- Open Access
Increased B3GALNT2 in hepatocellular carcinoma promotes macrophage recruitment via reducing acetoacetate secretion and elevating MIF activity
- Tianxiao Yang†1, 2,
- Yilin Wang†3, 4,
- Wenjuan Dai†1, 2,
- Xixi Zheng1, 2,
- Jing Wang1, 2,
- Shushu Song1, 2,
- Lan Fang5,
- Jiangfan Zhou6,
- Weicheng Wu1, 2Email authorView ORCID ID profile and
- Jianxin Gu1, 2
© The Author(s). 2018
- Received: 18 January 2018
- Accepted: 20 March 2018
- Published: 4 April 2018
Hepatocellular carcinoma (HCC) ranks as the sixth most prevalent cancer and the third leading cause of tumor-related death, so it is urgently needed to discover efficient markers and targets for therapy. β-1,3-N-acetylgalactosaminyltransferase II (B3GALNT2) belongs to the β-1,3-glycosyltransferases (b3GT) family and has been reported to regulate development of both normal and tumor tissues. However, studies on the functions of B3GALNT2 in cancer are quite limited. Here we investigated the potential role of B3GALNT2 in HCC progression.
Western blot, qPCR, and immunohistochemistry assays were performed to quantify the relative expression of B3GALNT2 in HCC. The functions of B3GALNT2 in tumor progression were evaluated in HCC cell lines and nude mice. Metabolomics analysis was applied to detect alternatively expressed small molecules. Enzyme activity assays were employed to determine the tautomerase activity of macrophage inhibitory factor (MIF).
For expression analysis, higher levels of B3GALNT2 were observed in tumor tissues compared with adjacent normal tissues, and upregulation of B3GALNT2 correlated with increased tumor size and worse overall survival. Changing levels of B3GALNT2 did not influence cell viability in vitro but promoted tumor growth via enhancing macrophage recruitment in vivo. Furthermore, acetoacetate was identified as a key molecule in B3GALNT2-mediated macrophage recruitment. Mechanistically, B3GALNT2 downregulated expression of enzymes involved in acetoacetate-related metabolism, and reduction of acetoacetate revived MIF activity, thus promoting macrophage recruitment.
This study evaluated B3GALNT2 as a tumor marker in HCC and revealed functions of B3GALNT2 in metabolic transformation and microenvironmental remodeling in HCC. Mechanistically, B3GALNT2 reduced expression of some metabolic enzymes and thus downregulated levels of secreted acetoacetate. This relieved the activity of MIF and enhanced macrophage recruitment to promote tumor growth.
- Hepatocellular carcinoma
- Macrophage recruitment
Hepatocellular carcinoma (HCC) ranks as the sixth most prevalent cancer and the third leading cause of tumor-related death [1, 2]. Despite huge developments in HCC diagnosis and treatment, 5-year survival rates of most HCC patients remain dismal . Therefore, it is urgently needed to discover efficient markers and therapeutic targets for HCC. Emerging evidence has proven that abnormal glycosylation in HCC is closely associated with cancer progression. Some glycosylation modifications have been reported to promote HCC tumor growth, angiogenesis, and metastasis, and some of them are capable of predicting the prognosis of HCC patients . However, due to instability of this glycosylation and the specialized equipment required for detection, it is still hard to use these as rapid markers for clinical application. Abnormal glycosylation usually results from aberrant expression of glycosyltransferases that are relatively stable in tissues and easy to detect [4, 5]. Therefore, some of them like N-acetylglucosaminyltransferase V (GnT-V), N-acetylglucosaminyltransferase III (GnT-III), and α1-6 fucosyltransferase (a1-6FT) have been used as tumor markers and therapeutic targets in HCC [6–9].
Macrophages originate from immature monocyte in the bone marrow and migrate throughout the entire body through the circulation. Final differentiation occurs in tissues to form macrophages, including Kupffer cells which are found in the liver. It is already known that all kinds of macrophages coexist in tumors, but recruited macrophages may account for the majority of tumor-associated macrophages (TAMs). Peripheral blood monocytes from the bone marrow are recruited and differentiate into TAMs in response to chemokines and growth factors in the tumor microenvironment [10, 11]. TAMs promote solid tumor development through providing factors which can establish a pre-malignant niche and enhance metastasis. TAMs may also play a role in forming pre-metastatic niches in organs where the tumor will eventually metastasize . There is also evidence that TAMs are closely associated with formation of stem-like cells in human cancers . Dysregulation of glycosyltransferases has been reported to regulate the functions of TAMs , but whether some glycosyltransferases influence TAM recruitment remains to be elucidated.
β-1,3-N-acetylgalactosaminyltransferase II (B3GALNT2) belongs to the β-1,3-glycosyltransferases (b3GT) family, consisting of β-1,3-galactosyltransferases (B3GALT), β-1,3-N-acetylglucosaminyltransferases (B3GNT), and β-1,3-N-acetylgalactosaminyltransferases (B3GALNT). B3GALNT2 efficiently adds N-acetylgalactosamine (GalNAc) on both N-glycans and O-glycans by β-1,3-linkage and generates GalNAcb1 → 3GlcNAcb1-R structure . B3GALNT2 has been reported to regulate the development of both normal tissues and tumor tissues . Upregulation of B3GALNT2 in breast cancer predicts poor prognosis . Since knockdown of B3GALNT2 in zebrafish leads to degeneration of the extracellular matrix , B3GALNT2 might also exert functions in cancer progression via altering secretion or remodeling the extracellular environment. However, studies on the functions of B3GALNT2 are quite limited. Whether and how B3GALNT2 functions in HCC remain to be elucidated.
