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. 2024 Dec 30;74(1):35. doi: 10.1007/s00262-024-03912-1

Q11, a CYP2E1 inhibitor, exerts anti-hepatocellular carcinoma effect by inhibiting M2 macrophage polarization

Cunzhen Zhang 1,#, Yan Fang 1,#, Mengxue Guo 1, Liming Tang 1, Yurong Xing 2, Jun Zhou 1, Yuanyuan Guo 1,2, Yuhan Gu 1, Qiang Wen 1, Na Gao 1, Haiwei Xu 3, Hailing Qiao 1,✉
PMCID: PMC11685367  PMID: 39738913

Abstract

Despite significant advancements in cancer immunotherapy, many patients continue to respond poorly. Novel therapeutic strategies and drugs are urgently needed. Here, we found that CYP2E1 is upregulated in M2 macrophages. The CYP2E1 inhibitor, Q11, could inhibit M2 macrophage polarization, while CYP2E1 overexpression could promote it. Increased levels of CYP2E1 and M2 macrophages in the tumor microenvironment of HCC patients correlate with poor prognosis. Q11 could inhibit tumor cells by targeting M2 macrophages rather than directly attacking tumor cells. Both Q11 and Cyp2e1 knockout could effectively suppress tumor growth. Q11 reduces the production of CYP2E1 metabolites ( ±)9(10)-DiHOME and ( ±)12(13)-DiHOME, thus attenuating PPARγ activation and M2 macrophage polarization. In summary, our findings suggest that Q11 could suppress M2 macrophage polarization by modulating the CYP2E1/( ±)9(10)-DiHOME or ( ±)12(13)-DiHOME/PPARγ axis, indicating that CYP2E1 may be a potential therapeutic target for HCC, and its inhibitor Q11 may be a potential drug for the treatment of HCC.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00262-024-03912-1.

Keywords: Hepatocellular carcinoma, CYP2E1, Inhibitor, M2 macrophage, Metabolites

Introduction

Cytochrome P450 2E1 (CYP2E1), primarily located in hepatic endoplasmic reticulum and mitochondria, plays a crucial role in regulating metabolism and inflammation [1, 2]. CYP2E1 has been implicated in the development of numerous diseases, such as liver cancer [3–5]. Previous studies have shown that CYP2E1 inhibitors can suppress nitrosamine-induced HCC [6, 7], however, this primarily reflects the role of CYP2E1 in activating the procarcinogen nitrosamine. Our recent studies have demonstrated that CYP2E1 activity is significantly increased in HCC patients and is associated with the Stat4 rs7574865 polymorphism [8, 9]. Among the three HCC subtypes classified by proteomics, patients with increased CYP2E1 activity tend to have shorter survival times [10].

Hepatocellular carcinoma (HCC) is the fifth most common cancer globally and the third leading cause of cancer-related deaths [11], posing a significant burden on global health. Most HCC patients have a history of viral hepatitis infection, leading to chronic inflammation and macrophage reprogramming, ultimately resulting in the cancer-inflammation cycle [12]. Multivariate analyses have revealed a unique tumor microenvironment (TME) in HCC, characterized by inflammation and immune escape [13, 14]. Additionally, the liver contains a large number of Kupffer cells, which share similar characteristics with macrophages. In recent years, targeting the TME has emerged as a promising strategy for HCC prevention and treatment [15, 16]. However, drugs such as sorafenib and lenvatinib still face challenges such as drug resistance, limited indications, and short survival benefits [17, 18]. Therefore, there is an urgent need for further research into the pathogenesis of HCC and the development of new therapeutic strategies and drugs.

Tumor-associated macrophages (TAMs) are one of the most abundant immune cells in the TME and can promote tumor signaling and progression by reprogramming [19]. TAMs can be divided into M1 and M2 types [13]. M1 macrophages primarily secrete pro-inflammatory cytokines, promoting immunity and inhibiting tumor progression [20]. M2 macrophages primarily secrete anti-inflammatory cytokines, suppressing immunity and promoting tumor growth [21]. Targeting TAMs has become one of the most popular immunotherapy strategies for HCC, mainly including depleting TAMs, blocking TAM recruitment, or reprogramming M2 macrophages to M1 macrophages [22–24], and some clinical trials have shown promising results. Macrophages play an important role in inflammation and immunity [25, 26]. CYP2E1 also plays an important role in the inflammatory response [27, 28]. However, the relationship between the two remains unclear.

