Hepatocellular carcinoma (HCC) is one of the primary liver malignancies with a poor prognosis. Glutamic-oxaloacetic transaminase 2 (GOT2) is a highly tissue-specific gene in the liver, but the roles GOT2 plays in the progression of HCC remain unclear. Here, we report that GOT2 is downregulated in HCC tumor tissues and that low expression of GOT2 is associated with advanced progression and poor prognosis. In HCC cells, knockdown of GOT2 promoted proliferation, migration, and invasion. In mouse models of HCC, loss of GOT2 promoted tumor growth as well as hematogenous and intrahepatic metastasis. Mechanistically, silencing of GOT2 enhanced glutaminolysis, nucleotide synthesis, and glutathione synthesis by reprogramming glutamine metabolism to support the cellular antioxidant system, which activated the PI3K/AKT/mTOR pathway to contribute to HCC progression. Furthermore, HCC with low expression of GOT2 was highly dependent on glutamine metabolism and sensitive to the glutaminase inhibitor CB-839 in vitro and in vivo. Overall, GOT2 is involved in glutamine metabolic reprogramming to promote HCC progression and may serve as a therapeutic and diagnostic target for HCC.

Significance:

Altered glutamine metabolism induced by GOT2 loss supports HCC growth and metastasis but confers a targetable vulnerability to glutaminase inhibitors.

Hepatocellular carcinoma (HCC) is one of the most common malignancies worldwide and has a poor prognosis (1). Hepatectomy or liver transplantation is the mainstay of durable curative therapy for early-stage HCC (2). However, most patients are diagnosed with moderate or late-stage HCC, which is unresectable (3). Even with targeted drugs, such as sorafenib, regorafenib, and lenvatinib, the treatment outcome is unsatisfactory, with low drug-response rates in advanced HCC (4–6). Hence, novel therapeutic strategies for aggressive HCC are urgently needed.

Metabolic reprogramming is an essential feature of cancer (7). Human liver cancer is dependent on extracellular glutamine (Gln) and shows glutamine addiction (8). Glutamine participates in macromolecular synthesis, energy formation, and the provision of nicotinamide adenine dinucleotide phosphate (NADPH) and glutathione (GSH) to maintain redox homeostasis (9). Mitochondrial glutamine metabolism involves oxidative or reductive metabolic pathways, including glutaminolysis and reductive carboxylation (RC) in the tricarboxylic acid (TCA) cycle (10). Glutaminolysis begins with the conversion of glutamine to glutamate (Glu) catalyzed by mitochondrial glutaminase (GLS) and then to α-ketoglutarate (αKG), which is oxidized to succinate (Suc) and subsequently TCA cycle metabolites, generating ATP and NADPH. Alternatively, αKG can undergo RC, pushing the reverse TCA cycle toward citrate for lipogenesis (11). GLS regulates reactive oxygen species (ROS) homeostasis by providing not only glutamate and cysteine precursors for GSH synthesis but also promoting the production of NADPH (12). Targeting GLS is suggested to be a valuable therapeutic approach in cancer (13, 14). GLS1, a major isoform of GLS, regulates the stemness properties of HCC, and targeting GLS1 inhibits tumorigenicity in vivo (15). CB-839, 968, and BPTES, chemical agents that target GLS, exhibit tumor-specific antiproliferative effects (16). Among these agents, CB-839 (also named telaglenastat) was the first to proceed to clinical trials (13). It is a potent, orally bioavailable GLS inhibitor with antitumor activity in several cancer cell lines and xenografts, such as triple-negative breast cancer (13), lung adenocarcinoma (17), chondrosarcoma (18), and lymphoma (19). However, monotherapy with the glutaminase inhibitor CB-839 has a weak anticancer effect in HCC cells (20). Therefore, a more effective strategy is needed for the treatment of HCC with glutamine addiction.

Glutamic-oxaloacetic transaminase (GOT) exists as a cytoplasmic form, GOT1, and a mitochondrial form, GOT2 (21). The transaminase GOT2 catalyzes the conversion of Glu and oxaloacetate to αKG and aspartate (Asp), whereas GOT1 catalyzes the reverse reaction (22). It has been reported that GOT2 promotes breast cancer (23) and colorectal cancer (24) via aspartate biosynthesis. GOT2 is required to sustain pancreatic ductal adenocarcinoma (PDAC) growth by repressing senescence (25). Our previous studies showed that GOT2 was a highly enriched gene in the liver (26), and GOT2 was associated with the clinical outcomes of HCC (27). However, the biological mechanisms of GOT2 in HCC need to be further investigated.

In this study, GOT2 was significantly downregulated in HCC tumor tissues, and low expression of GOT2 was associated with advanced progression and poor prognosis at both the mRNA and protein levels. Knockdown of GOT2 promoted proliferation and hematogenous and intrahepatic metastasis in vitro and in vivo. Metabolic flux analysis and seahorse assays showed that GOT2 silencing enhanced GSH synthesis and mitochondrial respiration in a manner dependent on glutamine metabolic reprogramming. Silencing GOT2 activated the PI3K/AKT/mTOR pathway via GSH/ROS redox homeostasis to promote HCC progression. Furthermore, HCC with low GOT2 expression showed enhanced glutamine dependency and glutaminase-inhibitor sensitivity. Therefore, GOT2 has potential as a therapeutic and diagnostic target for HCC.

Additional information on methods can be found in the Supplementary Data.

Clinical samples

The study was conducted in accordance with Declaration of Helsinki. The tumor and adjacent nontumor tissues used in this study were obtained written informed consent from the patients with HCC undergoing partial hepatectomy in the Department of Hepatobiliary Surgery of Nanjing Drum Tower Hospital. The experimental protocols were approved by the Ethics Committee of Nanjing Drum Tower Hospital, which granted research ethics approval for this study.

Cell culture and reagents

Human HCC cell lines (HCCLM3, Huh7, and MHCC97L) were cultured in DMEM supplemented with 9% FBS, 100 U/mL penicillin, and 100 μg/mL streptomycin (#C100C5 NCM Biotech). The cell lines were authenticated by the vendors (Cell STR certificates are listed in the Supplementary Data). The cell lines were routinely tested for Mycoplasma and maintained at 37°C in a humidified atmosphere with 5% CO2 and in culture for a maximum of 20 passages (2 months). Compound 968 (#352010) was purchased from Millipore. CB-839 (Telaglenastat, #HY-12248), N-acetylcysteine (NAC, #HY-B0215), LY294002 (#HY-10108), buparlisib (BKM120, #HY-70063), capivasertib (#HY-15431), GOT1 inhibitor-1 (#HY-122723), EGCG (#HY-13653), R162 (#HY-103096), and AOA (#HY-107994) were purchased from MedChemExpress. DMEM without glutamine (#HY-B0215) was purchased from Thermo Fisher Scientific.

Lentiviral infection

Lentiviruses carrying short hairpin RNAs (shRNA) targeting GOT2 and overexpressing GOT2 with flag labels were purchased from Shanghai Genechem Co. Ltd. Lentiviruses carrying knockdown or overexpression elements were used to infect HCCLM3, Huh7, and MHCC97L cells according to the manufacturer's protocol. Infected cells were selected using puromycin.

