeogenesis is a central metabolic pathway tightly regulated by hormones such as insulin and glucagon, along with transcription factors including FoxO1, CREB, PGC1α, and GR [60–63]. In our study, modulation of TAZ expression did not alter the protein levels of these transcription factors or YAP, suggesting that TAZ acts primarily through co-transcriptional regulation rather than altering transcription factor abundance. Consistent with its role as a transcriptional co-regulator, TAZ has been reported to interact with TEADs, suppressor of mothers against decapentaplegics (SMADs), erb-b2 receptor tyrosine kinase 4 (ErbB4), and T-box transcription factor 5 (TBX5) to promote tumorigenesis [64–67].

GR, a nuclear hormone receptor, directly activates gluconeogenic genes such as PCK1 and G6PC by binding to glucocorticoid response elements upon ligand stimulation [68,69]. Previous studies have shown that GR expression is reduced in several cancers, and efforts to target GR therapeutically are ongoing [70]. While YAP suppresses gluconeogenesis through PGC1α, TAZ has been shown to interact with GR in hepatocytes [35,40]. Extending this observation to HCC, our results confirmed a direct TAZ-GR interaction by Co-IP and IF. ChIP-qPCR demonstrated that TAZ-OE decreased GR occupancy and histone acetylation at the PCK1 and G6PC promoters, whereas TAZ-knockdown produced the opposite effect. Pharmacological inhibition of GR using RU486 abolished TAZ-mediated repression of gluconeogenesis, highlighting the functional importance of TAZ-GR crosstalk [71]. Furthermore, subcellular fractionation revealed that TAZ localizes to both cytoplasmic and nuclear compartments of HCC cells (Supplemental Fig. S1). This supports a dual regulatory model: cytoplasmic TAZ sequesters GR, restricting its nuclear translocation, while nuclear TAZ cooperates with other transcription factors to maintain oncogenic transcriptional programs. Consistently, GR silencing reversed the effects of TAZ-knockdown on PCK1 and G6PC expression and partially restored proliferative capacity, confirming that GR is indispensable for mediating the oncogenic and metabolic functions of TAZ in HCC (Supplemental Fig. S4).

Beyond TEAD binding, the WW domain constitutes another key structural feature of YAP/TAZ, mediating interactions with proline-rich motifs [72–74]. TAZ, which contains two WW domains, engages in signaling distinct from YAP; for example, the TAZ WW domain interacts with WW domain binding protein 2 (WBP2) to promote cell migration and invasion [75].

Given that GR contains a conserved I/LPxY motif [41,76], we hypothesized and confirmed that TAZ-GR binding is dependent on the WW domain. Deletion of this domain markedly reduced GR binding, restored PCK1 and G6PC expression, and impaired TAZ-mediated glucose suppression in HCC cells. In vivo xenograft studies further demonstrated attenuated tumor growth in WW domain-deficient TAZ mutants, underscoring the domain’s essential role in metabolic and oncogenic regulation. Collectively, these findings suggest that therapeutic strategies targeting the TAZ WW domain may effectively complement TEAD-binding inhibition, thereby broadening potential avenues for intervention in HCC.

In conclusion, this study underscores the critical role of the TAZ-GR interaction in regulating gluconeogenesis in liver cancer. Disrupting this interaction may serve as a novel strategy to inhibit the oncogenic effects of TAZ. Future research will focus on understanding the specific binding sites and mechanisms of TAZ-GR interaction to develop more targeted anti-cancer therapies.

Supplementary Material

Article information

CONFLICTS OF INTEREST

No potential conflict of interest relevant to this article was reported.

ACKNOWLEDGMENTS

This study was supported by grants from the National Natural Science Foundation for Young Scientists of China (No. 821033 39); the National Natural Science Foundation for Regional Fund (No. 82560474, and 82360507); the Natural Science Fund for Youths of Jiangxi Province (No.20224BAB216067); and the Hospital Clinical Research Cultivation Project (No. YFYLCYJPY202302).

AUTHOR CONTRIBUTIONS

Conception or design: H.H., L.C., Y.L. Acquisition, analysis, or interpretation of data: H.H., J.C., X.T., P.Z., Y.M., S.P., W.L., Z.H., S.L., X.Z., Z.L. Drafting the work or revising: H.H., L.C., Y.L. Final approval of the manuscript: H.H., J.C., X.T., P.Z., Y.M., S.P., W.L., Z.H., S.L., X.Z., Z.L., L.C., Y.L.