Researchers have devoted significant efforts to developing CYP2E1 inhibitors for the treatment and intervention of diseases, such as chlormethiazole [29], disulfiram [30], and 12-imidazolyl-1-dodecanol [31]. However, no CYP2E1 inhibitor has been approved for clinical use to date. To address this gap, we synthesized and screened a novel CYP2E1-specific inhibitor, 1-(4-methyl-5-thiazolyl)ethenone, named Q11, which exhibits potent inhibitory activity (Ki < 1 μM), high selectivity, and low toxicity [32] and has demonstrated efficacy in preclinical models of glioma, lung cancer, and sepsis [32–34].

Materials and methods

Specimen collection and survival time of HCC patients

A total of 197 liver tissue samples were collected from Chinese patients undergoing hepatic surgery at the First Affiliated Hospital of Zhengzhou University, Affiliated People's Hospital of Zhengzhou University, and Affiliated Cancer Hospital of Zhengzhou University. These samples comprised two groups: normal liver (n = 95), from most patients with angioma and peritumoral tissue from HCC patients (n = 102). The latter were obtained at least 2 cm from tumor margins. Following hepatectomy, a combined telephone and door-to-door follow-up was conducted every 6 months for 42–54 months. Ethical approval for this study was provided by the Medical Ethics Committee of Zhengzhou University (No: 2018-KY-0749–002). All research was conducted in accordance with both the Declarations of Helsinki and Istanbul. Written consent was given in writing by all subjects. (The patient's clinical information was provided in the supplementary Table S1).

Microsomes preparation and CYP2E1 activity determination

The preparation of microsomes is described in our previous study [35]. A 100 μL microsomal incubation system containing 160 mM diethylnitrosamine as substrate was used to measure the amount of metabolite described previously to characterize CYP2E1 activity [36].

Liver orthotopic transplantation tumor model

Cyp2e1 systemic knockout (Cyp2e1−/−) rats were generated by Biocytogen (Beijing, China) using CRISPR-Cas9 technology and validated by qPCR and WB (Detail in Supplementary materials). Cyp2e1−/− rats and their wild-type littermates were used in this study. Additionally, male Balb/c mice (7–8 weeks) and SD rats (180–200 g) were purchased from Beijing Vital River Laboratory Animal Technology Co., Ltd. (Beijing, China). All animals were bred and maintained under specific pathogen-free (SPF) conditions, fed standard laboratory chow, and housed on a 12 h light/dark cycle with temperature and humidity controlled at 22 ± 2 °C and 55% ± 5%. After a one-week acclimation period, mice were randomly assigned to 6 groups: a blank control group, a model group, a 5-FU positive drug group (20 mg/kg), and three Q11 dose groups (3.3 mg/kg, 10 mg/kg, and 30 mg/kg). Except for the normal group with 8 mice, each group consisted of approximately 19 mice. The 5-FU group began treatment on the day following model establishment and continued for two weeks; the Q11 group started treatment three days prior to model establishment and continued until sacrifice. The model and control groups received the same volume of saline. Animals were sacrificed approximately 21 days post-modeling, with the endpoint defined as the death of the first animal.

Model establishment: Animals were anesthetized, fixed on a board, depilated, and disinfected with iodine. A small (1 cm) left abdominal incision exposed the left liver lobe, which was gently rotated out with a saline-moistened cotton swab. Using a micropipette, either 50 μL of Walker-256 cell suspension (2 × 107 cells/mL) or 10 μL of H22 cell suspension (1 × 106 cells/mL) was slowly injected into the left lobe. Following injection, the needle remained in place for 1 min before withdrawal. The liver was then returned to its original position, the incision sutured layer-by-layer, and antibiotics administered to prevent infection. We closely monitored the postoperative status of the animals and maintained warmth. All animal experiments were approved by the Animal Experiment Administration Committee of Zhengzhou University (ZZUIRB2022‐152).

Proteomic data

The proteomic data and clinical information were obtained from our previous study [10]. Among the 95 liver tissues, 60 peritumoral tissues and 34 normal liver tissues passed the quality-control filter and were analyzed. Raw data is available via ProteomeXchange (https://www.ebi.ac.uk/pride/) with identifier PXD023118.

Transcriptomic data

The transcriptome data of HCC (n = 50) were obtained from TCGA-LIHC data set. The normal liver transcriptome data (n = 110) were obtained from the GTEx database. The data in TPM or log2(TPM + 0.001) format is used for quantification.

M2 markers and Gene set enrichment analysis

The M2 macrophage marker gene set was obtained from the “Cell Marker” database. The marker genes for different cell types were classified and organized by species. The Gene set enrichment analysis (GSEA) analysis was performed using GSEA software (Version v4.3.2).