Cell proliferation, migration, and invasion assay analysis

Cell viability was measured by Cell Counting Kit 8 (CCK-8) proliferation assay (#HY-K0301, MedChemExpress) according to the manufacturer's instructions.

Wound-healing migration was assayed by using an assay (Ibidi GmbH). Photographs were taken at 0, 24, and 48 hours. ImageJ software was used to measure the scratched area. Cell migration related to wound healing was assessed using the following formula: [(wound area at 0 hours) – (wound area at indicated 24 or 48 hours)]/(wound area at 0 hours) × 100%.

Transwell migration assays were performed using cell culture inserts (8 μm; 24-well plate; Corning Inc.). Genetically modified cells (1 × 106 cells/mL in FBS-free DMEM, 100 μL) were washed with PBS and seeded in the top chamber; 600 μL DMEM (supplemented with 20% FBS) was added to the bottom chamber. After incubation for 24 hours, the cell culture inserts were fixed with 1% paraformaldehyde for 30 minutes and stained using 0.1% crystal violet for 20 minutes. Nonmigrating cells were removed by gently wiping with a swab. The average number of stained cells per field was determined (100×, four separate fields).

Cell invasion was assessed by Matrigel-coated Transwell invasion assay (8 μm; 24-well plate; Corning Inc.). Matrigel (200 μg/mL, 100 μL) was precoated in the top chamber on ice. Subsequent procedures were as for the migration assay.

Western blotting

Proteins were isolated using RIPA lysis buffer (#P0013C, Beyotime Biotechnology) and qualified using a BCA detection kit (#P0012, Beyotime Biotechnology) following the manufacturer's protocol. Equal amounts of protein were separated by SDS-PAGE, electrophoretically transferred onto a polyvinylidene difluoride membrane (#03010040001, Roche), and blocked with 5% nonfat milk in Tris-buffered saline for 1 hour at room temperature. The membrane was incubated with specific primary antibodies at 4°C overnight, followed by washing and incubation with secondary antibodies for 1 hour at room temperature. Protein bands were detected using an enhanced chemiluminescence reagent (#E412-02, Vazyme, Nanjing, China). The antibodies used are listed in Supplementary Table S1.

Immunohistochemistry

Specimens were paraffin-embedded. Serial 4 μm sections were cut, deparaffinized, blocked, and incubated at 4°C overnight with the primary antibody, followed by a horseradish peroxidase-labeled secondary antibody. The primary antibodies used are listed in Supplementary Table S1. Human HCC tissue microarrays (ZL-LVC1606) were purchased from Shanghai Zhuolibiotech Company Co., Ltd., and GOT2 expression was evaluated using the automated VIS DIA VisioMorph System (Visiopharm). Clinical and pathologic information of the samples was obtained from the array manufacturer.

RNA isolation and quantitative real-time PCR

Total RNA was extracted using Total RNA Extraction Reagent (#R401-01, Vazyme) and reverse-transcribed using HiScript III-RT SuperMix for qPCR (+gDNA wiper; #R323-01, Vazyme). Quantitative real-time PCR was performed using ChamQ Universal SYBR qPCR Master Mix (#Q711-02, Vazyme) on a real-time PCR system (Applied Biosystems QuantStudio 6 Flex). Expression was normalized to GAPDH by the 2–ΔΔCt method. The primer sequences are listed in Supplementary Table S2.

Flow cytometry

Trypsin-digested cells were washed with PBS, followed by incubation with DAPI (#C1002, Beyotime) and DHE (#KGAF019, KeyGEN BioTECH). Samples were subjected to flow cytometry (FACSCalibur, BD Biosciences), and data were analyzed using FlowJo software (version 7.6.5).

ROS, MDA, GSH/GSSG, and NADP+/NADPH ratio measurement

Intracellular ROS accumulation was assayed using the DHE Kit (#KGAF019, KeyGEN BioTECH) and MitoSOX Red (#M36008, Thermo Fisher Scientific) as fluorescent probes. The MDA Test Kit (#G0110W) and LPO Inhibition Ratio Kits (#G0149W) were purchased from Suzhou Grace Biotechnology Co., Ltd. GSH/GSSG and NADPH/NADP+ ratios were measured using a GSH/GSSG Ratio Assay Kit (Beyotime; S0053) and NADP+/NADPH Ratio Assay Kit (Beyotime; S0179), respectively, according to the manufacturer's instructions.

Metabolic flux analysis

HCCLM3 shCON and shGOT2 cells were treated with 4 mmol/L 13C5,15N2 – glutamine for 24 hours and subjected to UHPLC-HRMS to determine metabolic changes. Cell protein extraction, separation, and identification were performed by Shanghai Profleader Biotech Co., Ltd.

Seahorse assay

HCCLM3 shCON and shGOT2 cells were plated at 25,000/well onto an Agilent Seahorse XF24 cell-culture microplate, and after 6 hours, 5 μmol/L CB-839 was added and allowed to grow for 24 hours. The oxygen consumption rate (OCR) was assayed on a Seahorse XF24 Extracellular Flux Analyzer (Seahorse Bioscience, Agilent Technologies) according to the manufacturer's instructions. The OCR was measured in the presence of oligomycin (1 μmol/L), FCCP (carbonyl cyanide-p-trifluorome-thoxyphenylhydrazone, 1 μmol/L), and rotenone/antimycin A (1 μmol/L/1 μmol/L). OCR values were normalized to the protein concentration and analyzed using WAVE software (Agilent Technologies).

Animal studies

BALB/c male nude mice (6–8 weeks old) were purchased from GemPharmatech Co., Ltd. Mice were housed in the specific pathogen-free facility of the Laboratory Animal Research Center (22 ± 2°C, 60 ± 10% humidity, 12/12 hour light/dark cycle). Animal care protocols and experiments were performed in accordance with the Guide for the Care and Use of Laboratory Animals and approved by the Institutional Animal Care and Use Committee of Experimental Animal Center of Drum Tower Hospital, China (approval no. 2020AE01049). Stable HCCLM3 shCON and shGOT2 cells were subcutaneously injected (5 × 106 cells in 200 μL PBS) into the left flank of nude mice. Tumor short and long diameters were measured using calipers every 3 days. At 30 days after injection, the mice were euthanized. Tumor volume was calculated as: [volume = (width × width × length)/2].

For splenic injection, nude mice were anesthetized with isoflurane. Next, the skin was sterilized with iodophor. A left-side flank incision was made, and the spleen was exposed. Cells (2 × 106/100 μL) were injected slowly into the splenic parenchyma. The puncture site was pressed closed with a cotton swab to prevent bleeding and cell leakage. The spleen was returned to the abdomen, and the incision was closed with 4-0 sutures. The mice recovered from anesthesia and were returned to their cages.

For surgical orthotopic implantation, when the size of the subcutaneous xenograft tumor exceeded 0.5 cm3, the mice were euthanized, and the tumors were removed and divided into pieces of approximately 1 mm3. Nude mice were anesthetized, and a piece of tumor was placed in the liver. The incisions in the liver and skin were closed with sutures.