In this study, we investigated expression of B3GALNT2 in HCC and analyzed its potential role in HCC progression. Our study also determined how B3GALNT2 remodels the tumor microenvironments to promote tumor growth.
Hepatocellular carcinoma patient samples
Usage of human pathological tissues and clinical data was approved by the Ethics Committee at the Shanghai Cancer Center of Fudan University (Shanghai, China; approval no. 050432-4-1212B). Written consent for all patients conformed to the ethical guidelines of the Helsinki Declaration. A total of 139 patients with primary HCC resected between 2010 and 2012 in the Department of Hepatic Surgery, Shanghai Cancer Center of Fudan University (Shanghai, China) were collected. None of the patients had received pre-operative therapy. Clinical tumor stages were determined according to the TNM classification system of International Union against Cancer. Follow-up was done until December 9, 2016. These patients were followed every 3 months. The median follow-up was 33.3 months (ranging from 0.8 to 60.4 months). Among all of the primary tumor specimens, 24 were used for Western blot and quantitative real-time PCR assays.
All HCC cell lines, human THP1 cells, and mouse RAW264.7 cells were obtained from the Cell Bank of Type Culture Collection of the Chinese Academy of Sciences (Shanghai, China) and cultured in Dulbecco’s Minimum Essential Medium (DMEM) supplemented with 10% fetal bovine serum at 37 °C in a humidified atmosphere containing 5% CO2. Fetal bovine serum and DMEM culture media were purchased from Sigma (St. Louis, MO, USA). The THP1 cell line was maintained in RPMI 1640 medium supplemented with 10% FBS and 2 mmol/L L-glutamine. THP1 cells were differentiated using 200 nM phorbol-12-myristate-13-acetate (PMA, Sigma-Aldrich) for 3 days.
The cDNA encoding B3GALNT2 was obtained by PCR and was inserted into the pCMV-Flag vector (Sigma, St. Louis, MO, USA). The sequence of the shRNA inserted in the pENTR vector (Thermo, USA) was as follows: B3GALNT2: 5′-CACCGGTCATATAATTGTGTGTTTACGAATAAACACACAATTATATGACC-3′, BDH2: 5′-CACCGGAACAGTTGATACGCCATCTCGAAAGATGGCGTATCAACTGTTCC-3′, and MIF: 5′-TCGAGGACACCAACGTGCCCCGCGCTTCAAGAGAGCGCGGGGCACGTTGGTGTCTTTTTTA-3′. Transfections were performed with Lipofectamine 3000 (Life Technologies, CA, USA), according to the manufacturer’s instructions. Stable cell lines were generated with G418 (200 μg/mL) in the medium.
Cell viability assay
Cell viability was quantified with a Cell Counting Kit-8 (CCK-8) (Dojindo, Japan), according to the manufacturer’s instructions. The cells were plated at a density of 3000 cells per well in 96-well plates. The CCK-8 assays were assessed by measuring the absorbance at 450 nm.
Cell cycle and apoptosis assay
Cycle arrest and apoptotic cells were detected by flow cytometric analysis. Cells were collected by trypsinization and washed twice with PBS. For cell cycle assay, the collected cells were stained with propidium iodide (PI) using a Cell Cycle Staining Kit (Lianke Bio, Hangzhou, China). Cellular apoptosis was determined using PE Annexin-V Apoptosis Detection Kit I (BD Biosciences, CA, USA). Stained cells were assessed by flow cytometry and the data were analyzed by FlowJo software (TreeStar, Ashland, OR, USA).
Transwell invasion was assessed using 8-μm transwell filters (Milliporem, Billerica, MA, USA) in a 12-well plate. The bottom of the transwell chamber was coated with BD Matrigel Basement Membrane Matrix (BD Biosciences, San Diego, CA, USA). Macrophages were added into the upper chamber containing basic culture medium without serum, and the lower chamber was filled with HCC tumor cell lines in serum-free culture medium. Macrophage infiltration was determined 48 h later. Cells on the upper side of the chamber were removed from the surface of the membrane by scrubbing, and cells on the lower surface of the membrane were fixed with 4% paraformaldehyde and stained with 0.1% crystal violet. The number of infiltrating cells was counted in five randomly selected microscopic fields of each filter.
HCC tissues and cells were homogenized in SDS sample buffer (10% glycerol, 2% SDS, 0.01% bromophenol blue, 1.25% 2-beta-mercaptoethanol, 25 mM Tris–HCl, pH 6.8) with ULTRA-TURRAX (IKA, Germany) at 4 °C. Protein concentration was determined using the Quick Start™ Bradford protein assay kit (Bio-Rad, USA). Ten micrograms of total protein extracts was loaded in 10% SDS-PAGE and transferred to 0.45-μm PVDF membranes (Millipore, USA) using an electro-blotting apparatus (Bio-Rad, USA). Anti-B3GALNT2, anti-BDH2, and anti-GAPDH antibodies were purchased from Proteintech. The Immobilon™ Western Chemiluminescence HRP substrate kit (Millipore, USA) was used for chemiluminescence. Images were obtained with the ImageQuant™ LAS-4000 (Amersham Biosciences, GE, USA) and quantified using the ImageQuant™ TL software (version 7.0, Amersham Biosciences, GE, USA).