Immune cell infiltration analysis

The R (version 4.3.3) package “immunedeconv” was used to evaluate the immune cells infiltration with the methods of CIBERSORT [37] and XCELL [38].

Cell experiments

Hepatocarcinoma cell lines HepG2 and H22, and macrophage cell lines THP-1 and J774A.1, were obtained from the National Collection of Authenticated Cell Cultures. HepG2 and J774A.1 cells were cultured in DMEM (Gibco), while H22 and THP-1 cells were cultured in RPMI 1640 medium (Gibco). All medium were supplemented with 10% fetal bovine serum (FBS, Gibco) and 1% penicillin–streptomycin solution (100 units/mL penicillin, 100 µg/mL streptomycin, Invitrogen) in a humidified atmosphere containing 5% CO2 at 37 °C. THP-1 and J774A.1 cells were further induced with human or mouse IL-4 (10 ng/mL) and IL-13 (10 ng/mL) before treatment with various concentrations of Q11 for 48 h. The resulting conditioned medium was collected for co-culturing with tumor cells, and the cells themselves were harvested for further experimentation. Proliferation, migration, invasion, and apoptosis were then assessed using CCK-8 kit, scratch assay, trans-well assay, annexin V-FITC/PI staining, respectively, following the manufacturer instructions.

qPCR

Cells or tissues were lysed with TRIZOL (vazyme, Nanjing, China), followed by RNA extraction according to the manufacturer’s protocol. We determined the concentration and purity of total RNA using an ultraviolet-spectrophotometer. cDNA synthesis reactions were performed using the HiScript® III All-in-one RT SuperMix kit. Real-time PCR was conducted using the Taq Pro Universal SYBR qPCR Master Mix kit, following the manufacturers’ protocols. The primer information is available in the Supplementary Table S2.

Western blotting

Harvested tissues and cells were lysed with RIPA buffer (Solarbio Science & Technology, Beijing, China). Protein concentration were determined using BCA kit (GLPBIO, California, USA). Samples were separated on 10% SDS‐PAGE gels and blotted with transfer buffer onto nitrocellulose membranes (Millipore). The membranes were incubated with primary antibodies at 4 °C overnight, washed three times with TBST (Tris-buffered saline with Tween-20), and then incubated with HRP‐conjugated anti‐mouse IgG or anti‐rabbit IgG diluted in TBST containing 1% non‐fat milk at room temperature for 1 h. After final washing with TBST, the membranes were developed using ECL. The antibody information is available in the Supplementary Table S3.

Immunohistochemistry and immunofluorescence

Paraffin‐embedded specimens were cut into 4‐µm sections and then subjected to antigen retrieval. Endogenous peroxidase activity was blocked with 3% H2O2 in methanol. Antibodies contains MRC1 (1:150, Servicebio), CD86 (1:150, Servicebio). Results were evaluated blindly by two pathologists and quantified by IPP 6.0 soft. Immunofluorescence staining was performed as described previously [39].

ELISA

The levels of TGF-β, IL-10, TNF-α and IL-1β were measured using correspondingly commercial ELISA Kit (Multi Sciences Biotech, Shanghai, China) according to the manufacturer's instructions. Briefly, samples (100 µL) were added to the plate and incubated for 2 h at room temperature. After washing, the plate was incubated with HRP (100 µL) for 45 min at room temperature. After washing, the plate was then incubated with TMB (100 µL) for 20 min at room temperature. The reaction was stopped with Stop Solution (100 µL). The absorbance was measured at 450 and 630 nm using microplate reader.

Transcriptomics and metabolomics

Tissues transcriptome sequencing was performed on normal liver tissues from control mice and tumor tissues from both the model and Q11 groups of HCC orthotopic xenograft mouse model. J774A.1 cells were treated with mouse IL-4 (10 ng/mL) and IL-10 (10 ng/mL) for 48 h to induce M2 macrophage polarization, followed by transcriptomics and metabolomics analysis. Transcriptomics and metabolomics were performed in collaboration with Wekemo Tech Group Co., Ltd. Shenzhen China.

Molecular docking

PPARγ protein structure files (5GTN) were downloaded from the PDB database and processed by removing ligands, dehydrating, and hydrogenating. Small molecule metabolite structures were obtained from databases such as HMDB, PubChem, and LIPO MAPS. After processing into ligands by removing water and hydrogenating, molecular docking was performed using Autodock vina 1.1.2 software. Molecular docking results were visualized using PyMOL 2.5.5 software.