CB-839 was suspended at 20 mg/mL (w/v) in vehicle consisting of 25% (w/v) hydroxypropyl-b-cyclodextrin (HPBCD; MCE) in 10 mmol/L citrate (pH 2.0). When tumors reached approximately 100–150 mm3, mice were randomized to receive 200 μL of prepared vehicle (as described above) or CB-839 (20 mg/mL in vehicle) twice daily by oral gavage for approximately 20 days.

Data availability

RNA-seq and clinical data of cancers were retrieved from the The Cancer Genome Atlas (TCGA) database (https://gdc.cancer.gov). The microarray datasets (GSE14520, GSE54236, GSE9843, GSE29721, GSE14811, GSE17548, GSE17856, GSE45436, GSE36411, GSE39791, GSE41804, GSE62232, GSE50579, GSE55092, GSE56140, GSE36376, GSE65484, GSE45267, GSE6764, GSE57957, and GSE79098) were downloaded from Gene Expression Omnibus (GEO; http://www.ncbi.nlm.nih.gov/geo/). HCC protein expression profiles were downloaded from the Clinical Proteomic Tumor Analysis Consortium (CPTAC; https://cptac-data-portal.georgetown.edu). Mouse organ single-cell RNA-seq data from the Tabula Muris project were downloaded from https://tabula-muris.ds.czbiohub.org/. Statistical analysis and graphing were performed using R software (https://www.r-project.org). The R package and computer code are available from the corresponding author upon request.

The datasets generated in this study are available as Supplementary Data. Additional data are available from the corresponding author upon reasonable request.

Statistical analysis

Quantitative data were analyzed using Student t test or Mann–Whitney U test. The paired t test or Wilcoxon signed-rank test was used for paired samples to perform between-group statistical comparisons. Count data were analyzed using Pearson χ2 test. The Kruskal–Wallis or Mann–Whitney U test, as appropriate, was used for ranked data. Repeated-measures ANOVA was conducted to compare tumor volumes. Survival curves were generated by the Kaplan–Meier method. The log-rank test was used to determine the significance of differences between survival curves. Statistical analysis was performed using Prism (version 9.0; GraphPad), SPSS (version 25.0), or R (version 4.1.0). A value of P < 0.05 was regarded as indicative of statistical significance. Data are provided as means ± SDs.

GOT2 downregulation predicts advanced progression and poor prognosis in HCC

We analyzed the mRNA level of GOT2 across cancers based on the TCGA dataset (Fig. 1A). GOT2 expression was upregulated in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC). GOT2 was upregulated in kidney chromophobe (KICH) and downregulated in kidney renal clear cell carcinoma (KIRC). However, it was significantly downregulated in cholangiocarcinoma (CHOL) and liver hepatocellular carcinoma (LIHC). These results suggest that GOT2 has different regulatory mechanisms in different cancers. GOT2 is a survival-related gene in HCC (27). In several independent HCC datasets, GOT2 was significantly downregulated in HCC tumor tissues (Supplementary Fig. S1). In addition, patients with low GOT2 expression had shorter overall survival (OS) based on the TCGA and GEO datasets (GSE14520, GSE54236; Supplementary Fig. S2A–S2F). In the CPTAC dataset, HCC tumor tissues had a low GOT2 protein level, which was associated with worse OS and recurrence-free survival (RFS; Fig. 1BD). Furthermore, the GSE9843 dataset indicated that HCC with vascular invasion had low GOT2 expression (Fig. 1E). GOT2 expression was associated with a progressive decrease in para-carcinoma liver, liver tumor tissue, and portal vein tumor thrombus in the GSE74656 dataset (Supplementary Fig. S2G). Lower GOT2 expression was significantly correlated with advanced pathologic stage and histologic grade in the TCGA dataset (Fig. 1F and G). In addition, the OS of patients with low GOT2 expression was markedly decreased in different T stages (Supplementary Fig. S2H). Therefore, reduced GOT2 expression was significantly associated with advanced progression and has potential as a prognostic biomarker for HCC.

Figure 1.

GOT2 expression was reduced, and GOT2 downregulation was predictive of advanced progression in HCC. A, Pan-cancer analysis of GOT2 expression in cancers from the TCGA database. B, GOT2 protein level of HCC in the CPTAC dataset. C and D, Low GOT2 protein levels predict worse OS and RFS based on the CPTAC dataset. E, The mRNA level of GOT2 is correlated with HCC vascular invasion in the GSE9843 dataset. F and G, GOT2 expression according to pathologic stage and histologic grade of HCC in the TCGA dataset. H, Western blot analysis of GOT2 expression in HCC tumor tissues and matched nontumor tissues. N, normal liver tissue; C, cancer tissue. I, Relative protein level of GOT2 in tumor tissues and nontumor tissues. J, Representative IHC images of GOT2 in human HCC tissues (100×, 200×). K, IHC scores of GOT2 in human HCC tissues and nontumor tissues. L, Representative figures of tissue microarray staining for GOT2 in normal tissues (NT) and HCC tumor tissues (TT). M, IHC scores of GOT2 in the human HCC tissue microarray. N, Representative figures of tissue microarray staining for GOT2 in HCC of different pathological stages. O and P, Low IHC scores of GOT2 predict poor OS (P = 0.01013) and TFS (P = 0.02758). Scale bars, 500 μm. Data are means ± SDs. **, P < 0.01; ****, P < 0.0001. TCGA abbreviations are attached in the Supplementary Materials and Methods.

Figure 1.

GOT2 expression was reduced, and GOT2 downregulation was predictive of advanced progression in HCC. A, Pan-cancer analysis of GOT2 expression in cancers from the TCGA database. B, GOT2 protein level of HCC in the CPTAC dataset. C and D, Low GOT2 protein levels predict worse OS and RFS based on the CPTAC dataset. E, The mRNA level of GOT2 is correlated with HCC vascular invasion in the GSE9843 dataset. F and G, GOT2 expression according to pathologic stage and histologic grade of HCC in the TCGA dataset. H, Western blot analysis of GOT2 expression in HCC tumor tissues and matched nontumor tissues. N, normal liver tissue; C, cancer tissue. I, Relative protein level of GOT2 in tumor tissues and nontumor tissues. J, Representative IHC images of GOT2 in human HCC tissues (100×, 200×). K, IHC scores of GOT2 in human HCC tissues and nontumor tissues. L, Representative figures of tissue microarray staining for GOT2 in normal tissues (NT) and HCC tumor tissues (TT). M, IHC scores of GOT2 in the human HCC tissue microarray. N, Representative figures of tissue microarray staining for GOT2 in HCC of different pathological stages. O and P, Low IHC scores of GOT2 predict poor OS (P = 0.01013) and TFS (P = 0.02758). Scale bars, 500 μm. Data are means ± SDs. **, P < 0.01; ****, P < 0.0001. TCGA abbreviations are attached in the Supplementary Materials and Methods.

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To confirm the prediction of GOT2 in HCC, we collected seven paired HCC tumors and corresponding adjacent nontumor tissues. Western blotting showed that GOT2 protein levels in HCC tumor tissues were lower than those in nontumor tissues (Fig. 1H and I). This finding was confirmed by IHC staining using an anti-GOT2 antibody in 82 clinical specimens (Fig. 1J and K). Consistently, a tissue microarray showed that GOT2 expression was significantly decreased in HCC tissues (Fig. 1L and M). Moreover, the higher the pathologic stage was, the lower the GOT2 expression in HCC tumors (Fig. 1N). HCC tumors with low GOT2 expression had worse OS (P = 0.01013; Fig. 1O) and tumor-free survival (TFS) times (P = 0.02758; Fig. 1P). Therefore, downregulation of GOT2 is predictive of a poor prognosis and advanced progression in patients with HCC.