Quantitative real-time PCR
Total RNA of the samples was purified using TRIzol (Invitrogen, Carbad, CA, USA) and then reverse-transcribed to cDNA using the PrimeScript RT reagent kit (Takara, Tokyo, Japan). Real-time PCR was performed with cDNA production using SYBR Premix Ex Taq (Takara, Tokyo, Japan) on an ABI StepOne Plus (Applied Biosystems, USA) instrument. GAPDH was used as an internal control. Primers used in this study are listed as follows: B3GALNT2 forward: GATGTGGTAGTTGGCGTGTTG, reverse: CGTTGACTTAATGTGGGATGCTG, GAPDH forward: GAGTCAACGGATTTGGTCGT, reverse: TTGATTTTGGAGGGATCTCG, BDH2 forward: GCTTCCA GCGTCAAAGGAGTT, reverse: CAGTTGCGAATCTTCCCGTC, MIF forward: TACACCCAGACCAAATGATG, reverse: TTCTCCTAATGCTCCAATACTG.
Immunohistochemistry (IHC) tests on tissue microarray and paraffin sections were performed using a Dako REAL EnVision Detection System (Dako, Denmark) following the protocol recommended, and hematoxylin was used for counterstaining. Anti-B3GALNT2, anti-CD206, anti-F4/80, and anti-CD68 antibody were used to quantify relative expression levels. Immunohistochemical scoring was determined as previously described . The staining intensity was scored as 0 for negative, 1 for weak, 2 for moderate weak, 3 for moderate strong, and 4 for strong. The score for the stained area was set as 0 for 0–33%; 1, 33–66%; and 2, 66–100%. The final staining score was obtained by multiplying the staining intensity score by the staining area score, and the results are a series of numbers ranging from 0 to 8.
MIF, CSF1, CCL2, VEGF, and MIP-1α in the culture supernatants of HCC cell lines were measured using ELISA kits (R&D Systems). The culture supernatants of the cells were collected and centrifuged at 500×g for 5 min to remove cellular debris. The ELISA was performed according to the manufacturer’s instructions.
Protein and small molecule components were separated using a 3-kDa ultrafiltration tube purchased from Millipore. Fraction containing small molecules (< 3 kDa) was collected from the residual liquid at the first ultrafiltration. The concentrated fraction (> 3 kDa) was further washed three times using PBS and finally concentrated in PBS.
MIF tautomerase activity assay
The tautomerase activity of MIF in the medium was detected as previously described with minor modifications . Phenylpyruvate could be conversed from enol- to keto- type by MIF, and this reaction was monitored by the decrease of absorbance at 288 nm on a spectrophotometer at room temperature. The assay mixture contained 50 mM sodium-phosphate buffer (pH 6.5) and a series of diluted MIF-containing culture medium. The assay was initiated by addition of ethanol-diluted phenylpyruvic acid with final concentration of 100 μm. Absorbance values for each group were normalized with the control group that contained buffer and fresh DMEM medium, thus yielding the relative percent of enzyme activity. For acetoacetate addition, 10 μM lithium acetoacetate was used. All the chemicals were purchased from Sigma (St. Louis, MO, USA).
For MIF inhibition, N-acetyl-p-benzoquinone (NAPQI) was purchased from Sigma (St. Louis, MO, USA), and it could inhibit 96% of MIF by incubating with cells for 5 min at 200 μM as previously reported .
All animal experiments were approved by the research medical ethics committee of Fudan University (Shanghai, China; approval no. 170013-0056) and were performed in accordance with the approved guidelines. Nude mice were purchased from the Shanghai Laboratory Animal Center of Chinese Academy Sciences (Shanghai, China) and were housed in individual ventilated cages. All of the mice were randomly grouped (n = 6 in each group).
For the subcutaneous xenograft model, Huh7-luc cells were resuspended in PBS (5 × 106/mouse) and subcutaneously inoculated into the axillaries of 4-week-old male nude mice. The mice were sacrificed after 4 weeks, and tumor tissues were harvested and weighted. For the orthotopic translation model, tumors from xenograft models were separated and chopped in PBS at 4 °C. The diameter of each fragment was modified to 1 mm. The fragments were then transplanted into nude mice in the left lobes of the liver. Bioluminescent imaging was performed with an IVIS200 (Xenogen, Caliper, CA) 10 min after intraperitoneal injection of luciferin (3 mg/mouse) (Promega, WI, USA). The intensity of luciferase signals was quantified using ROI analysis.
For metabolomics analysis, 7402-B3GALNT2 and 7402-control cells were cultured to 80% confluence and the medium was removed, followed by washing the cells with ice-cold PBS. The cells were then collected into tubes with PBS by scraping. For targeted metabolomics analysis, the culture medium was changed to serum-free medium 2 h before metabolite collection. Metabolite fractions of the culture media were collected and analyzed by targeted LC-MS/MS. LC-MS analysis was performed as described previously .
An ACQUITY UHPLC System (Waters Corporation, Milford, USA) coupled with an AB SCIEX Triple TOF 5600 System (AB SCIEX, Framingham, MA) was used to analyze the metabolic profiling. An ACQUITY UPLC BEH C18 column (1.7 μm, 2.1 × 100 mm) was employed with a binary gradient method. Data acquisition was performed in full scan mode (m/z ranges from 70 to 1000) combined with information-dependent acquisition (IDA) mode. For IDA analysis, range of m/z was set as 50–1000 and the collision energy was 30 eV. The QCs were injected at regular intervals (every eight samples) throughout the analytical run to provide a set of data from which repeatability could be assessed.