Statistical analysis

Data analysis was performed using SPSS 27.0 software (IBM Corp.) and plotted using GraphPad Prism 8.0.1 software (GraphPad). For normally distributed data, two-group comparisons utilized the t-test, multiple-group comparisons employed one-way ANOVA, and correlation analysis was conducted using the Pearson correlation test. For non-normally distributed data, two-group comparisons employed the Mann–Whitney U test, multiple-group comparisons utilized the Kruskal–Wallis test, and correlation analysis was conducted using the Spearman correlation test. Statistical significance was defined as P < 0.05, with significance levels indicated as follows: *P < 0.05, **P < 0.01, ***P < 0.001, #P < 0.05, ##P < 0.01, ###P < 0.001.

Results

CYP2E1 specific inhibitors Q11 could inhibit M2 macrophage polarization

To investigate the role of CYP2E1 in macrophage, we examined the expression of CYP2E1 in J774A.1-induced M2 macrophages. The results suggest a potential positive correlation between CYP2E1 and M2 macrophages (Fig. 1a, b). Then, we found that Q11 could inhibit the expression of M2 macrophage related factors (CD163, TGF-β and IL-10), and increase the M1 macrophage related factors (CD86 and TNF-α) (Fig. 1c). Moreover, we found that Q11 could decrease the levels of TGF-β and IL-10, and increase the levels of TNF-α and IL-1β in the supernatants of M2 macrophages (Fig. 1d, e). These results suggest that Q11 could suppresses M2 macrophage polarization.

Fig. 1.

Fig. 1

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Q11 could suppress M2 macrophage polarization a Relative mRNA expression of Cyp2e1 in M2 macrophage which were induced by J774A.1 with mouse IL-4 (10 ng/mL) and IL-13 (10 ng/mL) for 48 h. b Immunofluorescence staining for DAPI (blue), ARG-1 (red), CYP2E1 (green) and Merge in M2 macrophages, scale bar = 100 μm. c Effect of Q11 on M2 polarization related factors (CD163, TGF-β, and IL-10) and M1 polarization related factors (CD86 and TNF-α) in M2 macrophages. t-test. *P < 0.05, **P < 0.01, ***P < 0.001 versus Ctrl group; #P < 0.05, ##P < 0.01 versus M2 group. (d, e) Effect of Q11 on M2 polarization related factors (TGF-β, IL-10) and M1 polarization related factors (TNF-α, IL-1β) in the supernatant of J774A.1-induced M2 macrophages. t-test. *P < 0.05, **P < 0.01, ***P < 0.001 versus M2 group. f, g Changes of the M2 marker pathway between Control and M2 groups (f), M2 and Q11 groups (g) were analyzed by GSEA, h Relative mRNA expression of cyp2e1 in J774A.1 transfected with cyp2e1 plasmid was measured by qPCR. t-test. i Relative protein expression of CYP2E1 in J774A.1 transfected with cyp2e1 plasmid was measured by WB. t-test. j Effect of CYP2E1 overexpression (OE) on M2 and M1 polarization related factors (CD163, IL-10, CD86) in J774A.1 cells. t-test. In h, i, and j: *P < 0.05, **P < 0.01, ***P < 0.001 versus Ctrl group

To further investigate the effects of Q11 on M2 macrophages, transcriptomic was performed. The results showed that M2 marker pathway was significantly upregulated in the M2 group and downregulated in the Q11 group (Fig. 1f, g, Supplementary Table S4). The finding further confirm that Q11 could inhibit M2 macrophages polarization.

Furthermore, we investigated the effects of CYP2E1 overexpression on J774A.1 macrophages. The results showed that CYP2E1 overexpression could upregulate the M2 macrophage related factors CD163 and IL-10, and downregulate the M1 macrophage related factor CD86 (Fig. 1h–j). The findings suggest that upregulation of CYP2E1 could promote M2 macrophage polarization.

CYP2E1 and M2 macrophages infiltration were both increased in HCC patients and associated with poor prognosis

Database analysis revealed a significant upregulation of CYP2E1 expression in HCC microenvironment (Fig. 2a). The findings were further confirmed by Western blot and Immunofluorescence (Fig. 2b, c). Subsequently, we found that the CYP2E1 activity was significantly increased in peritumoral tissues of HCC patients (696 vs 1427 pmol/min/mg, P < 0.001) (Fig. 2d). The median overall survival (OS) time of the V2E1low group was significantly longer than V2E1high group (522 vs 268 days, P < 0.05) (Fig. 2e). V2E1 exhibited a high area under the curve (AUC) on predicting the occurrence and progression of HCC (Fig. 2f, g).

Fig. 2.