Knockdown of GOT2 promotes HCC proliferation and metastasis in vitro and in vivo

A lentivirus-mediated GOT2 short hairpin RNA vector was used to generate HCCLM3-shGOT2, Huh7-shGOT2, and control stable cell lines. We first confirmed that GOT2 was knocked down in HCCLM3 and Huh7 cells by Western blotting and qRT-PCR (Fig. 2A; Supplementary Fig. S3A). CCK-8 assays indicated that knockdown of GOT2 enhanced the proliferation of HCC cells (Fig. 2B). The shGOT2 groups had a smaller gap by wound healing assay (Fig. 2C and D) and more HCCLM3 and Huh7 cells by Transwell migration (Fig. 2E and F). Similarly, the number of invading cells in the shGOT2 groups was greater than that in the control groups (Fig. 2G and H). On the other hand, MHCC97L cells were stably transfected with GOT2 lentivirus (Supplementary Fig. S3B and S3C). GOT2 overexpression reduced cell migration and invasion in Transwell assays (Supplementary Fig. S3D–S3G). Therefore, knockdown of GOT2 promoted HCC cell proliferation, migration, and invasion in vitro.

Figure 2.

Knockdown of GOT2 promoted proliferation and metastasis in vitro and in vivo. A, Western blot analysis of the effect of lentiviral infection with GOT2 knockdown. B, Effect of GOT2 knockdown on HCC cell proliferation by CCK-8 assay. C, Wound healing assays at 0, 24, and 48 hours. D, Quantitation of the extent of wound closure. E and G, Representative images of the effect of GOT2 knockdown on cell migration and invasion (100×). F and H, Graphical representation of the migration and invasion of HCCLM3 and Huh7 GOT2 knockdown cells. I, The indicated HCCLM3 cells were subcutaneously injected into nude mice (n = 8 mice/group). J, Tumors (n = 8) were isolated and weighed 30 days after injection. K, Growth curves of tumors in nude mice (n = 8 mice/group) injected with the indicated HCCLM3 cells. L, Macroscopic views of spleens with tumors and liver metastases. Black arrow, spleen with tumor; red arrow, metastatic foci. M, Incidence of liver metastasis in the indicated HCCLM3 groups. N, Representative in vivo images of mouse spleens injected with the indicated HCCLM3 cells. O, Hematoxylin and eosin staining of liver metastatic focus sections of spleens injected with HCCLM3 cells. Red arrow, metastatic foci. Scale bars, 800 and 100 μm. P, Bioluminescence images of the indicated HCCLM3 orthotopically transplanted tumors in the livers of nude mice. Q, Representative macroscopic views of intrahepatic metastasis. Black arrow, orthotopically transplanted tumor; red arrow, metastatic foci. R, Number of metastatic liver foci. S, IHC images of GOT2, Ki-67, and cleaved caspase-3 in liver orthotopically transplanted tumors. Scale bars, 100 μm. **, P < 0.01; ***, P < 0.01; ****, P < 0.0001; ns, nonsignificant.

Figure 2.

Knockdown of GOT2 promoted proliferation and metastasis in vitro and in vivo. A, Western blot analysis of the effect of lentiviral infection with GOT2 knockdown. B, Effect of GOT2 knockdown on HCC cell proliferation by CCK-8 assay. C, Wound healing assays at 0, 24, and 48 hours. D, Quantitation of the extent of wound closure. E and G, Representative images of the effect of GOT2 knockdown on cell migration and invasion (100×). F and H, Graphical representation of the migration and invasion of HCCLM3 and Huh7 GOT2 knockdown cells. I, The indicated HCCLM3 cells were subcutaneously injected into nude mice (n = 8 mice/group). J, Tumors (n = 8) were isolated and weighed 30 days after injection. K, Growth curves of tumors in nude mice (n = 8 mice/group) injected with the indicated HCCLM3 cells. L, Macroscopic views of spleens with tumors and liver metastases. Black arrow, spleen with tumor; red arrow, metastatic foci. M, Incidence of liver metastasis in the indicated HCCLM3 groups. N, Representative in vivo images of mouse spleens injected with the indicated HCCLM3 cells. O, Hematoxylin and eosin staining of liver metastatic focus sections of spleens injected with HCCLM3 cells. Red arrow, metastatic foci. Scale bars, 800 and 100 μm. P, Bioluminescence images of the indicated HCCLM3 orthotopically transplanted tumors in the livers of nude mice. Q, Representative macroscopic views of intrahepatic metastasis. Black arrow, orthotopically transplanted tumor; red arrow, metastatic foci. R, Number of metastatic liver foci. S, IHC images of GOT2, Ki-67, and cleaved caspase-3 in liver orthotopically transplanted tumors. Scale bars, 100 μm. **, P < 0.01; ***, P < 0.01; ****, P < 0.0001; ns, nonsignificant.

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To investigate the biological function of GOT2 in vivo, we injected HCCLM3 shCON and shGOT2 cells into nude mice (n = 8 mice/group) to establish subcutaneous and splenic injection mouse models. The growth rate and average tumor weights derived from shGOT2 group cells were significantly faster and heavier than those of the control (Fig. 2IK). In splenic injection mice, it was found that the shGOT2 group had more liver metastasis (Fig. 2L and M) and systemic metastases (Fig. 2N). Hematoxylin and eosin (H&E) staining revealed an increased number of liver metastatic nodules in the shGOT2 group (Fig. 2O). Furthermore, we established surgical orthotopic implantation mouse models. The HCCLM3 shGOT2 group had a greater bioluminescence intensity and more liver metastasis (Fig. 2P). Knockdown of GOT2 promoted intrahepatic metastasis (Fig. 2Q and R). IHC staining for Ki-67, a proliferation marker, was increased in orthotopic tumors (Fig. 2S). Therefore, knockdown of GOT2 not only accelerated the growth of subcutaneous and intrahepatic tumors but also promoted hematogenous and intrahepatic metastases in HCC.

In mitochondria, Gln is metabolized via GLS to Glu and ammonia (NH4+) and further catabolized to αKG mainly by glutamate dehydrogenase (GLUD1) or transaminases (GOT2; ref. 25). GLUD1 expression was unchanged in HCCLM3 shGOT2 cells and tumors (Supplementary Fig. S4A–S4C). To confirm that GOT2 regulation of HCC progression was independent of GLUD1 compensation, we treated cells with the GLUD1 inhibitors, epigallocatechin gallate (EGCG) and R162. Inhibition of GLUD1 activity did not rescue GOT2 knockdown-mediated promotion of cell migration (Supplementary Fig. S4D–S4G). Aminooxyacetate (AOA), a pan-transaminase inhibitor, rescued the GOT2 knockdown–mediated promotion of cell migration (Supplementary Fig. S5A and S5B), as did GOT1 inhibitor-1 (Supplementary Fig. S5C and S5D). GOT1 was upregulated in the HCCLM3 shGOT2 group (Supplementary Fig. S3A). Therefore, GOT2 knockdown was dependent on compensatory cytoplasmic GOT1.

