Targeting MED8 enhances sorafenib sensitivity in hepatocellular carcinoma by disrupting epithelial–mesenchymal transition mechanisms

Abstract

HCC is a highly lethal cancer characterised by significant sorafenib resistance, leading to poor patient outcomes. Recent studies have suggested that MED8 plays a role in enhancing tumour resistance to drugs, but its role in drug resistance in HCC has not yet been reported. This study found significantly higher MED8 expression in HCC tissues compared to adjacent noncancerous tissues. Increased MED8 expression in HCC correlates with poorer overall survival. Functional assays demonstrated that reduced MED8 expression inhibited HCC cell proliferation and epithelial–mesenchymal transition, promoted apoptosis, and increased sensitivity to sorafenib. Overexpression of MED8 elevated TRIP4 protein levels. TRIP4 overexpression negated the effects of MED8 knockdown, whereas TRIP4 suppression inhibited MED8-driven EMT. Mechanistically, MED8 interacts with TRIP4, reducing its ubiquitination and stabilising TRIP4 protein levels. Our findings indicate that the MED8–TRIP4 axis plays a role in sorafenib resistance in HCC and could serve as a therapeutic target for HCC treatment.

Highlights

Elevated MED8 is closely associated with sorafenib resistance in HCC cells. MED8 contributes to sorafenib resistance of HCC by enhancing TRIP4-mediated EMT. MED8 interacts with TRIP4’s N-terminal (aa 1–150) and C-terminal (aa 301–581) domains.

Introduction

Hepatocellular carcinoma (HCC) ranks as the sixth most prevalent malignancy and the third leading cause of cancer-related mortality globally¹. Despite being a key targeted therapy for advanced HCC, sorafenib’s clinical efficacy is greatly compromised by drug resistance². Intrinsic and acquired resistance mechanisms undermine sorafenib’s efficacy, leading to disease progression, recurrence, and metastasis^3,4. Consequently, elucidating the mechanisms underlying therapeutic resistance is critical for advancing treatment strategies and improving the clinical outcomes of patients with HCC.

Mediator is a large multiprotein complex comprising the head, middle, and tail regions, along with a kinase module⁵. Mediator complex subunit 8 (MED8), part of the head module, is crucial for RNA polymerase II regulation and mediates interactions with regulatory proteins during mRNA transcription⁶. Multiple studies have indicated that MED8 contributes to tumour progression. MED8 is crucial in the pathogenesis and progression of clear cell renal cell carcinoma (RCC)⁷. A previous study indicated that MED8 inhibits interferon responses in human lung adenocarcinoma A549 cells⁸ and promotes the proliferation of gastric cancer cells⁹. Although MED8 expression is elevated in HCC cells¹⁰, its role and regulatory mechanisms in HCC drug resistance have not yet been reported.

Epithelial–mesenchymal transition (EMT) is a process by which epithelial cells acquire mesenchymal traits¹¹. Cancer cells can exploit the EMT process to enhance tumorigenesis, invasion, metastasis, stemness, therapy resistance, and plasticity¹². Evidence increasingly shows that EMT significantly contributes to sorafenib resistance in HCC, underscoring its role as a key mechanism in HCC treatment resistance^13,14. However, the mechanisms through which EMT affects sorafenib resistance in HCC remain poorly understood. Thyroid hormone receptor interactor 4 (TRIP4), a component of the tetrameric ASC-1 transcriptional cointegrator complex, is essential for EMT¹⁵. TRIP4 regulates cellular processes such as proliferation, differentiation, DNA repair, and apoptosis¹⁶. It affects EMT and contributes to therapeutic resistance in various cancers. Reducing TRIP4 expression has been shown to enhance the sensitivity of BRAF-mutant melanoma cells and xenografts to BRAF-targeted therapies by modulating EMT-related biomarkers¹⁷. Furthermore, TRIP4 promotes EMT and tumour progression and reduces radiosensitivity in cervical cancer through activation of the MAPK, PI3K/AKT, and hTERT pathways¹⁵. However, the role of TRIP4-mediated EMT in sorafenib resistance in HCC and its upstream regulatory mechanisms remain unclear.

To address this, we aimed to examine the role and mechanism of MED8 in the development of sorafenib resistance in HCC.

Materials and methods

Bioinformatics analysis

MED8 mRNA and protein expression data for HCC were obtained from The Cancer Genome Atlas (TCGA) and the Clinical Proteomic Tumour Analysis Consortium (CPTAC) is accessible through the University of Alabama at Birmingham’s Cancer Data Analysis platform, known as UALCAN (https://ualcan.path.uab.edu/analysis.html). A Kaplan-Meier survival analysis for the MED8 gene in HCC was performed using the Kaplan-Meier Plotter tool, which is available at http://kmplot.com/analysis/. The RNA sequencing data and clinical profiles for 371 liver HCC cases were obtained from The Cancer Genome Atlas (TCGA) database, accessible through their portal at https://portal.gdc.cancer.gov/. Gene sets related to the relevant pathways were analysed using the Gene set variation analysis (GSVA) method in the GSVA package. The GSVA algorithm calculates enrichment scores for each sample across various pathways, identifying sample-pathway associations. Pearson correlation analysis was used to assess the relationship between MED8 expression and EMT pathway scores, with statistical analyses and data processing conducted in R.