Fig. 2

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CYP2E1 and M2 macrophages infiltration were upregulated in HCC patients a mRNA expression of CYP2E1 in tumor and peritumoral tissues of HCC patients (n = 50) and normal tissues (n = 110). Data were from TCGA-LIHC and GTEx and presented as mean ± SD. Mann–Whitney U test. b The protein expression of CYP2E1 in peritumoral tissues of HCC patients and normal tissues (n = 6). Data were presented as mean ± SD. t-test. c Representative micrographs of immunofluorescence staining for DAPI (blue), CYP2E1 (red) and Merge in peritumoral tissues of HCC patients and normal tissues. scale bar = 100 μm. d CYP2E1 activity (V2E1) in peritumoral (n = 101) tissues of HCC patients and normal tissues (n = 95). Data were presented as median with min ~ max. Mann–Whitney U test. e Kaplan–Meier (K-M) survival analysis on median survival time of HCC patients with high (n = 36) or low (n = 47) V2E1. Grouped by median. f, g The receiver operating characteristic (ROC) curve of V2E1 for predicting occurrence and prognosis of HCC patients. h The infiltration of M2 macrophages in tumor and peritumoral tissues of HCC patients and normal tissues was evaluated by CIBERSORT, data from TCGA-LIHC database, n = 110 for normal tissues, n = 50 for tumor and peritumoral tissues. Mann–Whitney U test. i Representative micrographs of immunofluorescence staining for DAPI (blue) and CD163 (red) and Merge in tumor and peritumoral tissues of HCC patients and normal tissues, n = 3 for each group, scale bar = 100 μm. j-n Correlations between protein expression of M2 markers (VSIG4, j; STAT6, k; CD163, l; CD14, m; MRC1, n) and tumor maximum diameter (TMD), n = 55 for each group. Spearman correlation test. o-q Correlations between protein expression of M2 markers (CD163, o; MRC1, p; VSIG4, q) and CYP2E1, n = 55 for each group. Spearman correlation test. *P < 0.05, ***P < 0.001 versus Normal group

Next, we investigated the alterations of M2 macrophages in HCC patients. The results showed that M2 macrophages infiltration were significantly increased (Fig. 2h). Immunofluorescence staining was employed to further validate the above findings (Fig. 2i). Of the 20 M2 markers (Supplementary Table S5) analyzed in our previous proteomics data, 8 were detected. Five of these were associated with tumor maximum diameter (TMD) (Fig. 2j–n), and among these, three were correlated with CYP2E1 expression (Fig. 2o–q).

Taken together, these findings suggest that both CYP2E1 expression and M2 macrophage infiltration were significantly elevated in HCC patients and were associated with poor prognosis. Furthermore, there was a significant correlation between the two.

Q11 could inhibit tumor cells phenotype via M2 macrophage

We next investigated whether Q11 could influence tumor cell phenotype through M2 macrophages in vitro. Direct treatment of HepG2 and H22 cells with various concentrations of Q11 showed a weak inhibitory effect on cell viability only at a concentration of 500 μM (Fig. 3a, b). Furthermore, HepG2 and H22 cells co-cultured with M2 macrophages exhibited significant promotion in proliferation (Fig. 3c, d), invasion (Fig. 3e, f), and migration (Fig. 3g), whereas Q11 could reverse these increases and promote apoptosis of HepG2 in a concentration-dependent manner (Fig. 3h).

Fig. 3.

Fig. 3

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Q11 could inhibit the proliferation, invasion, migration and promotes the apoptosis of HepG2 and H22 cells through M2 macrophages a, b The effect of Q11 on the proliferation of HepG2 (a) and H22 (b) cells was determined using CCK-8 assay. t-test. c, d The proliferation of HepG2 (c) and H22 (d) cells co-cultured with THP-1 or J774A.1 M2-conditioned medium (CM) was determined using CCK-8 assay. t-test. e, f Invasion of HepG2 (e) and H22 (f) cells co-cultured with THP-1 or J774A.1 M2-CM was detected by the trans-well assay, and the number of invasive cells was quantified. t-test. g Migration of HepG2 cells co-cultured with THP-1 M2–CM was detected using a scratch assay, and the cell migration rate was quantified. t-test. h Apoptosis of HepG2 cells co-cultured with THP-1 M2–CM was determined via FACS. The apoptosis rate of the control group was normalized as 100%. t-test. *P < 0.05, **P < 0.01, ***P < 0.001, #P < 0.05, ##P < 0.01, ###P < 0.001. In c, e, g, and h: *versus THP-1 M2-CM (-) Q11 (-) group, #versus THP-1 M2-CM ( +) Q11 (-) group; In d and f: *versus J774A.1 M2-CM (-) Q11 (-) group, #versus J774A.1 M2-CM ( +) Q11 (-) group. All data were presented as the mean ± SD

Q11 could inhibit the growth of liver cancer in mice

Next, we investigated the in vivo effects of Q11 on liver cancer. Orthotopic liver tumor models were treated with 5-FU (positive control) and various doses of Q11. The results showed that Q11 could significantly inhibit tumor growth of mice, with the highest dose (30 mg/kg) exhibiting the most potent inhibitory effect (Fig. 4a).