Taken together, the above data indicate that GOT2 silencing promotes the malignant progression of HCC.

GOT2 silencing enhances glutaminolysis and GSH synthesis via glutamine metabolic reprogramming

To reveal the metabolic signature of the stable cell lines, the contribution of each stable isotopic tracer to TCA cycle metabolites was evaluated by 13C5,15N2-glutamine metabolic flux analysis (Fig. 3A). Once taken up by the cell, glutamine is converted to 13C5,15N1 – glutamate with the loss of amido nitrogen as ammonia by GLS. Next, glutamate-derived αKG is further metabolized in the TCA cycle via glutaminolysis or RC. Mass isotopomers generated by glutaminolysis included M4 Suc, M4 malate (Mal), M4 Asp, M4 Cit, and M4 cis-aconitate. The second mass isotopomers generated by RC included M5 cis-Aco, M5 Cit, M3 Asp, and M3 Mal (Supplementary Fig. S6). GOT2 silencing enhanced glutaminolysis (Fig. 3B; Supplementary Fig. S7A) and reduced RC (Fig. 3C; Supplementary Fig. S7B), contributing to ATP by generating substrates for oxidation in aerobic respiration and enabling redox control by NADPH. The HCCLM3 shGOT2 group had a higher level of ATP (Fig. 3D) and nucleotide synthesis, including M3 uridine, M3 UMP, M3 UDP, and M3 UTP (Fig. 3E; Supplementary Fig. S6), based on 13C isotopomers.

Figure 3.

GOT2 knockdown enhanced glutaminolysis and GSH synthesis as shown by 13C5,15N2 - glutamine metabolic flux analysis. A, Diagram of 13C- and 15N-labeling pattern products with13C5,15N2-Gln as the tracer. Glutamine is labeled at all five-carbon atoms and both nitrogen atoms. The indicated HCCLM3 cells were cultured for 24 hours with 4 mmol/L 13C5,15N2-Gln. Red and blue circles, 13C and 15N from glutamine, respectively; blank circle, unlabeled C; red, increase; green, decrease. Mal, malate; OAA, oxaloacetate; Cit, citrate; cis-Aco, cis-aconitate; Lac, lactate. B, Glutaminolysis capacity of the indicated HCCLM3 cells, including Glu-m5, αKG-m5, Suc-m4, Mal-m4, Asp-m4, Cit-m4, and cis-Aco-m4. C, Reductive carboxylation capacity of the indicated HCCLM3 cells, including αKG-m5, cis-Aco-m5, Cit-m5, Mal-m3, and Asp-m3. D, ATP fractional enrichment was increased by 13C labeling in the HCCLM3-shGOT2 group. E, Nucleotide fractional enrichment was increased by 13C labeling in the HCCLM3-shGOT2 group. F–J, Fractional enrichment of 13C- and 15N-labeled glutamine (F), glutamate (G), αKG (H), GSH (I), and GSSG (J). K, The total metabolite abundances of 13C- and 15N-labeled Asp. Data are means ± SDs. *, P < 0.05; **, P < 0.01; ***, P < 0.001; ****, P < 0.0001; ns, nonsignificant. Red ellipse, glutaminolysis; blue ellipse, reductive carboxylation.

Figure 3.

GOT2 knockdown enhanced glutaminolysis and GSH synthesis as shown by 13C5,15N2 - glutamine metabolic flux analysis. A, Diagram of 13C- and 15N-labeling pattern products with13C5,15N2-Gln as the tracer. Glutamine is labeled at all five-carbon atoms and both nitrogen atoms. The indicated HCCLM3 cells were cultured for 24 hours with 4 mmol/L 13C5,15N2-Gln. Red and blue circles, 13C and 15N from glutamine, respectively; blank circle, unlabeled C; red, increase; green, decrease. Mal, malate; OAA, oxaloacetate; Cit, citrate; cis-Aco, cis-aconitate; Lac, lactate. B, Glutaminolysis capacity of the indicated HCCLM3 cells, including Glu-m5, αKG-m5, Suc-m4, Mal-m4, Asp-m4, Cit-m4, and cis-Aco-m4. C, Reductive carboxylation capacity of the indicated HCCLM3 cells, including αKG-m5, cis-Aco-m5, Cit-m5, Mal-m3, and Asp-m3. D, ATP fractional enrichment was increased by 13C labeling in the HCCLM3-shGOT2 group. E, Nucleotide fractional enrichment was increased by 13C labeling in the HCCLM3-shGOT2 group. F–J, Fractional enrichment of 13C- and 15N-labeled glutamine (F), glutamate (G), αKG (H), GSH (I), and GSSG (J). K, The total metabolite abundances of 13C- and 15N-labeled Asp. Data are means ± SDs. *, P < 0.05; **, P < 0.01; ***, P < 0.001; ****, P < 0.0001; ns, nonsignificant. Red ellipse, glutaminolysis; blue ellipse, reductive carboxylation.

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More importantly, in the shGOT2 group, glutamine uptake (Fig. 3F; Supplementary Fig. S7C) and glutamate production (Fig. 3G; Supplementary Fig. S7D) were increased in both fraction and total metabolite abundances by 13C and 15N isotopomers. However, the increase in αKG synthesis from glutamate was not significantly different between the shCON and shGOT2 groups (M5-αKG) (Fig. 3H). GSH was significantly increased with M5-13C and M1-15N isotopomers from glutamate (Fig. 3I; Supplementary Fig. S7E). In addition, the GSSG levels in the M10-13C and M2-15N groups were higher than those in the HCCLM3 shCON group (Fig. 3J; Supplementary Fig. S7F). Therefore, silencing GOT2 promotes GSH production via glutamine metabolism, which provides precursors for GSH biosynthesis. GOT2 knockdown increased the intracellular and supernatant Glu and ammonia levels (Supplementary Fig. S8A and S8B). Cancer cells mainly obtain cysteine from the extracellular environment via nutrient transporters (28). The intracellular and supernatant cysteine levels were decreased, suggesting the consumption of cysteine for GSH synthesis (Supplementary Fig. S8C). GOT2 knockdown tended to decrease Asp (Fig. 3K; Supplementary Fig. S8D) but did not affect the alanine (Ala) and αKG levels (Supplementary Fig. S8E and S8F). Alanine transaminase (GPT2) was not increased in the HCCLM3 shGOT2 group (Supplementary Fig. S4C). Therefore, GOT2 knockdown was not dependent on compensation of alanine transaminase. Taken together, these results suggest that GOT2 silencing enhanced glutaminolysis and GSH synthesis via glutamine metabolic reprogramming to support HCC progression.