Tissue specimens

Paraffin-embedded tissue specimens were collected from 98 patients who underwent HCC resection at the Jiangxi Cancer Hospital between January 2016 and December 2018. Fresh HCC tissues and adjacent liver tissues were collected from 40 patients undergoing HCC resection between July 2021 and December 2022 at Jiangxi Cancer Hospital. The study adheres to the ethical principles outlined in the Declaration of Helsinki, ensuring that the basic rights and welfare of all participants are respected and protected. Informed consent was obtained from each patient, and the study protocol received approval from the Ethics Committee of Jiangxi Cancer Hospital (filing number 2022ky308).

Cell lines and culture

Human HCC cell lines (MHCC97H, HCCLM03, and HuH7) were obtained from the National Collection of Authenticated Cell Cultures, Chinese Academy of Sciences, and the HepG2 cell line was sourced from ProCell. The L02 and SMMC7721 cell lines were maintained in our laboratory. Authentication of the cell lines was confirmed via short tandem repeat (STR) sequence analysis, and all cell lines were verified to be free of mycoplasma contamination. Cells were cultured in DMEM with 10% FBS and 1% P/S at 37 °C in a 95% air, 5% CO2 incubator. MHCC97H, HCCLM03, and HepG2 cells were exposed to progressively higher concentrations of sorafenib to induce resistance. Sorafenib-resistant HCC cell lines (MHCC97H/SR, HCCLM03/SR, and HepG2/SR) were established after six months^18–21. Resistant cells were cultured in DMEM with 10% FBS at 37 °C in a 5% CO2 incubator, with the addition of low-dose sorafenib. Drug-resistant cell cultures underwent EMT induction by treating them with 10 ng/mL of TGF-β for 48 h, following an initial 24-h culture under standard conditions.

Other assays

Western blotting, qRT-PCR, and co-immunoprecipitation assays were performed as described previously²². Cellular function assays, such as CCK-8 and EdU staining, were conducted as previously described²³. All primers were synthesised by Sangon Company, the specific primers used for PCR amplification were shown in Supplementary Table S1.

Reagents and antibodies

Reagents and antibodies are described in detail in Supplementary Tables S2 and S3.

Plasmid and shRNA transfection

Sangon Biotech (Shanghai, China) synthesised shRNA-mediated RNA duplexes targeting MED8 and TRIP4. Plasmids for FLAG-MED8, MYC-TRIP4, MYC-TRIP4-Zinc Finger (ZF), MYC-TRIP4-ΔActivating Signal Cointegrator 1 complex Homology (ASCH), and MYC-TRIP4-ASCH were constructed and supplied by Ademan Biotechnology (Nanchang, China). The plasmid MYC-Ub encoding fusion protein was purchased from Miaolingbio (Wuhan, China). HCC cells were transfected with shRNAs or plasmids using Lipofectamine 3000. Stable HCC cell lines transfected with sh-NC, shMED8-1, or shMED8-2 plasmids were generated using puromycin. Supplementary Table S4 contains the sequences of the plasmids and shRNAs utilised in this study. Primers for the plasmids construction are shown in Supplementary Table S5.

Liquid chromatography–tandem mass spectrometry (LC-MS/MS) analysis

MHCC97H/SR cells were transfected with sh-NC or shMED8, and subjected to trypsin digestion and quantification. Approximately 20 μL of 1× electrophoresis buffer was used for the procedure. The sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) was stained using Coomassie brilliant blue for a duration of 3 h. After staining, the gel was thoroughly washed with the elution buffer overnight. After three 15-min washes at 37 °C and 900 rpm, it was vacuum-dried for 30 min. The gel sheet was hydrated with a 2 ng/μL trypsin solution (Thermo Scientific Pierce) for 30 min. Following removal of excess solution, overnight enzymatic digestion was performed at 37 °C. 40 μL distilled water was added, and peptide elution was ultrasonicated for 15 min. The mixture was then SpeedVac-dried, dissolved in 0.1% formic acid, and transferred to HPLC vials to liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis²⁴.

Molecular docking

In order to obtain the crystal structure of the key target, we retrieved the corresponding structural information from the RCSB PDB and UniProt database. After preprocessing the target proteins, which included the removal of water molecules and ligands, molecular docking experiments were conducted using AutoDockTools 1.5.6. The binding affinity between the molecules and the protein targets was assessed based on the binding energy. The results were then visualised in three dimensions using PyMOL 2.5.8 software.

Immunohistochemical analysis

Immunohistochemical (IHC) analysis was performed according to established protocols. The primary antibodies used included anti-MED8 from Proteintech (Wuhan, China). Two pathologists from the Department of Pathology at Jiangxi Cancer Hospital independently evaluated the protein expression intensity in normal adjacent tissues and HCC tissues. Staining intensity was scored based on previously established criteria²⁵.

Cell apoptosis analysis

Apoptosis was evaluated via flow cytometry using Annexin V-FITC/PI Apoptosis Detection kits, following the manufacturer’s guidelines. In brief, 3 × 10^5 cells were seeded into a 6-well plate. Cells were harvested after sorafenib treatment, stained with Annexin V-FITC and propidium iodide (PI) for 15 min at room temperature in the dark, and analysed via flow cytometry. All experiments were conducted at least thrice.