Fig. 4.

Fig. 4

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Q11 could inhibit HCC a Q11 suppresses the tumor growth of orthotopic xenograft HCC mouse that established with H22 cells. Control, n = 8; Model, n = 19; 5-FU (20 mg/kg, n = 16); Q11-L (3.3 mg/kg, n = 19); Q11-M (10 mg/kg, n = 18) and Q11-H (30 mg/kg, n = 19). Time of execution, 21 days. Mann–Whitney U test. b, c Identification of Cyp2e1−/− rats via qPCR (b) and WB (c). d Cyp2e1 knockout suppressed the tumor growth of orthotopic xenograft HCC rat that established with Waker256 cells, Model, n = 9; Cyp2e1−/−, n = 7. Mann–Whitney U test. e CYP2E1 activity in Control, Tumor, and Peritumoral tissues of orthotopic xenograft HCC mouse. n = 8 for Control, n = 10 for Tumor and Peritumoral. Data were presented as mean ± SD. Mann–Whitney U test. f, g Correlation between tumoral, peritumoral CYP2E1 activity and tumor weight, n = 10 for each group. Spearman correlation test. *P < 0.05, **P < 0.01 versus Model group; ##P < 0.01 versus Control group

To explore the efficacy and safety on targeting CYP2E1, we generated systemic Cyp2e1−/− rats and screened them by qPCR and western blotting (Fig. 4b, c). Then, we found that Cyp2e1 knockout could significantly suppress the tumor growth of orthotopic rat liver cancer model (Fig. 4d).

We observed a significant increase in CYP2E1 activity in peritumoral tissues, while it remained unchanged in intratumor (Fig. 4e). Moreover, peritumoral CYP2E1 activity was significantly correlated with tumor weight (r = 0.68, P = 0.03), whereas no correlation was observed between intratumoral CYP2E1 activity and tumor weight (Fig. 4f, g).

Q11 suppressed the M2 macrophages infiltration of liver cancer mouse

Next, we investigated the effects of Q11 on M2 macrophages in liver cancer mouse. We found that Q11 could significantly decrease the expression of M2 macrophage related factors (CD163, TGF-β, and IL-10), and increase the expression of M1 macrophage related factors (CD86 and TNF-α) (Fig. 5a). Immunohistochemical results showed that MRC1 (M2 marker) was decreased and CD86 (M1 marker) was increased after Q11 treatment (Fig. 5b).

Fig. 5.

Fig. 5

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Q11 suppressed the M2 macrophages infiltration of liver cancer mouse a Effect of Q11 on M2 polarization related factors (CD163, TGF-β and IL-10) and M1 polarization related factors (CD86 and TNF-α) in tumor tissues of HCC mice. t-test. b Immunohistochemical staining for MRC1 (M2 marker) and CD86 (M1 marker) in tumor tissues of HCC mice, n = 3 for each group, scale bar = 50 μm, t-test. c Heatmap of different expression genes between Model and Q11 groups, n = 3 for each group. d GO enrichment on downregulated genes between Model and Q11 groups, n = 3 for each group. e Changes on M2 macrophages infiltration of HCC mice among Control, Model and Q11 groups were analyzed by XCELL, n = 3 for each group, t-test. f, g Changes in M2 marker pathway between Control and Model groups (f), Model and Q11 groups (g) were analyzed by GSEA. All data were presented as mean ± SD, *P < 0.05, **P < 0.01 versus Control group; #P < 0.05, ##P < 0.01 versus Model group

To gain a more comprehensive understanding of the effects of Q11 on HCC, transcriptomic was performed. Gene-expression profiling showed highly significant differential expression profiles for 162 genes, including 123 upregulated and 39 downregulated genes after Q11 intervention, confirmed by volcano plot analysis (Fig. 5c, Supplementary Fig. S1). GO enrichment for the downregulated genes in the Q11 group showed that biological processes related to CYP2E1 inhibition mainly involved inflammation, immunity, and metabolism (Fig. 5d). KEGG enrichment on the differentially expressed genes revealed that CYP2E1 inhibition-related signaling pathways involved in chemical carcinogenesis, HCC, and arachidonic acid metabolism, etc. (Supplementary Fig. S2). Subsequently, we found that the M2 macrophages infiltration were significantly higher in model group, while significantly reduced in Q11 group (Fig. 5e). Gene set enrichment analysis (GSEA) analysis on the M2 marker pathway further confirmed above finding (Fig. 5f, g).