Fig. 1

Transcriptional co-activator with PDZ-binding motif (TAZ) expression in liver cancer. (A) TAZ expression across 33 cancer types and normal tissues in The Cancer Genome Atlas (TCGA) database; (B) TAZ expression in 371 liver cancer and 50 adjacent non-tumor tissues from TCGA; (C) TAZ expression in 20 paired liver cancer and adjacent non-tumor tissues; (D, E, F) Western blot and quantitative real-time polymerase chain reaction (qRT-PCR) of TAZ in liver cancer and adjacent non-tumor tissues; (G, H) qRT-PCR and Western blot of TAZ mRNA in LO2 and liver cancer cell lines; (I, J, K, L) Validation of TAZ overexpression (OE) and knockdown in HepG2 and Huh7 cells by qRT-PCR and Western blot. TPM, transcripts per million; FPKM, fragments per kilobase of exon model per Million mapped fragments; N, normal; T, tumor; NC, negative control; shNC, short-hairpin NC; shTAZ, short-hairpin TAZ. ^aP<0.05; ^bP<0.01; ^cP<0.001.

Fig. 2

Transcriptional co-activator with PDZ-binding motif (TAZ) influences proliferation, cell cycle, migration, and invasion in liver cancer cells. (A) Cell counting kit-8 (CCK-8) assay of TAZ overexpression (OE); (B) CCK-8 assay of TAZ-knockdown; (C, D) Flow cytometry and cell cycle analysis of TAZ-OE; (E, F) Flow cytometry and cell cycle analysis of TAZ-knockdown; (G) Migration and invasion assays of TAZ-OE in Huh7 cells; (H) TAZ-knockdown in Huh7 cells. Data are presented as mean±standard deviation (n=3). NC, negative control; shNC, short-hairpin NC; shTAZ, short-hairpin TAZ; OD, optical density; G, gap phase; S, synthesis phase; M, mitosis phase. ^aP<0.05; ^bP< 0.01; ^cP<0.001.

Fig. 3

The transcriptional co-activator with PDZ-binding motif (TAZ)-TEA domain family member (TEAD) binding domain influences proliferation and apoptosis in liver cancer cells. (A, B) CCK-8 proliferation assays in HepG2 and Huh7 cells transfected with TAZ overexpression (OE), TAZ-TEAD binding domain deletion, and control plasmids; (C, D) Flow cytometry and apoptosis analysis in HepG2 and Huh7 cells transfected with TAZ-OE, TAZ-TEAD binding domain deletion, and control plasmids. Data are presented as mean±standard deviation (n=3). NC, negative control; OD, optical density; AAD, aminoactinomycin D. ^aP<0.05; ^bP<0.01; ^cP<0.001.

Fig. 4

Transcriptional co-activator with PDZ-binding motif (TAZ) regulates phosphoenolpyruvate carboxykinase 1 (PCK1) and glucose-6-phosphatase (G6PC) expression, correlating with metabolic changes in liver cancer. (A, B) PCK1 and G6PC expression in liver cancer and adjacent tissues from The Cancer Genome Atlas (TCGA); (C, D) PCK1 and G6PC expression in 50 paired liver cancer and adjacent tissues; (E, F) Survival analysis based on PCK1 and G6PC expression in TCGA; (G) Correlation between TAZ and PCK1/G6PC in liver cancer from the R2 database; (H, I, J) Quantitative real-time polymerase chain reaction (qRT-PCR) and Western blot of TAZ, PCK1, and G6PC expression in liver cancer and adjacent tissues; (K, L, M, N) qRT-PCR of PCK1 and G6PC in TAZ-overexpressing (OE) and TAZ-knockdown HepG2 and Huh7 cells; (O, P, Q, R) Western blot of PCK1 and G6PC protein in TAZ-OE and TAZ-knockdown HepG2 and Huh7 cells; (S) Glucose production in TAZ-OE and knockdown HepG2 and Huh7 cells. TPM, transcripts per million; FPKM, fragments per kilobase of exon model per million mapped fragments; HR, hazard ratio; N, normal; T, tumor; NC, negative control; shNC, short-hairpin NC; shTAZ, short-hairpin TAZ. ^aP<0.05; ^bP<0.01; ^cP<0.001.