Immunofluorescence analysis

Immunofluorescence analysis followed standard protocols. Briefly, the HCC cells were seeded onto coverslips and cultured for 24 h under different treatment conditions. Direct immunostaining for E-cadherin and vimentin was conducted on HCC cells. Following fixation and permeabilization, the cells were incubated overnight at 4 °C with Alexa-Fluor 594 anti-E-cadherin and Alexa-Fluor 488 anti-vimentin antibodies. Nuclei were stained with DAPI, and all antibodies were diluted in PBS with 1% bovine serum albumin. The cells’ fluorescence intensity was examined using a fluorescence microscope.

Subcutaneous xenograft experiments

A mouse xenograft model, established via subcutaneous injection, evaluated the tumorigenic potential of MHCC97H cells that had undergone stable MED8 knockdown, both in the presence and absence of sorafenib therapy. Six-week-old male BALB/c nude mice were sourced from Beijing Sibeifu Bioscience, Co. Ltd. (Beijing, China). MHCC97H-shNC and MHCC97H-shMED8-1# cell lines were constructed. A total of 1 × 10^7 cells were suspended in 200 μL of PBS and subcutaneously injected into the right side of each mouse. After one week, the treatment group mice received daily intraperitoneal sorafenib injections (30 mg/kg). Tumour dimensions were recorded every 5 days with callipers, and volume (V) was computed using the formula (L × W^2)/2. Upon completion of the study, the mice were euthanized, and the tumours were excised and photographed. All animal experiments were carried out in Kangtai Medical Laboratory Services Hebei Co. Ltd. (KT2024-06–24-01). All experimental protocols concerning the handling of mice were approved by the institutional animal care and use committee of Kangtai Medical Laboratory Services Hebei Co. Ltd.

Proteasome activity assay

Proteasomal activity was evaluated using the Abcam Proteasome Activity Assay Kit (ab107921). MHCC97H/SR, HCCLM03/SR, and HepG2/SR cells were transfected with specific plasmids and shRNA, and subjected to trypsin digestion and quantification. Each 1 mg of protein extract was combined with Assay Buffer to a final volume of 100 μL and added in triplicate to separate wells of a 96-well black plate. Proteasome activity was assessed in each well by monitoring the changes in fluorescence after introducing the proteasome substrate. Excitation and emission were measured at 350/440 nm using an Infinite 200 PRO microplate reader at 37 °C for 25 min.

Statistical analysis

Statistical analyses were conducted using the SPSS 22.0 or GraphPad Prism 9 software, and all results are presented as standard deviation (SD). Statistical comparisons between two groups utilised Student’s t-test, whereas analysis of variance with post-hoc tests was applied for multiple group comparisons. Spearman’s correlation analysis was used to assess the correlations, with statistical significance set at p < 0.05. The software tools used in this study were shown in Supplementary Table S6.

Results

MED8 is highly expressed in HCC and linked to unfavourable clinical prognosis in patients

We used the UALCAN server to assess MED8 expression in HCC and normal tissues to understand its significance in HCC. Analyses of TCGA and CPTAC databases indicated significant MED8 overexpression in HCC compared to noncancerous tissues (Figure 1(A,B)). A total of 98 HCC samples along with their corresponding adjacent tissues were analysed. Immunohistochemical staining indicated an elevated expression of MED8 in 57 out of 98 HCC samples, which accounts for 58.16% (Figure 1(C,D)). Furthermore, 40 newly procured tissue pairs were also assessed. qRT-PCR and western blot assays confirmed a marked increase in MED8 mRNA and protein expression in the HCC tissues as opposed to the surrounding non-neoplastic tissues (Figure 1(E,F)). We evaluated the correlation between MED8 expression and clinicopathological characteristics in HCC patients. Elevated MED8 expression was significantly linked to gender, number of tumours, tumour microsatellite, venous permeation, and advanced TNM stage but showed no significant association with age, HBsAg, tumour size, tumour encapsulation, cirrhosis, or lymph node metastasis (Table 1). Kaplan–Meier survival analysis revealed that patients with HCC with low MED8 expression had significantly better overall survival (OS) rates than those with high MED8 expression (Figure 1(G)). An analysis of HCC cases from TCGA using the Kaplan–Meier Plotter revealed that high MED8 expression correlated with poor OS (Figure 1(H)), supporting our conclusions. Additionally, both univariate and multivariate Cox regression analyses revealed that MED8 expression serves as an independent predictor of poor prognosis in HCC patients (Supplementary Table S7). Overall, these findings suggest that MED8 is upregulated in HCC and correlates with patient prognosis.

Figure 1.

