RNA-seq data and associated clinical records for HCC (n = 374) and normal liver tissues (n = 50) were retrieved from The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/). MED10 expression patterns across multiple tumor types were further evaluated using the TIMER2.0 platform (http://timer.cistrome.org/) [14].

TCGA-LIHC patient data were stratified into high- and low-MED10 expression groups based on median expression values. GSEA was conducted using the clusterProfiler R package (version 4.10.0, https://bioconductor.org/packages/clusterProfiler/) to delineate functionally relevant pathways between these groups [15]. Statistical significance was defined as p 0.05, with a false discovery rate threshold of 0.25 and a normalized enrichment score absolute value $>$1.

Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed using the Metascape platform (https://metascape.org/gp/index.html#/main/step1) [16].

To investigate potential functional associations, we generated a PPI network using the STRING database (https://cn.string-db.org/) by analyzing MED10 alongside cell cycle regulatory genes [17].

Target genes of the transcription factor, c-Myc, were predicted using the TRRUST (https://www.grnpedia.org/trrust/), CHEA (https://maayanlab.cloud/chea3/), ENCODE (https://www.encodeproject.org), and CHIP Atlas (https://chip-atlas.org/) databases [18, 19, 20, 21]. Biological processes involved in c-Myc expression were predicted using the TRRUST database.

Bioinformatic analysis utilizing the JASPAR database (http://jaspar.genereg.net) [22] predicted the presence of c-Myc response elements within the promoter sequences of cyclin-dependent kinase 4 (CDK4), cyclin-dependent kinase 6 (CDK6), and cyclin D1 (CCND1).

This retrospective analysis utilized archived paraffin-embedded tissues and clinical records from 215 HCC patients who underwent surgical resection at Ningxia Medical University General Hospital between 2010–2020. The study cohort was selected based on strict inclusion criteria: (1) histologically confirmed HCC diagnosis, (2) treatment-naïve status prior to surgery, (3) availability of complete clinical datasets, and (4) paired tumor and normal liver tissue samples (collected $>$2 cm from tumor margins). Exclusion criteria: (1) combination with other malignant tumors; (2) combination with cardiovascular and respiratory disease. The study was carried out in accordance with the guidelines of the Declaration of Helsinki. The Institutional Review Board of Ningxia Medical University approved all protocols, and written informed consent was obtained from each participant prior to sample collection.

Antibodies against the following were used: p-RAF1 (9427, Cell Signaling Technology, Danvers, MA, USA), MED10 (DF13141, Affinity, Nanjing, Jiangsu, China), E-cadherin (60335-1-Ig, Proteintech, Wuhan, Hubei, China), p-ERK1/2 (AF1015, Affinity, Nanjing, Jiangsu, China), Cyclin D1 (ab16663, Abcam, Cambridge, Cambridgeshire, UK), RAF1 (ab181115, Abcam, Cambridge, Cambridgeshire, UK), MEK1/2 (9122, Cell Signaling Technology, Danvers, MA, USA), Vimentin (PA1-16759, Invitrogen, Carlsbad‌, CA, USA), ERK1/2 (11257-1-AP, Proteintech, Wuhan, Hubei, China), CDK6 (ab222395, Abcam, Cambridge, Cambridgeshire, UK), matrix metallopeptidase 13 (MMP13, ab39012, Abcam, Cambridge, Cambridgeshire, UK), c-Myc (5605, Cell Signaling Technology, Danvers, MA, USA), N-cadherin (66219-1-Ig, Proteintech, Wuhan, Hubei, China), p-MEK1/2 (9154, Cell Signaling Technology, Danvers, MA, USA), CDK4 (ab108357, Abcam, Cambridge, Cambridgeshire, UK), marker of proliferation ki-67 (Ki67, 9027, Cell Signaling Technology, Danvers, MA, USA) and $\beta$-actin (ab213262, Abcam, Cambridgeshire, Cambridge, UK).