Q11 inhibits M2 macrophage polarization by modulating CYP2E1/( ±)9(10) -DiHOME or ( ±)12(13)-DiHOME/PPARγ axis

To investigate the mechanism of how Q11 inhibits macrophage M2 polarization, we conducted a transcriptional regulation analysis on M2 markers that exhibited significant alterations in M2 macrophages after Q11 treatment. The results showed that STAT6, STAT1, and PPARγ were closely associated with the regulation on M2 markers (Fig. 6a). Moreover, protein–protein interaction analysis in STRING database exhibited a possible interaction between CYP2E1 and PPARγ (Fig. 6b). Given that CYP2E1 is a metabolic enzyme and PPARγ is a ligand-inducible nuclear receptor, we hypothesized that Q11 might decrease the levels of certain CYP2E1-derived metabolites in M2 macrophages, thereby attenuating PPARγ activation and subsequently affecting the transcription of M2 markers.

Fig. 6.

Fig. 6

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Q11 inhibits M2 macrophage polarization through CYP2E1/ ( ±)9(10) -DiHOME or ( ±)12(13)-DiHOME/PPARγ axis a Regulatory network diagram between M2 markers (inner circle) and their regulatory factors (outer circle) from the “Cistrome Data Browser” were visualized by “Cytoscape”. b Protein–protein interaction between CYP2E1 and PPARγ according to the STRING database. c Ranking of binding affinities between PPARγ and decreased metabolites in Q11 group compared to M2 group, as well as the fold change. Affinity < -7 indicates strong binding. t-test. d Effect of PPAR-γ high-affinity metabolites on macrophage M2 polarization. Concentration of compounds were 10 μM and co-culture with J774A.1 cells for 24 h. Data were presented as mean ± SD, *P < 0.05, **P < 0.01, versus Control group. e Relative contents of ( ±)9(10)-DiHOME and ( ±)12(13)-DiHOME in the M2 group and Q11 group. f Molecular docking schematic of ( ±)9(10)-DiHOME and ( ±)12(13)-DiHOME with PPARγ. (g) Effect of ( ±)9(10)-DiHOME, ( ±)12(13)-DiHOME, GW9662 (PPARγ antagonist), and rosiglitazone (PPARγ agonist) on ARG1. *P < 0.05, ***P < 0.001 versus A group; ##P < 0.01, ###P < 0.001versus B group; $$P < 0.01, $$$P < 0.001 versus C group; &&&P < 0.001 versus E group. t-test

To verify above hypothesis, we performed metabolomic analysis on J774A.1-induced M2 macrophages. Molecular docking analysis on metabolites with decreased levels in the Q11 group exhibited a large number of compounds with high binding affinities on PPARγ (Fig. 6c, Supplementary Table S6). Furthermore, we found that only ( ±)9(10)-DiHOME (9-dHOME) and ( ±)12(13)-DiHOME (12-dHOME) could promote M2 polarization (Fig. 6d). After confirming the decreased levels of these two metabolites and their stable binding to PPARγ (Fig. 6e, f), cell-based experiments showed that 9-dHOME and 12-dHOME could promote M2 polarization, and these effects could be reversed by GW9662, a PPARγ antagonist (Fig. 6g). These results suggest that Q11 could inhibit M2 polarization by suppressing the production of 9-dHOME and 12-dHOME, which were activators of PPARγ. Additionally, KEGG database showed that 9-dHOME and 12-dHOME were CYP2E1 metabolites (Supplementary Figs. S3, S4). These findings suggest that the inhibitory effect of Q11 on M2 polarization was due to the inhibition of CYP2E1 metabolites (9-dHOME, 12-dHOME), which leads to a decrease in the level of endogenous PPARγ agonists, resulting in a decrease in the agonistic effect of PPARγ.

In summary, Q11 could inhibit M2 macrophage polarization by suppressing the fatty acid-PPARγ pathway, thereby exerting anti-tumor effects through modulation of the TME. The overall mechanism is illustrated in the diagram (Fig. 7).

Fig. 7.