High MED8 expression is associated with poor prognosis in patients with HCC. (A) The TCGA/UALCAN server was used to analyse mRNA expression of MED8 in liver hepatocellular carcinoma (LIHC, ****, p < 0.0001). (B) The CPTAC/UALCAN server was used to analyse protein expressions of MED8 in hepatocellular carcinoma (****, p < 0.0001). (C) The score of MED8 expression in 98 paired HCC tissue sections determined by IHC assay (**, p = 0.0025). (D) Representative IHC image showing the increase in MED8 protein levels in HCC tissue (magnification: 100×, inset magnification: 200×). (E) qRT–PCR analysis of MED8 mRNA levels in HCC tissues and corresponding adjacent tissues (n = 40, p = 0.0008; Student’s t-test). GAPDH was used as a loading control. (F) Western blot analysis of MED8 protein expression in HCC tissues and corresponding adjacent noncancerous tissues (n = 40, p = 0.0030, Student’s t-test). Tubulin was used as a loading control. On the left is a representative western blot image, and on the right is a summary of the statistical data. (G) Kaplan–Meier survival curve of the relationship between MED8 high and low expression groups and the prognosis of HCC patients (n = 98, p<0.0001; Log-rank test). (H) Kaplan–Meier survival curves of prognosis-related MED8 expression in the Kaplan–Meier plotter cohort (n = 364, p = 0.001; Log-rank test).

Table 1.

The association between MED8 expression and clinicopathological characteristics of HCC patients in training and validation cohort.

Characteristic	n	MED8		P-value
		Low expression	High expression
		n (%)	n (%)
Gender
Female	16	11(11.22%)	5(5.10%)	0.0170*
Male	82	30(30.61%)	52(53.06%)
Age
<50	51	21(21.43%)	30(30.61%)	0.8902
≥ 50	47	20(20.41%)	27(27.55%)
HBsAg
Negative	43	20(20.41%)	23(23.47%)	0.4068
Positive	55	21(21.43%)	34(34.69%)
AFP (ng/mL)
<400	61	28(28.57%)	33(33.67%)	0.7600
≥400	37	13(13.27%)	24(24.49%)
Tumour size (cm)
<5	25	14(14.28%)	11(11.22%)	0.0962
≥5	73	27(27.55%)	46(46.94%)
Number of tumours
1	77	40(40.82%)	37(37.76%)	0.0001***
≥2	21	1(1.02%)	20(20.41%)
Tumour microsatellite
Absent	83	41(41.84%)	42(42.86%)	0.0004***
Present	15	0(0%)	15(15.31%)
Tumour encapsulation
Absent	66	25(25.51%)	41(41.84%)	0.2540
Present	32	16(16.33%)	16(16.33%)
Venous permeation
Absent	72	40(40.82%)	32(32.65%)	<0.0001****
Present	26	1(1.02%)	25(25.51%)
Lymph node metastasis
Negative	90	40(40.82%)	50(51.02%)	0.0792
Positive	8	1(1.02%)	7(7.14%)
TNM stage
I	63	39(39.80%)	24(24.49%)	<0.0001****
II-IV	35	2(2.04%)	33(33.67%)
Nontumorous liver
C	89	39(39.80%)	50(51.02%)	0.2107
CH	9	2(2.04%)	7(7.14%)

NOTE: The p values represent probabilities for MED8 expression level between variable subgroups determined by a test. Abbreviations: AFP, a-fetoprotein; HBsAg, hepatitis B surface antigen; TNM, tumour–node–metastasis.

MED8 overexpression correlates with sorafenib resistance in HCC cells

The correlation between MED8 expression and sorafenib sensitivity in HCC cells was evaluated in vitro. qRT-PCR and western blot analyses showed that MED8 levels were elevated in HCC cells compared to noncancerous human liver cells. Among HCC cell lines, MHCC97H showed the highest MED8 levels, whereas HepG2 had the lowest (Figure 2(A–C)). The cell viability upon sorafenib treatment at different concentrations was assessed, and the IC50 values were calculated (Figure 2(D)). Higher MED8 expression correlated with increased resistance to sorafenib, as observed in sorafenib-resistant (SR) HCC cell lines compared to the parent HCC cell line (Figure 2(E,F)). These data suggested that MED8 is highly expressed in HCC cell lines and contributes to their resistance to sorafenib treatment.

Figure 2.

MED8 expression is correlated with sorafenib sensitivity of HCC cells. (A, B) Protein expression of MED8 in noncancerous liver cells and HCC cell lines was analysed by western blotting. (C) The mRNA expression of MED8 in normal liver cells and HCC cell lines was analysed by qRT-PCR. (D) Viabilities of HCC cells were determined in response to different concentrations of sorafenib. Inhibition curves were fitted by nonlinear regression, and sorafenib IC50s were calculated using GraphPad Prism 10. (E, F) Western blotting was used to analyse expression levels of MED8 in sorafenib-resistant (SR) HCC cells. Data represent the mean ± SD of triplicate experiments and were statistically analysed with Student’s t-test, *p < 0.05, **p < 0.01, ***p < 0.001.

Silencing MED8 enhances HCC sensitivity to sorafenib in both in vivo and in vitro studies

The study examined MED8’s influence on HCC sensitivity to sorafenib by treating HCC cells with varying sorafenib concentrations. Cell viability assays revealed that inhibiting MED8 heightened the sensitivity to sorafenib in MHCC97H and HCCLM03 cells (Figure 3(A,B)). Elevated MED8 expression decreased the sensitivity of HepG2 cells to sorafenib (Figure 3(C)). EdU and flow cytometry experiments confirmed that MED8 downregulation enhances sorafenib-induced inhibition of HCC cell proliferation (Figure 3(D,E)) and the induction of HCC cell apoptosis (Figure 3(F,G)). After 35 days, the tumour volumes and weight in the shMED8-1# and sh-NC + sorafenib groups were lower than in the sh-NC group, with the most significant reduction observed in the shMED8-1# + sorafenib group (Figure 3(H–J)). These data suggest that MED8 inhibition enhances the sensitivity of HCC cells to sorafenib.