The raw data were converted to common data format (mzML) files using a conversion software program MSconventer. Metabolomics data were acquired using software XCMS 1.50.1 version, which produced a matrix of features with the associated retention time, accurate mass, and chromatographic separation. The positive and negative data were combined to get a combined data set which was imported into the SIMCA software package (version 14.0, Umetrics, Umea, Sweden). Principal component analysis (PCA) was carried out to visualize metabolic alterations among experimental groups, after mean centering and unit variance scaling. The differential metabolites were selected on the basis of p values from a two-tailed Student’s t test on the normalized peak areas, where metabolites with p values less than 0.05 were included. A reference material database built by the Dalian Institute of Chemical Physics, Chinese Academy of Sciences and Dalian ChemData Solution Information Technology Co., Ltd., HMDB, and METLIN was used.
All analyses were performed with SPSS 13.0 (Chicago, IL, USA). Results were presented as the mean ± standard deviation with at least three replicates for each sample. Optimal cut-off values for B3GALNT2 expression were determined by ROC curve analysis. Pearson’s chi-square test was used to identify the correlation between B3GALNT2 expression and other factors. Survival probability was determined by the Kalan-Meier curve, and the differences between groups were assessed by Log-rank test. Univariate and multivariate survival analyses were applied using Cox regression. Differences between groups were determined with Student’s t test. Statistical significance was set at two tails p < 0.05.
Upregulation of B3GALNT2 in HCC is associated with poor prognosis
Receiver operating characteristic (ROC) curve analysis grouped all the patients according to B3GALNT2 levels in tumor tissues, and representative IHC staining on B3GALNT2high and B3GALNT2low samples is shown respectively (Fig. 1a, and Additional file 1: Figure S1e). Chi-square test was applied to analyze correlations between intra-tumoral B3GALNT2 levels and clinicopathological features in HCC patients, and the data show that upregulation of B3GALNT2 is significantly correlated with tumor size (p = 0.01188) and T stage (p = 0.00328) among all the pathological factors (Fig. 1g, Additional file 2: Table S1). Furthermore, Kaplan-Meier analysis on both the IHC cohort and the TCGA cohort revealed that overexpressed B3GALNT2 was significantly associated with poorer overall survival (p = 0.0119 for IHC cohort and p = 0.0052 for TCGA cohort) (Fig. 1h, i). Meanwhile, univariate and multivariate Cox analysis revealed the prognostic significance of B3GALNT2 expression for overall survival of HCC patients (HR, 0.468; 95% CI, 0.252–0.872; p < 0.001) (Additional file 3: Table S2). Taken together, these data indicate that upregulation of B3GALNT2 is closely associated with HCC progression.
B3GALNT2 in HCC cells confers no significant function in vitro whereas it promotes tumor progression in vivo
B3GALNT2 knockdown inhibits macrophage recruitment in HCC cells
Acetoacetate is identified as the key secreted molecule from HCC cells in B3GALNT2-mediated macrophage recruitment
Since most metabolites were smaller than 3 kDa, we speculated that the key factor might be a metabolite. We performed metabolomics analysis on B3GALNT2-overexpressed 7402 cells and 7402 parental cells. The total ion chromatogram (TIC) and principal component analysis (PCA) (eight samples in each group) revealed that overexpression of B3GALNT2 altered the metabolomic pattern in 7402 cells (Fig. 4e and Additional file 6: Figure S4). Levels of several metabolites (n = 66) were significantly changed by B3GALNT2 (Additional file 7: Table S3), and the functional enrichment analysis of KEGG pathways on these metabolites revealed eight significantly enriched metabolic pathways, including the metabolism of butanoate (n = 6, p = 4.87E−06); ketone bodies (n = 3, p = 1.13E−04); alanine, aspartate, and glutamate (n = 4, p = 1.98E−04); pyruvate (n = 4, p = 4.87E−04); arginine and proline (n = 4, p = 1.55E−02); taurine and hypotaurine (n = 2, p = 0.045); and citrate cycle (n = 2, p = 0.045) (Fig. 4f and Additional file 7: Table S3). Most (68.2%, 45/66) of these metabolites were significantly downregulated by B3GALNT2 (p < 0.05), and acetoacetate conferred the lowest p value (p = 3.00E−12) and the largest fold change (FC = 22.64) among all downregulated metabolites (Fig. 4g, h, and Additional file 7: Table S3), suggesting that acetoacetate and its related metabolic pathways might be critical for functions of B3GALNT2 in macrophage recruitment. To verify this, we added acetoacetate into the lower chamber containing 7402 cells and found that the overexpression of B3GALNT2 in HCC cells failed to promote macrophage infiltration in addition with acetoacetate (Fig. 4i, j), proving that acetoacetate attenuates the effects of B3GALNT2. Taken together, our data indicate that B3GALNT2 facilitates macrophage recruitment via downregulating acetoacetate levels.