GOT2 knockdown reduces ROS levels via the GSH antioxidant system and increases ATP production to support HCC progression

Glutamine uptake and GSH synthesis were markedly increased in the HCCLM3 shGOT2 group. Moreover, glutamine increased the cellular GSH level in a concentration-dependent manner (29). Glutamine carbon flow through GOT2 is required to generate NADPH and maintain the cellular redox state (25). Therefore, we measured the GSH/GSSG and NADPH/NADP+ ratios to evaluate antioxidant activity. The GSH/GSSG and NADPH/NADP+ ratios were increased in the GOT2 knockdown group (Fig. 4A and B). ROS production was visualized by DHE (red; Fig. 4C). The shGOT2 group had low ROS levels (Fig. 4D) and lipid peroxidation (Fig. 4E). Tumors under hypoxic conditions have increased ROS levels (30). Low GOT2 expression can adapt to the ROS levels under hypoxic conditions (Fig. 4F). We also explored whether ROS influenced HCC progression. H2O2 exposure significantly decreased the migration of HCC cells in the HCCLM3 shGOT2 and MHCC97L lvCON groups (Fig. 4G and H). The average number of migrating cells was increased by 0 to 10 mmol/L NAC, especially in the HCCLM3 shCON and MHCC97L lvGOT2 groups (Fig. 4I and J). The migration of MHCC97L lvGOT2 cells was restored by 10 mmol/L NAC. CB-839 increased the ROS levels (Fig. 4K) and decreased the inhibition capacities of lipid peroxidation in the HCCLM3 shCON and shGOT2 groups (Fig. 4L). Therefore, GOT2 downregulation and associated glutamine metabolism participate in antioxidant defense in HCC.

Figure 4.

GOT2 knockdown supported cellular antioxidant activity and mitochondrial respiration, thereby promoting HCC migration. A and B, GSH/GSSG (A) and NADPH/NADP+ (B) ratios in HCCLM3 shCON and shGOT2 cells. C, DHE immunofluorescence of ROS in the indicated HCCLM3 cells (red). D, Intracellular ROS levels in the indicated HCCLM3 cells by flow cytometry. E, Intracellular and supernatant oxidative stress by MDA assay. F, Immunofluorescence of ROS in the indicated HCCLM3 cells under normoxic or hypoxic conditions using MitoSOX Red. G and H, Representative images and graphical representation of the migration of the indicated HCCLM3 and MHCC97L cells with or without H2O2 treatment for 24 hours. I and J, Representative images and graphical representation of the migration of the indicated HCCLM3 and MHCC97L cells treated with NAC for 24 hours. K, Intracellular ROS levels of the indicated HCCLM3 cells with or without CB-839 (10 μmol/L) treatment using a microplate reader (EX/EM = 518/605 nm). L, Inhibition (%) of LPO in the indicated HCCLM3 cells with or without CB-839 (10 μmol/L) treatment. M, Mitochondrial respiration in the indicated HCCLM3 cells pretreated with DMSO or CB-839 (5 μmol/L) for 24 hours. OCR was assayed after consecutive injections of oligo (1 μmol/L), FCCP (1 μmol/L), rotenone (1 μmol/L), and antimycin (1 μmol/L). N, Graphs of basal respiration and ATP production. *, P < 0.05; **, P < 0.01; ***, P < 0.001; ****, P < 0.0001; ns, nonsignificant.

Figure 4.

GOT2 knockdown supported cellular antioxidant activity and mitochondrial respiration, thereby promoting HCC migration. A and B, GSH/GSSG (A) and NADPH/NADP+ (B) ratios in HCCLM3 shCON and shGOT2 cells. C, DHE immunofluorescence of ROS in the indicated HCCLM3 cells (red). D, Intracellular ROS levels in the indicated HCCLM3 cells by flow cytometry. E, Intracellular and supernatant oxidative stress by MDA assay. F, Immunofluorescence of ROS in the indicated HCCLM3 cells under normoxic or hypoxic conditions using MitoSOX Red. G and H, Representative images and graphical representation of the migration of the indicated HCCLM3 and MHCC97L cells with or without H2O2 treatment for 24 hours. I and J, Representative images and graphical representation of the migration of the indicated HCCLM3 and MHCC97L cells treated with NAC for 24 hours. K, Intracellular ROS levels of the indicated HCCLM3 cells with or without CB-839 (10 μmol/L) treatment using a microplate reader (EX/EM = 518/605 nm). L, Inhibition (%) of LPO in the indicated HCCLM3 cells with or without CB-839 (10 μmol/L) treatment. M, Mitochondrial respiration in the indicated HCCLM3 cells pretreated with DMSO or CB-839 (5 μmol/L) for 24 hours. OCR was assayed after consecutive injections of oligo (1 μmol/L), FCCP (1 μmol/L), rotenone (1 μmol/L), and antimycin (1 μmol/L). N, Graphs of basal respiration and ATP production. *, P < 0.05; **, P < 0.01; ***, P < 0.001; ****, P < 0.0001; ns, nonsignificant.

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Next, we assessed the OCR (oxygen consumption rate) of the indicated HCCLM3 groups with or without CB-839 (Fig. 4M). Basal respiration and ATP production were significantly increased in the HCCLM3 shGOT2 groups (Fig. 4N). CB-839 reduced basal respiration, ATP production, and maximum respiration in the two groups (Fig. 4N; Supplementary Fig. S9A), similarly as did 968 in the indicated HCCLM3 groups (Supplementary Fig. S9B).

Taken together, GOT2 knockdown reprogrammed glutamine metabolism to promote GSH synthesis and support the cellular antioxidant system, thereby enhancing HCC progression.

GOT2 silencing activated the PI3K/AKT/mTOR pathway

We performed RNA-seq to characterize gene expression in the HCCLM3 shCON and shGOT2 groups (Supplementary Fig. S10A). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis revealed that the PI3K/AKT signaling pathway was significantly enriched in the HCCLM3 shGOT2 group (Fig. 5A). The PI3K/AKT signaling pathway is regulated by ROS and is implicated in cancer progression (31). GOT2 knockdown induced the phosphorylation of p-PI3K, pAKT (Ser473), and p-mTOR (Ser2448) (Fig. 5B; Supplementary Fig. S10B). This was confirmed by IHC staining of mouse orthotopically implanted tumors (Fig. 5C). These were downregulated by suppression of glutamine metabolism by CB-839 (Fig. 5B). The PI3K/AKT inhibitor LY294002 significantly inhibited the migration of HCC cells, especially in the HCCLM3 shGOT2 and MHCC97L lvCON groups (Fig. 5D and E), as did buparlisb, a pan-PI3K inhibitor, and capivasertib, a pan-AKT inhibitor (Supplementary Fig. S10C–S10F). AKT-Ser473 phosphorylation is a prerequisite for full AKT activation (32). We evaluated the expression of GOT2 and pAKT (Ser473) in patients with HCC. pAKT (Ser473) was increased in HCC tissues with low GOT2 expression (Supplementary Fig. S11A) and was negatively correlated with GOT2 expression (r = −0.495; Supplementary Fig. S11B). ROS repress the PI3K/AKT pathway (33). H2O2 inhibited the PI3K/AKT pathway, and this effect was rescued by the ROS scavenger NAC (Fig. 5F). These data indicated that GOT2 knockdown activated the PI3K/AKT/mTOR pathway in a manner dependent on low ROS levels. In the TCGA dataset, GOT2 was positively correlated with ROS-related genes and negatively correlated with glutamine metabolic genes such as GLS and SLC1A5 (ASCT2; Fig. 5G). In HCCLM3 shGOT2 cells, a glutamine metabolic gene (GLS) and glutamine transporter genes (SNAT3, ACST2) were upregulated (Supplementary Figs. S3A and S4C). Gene set enrichment analysis showed enrichment of genes associated with the GO_glutamate, GSH, and ROS metabolic processes in tissues with low GOT2 expression (NES = 2.638; NES = 1.806; NES = 2.286; Fig. 5H). The KEGG_GSH metabolism, hallmark_ROS, and hallmark_oxidative phosphorylation pathways were also enriched (Supplementary Fig. S11C).