Figure 3.

Inhibition of MED8 increases sorafenib sensitivity of HCC. (A–C) The IC50 of HCC cells (MED8 knockdown or overexpression) exposed to different concentrations of sorafenib was determined by cell viability experiments. (D, E) The effect of knocking down MED8 on the proliferation rate of HCC cells treated with sorafenib was detected by EdU staining. (F, G) The apoptosis rate of HCC cells with MED8 knockdown was detected by flow cytometric analysis after sorafenib treatment. Data represent the mean ± SD of triplicate experiments and were statistically analysed with Student’s t-test. (H) Representative images of different groups of tumours removed from mice are shown. (I, J) Tumour growth curve and tumour weight were shown. Growth curve showing changes in tumour volume in mice from different groups. Growth was assessed every 5 days beginning from the day of injection and during sorafenib treatment. At the end of the experiment, the tumour was dissected and photographed, and the tumour volume (V) was calculated as follows: V = 0.5 × length × width² and analysed by one-way ANOVA. **p < 0.01.

MED8 modulates EMT, enhancing sorafenib resistance in HCC cells

EMT contributes to drug resistance in various tumours²⁶. We hypothesised that MED8 regulates EMT in HCC cells, thereby contributing to sorafenib resistance. This hypothesis was assessed using RNA-seq data and clinical information from HCC samples obtained from the TCGA database. Our GSVA analysis revealed a positive correlation between MED8 expression and multiple EMT hallmarks (Figure 4(A)). Immunofluorescence analysis and assessment of EMT marker expression post-MED8 KD revealed the correlation between MED8 and EMT features, suggesting that decreased MED8 expression increased E-cadherin and decreased vimentin levels, hinting that MED8 downregulation might suppress EMT in HCC cells (Figure 4(B)). To validate the involvement of MED8 in sorafenib resistance through EMT, we downregulated MED8 in sorafenib-resistant HCC cells and introduced an EMT activator to assess the sorafenib sensitivity of changes. Our findings indicated that reduced MED8 expression increased the sensitivity of drug-resistant HCC cells, but this effect was counteracted by the EMT activator (Figure 4(C–F)). These findings suggest that MED8 modulates EMT, influencing the sensitivity of HCC cells to sorafenib.

Figure 4.

MED8 regulates EMT to promote sorafenib resistance in HCC cells. (A) Pearson correlation analysis of MED8 expression and EMT pathway score in TCGA-LIHC patients. (B) Immunofluorescence analysis of the effect of inhibiting MED8 on the expression of EMT-related proteins (E-cadherin and Vimentin) in HCC cells. (C–F) Sorafenib-resistant HCC cell lines (MHCC97H/SR and HCCLM03/SR) were transfected with interfering plasmid sh-MED8-1# and or EMT activator (TGF-β, 10 ng/mL) for 48 h. (C, E) Western blot analysis was used to observe the expression of EMT-related proteins in each treatment group. (D, F) Cell viability experiments were used to calculate the half-inhibitory concentration (IC50) of sorafenib-treated HCC-resistant cells in each treatment group.

MED8 regulates EMT through TRIP4

To elucidate the role of MED8 in regulating EMT and influencing sorafenib sensitivity in HCC cells, we investigated the genes interacting with MED8 using the STRING interaction network and predicted that TRIP4 and MED8 interact with each other (Figure 5(A)). We carried out triplicate LC-MS/MS analyses of MED8-silenced and control HCC cells. TRIP4 protein expression decreased in MED8-silenced HCC cells (Figure 5(B)). We subsequently examined MED8 and TRIP4 correlations in HCC tissues. Western blot analysis demonstrated high MED8 and TRIP4 expression in HCC tissues, showing a positive correlation between their levels (Figure 5(C–F)). Additionally, we observed that TRIP4 protein expression decreased when MED8 levels decreased and increased when MED8 expression was upregulated (Figure 5(G–I)). To determine whether MED8 regulates EMT via TRIP4, we overexpressed TRIP4 in sorafenib-resistant HCC cell lines with MED8-silenced and analysed the EMT changes by western blotting. TRIP4 upregulation counteracted the inhibition of EMT caused by MED8 downregulation (Figure 5(J,K)). Downregulating TRIP4 in sorafenib-resistant HCC cells with stable MED8 overexpression abrogated the MED8-driven EMT process (Figure 5(L)). These findings suggest that MED8 regulates EMT through TRIP4 in sorafenib-resistant HCC cells.

Figure 5.