B3GALNT2 regulates the transcription of some enzymes involved in acetoacetate-related metabolism
HCC cell-derived acetoacetate suppresses macrophage recruitment by inhibiting MIF activity
In this study, our data reveal that B3GALNT2 is upregulated in HCC, and this upregulation is associated with tumor growth and poor prognosis. Mechanistically, B3GALNT2 reduced the expression of some metabolic enzymes and thus downregulated the secretion of acetoacetate levels, which relieved the activity of MIF and enhanced macrophage recruitment. Finally, recruited macrophages promoted tumor growth.
HCC is closely related with inflammation. A chronic inflammatory state is required for initiation, and the development of HCC and tumor cells also promote the construction and assist with the maintenance of the inflammatory extracellular environment [24, 33]. Consistent with this, our results prove that HCC cells and their inflammatory microenvironment are mutually beneficial for each other. HCC cells recruit macrophages to maintain the inflammatory environment, and recruited macrophages promote tumor growth in return. Notably, continuous recruitment of inflammatory cells is commonly observed in inflammatory environment. Among these cells, tumor-associated macrophages (TAMs) occupy a major fraction, especially in HCC [24, 25]. Liver-resident Kupffer cells and TAM in HCC are also polarized from peripheral blood mononuclear cell (PMBC), for which monocyte recruitment is indispensable .
Monocyte recruitment depends on some cytokines, such as chemokine (C–C motif) ligand 2 (CCL2), macrophage colony-stimulating factor (M-CSF), macrophage inflammatory protein 1a (MIP-1a), vascular endothelial growth factor (VEGF), CCL4, CCL5, CCL8, angiopoietin-2, and MIF [26, 27]. Usually, tumor cells elevate the secretion of cytokines to promote TAM recruitment. In our study, instead of increasing cytokine levels directly, B3GALNT2 elevated the activity of one cytokine, MIF. Although MIF was first identified as an inhibitor of macrophage migration , later studies revealed that it has pleiotropic effects on cell migration and chemotaxis [36, 37]. Actually, MIF can induce macrophage recruitment through CCL2 and its receptor CCR2 . Sometimes, MIF acts as a counter-regulation factor against anti-inflammatory and immunosuppressive machinery by overriding the glucocorticoid inhibition to immune response like T cell proliferation and cytokine production [38, 39]. MIF has also been identified as a key cytokine for TAM polarization in melanoma-bearing mice. MIF deficiency or treatment with a MIF antagonist attenuated tumor-induced TAM polarization and reduced expression of angiogenesis-related genes in TAMs .
Different from most cytokines, MIF exhibits perplexing tautomerase activity which is conversion of d-dopachrome and phenylpyruvate , but its natural substrate is still not clear. Early studies considered that the tautomerase active site is vestigial with no true physiological function . But some researchers revealed that MIF interplays with CD74 as a cytokine and initiates signaling [43, 44]. This interplay could be disrupted when the tautomerase pocket of MIF is mutated or occupied by some molecules [31, 32]. NAPQI that we used in this study is one of the inhibitors targeting the tautomerase pocket of MIF. Our data verify that suppressing the tautomerase activity of MIF attenuates its promotion of macrophage recruitment.
Some natural small molecules, like ketone bodies, also show the ability to inhibit tautomerase activity of MIF . As one of the ketone bodies, acetoacetate inhibited MIF activity in our study and we proved that acetoacetate was the key molecule by which B3GALNT2 regulated MIF activity and macrophage recruitment. Although acetoacetate was reported to promote tumor growth in melanoma [45, 46], ketone bodies including acetoacetate inhibit tumor progression in other cancers [47–50]. It is reported that cancer cell lines grown in glucose plus acetoacetate medium show tightly coupled reduction of growth and ATP concentration . And an acetoacetate-related ketogenic diet decreases tumor cell viability and prolongs survival of mice with metastatic cancer . Meanwhile, since ketone bodies could serve as energy sources in tumor cells, the consumption of these ketone bodies might result in their decrease in extracellular microenvironment. In liver cancers, the consumption of acetoacetate was elevated for lipogenesis to compensate the energy from truncated TCA cycle [51, 52]. Our data suggested that this persistent consumption decreases the extracellular acetoacetate levels and preserves an acetoacetate-low microenvironment benefit for tumor development. Our data also indicated that besides the direct inhibition on cell proliferation, acetoacetate could suppress tumor growth via inhibiting TAM recruitment.
The chronic inflammatory microenvironment is required for both tumor initiation and tumor progression, and increased cytokine secretion in the tumor microenvironment promotes recruitment of immune cells including TAMs [24, 25]. TAMs play important roles in different cancers including HCC [24, 33]. Targeting TAMs is becoming a promising strategy in treating tumors. Some studies suggest that suppressing TAM recruitment via targeting chemokines could also inhibit tumor progression. However, blocking chemokines directly with antibodies sometimes leads to unpredictable tumor growth and distant metastasis. Here, our study provides a new approach to treat HCC by increasing the small molecule acetoacetate. Due to its endogenous derivation, this strategy might be safer and have more efficiency.
In summary, this study evaluated B3GALNT2 as a tumor marker in HCC and revealed the role of B3GALNT2 in metabolism which transformed the microenvironment of HCC. Our mechanistic study also emphasizes critical roles for acetoacetate and macrophages in HCC tumor growth. Therefore, this study provides more evidence for an advantage of a ketogenic diet to treat HCC and suggests an approach for immunotherapeutic treatment of HCC. Further studies on the molecular mechanism of how B3GALNT2 regulates acetoacetate-related enzymes in HCC progression are required.