Figure 5.

GOT2 downregulation promoted HCC progression via PI3K/AKT/mTOR signaling in a glutamine-dependent manner. A, Top 8 enriched KEGG pathways for the HCCLM3 shGOT2 group vs. the shCON group. B, Western blot analysis of PI3K/AKT/mTOR pathway targets in stable cell lines of the indicated HCCLM3 cells with or without CB-839 (10 μmol/L). C, IHC of PI3K/AKT/mTOR in mouse liver orthotopically implanted tumors. D and E, Representative images and graphical representation of the migration of the indicated HCCLM3 and MHCC97L cells treated with DMSO or LY294002 (20 μmol/L) for 24 hours. F, Western blot analysis of pAKT (Ser473) and p-PI3K in stable HCCLM3 shCON and shGOT2 cells treated with H2O2 and NAC. G, GOT2 correlation with ROS and glutamine metabolism-related genes plotted with an upper triangle in the TCGA dataset. Color intensity and circle size are proportional to the correlation coefficients. Red, positive correlation; blue, negative correlation. H, Genes associated with low GOT2 expression by gene set enrichment analysis based on the TCGA dataset. GO_Gln metabolic processes (NES = 2.638, P = 0.003), GO_GSH metabolic processes (NES = 1.806, P = 0.004), and GO_ROS metabolic processes (NES = 2.286, P = 0.004) were enriched. **, P < 0.01; ****, P < 0.0001; ns, nonsignificant.

Figure 5.

GOT2 downregulation promoted HCC progression via PI3K/AKT/mTOR signaling in a glutamine-dependent manner. A, Top 8 enriched KEGG pathways for the HCCLM3 shGOT2 group vs. the shCON group. B, Western blot analysis of PI3K/AKT/mTOR pathway targets in stable cell lines of the indicated HCCLM3 cells with or without CB-839 (10 μmol/L). C, IHC of PI3K/AKT/mTOR in mouse liver orthotopically implanted tumors. D and E, Representative images and graphical representation of the migration of the indicated HCCLM3 and MHCC97L cells treated with DMSO or LY294002 (20 μmol/L) for 24 hours. F, Western blot analysis of pAKT (Ser473) and p-PI3K in stable HCCLM3 shCON and shGOT2 cells treated with H2O2 and NAC. G, GOT2 correlation with ROS and glutamine metabolism-related genes plotted with an upper triangle in the TCGA dataset. Color intensity and circle size are proportional to the correlation coefficients. Red, positive correlation; blue, negative correlation. H, Genes associated with low GOT2 expression by gene set enrichment analysis based on the TCGA dataset. GO_Gln metabolic processes (NES = 2.638, P = 0.003), GO_GSH metabolic processes (NES = 1.806, P = 0.004), and GO_ROS metabolic processes (NES = 2.286, P = 0.004) were enriched. **, P < 0.01; ****, P < 0.0001; ns, nonsignificant.

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Taken together, these results indicate that GOT2 silencing activated the PI3K/AKT/mTOR pathway by shifting glutamine metabolism to GSH synthesis to maintain the GSH/ROS balance, thereby supporting HCC progression.

Low GOT2 expression enhances glutamine dependency and glutaminase inhibitor sensitivity

The HCCLM3 shGOT2 groups showed activation of the PI3K/AKT/mTOR pathway, which may depend on the maintenance of redox homeostasis by glutamine metabolism. Therefore, we assayed the glutamine dependence of GOT2-knockdown cells. We analyzed the viability of the indicated HCCLM3 group cells with increasing concentrations of glutamine in Gln-free medium (Fig. 6A). In the absence of glutamine, cell proliferation was inhibited in the shGOT2 groups; the effect was rescued by glutamine but GOT2-knockdown cells were more sensitive to glutaminase inhibitors (Fig. 6B). HCCLM3 and MHCC97L cell migration was suppressed by CB-839, especially in the HCCLM3 shGOT2 and MHCC97L lvCON groups (Fig. 6C and D), as did 968 in the HCCLM3 and Huh7 groups (Supplementary Fig. S12A and S12B). There is no correlation between the GLS protein level and CB-839 sensitivity (20). HCC cell lines with low GOT2 expression, such as MHCC97L and Huh7 (Fig. 6E), were more sensitive to CB-839 than those with high GOT2 expression, such as HepG2 and C3A (Fig. 6F). The IC50 of CB-839 was decreased in HCCLM3 shGOT2 cells (Fig. 6G). The GSE97098 dataset showed that GOT2 expression in HCC cells was related to CB-839 sensitivity. The expression of GOT2 was positively correlated with the IC50 (r = 0.2) and Emax (r = 0.224) of CB-839 (Supplementary Fig. S12C and S12D) and was significantly negatively correlated with the activity area of CB-839 (r = −0.298), reflecting the magnitude of the drug response (Fig. 6H). Therefore, HCC cells with low GOT2 expression were more sensitive to glutamine deprivation and the glutaminase inhibitor CB-839, which thus has potential as a CB-839 response marker for HCC.

Figure 6.

Low GOT2 expression increased glutamine dependency and CB-839 sensitivity. A, Viability of the indicated HCCLM3 stable cell lines cultured with Gln by CCK-8 assay. B, Viability of the indicated HCCLM3 stable cell lines treated with 968. C and D, Representative images and graphical representation of the migration of the indicated HCCLM3 and MHCC97L cells treated with CB-839 (10 μmol/L) for 24 hours. E, Western blot analysis of GOT2 expression in HCC cell lines and two normal liver tissues (NT). F, IC50 of CB-839 in high-GOT2-expression cell lines (C3A, HepG2) and low-GOT2-expression cell lines (Huh7, MHCC97L). G, IC50 values of CB-839 in the HCCLM3 shCON and shGOT2 stable cell lines. H, Correlation between GOT2 expression and CB-839 activity area in the GSE97098 dataset (r = −0.298, P = 0.007; Spearman correlation analysis). Activity area, the area upon the dose–response curve. I and J, Representative images of subcutaneous xenograft tumors and tumor weights with vehicle or CB-839 oral gavage (n = 8 mice/group). A total of 5 × 106 shCON or shGOT2 HCCLM3 cells were subcutaneously inoculated into both flanks of male BALB/c nude mice. Data are means ± SDs. *, P < 0.05; **, P < 0.01; ***, P < 0.001; ****, P < 0.0001; ns, nonsignificant.

Figure 6.