MED8 regulates EMT through TRIP4. (A) MED8 interaction network from STRING website. (B) Mass spectrum showing unique peptides of TRIP4 identified by 2D-LC-MS/MS from protein lysates prepared from MHCC97H/SR cells after silencing MED8. (C–E) Representative western blot analysis of MED8 and TRIP4 protein expression in HCC and paired paracancerous tissues (n = 40, T, tumour; NT, non-tumour tissue). (F) Scatter plots showing a positive correlation between MED8 and TRIP4 protein expression levels in 40 HCC samples (n = 40, r = 0.3167, p = 0.0002, Pearson test). (G–I) Western blot analyses were used to detect MED8 and TRIP4 protein expression in cells stably transfected with the shMED8 or pcDNA-MED8 plasmid. (J, K) Western blot analysis confirming MED8 silencing and TRIP4 restoration and their effects on EMT-related biomarkers. (L) Western blot analysis showing the levels of MED8 overexpression and TRIP4 inhibition and their effects on EMT-related biomarkers.

MED8 interacts with TRIP4

The impact of MED8 on TRIP4 expression was assessed by measuring TRIP4 mRNA levels in sorafenib-resistant HCC cells with MED8 overexpression or knockdown using qRT-PCR. The analyses revealed that MED8 does not affect TRIP4 mRNA levels, implying that MED8 regulates TRIP4 expression post-transcriptionally rather than transcriptionally (Figure 6(A)). Immunofluorescence experiments confirmed the co-localisation of TRIP4 and MED8 in sorafenib-resistant HCC cells, supporting their protein-protein interactions (Figure 6(B)). Subsequent co-IP analyses confirmed the interaction between MED8 and TRIP4 (Figure 6(C)). Docking analysis identified interactions between TRIP4 and MED8 (Figure 6(D)). An array of MYC-tagged TRIP4 truncated plasmids was constructed to characterise the structural domains interacting directly with MED8. TRIP4 comprises three primary domains: the N-terminal domain, middle ZF domain responsible for DNA binding, and C-terminal ASCH domain (Figure 6(E)). Using structural information, a truncated TRIP4 mutant was constructed to identify the domain that interacts with MED8. Localisation experiments identified that the N-terminal domain (amino acids 1–150) and the ASCH domain within the C-terminal region (amino acids 301–581) of TRIP4 interact with MED8 (Figure 6(F)). These findings suggest an interaction between MED8 and TRIP4 in sorafenib-resistant HCC cells.

Figure 6.

MED8 interacts with TRIP4. (A) qRT-PCR analysis of MED8 and TRIP4 mRNA expression in HCC/SR. Left, HCCLM03/SR and MHCC97H/SR cells transfected with either shNC or shMED8. Right, HepG2/SR cells transfected with either vector or pcDNA-MED8. Data represent the mean ± SD of triplicate experiments and were statistically analysed with Student’s t-test; ns, not significant, **p < 0.01. (B) MHCC97H/SR and HCCLM03/SR cells were fixed and stained using TRIP4 (green) and MED8 (red) antibodies. Cell nuclei were stained using DAPI (blue). (C) Co-IP test was implemented to identify the protein binding of MED8 and TRIP4 in MHCC97H/SR cells. (D) Top ranked docking conformations and 3D structures of TRIP4 and MED8. TRIP4 and MED8 are displayed in cyan and green, respectively. (E) Diagram of TRIP4 truncated mutant constructs. (F) The N-terminal domain and the ASCH domain within the C-terminal region of TRIP4 interact with MED8. Mapping the TRIP4 domain that interacts with MED8. Constructs for MYC-tagged TRIP4 (full-length or truncated mutants) and FLAG-tagged MED8 were cotransfected into MHCC97H/SR cells. IP was performed using anti-MYC antibodies, and precipitates and input were analysed by western blotting with antibodies to FLAG and MYC.

MED8 stabilises TRIP4 protein expression by inhibiting its proteasomal degradation

Existing literature suggests that MED8 is involved in the formation of a ubiquitin ligase complex²⁷, we hypothesised that MED8 influences TRIP4 degradation and ubiquitination in sorafenib-resistant HCC cells. Our findings indicated that the proteasome inhibitor MG132 inhibited TRIP4 degradation (Figure 7(A–C)), implying that TRIP4 underwent proteasomal degradation in sorafenib-resistant HCC cells. We investigated whether MED8 affects TRIP4 protein stability by regulating proteasomal degradation. MED8 knockdown significantly reduced TRIP4 levels, whereas MG132 treatment completely prevented this reduction (Figure 7(D,E)). In contrast, treatment with MG132 blocked the MED8-mediated upregulation of TRIP4 expression in HepG2/SR cell (Figure 7(F)). To investigate the impact of MED8 expression on TRIP4 half-life, sorafenib-resistant HCC cells were treated with CHX. Our findings indicated that silencing MED8 reduced the half-life of TRIP4 protein, while MED8 overexpression extended it. (Figure 7(G–I)). Together, these data indicate that MED8 regulates TRIP4 protein expression through the ubiquitin-proteasome system. Considering that MED8 is involved in the formation of ubiquitin ligases, this raises the possibility that MED8 may broadly regulate the function of the proteasomal degradation machinery, thereby leading to non-specific effects on TRIP4 expression. To verify this hypothesis, we performed in vitro proteasomal activity assays and discovered that neither upregulation nor downregulation of MED8 affected proteasomal function in sorafenib-resistant HCC cells (Figure 7(J–L)). These findings suggest that altering MED8 does not change overall proteasomal activity but specifically influences the expression of TRIP4. Co-IP analysis demonstrated that MED8 knockdown elevated endogenous TRIP4 ubiquitination, while MED8 overexpression reduced it (Figure 7(M–O)). These results imply that MED8 inhibits the degradation of TRIP4 protein, thus enhancing its stability.