This work was supported by grants from the National Basic Research Program of China (31630088), the National Natural Science Foundation of China (31501051), the Science Technology Commission of Shanghai Municipality (16142202600), Shanghai Municipal Commission of Health and Family Planning (201740023), and the “Fostering Project for Outstanding Young Talent” from Tongji University (2015KJ054).
Availability of data and materials
All data are fully available without restriction.
TY and WW performed all the experiments. TY, XZ, JW, SS, JZ, and YW performed the tissue treatments. TY and WD carried out analysis and interpretation of data; XZ and LF carried out statistical analysis; and YW and JZ collected clinical data. WW and TY co-wrote the manuscript. JG and WW conceived of the idea and designed the experiments. All authors have reviewed the manuscript. All authors read and approved the final manuscript.
Ethics approval and consent to participate
Usage of human pathological tissues and clinical data was approved by the Ethics Committee at the Shanghai Cancer Center of Fudan University (Shanghai, China; approval no. 050432-4-1212B). Written consent for all patients conformed to the ethical guidelines of the Helsinki Declaration.
Consent for publication
The authors declare that they have no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
- Forner A, Llovet JM, Bruix J. Hepatocellular carcinoma. Lancet. 2012;379(9822):1245–55.View ArticlePubMedGoogle Scholar
- Ferlay J, Shin HR, Bray F, Forman D, Mathers C, Parkin DM. Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008. Int J Cancer. 2010;127(12):2893–917.View ArticlePubMedGoogle Scholar
- Shimada K, Sano T, Sakamoto Y, Kosuge T. A long-term follow-up and management study of hepatocellular carcinoma patients surviving for 10 years or longer after curative hepatectomy. Cancer. 2005;104(9):1939–47.View ArticlePubMedGoogle Scholar
- Fuster MM, Esko JD. The sweet and sour of cancer: glycans as novel therapeutic targets. Nat Rev Cancer. 2005;5(7):526–42.View ArticlePubMedGoogle Scholar
- Pinho SS, Reis CA. Glycosylation in cancer: mechanisms and clinical implications. Nat Rev Cancer. 2015;15(9):540–55.View ArticlePubMedGoogle Scholar
- Ohno M, Nishikawa A, Koketsu M, Taga H, Endo Y, Hada T, Higashino K, Taniguchi N. Enzymatic basis of sugar structures of α-fetoprotein in hepatoma and hepatoblastoma cell lines: correlation with activities of α 1-6 fucosyltransferase and N-acetylglucosaminyltransferases III and V. Int J Cancer. 1992;51(2):315–7.View ArticlePubMedGoogle Scholar
- Song E-Y, Kang S-K, Lee Y-C, Park Y-G, Chung T-H, Kwon D-H, Byun S-M, Kim C-H. Expression of bisecting N-acetylglucosaminyltransferase-III in human hepatocarcinoma tissues, fetal liver tissues, and hepatoma cell lines of Hep3B and HepG2. Cancer Investig. 2001;19(8):799–807.View ArticleGoogle Scholar
- Yanagi M, Aoyagi Y, Suda T, Mita Y, Asakura H. N-Acetylglucosaminyltransferase V as a possible aid for the evaluation of tumor invasiveness in patients with hepatocellular carcinoma. J Gastroenterol Hepatol. 2001;16(11):1282–9.View ArticlePubMedGoogle Scholar
- Blomme B, Van Steenkiste C, Callewaert N, Van Vlierberghe H. Alteration of protein glycosylation in liver diseases. J Hepatol. 2009;50(3):592–603.View ArticlePubMedGoogle Scholar
- Yang L, Zhang Y. Tumor-associated macrophages: from basic research to clinical application. J Hematol Oncol. 2017;10(1):58.View ArticlePubMedPubMed CentralGoogle Scholar
- Yang L, Zhang Y. Tumor-associated macrophages, potential targets for cancer treatment. Biomarker Res. 2017;5:25.View ArticleGoogle Scholar
- Chen Y, Zhang S, Wang Q, Zhang X. Tumor-recruited M2 macrophages promote gastric and breast cancer metastasis via M2 macrophage-secreted CHI3L1 protein. J Hematol Oncol. 2017;10(1):36.View ArticlePubMedPubMed CentralGoogle Scholar
- Huang YJ, Yang CK, Wei PL, Huynh TT, Whang-Peng J, Meng TC, Hsiao M, Tzeng YM, Wu AT, Yen Y. Ovatodiolide suppresses colon tumorigenesis and prevents polarization of M2 tumor-associated macrophages through YAP oncogenic pathways. J Hematol Oncol. 2017;10(1):60.View ArticlePubMedPubMed CentralGoogle Scholar
- Shinzaki S, Ishii M, Fujii H, Iijima H, Wakamatsu K, Kawai S, Shiraishi E, Hiyama S, Inoue T, Hayashi Y, et al. N-acetylglucosaminyltransferase V exacerbates murine colitis with macrophage dysfunction and enhances colitic tumorigenesis. J Gastroenterol. 2016;51(4):357–69.View ArticlePubMedGoogle Scholar