Low GOT2 expression increased glutamine dependency and CB-839 sensitivity. A, Viability of the indicated HCCLM3 stable cell lines cultured with Gln by CCK-8 assay. B, Viability of the indicated HCCLM3 stable cell lines treated with 968. C and D, Representative images and graphical representation of the migration of the indicated HCCLM3 and MHCC97L cells treated with CB-839 (10 μmol/L) for 24 hours. E, Western blot analysis of GOT2 expression in HCC cell lines and two normal liver tissues (NT). F, IC50 of CB-839 in high-GOT2-expression cell lines (C3A, HepG2) and low-GOT2-expression cell lines (Huh7, MHCC97L). G, IC50 values of CB-839 in the HCCLM3 shCON and shGOT2 stable cell lines. H, Correlation between GOT2 expression and CB-839 activity area in the GSE97098 dataset (r = −0.298, P = 0.007; Spearman correlation analysis). Activity area, the area upon the dose–response curve. I and J, Representative images of subcutaneous xenograft tumors and tumor weights with vehicle or CB-839 oral gavage (n = 8 mice/group). A total of 5 × 106 shCON or shGOT2 HCCLM3 cells were subcutaneously inoculated into both flanks of male BALB/c nude mice. Data are means ± SDs. *, P < 0.05; **, P < 0.01; ***, P < 0.001; ****, P < 0.0001; ns, nonsignificant.

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To assess the effect of the glutaminase inhibitors on GOT2 in vivo, we subcutaneously implanted HCCLM3 shCON and shGOT2 cells into both flanks of nude mice. The glutaminase inhibitor CB-839 significantly impaired tumor growth (Fig. 6I). Mice treated with CB-839 (20 mg/mL in vehicle) had significantly smaller tumor weights, especially in the shGOT2 group (P < 0.01; Fig. 6J).

In summary, silencing GOT2 maintains GSH/ROS redox homeostasis by reprogramming glutamine metabolism, and low GOT2 expression enhances sensitivity to glutaminase inhibitors in vitro and in vivo. Therefore, the GOT2 level is a potential biomarker for glutaminase inhibitor therapy of HCC (Fig. 7).

Figure 7.

Effects of GOT2 KD and glutaminase inhibitors on HCC. Low GOT2 expression promoted glutamine metabolic reprogramming to GSH synthesis and enhanced the antioxidant system to maintain a low ROS level, which activates PI3K/AKT/mTOR signaling to promote HCC progression. The glutaminase inhibitors CB-839 and 968, which block glutamine metabolism, were more effective in inhibiting the progression of HCC with low GOT2 expression. OAA, oxaloacetate. Created with BioRender.com.

Figure 7.

Effects of GOT2 KD and glutaminase inhibitors on HCC. Low GOT2 expression promoted glutamine metabolic reprogramming to GSH synthesis and enhanced the antioxidant system to maintain a low ROS level, which activates PI3K/AKT/mTOR signaling to promote HCC progression. The glutaminase inhibitors CB-839 and 968, which block glutamine metabolism, were more effective in inhibiting the progression of HCC with low GOT2 expression. OAA, oxaloacetate. Created with BioRender.com.

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GOT2 is highly enriched in the liver (Fig. 1A; Supplementary Fig. S13), reflecting a tissue-specific biological function (34). Low GOT2 expression is associated with a poor prognosis. This means that the HCC tissue is farther from the normal liver and is thus dedifferentiated (35, 36). We postulated that replacing a lost liver-function gene would reverse dedifferentiation and improve the prognosis of HCC (26). In this study, GOT2 overexpression suppressed HCC prognosis. GOT2 has been reported to promote cancer processes (24, 37). However, low GOT2 expression is associated with a poor prognosis of HCC (38–41), possibly due to its heterogeneity and tissue-specific biological functions. GOT2 downregulation resulted in dedifferentiation and metabolic plasticity to support HCC cells themselves rather than systematic functions. In addition, shGOT2 group cells were more dependent on glutamine and had upregulated GLS (Supplementary Fig. S3A; Fig. 5G). Low GOT2 expression shifts glutamine metabolism to GSH synthesis. Redox homeostasis is the balance between ROS generation and removal by antioxidants (42). A high GSH level promotes protein S-glutathionylation (43), which is involved in cell functions (44). S-glutathionylation of GOT2 in HCC warrants further exploration.

There are no reports on the regulatory mechanisms of GOT2 in HCC. In NSCLC, miR-520a-5p interacts with the 3′-UTR of GOT2, inhibiting GOT2 at the post-transcriptional level (37). Hypoxia-inducible factor-1α (HIF1α) inhibits GOT2, thus suppressing colorectal cancer (22, 24). Hypoxia is an important factor that promotes tumor progression and maintains the malignant phenotype (45). Transarterial chemoembolization (TACE) was used for unresectable HCC, and one of the important mechanisms was obstruction of tumor blood supply, which concomitantly elicited tumor adaptation, invasion, and distant metastasis (46). Furthermore, HIF1α, a key transcriptional regulator under hypoxic conditions, was reported to specifically inhibit GOT2 (22). We found that HIF1α regulates GOT2 in HCC (not shown). Thus, the mechanisms underlying the relationship between HIF1a and GOT2 require further investigation. GOT2 downregulation may be linked to HCC metastasis and recurrence after TACE. Therefore, the combination of TACE with glutaminase inhibitors should be examined in further studies.

HCC tumors with low GOT2 expression are especially dependent on glutamine. Targeting glutamine metabolism by the GLS inhibitor CB-839 is effective in HCC cells with low GOT2 expression. CB-839 is selective for GLS1 and may not be effective in some types of HCC (47). Thus, GOT2 could guide the use of CB-839 for HCC. The SLC1A5 variant is the only glutamine transporter in the mitochondrial inner membrane (29). Targeting the SLC1A5 variant could be a more effective strategy for HCC with low GOT2 expression.

In conclusion, GOT2 silencing enhanced GSH synthesis via glutamine metabolic reprogramming to support the cellular antioxidant system. Low GOT2 expression enhanced glutamine dependency and glutaminase inhibitor sensitivity. The GOT2 level could be a prognostic biomarker for HCC and facilitate the development of effective therapeutics.

No disclosures were reported.

Y. Li: Conceptualization, resources, data curation, software, formal analysis, validation, investigation, visualization, writing–original draft, writing–review and editing. B. Li: Conceptualization, data curation, software, formal analysis, validation, investigation, visualization, methodology, writing–review and editing. Y. Xu: Data curation, investigation. L. Qian: Investigation. T. Xu: Validation. G. Meng: Validation. H. Li: Validation. Y. Wang: Validation. L. Zhang: Validation. X. Jiang: Validation. Q. Liu: Validation. Y. Xie: Validation. C. Cheng: Validation. B. Sun: Validation. D. Yu: Conceptualization, resources, data curation, supervision, funding acquisition, validation, project administration, writing–review and editing.

This work was supported by grants from the National Natural Science Foundation of China (no. 81871967, 82173129, 82002509, 81903147, 82103384), the China Postdoctoral Science Foundation (no. 2019M661803), the Social Development Foundation of Jiangsu Province of China (no. BE2018604), Jiangsu Provincial Medical Talent, and the Nanjing Science and Technology Project (no. 201803028).

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

Note: Supplementary data for this article are available at Cancer Research Online (http://cancerres.aacrjournals.org/).

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Supplementary data