Figure 7.

MED8 stabilises TRIP4 protein expression by suppressing its ubiquitination and degradation. (A–C) TRIP4 protein levels at various times were measured by western blotting after MG132 addition (10 µM) to MHCC97H/SR, HCCLM03/SR, and HepG2/SR cells. (D–F) Western blot analysis of TRIP4 and MED8 protein expression in MHCC97H/SR and HCCLM03/SR cells transfected with shMED8-1# or shNC and HepG2/SR cells transfected with pcDNA-MED8 or control vector, with or without treatment with 10 µM MG132. (G–I) MHCC97H/SR and HCCLM03/SR cells transfected with shMED8-1# or shNC and HepG2/SR cells transfected with pcDNA-MED8 or control vector were subjected to treatment with 20 µg/mL CHX, followed by assessment of TRIP4 protein levels using western blotting. n = 3, **p < 0.01. (J–L) MHCC97H/SR, HCCLM03/SR, and HepG2/SR cells were treated with either shMED8-1# or pcDNA-MED8 for 72 hours, and intracellular proteasome activity was assessed using a proteasome activity fluorometric assay kit. The relative proteolytic activities are presented as average percentages of the total fluorescence intensity of the control group at the end of the assay. Data are shown as mean ± SD, n = 5, with statistical analysis performed using a two-sided Student’s t-test; ns, not significant. (M–O) Lysates of MHCC97H/SR, HCCLM03/SR, and HepG2/SR cells transfected with sh-MED8-1#, FLAG-MED8, and MYC-ubiquitin (MYC-Ub) were analysed by immunoblotting and then immunoprecipitated using anti-TRIP4, followed by probing with anti-MYC.

Discussion

HCC ranks among the deadliest malignant tumours¹, with a high propensity for developing resistance to therapeutic drugs, thereby reducing the effectiveness of sorafenib treatment^2–4. Therefore, understanding the mechanisms underlying sorafenib resistance and identifying new targets for enhancing the sensitivity of HCC cells are crucial.

The 1.4-MDa mediator complex, consisting of 33 subunits, plays a crucial role in several fundamental biological processes^5,28. Current studies have established that other mediator complex subunits akin to MED8 are associated with tumour progression and drug resistance. MED1 affects tamoxifen resistance in breast cancer cells^29,30, while MED19 knockdown reduces proliferation and boosts chemosensitivity to cisplatin in non-small cell lung cancer cells and to adriamycin in breast cancer cells by decreasing autophagy^31,32. Furthermore, silencing of MED23 increased the sensitivity of everolimus-resistant BT474 and MCF-7/ADM breast cancer cells to everolimus treatment³³. MED31 upregulation enhanced the temozolomide resistance in glioma cells³⁴. However, the role and molecular mechanism of action of MED8 in sorafenib resistance in HCC have not yet been reported. This study is the first to investigate the role of MED8 in this context. Our research confirmed that MED8 is overexpressed in HCC tissues, correlating with poor prognosis in patients. MED8 expression was elevated in sorafenib-resistant HCC cell lines and showed a positive correlation with sorafenib resistance. In vivo and in vitro studies showed that decreased MED8 levels suppressed HCC cell growth, enhanced sorafenib responsiveness, and triggered apoptosis, indicating MED8’s potential as a biomarker for sorafenib sensitivity prediction in HCC.

EMT significantly contributes to cancer cell therapeutic resistance¹². Angiopoietin-like protein 1 significantly reduces EMT-driven sorafenib resistance in HCC cells by downregulating Slug expression³⁵. Chen et al. suggested that EMT is linked to sorafenib resistance and increased invasion of hepatoma subpopulations³⁶. Studies have also demonstrated that mediator complex subunits can induce EMT in various tumours. MED28 overexpression significantly enhances EMT and promotes breast cancer progression through the activation of the p-NF-κB/p65 signalling pathway³⁷, while its suppression reduces EMT features in human colorectal cancer cells³⁸. Lu et al. MED1 downregulation significantly inhibited breast cancer cell metastasis and EMT³⁹. More importantly, the knockdown of MED8 weakened the migration of HCC cells¹⁰. However, the role of MED8 in the regulation of EMT-mediated sorafenib resistance in HCC remains unclear. Our experiments demonstrated that silencing MED8 expression reversed EMT in sorafenib-resistant HCC cell lines, except when an EMT activator was present. These findings imply that MED8 modulates EMT and affects the sensitivity of targeted therapies in HCC.