- Hiruma T. A novel human 1,3-N-acetylgalactosaminyltransferase that synthesizes a unique carbohydrate structure, GalNAc 1-3GlcNAc. J Biol Chem. 2004;279(14):14087–95.View ArticlePubMedGoogle Scholar
- Stevens E, Carss KJ, Cirak S, Foley AR, Torelli S, Willer T, Tambunan DE, Yau S, Brodd L, Sewry CA, et al. Mutations in B3GALNT2 cause congenital muscular dystrophy and hypoglycosylation of alpha-dystroglycan. Am J Hum Genet. 2013;92(3):354–65.View ArticlePubMedPubMed CentralGoogle Scholar
- Matsuo T, Komatsu M, Yoshimaru T, Kiyotani K, Miyoshi Y, Sasa M, Katagiri T. Involvement of B3GALNT2 overexpression in the cell growth of breast cancer. Int J Oncol. 2014;44(2):427–34.View ArticlePubMedGoogle Scholar
- Song S, Peng P, Tang Z, Zhao J, Wu W, Li H, Shao M, Li L, Yang C, Duan F. Decreased expression of STING predicts poor prognosis in patients with gastric cancer. Sci Rep. 2017;7:39858.Google Scholar
- Garai J, Lóránd T, Molnár V. Ketone bodies affect the enzymatic activity of macrophage migration inhibitory factor. Life Sci. 2005;77(12):1375–80.View ArticlePubMedGoogle Scholar
- Senter PD, Al-Abed Y, Metz CN, Benigni F, Mitchell RA, Chesney J, Han J, Gartner CG, Nelson SD, Todaro GJ. Inhibition of macrophage migration inhibitory factor (MIF) tautomerase and biological activities by acetaminophen metabolites. Proc Natl Acad Sci. 2002;99(1):144–9.View ArticlePubMedPubMed CentralGoogle Scholar
- Dang CV. Links between metabolism and cancer. Genes Dev. 2012;26(9):877–90.View ArticlePubMedPubMed CentralGoogle Scholar
- Sung WK, Zheng H, Li S, Chen R, Liu X, Li Y, Lee NP, Lee WH, Ariyaratne PN, Tennakoon C, et al. Genome-wide survey of recurrent HBV integration in hepatocellular carcinoma. Nat Genet. 2012;44(7):765–9.View ArticlePubMedGoogle Scholar
- Villa E, Critelli R, Lei B, Marzocchi G, Camma C, Giannelli G, Pontisso P, Cabibbo G, Enea M, Colopi S, et al. Neoangiogenesis-related genes are hallmarks of fast-growing hepatocellular carcinomas and worst survival. Results from a prospective study. Gut. 2016;65(5):861–9.View ArticlePubMedGoogle Scholar
- Capece D, Fischietti M, Verzella D, Gaggiano A, Cicciarelli G, Tessitore A, Zazzeroni F, Alesse E. The inflammatory microenvironment in hepatocellular carcinoma: a pivotal role for tumor-associated macrophages. Biomed Res Int. 2013;2013:187204.View ArticlePubMedGoogle Scholar
- Solinas G, Germano G, Mantovani A, Allavena P. Tumor-associated macrophages (TAM) as major players of the cancer-related inflammation. J Leukoc Biol. 2009;86(5):1065–73.View ArticlePubMedGoogle Scholar
- Coussens LM, Werb Z. Inflammation and cancer. Nature. 2002;420(6917):860–7.View ArticlePubMedPubMed CentralGoogle Scholar
- Murdoch C, Giannoudis A, Lewis CE. Mechanisms regulating the recruitment of macrophages into hypoxic areas of tumors and other ischemic tissues. Blood. 2004;104(8):2224–34.View ArticlePubMedGoogle Scholar
- Bacher M, Meinhardt A, Lan HY, Mu W, Metz CN, Chesney JA, Calandra T, Gemsa D, Donnelly T, Atkins RC. Migration inhibitory factor expression in experimentally induced endotoxemia. Am J Pathol. 1997;150(1):235.PubMedPubMed CentralGoogle Scholar
- Calandra T, Thierry R. Macrophage migration inhibitory factor: a regulator of innate immunity. Nat Rev Immunol. 2003;3(10):791.View ArticlePubMedGoogle Scholar
- Gregory JL, Morand EF, McKeown SJ, Ralph JA, Hall P, Yang YH, McColl SR, Hickey MJ. Macrophage migration inhibitory factor induces macrophage recruitment via CC chemokine ligand 2. J Immunol. 2006;177(11):8072–9.View ArticlePubMedGoogle Scholar
- Cournia Z, Leng L, Gandavadi S, Du X, Bucala R, Jorgensen WL. Discovery of human macrophage migration inhibitory factor (MIF)-CD74 antagonists via virtual screening. J Med Chem. 2008;52(2):416–24.View ArticleGoogle Scholar
- Pantouris G, Syed MA, Fan C, Rajasekaran D, Cho TY, Rosenberg EM, Bucala R, Bhandari V, Lolis EJ. An analysis of MIF structural features that control functional activation of CD74. Chem Biol. 2015;22(9):1197–205.View ArticlePubMedPubMed CentralGoogle Scholar
- Berasain C, Castillo J, Perugorria MJ, Latasa MU, Prieto J, Avila MA. Inflammation and liver cancer: new molecular links. Ann N Y Acad Sci. 2009;1155:206–21.View ArticlePubMedGoogle Scholar