Fig. 5

Transcriptional co-activator with PDZ-binding motif (TAZ) regulates tumor growth and gene expression in Huh7 xenografts. (A) Tumor comparison between TAZ-overexpressing (OE) and control Huh7 xenografts; (B, C, D) Tumor size, volume, and growth in TAZ-OE xenografts; (E) Tumor comparison between TAZ-knockdown and control Huh7 xenografts; (F, G, H) Tumor size, volume, and growth in TAZ-knockdown xenografts; (I, J, K, L) Western blot and quantitative real-time polymerase chain reaction analysis of TAZ, phosphoenolpyruvate carboxykinase 1 (PCK1), and glucose-6-phosphatase (G6PC) expression. Data are presented as mean±standard deviation (n=6). NC, negative control; shNC, short-hairpin NC; shTAZ, short-hairpin TAZ. ^aP<0.01; ^bP<0.001.

Fig. 6

TAZ interacts with glucocorticoid receptor (GR), affecting gene expression and glucose metabolism in liver cancer cells. (A) Simulation of TAZ-GR interaction; (B, C) Co-immunoprecipitation (Co-IP) of TAZ-GR in HepG2 and Huh7 cells; (D) Immunofluorescence images of TAZ-GR colocalization; (E) chromatin immunoprecipitation-quantitative polymerase chain reaction (PCR) showing TAZ effect on GR binding to phosphoenolpyruvate carboxykinase 1 (PCK1) and glucose-6-phosphatase (G6PC) promoters; (F) TAZ effects on histone acetylation at PCK1 and G6PC promoters; (G, H) Quantitative real-time PCR and glucose production assays after RU486 treatment in TAZ-overexpressing (OE) cells; (I) RU486 reduces glucose production in TAZ-OE cells to control levels. Data are presented as mean±standard deviation (n=3). IP, immunoprecipitation; IgG, immunoglobulin G; IB, immunoblotting; DAPI, 4′,6-diamidino-2-phenylindole; NC, negative control; shNC, short-hairpin NC; shTAZ, short-hairpin TAZ; NS, no significance. ^aP<0.05; ^bP<0.01; ^cP<0.001.

Fig. 7

The WW domain of transcriptional co-activator with PDZ-binding motif (TAZ) influences gluconeogenesis, cell cycle, proliferation, migration, and invasion in liver cancer. (A, B, C, D) Quantitative real-time polymerase chain reaction and Western blot analysis of phosphoenolpyruvate carboxykinase 1 (PCK1) and glucose-6-phosphatase (G6PC) in HepG2 and Huh7 cells expressing TAZ overexpression (OE), WW domain deletion mutant, or control constructs; (E, F) Glucose production assays in these cells; (G, H) Flow cytometry of cell cycle distribution; (I) Cell counting kit-8 proliferation assays; (J, K) Transwell migration and invasion assays. Data are presented as mean±standard deviation (n=3). NS, no significance; NC, negative control; OD, optical density; G, gap phase; S, synthesis phase; M, mitosis phase. ^aP<0.05; ^bP<0.01; ^cP<0.001.

Fig. 8

The WW domain modulates tumor growth and gluconeogenesis through interaction with glucocorticoid receptor (GR). (A) Tumor comparison among transcriptional co-activator with PDZ-binding motif (TAZ)-overexpressing (OE), WW domain deletion mutant, and control Huh7 xenografts; (B, C, D) Tumor size, volume, and growth among different groups; (E, F) Western blot and quantitative real-time polymerase chain reaction of TAZ, phosphoenolpyruvate carboxykinase 1 (PCK1), and glucose-6-phosphatase (G6PC) in tumors; (G) Proposed mechanism: TAZ inhibits GR-mediated activation of PCK1 and G6PC when Hippo signaling is inactive, while Hippo activation or WW domain deletion restores GR-dependent gluconeogenesis. Data are presented as mean±standard deviation (n=6). NC, negative control; NS, no significance. ^aP<0.05; ^bP<0.01; ^cP<0.001.

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