Fig. 7

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Schematic representation of the anti-HCC mechanism of Q11 through M2 macrophages

Discussion

Although there have been several studies on the role of CYP2E1 in HCC, there are several limitations. For example, some studies have demonstrated that knocking out or inhibiting CYP2E1 could inhibit DEN-induced liver cancer [5, 27]. These findings mainly indicate the role of CYP2E1 in the metabolism of the procarcinogen DEN. Additionally, some studies have focused only on the role of CYP2E1 in tumor cells and have neglected its role in other cells in TME [31, 40]. In this study, an animal orthotopic xenograft tumor model was used to circumvent the influence of CYP2E1-mediated procarcinogens activation. Furthermore, studies on the role of CYP2E1 in macrophages provide a comprehensive understanding of the relationship between CYP2E1 and tumorigenesis.

CYP2E1 expression was significantly higher in the peritumoral tissues compared to the intratumoral tissues within the TME. On the one hand, this may be attributed to the more pronounced infiltration of macrophages in the peritumoral tissues compared to the intratumoral tissues. On the other hand, given that CYP2E1 is expressed in various cell types, its expression patterns in different cell populations may vary, including macrophages.

Numerous studies have reported a close association between CYP2E1 and fatty acid metabolism [41, 42]. Additionally, PPARγ can be activated by several fatty acid substrates [43]. Thus, fatty acids may be key mediators of the interaction between CYP2E1 and PPARγ. However, due to the unique structure of fatty acids, this could be a meaningful but challenging endeavor.

Our study has several limitations. Although, our study has shown that inhibiting CYP2E1 can suppress macrophage polarization, the TME is a highly heterogeneous environment composed of fibroblasts, immune cells, stromal cells, microvessels, biomolecules, and extracellular matrix. The impact of Q11 on these cells warrants further study. In addition, the therapeutic efficacy of Q11 against HCC needs to be further validated in more animal models and clinical trials.

In summary, our findings indicate that Q11 could suppress M2 macrophage polarization by modulating the CYP2E1/( ±)9(10)-DiHOME or ( ±)12(13)-DiHOME/PPARγ axis, and thereby exerting anti-HCC effects. These results suggest that CYP2E1 may be a potential therapeutic target for HCC, and its inhibitor Q11 may be a potential drug for the treatment of HCC.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 6107 KB) (6MB, docx)

Acknowledgements

We thank the Beijing Proteomics Center for quantitative protein analysis, and the National Natural Science Foundation of China for their financial support.

Author contributions

*CZ-Z and YF contributed equally to this work. All authors contributed to the study conception and design. Material preparation, data collection and analysis, write the original draft: CZ-Z and YF; Methodology, formal analysis and investigation: MX-G, YH-G, YY-G; Supervision: LM-T, YR-X, JZ, QW, NG, HW-X; Design experiments and funding acquisition: HL-Q. All authors read and approved the final manuscript.

Funding

This project was supported by the National Natural Science Foundation of China (NSFC) (No.81872931, No.82073930 and No.82274008), Henan Province Medical Science and Technology Research Project Joint Construction Project (LHGJ20230344).

Data availability

The datasets supporting the conclusions of this article are available in the ProteomeXchange, TCGA, GTEx repository, hyperlink to dataset in https://www.ebi.ac.uk/pride/, accession number: PXD023118, https://portal.gdc.cancer.gov/, https://www.genome.gov/. The datasets supporting the conclusions of this article are included within the article and its additional files. The transcriptomic data for mouse models can be obtained from https://ngdc.cncb.ac.cn/gsa (CRA020716). The transcriptomic profile of J774A.1 cells is accessible at https://www.ncbi.nlm.nih.gov/geo (GSE282907). Metabolomics data can be accessed at https://www.ebi.ac.uk/metabolights/index. (MTBLS11777).

Declarations

Conflict of interest

The authors declare no competing interests.

Ethics approval

This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of Zhengzhou University (ZZUIRB2022‐152).

Consent for publication

All the authors consent to publication.

Footnotes

Publisher's Note

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

Cunzhen Zhang and Yan Fang contributed equally to this work.

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Associated Data

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

Supplementary Materials

Supplementary file1 (DOCX 6107 KB) (6MB, docx)

Data Availability Statement

The datasets supporting the conclusions of this article are available in the ProteomeXchange, TCGA, GTEx repository, hyperlink to dataset in https://www.ebi.ac.uk/pride/, accession number: PXD023118, https://portal.gdc.cancer.gov/, https://www.genome.gov/. The datasets supporting the conclusions of this article are included within the article and its additional files. The transcriptomic data for mouse models can be obtained from https://ngdc.cncb.ac.cn/gsa (CRA020716). The transcriptomic profile of J774A.1 cells is accessible at https://www.ncbi.nlm.nih.gov/geo (GSE282907). Metabolomics data can be accessed at https://www.ebi.ac.uk/metabolights/index. (MTBLS11777).

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