- Shirabe K, Mano Y, Muto J, Matono R, Motomura T, Toshima T, Takeishi K, Uchiyama H, Yoshizumi T, Taketomi A, et al. Role of tumor-associated macrophages in the progression of hepatocellular carcinoma. Surg Today. 2012;42(1):1–7.View ArticlePubMedGoogle Scholar
- Bloom BR, Bennett B. Mechanism of a reaction in vitro associated with delayed-type hypersensitivity. Science. 1966;153(3731):80–2.View ArticlePubMedGoogle Scholar
- Hermanowski-Vosatka A, Mundt SS, Ayala JM, Goyal S, Hanlon WA, Czerwinski RM, Wright SD, Whitman CP. Enzymatically inactive macrophage migration inhibitory factor inhibits monocyte chemotaxis and random migration. Biochemistry. 1999;38(39):12841–9.View ArticlePubMedGoogle Scholar
- Bernhagen J, Krohn R, Lue H, Gregory JL, Zernecke A, Koenen RR, Dewor M, Georgiev I, Schober A, Leng L. MIF is a noncognate ligand of CXC chemokine receptors in inflammatory and atherogenic cell recruitment. Nat Med. 2007;13(5):587.View ArticlePubMedGoogle Scholar
- Flaster H, Bernhagen J, Calandra T, Bucala R. The macrophage migration inhibitory factor-glucocorticoid dyad: regulation of inflammation and immunity. Mol Endocrinol. 2007;21(6):1267–80.View ArticlePubMedGoogle Scholar
- Calandra T, Bernhagen J, Metz CN, Spiegel LA, Bacher M, Donnelly T, Cerami A, Bucala R. MIF as a glucocorticoid-induced modulator of cytokine production. Nature. 1995;377(6544):68–71.View ArticlePubMedGoogle Scholar
- Yaddanapudi K, Putty K, Rendon BE, Lamont GJ, Faughn JD, Satoskar A, Lasnik A, Eaton JW, Mitchell RA. Control of tumor-associated macrophage alternative activation by macrophage migration inhibitory factor. J Immunol. 2013;190(6):2984–93.View ArticlePubMedPubMed CentralGoogle Scholar
- Rosengren E, Åman P, Thelin S, Hansson C, Ahlfors S, Björk P, Jacobsson L, Rorsman H. The macrophage migration inhibitory factor MIF is a phenylpyruvate tautomerase. FEBS Lett. 1997;417(1):85–8.View ArticlePubMedGoogle Scholar
- Kudrin A, Ray D. Cunning factor: macrophage migration inhibitory factor as a redox-regulated target. Immunol Cell Biol. 2008;86(3):232.View ArticlePubMedGoogle Scholar
- Leng L, Metz CN, Fang Y, Xu J, Donnelly S, Baugh J, Delohery T, Chen Y, Mitchell RA, Bucala R. MIF signal transduction initiated by binding to CD74. J Exp Med. 2003;197(11):1467–76.View ArticlePubMedPubMed CentralGoogle Scholar
- Shi X, Leng L, Wang T, Wang W, Du X, Li J, McDonald C, Chen Z, Murphy JW, Lolis E. CD44 is the signaling component of the macrophage migration inhibitory factor-CD74 receptor complex. Immunity. 2006;25(4):595–606.View ArticlePubMedPubMed CentralGoogle Scholar
- Trousil S, Zheng B. Addicted to AA (acetoacetate): a point of convergence between metabolism and BRAF signaling. Mol Cell. 2015;59(3):333–4.View ArticlePubMedGoogle Scholar
- Xia S, Lin R, Jin L, Zhao L, Kang HB, Pan Y, Liu S, Qian G, Qian Z, Konstantakou E, et al. Prevention of dietary-fat-fueled ketogenesis attenuates BRAF V600E tumor growth. Cell Metab. 2017;25(2):358–73.View ArticlePubMedPubMed CentralGoogle Scholar
- Magee BA, Potezny N, Rofe AM, Conyers RA. The inhibition of malignant cell growth by ketone bodies. The Australian journal of experimental biology and medical science. 1979;57(5):529–39.View ArticlePubMedGoogle Scholar
- Poff AM, Ari C, Arnold P, Seyfried TN, D’Agostino DP. Ketone supplementation decreases tumor cell viability and prolongs survival of mice with metastatic cancer. Int J Cancer. 2014;135(7):1711–20.View ArticlePubMedPubMed CentralGoogle Scholar
- Shukla SK, Gebregiworgis T, Purohit V, Chaika NV, Gunda V, Radhakrishnan P, Mehla K, Pipinos II, Powers R, Yu F, et al. Metabolic reprogramming induced by ketone bodies diminishes pancreatic cancer cachexia. Cancer Metab. 2014;2:18.View ArticlePubMedPubMed CentralGoogle Scholar
- Fine EJ, Miller A, Quadros EV, Sequeira JM, Feinman RD. Acetoacetate reduces growth and ATP concentration in cancer cell lines which over-express uncoupling protein 2. Cancer Cell Int. 2009;9:14.View ArticlePubMedPubMed CentralGoogle Scholar
- Huang D, Li T, Wang L, Zhang L, Yan R, Li K, Xing S, Wu G, Hu L, Jia W, et al. Hepatocellular carcinoma redirects to ketolysis for progression under nutrition deprivation stress. Cell Res. 2016;26(10):1112–30.View ArticlePubMedPubMed CentralGoogle Scholar
- Briscoe DA, Fiskum G, Holleran AL, Kelleher JK. Acetoacetate metabolism in AS-30D hepatoma cells. Mol Cell Biochem. 1994;136(2):131–7.View ArticlePubMedGoogle Scholar