TRIP4 is essential for the regulation of cell migration, invasion, and EMT¹⁵. Lu et al. reported that LINC00668 promotes the migration and invasion of non-small cell carcinoma through TRIP4 upregulation⁴⁰. Furthermore, the silencing of miR-518-3p markedly enhanced colorectal cancer invasion and migration through the upregulation of TRIP4 ⁴¹. TRIP4 influences anti-BRAF drug sensitivity via EMT in BRAF-mutated melanoma cells and xenografts¹⁷. However, the upstream mechanism through which TRIP4 regulates sorafenib resistance in HCC remains unclear. Our study found elevated MED8 and TRIP4 expression in HCC, with a positive correlation between their levels. MED8 knockdown reduced TRIP4 protein expression. In vitro experiments demonstrated that TRIP4 upregulation mitigated the EMT-related changes induced by MED8 knockdown, whereas TRIP4 downregulation reversed EMT induced by MED8 overexpression. These findings suggested that TRIP4 is a key mediator of MED8-induced EMT in sorafenib-resistant HCC cells.

We performed a detailed analysis of the molecular mechanism of MED8-mediated regulation of TRIP4 expression. Studies have reported that MED8 is a crucial regulatory factor of RNA polymerase and that its primary function is to connect RNA polymerase II with regulatory proteins during mRNA transcription⁴². However, in this study, MED8 did not affect TRIP4 mRNA expression but rather regulated the TRIP4 protein levels. Mammalian MED8 interacts with Elongin BC and assembles with Cul2 and Rbx1 to form a ubiquitin ligase²⁷. Studies have demonstrated that ubiquitination and deubiquitination are closely associated with the progression of HCC, especially in therapy resistance, this agrees with our previous reports^43,44. More importantly, ubiquitination is a critical mechanism for modulating TRIP4 protein expression⁴⁵. We hypothesised that MED8 stabilised TRIP4 expression by influencing its ubiquitination. Our findings demonstrate that MED8 directly interacts with TRIP4 in HCC cells, resulting in TRIP4 degradation via the ubiquitin-proteasome pathway. Inhibition of proteasome function by MG132 did not alter TRIP4 protein expression, regardless of changes in MED8 protein levels. Blocking protein synthesis using CHX slowed the degradation of TRIP4 in HCC cells, which was upregulated by exogenous MED8, suggesting that MED8 was involved in TRIP4 proteasomal degradation. Meanwhile, MED8 knockdown increases TRIP4 ubiquitination, and MED8 overexpression significantly inhibits TRIP4 ubiquitination; however, altering MED8 expression does not affect the activity of the ubiquitin-proteasome system. In addition, in the present study, we demonstrated for the first time that MED8 interacts with the N-terminal domain (amino acids 1–150) and the ASCH domain within the C-terminal region (amino acids 301–581) of TRIP4. It will be intriguing to explore the mechanisms by which MED8 regulates TRIP4 ubiquitination in future studies. Although our current findings identify MED8 as a potential novel target for reversing sorafenib resistance in hepatocellular carcinoma—possibly through its ability to stably interact with partner proteins—the precise molecular mechanism remains to be fully clarified. Two plausible models can be proposed: (1) Direct mechanism – MED8 may exert deubiquitinase-like effects by directly occupying the ubiquitination site of its interacting protein, thereby preventing ubiquitin attachment; (2) Indirect mechanism – MED8 may act as a steric regulator by binding to a domain adjacent to the ubiquitination site(s) of its binding partner, physically shielding the site and thus impeding ubiquitin conjugation. Further investigation is required to distinguish between these possibilities. Collectively, our data indicate that MED8 inhibits the ubiquitination and degradation of TRIP4, thereby regulating its protein abundance, promoting EMT, and ultimately leading to sorafenib resistance in HCC (Figure 8). The novel identified MED8-TRIP4 axis provides new insight for HCC therapy.

Figure 8.

Diagram of the regulatory mechanism of MED8 promoting sorafenib resistance in HCC. This is a proposed model by which MED8 inhibits the ubiquitination and degradation of TRIP4, thereby regulating its protein abundance, promoting EMT, and ultimately leading to sorafenib resistance in HCC cells.

Funding Statement

The study was supported by the National Natural Science Foundation of China (No.82260467), the Provincial Natural Science Foundation of Jiangxi (No.20224BAB206118), and The Open Fund for Scientific Research of Jiangxi Cancer Hospital (No.2021K05) to Q.T.; the Provincial Natural Science Foundation of Jiangxi (No.20224BAB206061), The “Five-level Progressive” talent cultivation project of Jiangxi Cancer Hospital & Institute (No.WCDJ2024YQ05), and The Open Fund for Scientific Research of Jiangxi Cancer Hospital (No.2021J12) to M.L.

Author contribution statement

CRediT: Ming Li: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Software, Validation, Visualization, Writing – original draft; Xiaoxiang You: Methodology, Software; Tinghao Yuan: Data curation, Formal analysis, Validation; Jun He: Formal analysis, Visualization; Zhiliang Xu: Formal analysis; Siqin Liang: Formal analysis, Software; Lei Mao: Investigation, Methodology; Anan Jin: Project administration, Supervision; Xinwen Zhou: Methodology; Bo Yi: Formal analysis, Visualization; Jingao Li: Project administration, Supervision, Writing – review & editing; Qiang Tu: Conceptualization, Funding acquisition, Supervision, Writing – review & editing.

Disclosure statement

The authors report no conflicts of interest.

Ethics approval and consent to participate

Informed consent was obtained from each patient, and the study protocol received approval from the Ethics Committee of Jiangxi Cancer Hospital (filing number 2022ky308).

Consent for publication

Not applicable.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.