Following dewaxing of the paraffin-embedded tissue sections in xylene, a graded alcohol series was used for rehydration. To expose target antigens, the sections were microwaved in sodium citrate buffer (C1010, Solarbio, Beijing, China). After cooling to room temperature, the samples were rinsed three times with phosphate-buffered saline (PBS, P1010, Solarbio, Beijing, China) and then treated with a peroxidase-blocking solution (PV-9001, ZSGB-BIO, Beijing, China) for 10 minutes. Subsequent PBS washes were performed before blocking nonspecific binding sites with 5% bovine serum albumin (BSA, SW3015, Solarbio, Beijing, China) for an additional 10 minutes. Primary antibody incubation was carried out overnight at 4 °C, followed by PBS washes and a 1-hour room-temperature incubation with secondary antibodies (PV-9001, ZSGB-BIO, Beijing, China). The primary antibodies used were as follows: MED10 (1:100, DF13141; Affinity, Nanjing, China), Ki67 (1:200, 9027, Cell Signaling Technology, Danvers, MA, USA). Following another PBS wash, the sections were developed using a 3,3’-Diaminobenzidine (DAB) kit (ZLI9018, ZSGB-BIO, Beijing, China), counterstained with hematoxylin (G1120, Solarbio, Beijing, China), dehydrated through an alcohol gradient, cleared in xylene, and finally mounted for microscopic imaging. Two independent pathologists assessed immunohistochemical staining, scoring both the proportion of positive cells (1–3) and staining intensity (0–4). The product of these scores yielded a composite value, with the median score serving as the threshold to classify samples into low- and high-expression groups.

The tissue sections were first dewaxed in xylene and rehydrated using a graded alcohol series. Antigen retrieval was performed by heating the sections in sodium citrate buffer (C1010, Solarbio, Beijing, China). Afterward, the tissue sections were incubated with an endogenous peroxidase blocker (PV-9001, ZSGB-BIO, Beijing, China) for 10 minutes, followed by permeabilization with 0.3% Triton X-100 (T8200, Solarbio, Beijing, China) for 20 minutes. Nonspecific binding sites were then blocked with 5% BSA (SW3015, Solarbio, Beijing, China) for 1 hour. The sections were incubated with the primary antibody at 4 °C overnight, followed by a one-hour room-temperature incubation with a fluorescent secondary antibody. The primary antibodies used were as follows: MED10 (1:150, DF13141, Affinity, Nanjing, Jiangsu, China), CDK6 (1:150, ab222395, Abcam, Cambridge, UK), CDK4 (1:250, ab108357, Abcam, Cambridge, UK), Cyclin D1 (1:50, ab16663, Abcam, Cambridge, UK). Nuclei were counterstained with 4’,6-diamidino-2-phenylindole (DAPI, C0065, Solarbio, Beijing, China), and the slides were mounted for imaging using an inverted fluorescence microscope (DMi8, Leica, Wetzlar, Hesse, Germany).

The human hepatoma cell lines MHCC97L, MHCC97H, HCCLM3, Hep3B, Huh-7, and SK-Hep-1, along with the immortalized hepatocyte line LO2, were kindly provided by the Institute of Liver Diseases at Fudan University (Shanghai, China). All cell lines were maintained in Dulbecco’s modified Eagle medium (DMEM; VivaCell, Shanghai, China) supplemented with 10% fetal bovine serum (FBS, ExCell Bio, Shanghai, China) and 1% penicillin-streptomycin (P1400, Solarbio, Beijing, China) under standard culture conditions (37 °C, 5% CO₂). Prior to experimentation, cell line authenticity was confirmed through short tandem repeat (STR) profiling, and mycoplasma contamination testing yielded negative results.

For MED10 overexpression (ov-MED10 group), HCCLM3 cells were transduced with the lentiviral construct PDS279-PL-CMV-GFP-CC-DB-Puro-MED10, while control cells received the empty vector PDS279-pL-CMV-GFP-cc-dB-puro. MED10 knockdown (shMED10) was achieved using the lentiviral vector VP013-U6-MCS-CMV-ZsGreen-PGK-PURO-MED10-SHRNA, with the VP013-U6-MCS-CMV-ZsGreen-PGK-PURO vector serving as the shcontrol. All lentiviruses (Tsingke Biotech, Beijing, China) were transfected at 30–50% cell confluence using Lipo3000 (Invitrogen, Carlsbad‌, CA, USA) per manufacturer protocols. After 14 h, the viral medium was replaced with fresh complete medium. Transduced cells were selected with 2 µg/mL puromycin (P8230, Solarbio, Beijing, China) for 4–5 days, with transduction efficiency verified via green fluorescence microscopy. Where indicated, ov-MED10 cells were treated with 10 µM LY3009120 (S7842, Selleck Chemicals, Houston, TX, USA). MED10 targeting sequences are provided in Supplementary Table 1.

Total RNA was isolated from cells using the MolarPure Cell/Tissue Total RNA Kit (19221ES, Yeasen Biotechnology, Shanghai, China). cDNA synthesis was performed with PrimeScript RT Master Mix (RR036A, Takara Bio, Kyoto, Japan), followed by RT-qPCR analysis using TB Green Premix Ex Taq II (RR820A, Takara Bio, Kyoto, Japan) on a validated thermal cycler. Gene expression was calculated using the 2^{-Δ⁢Δ⁢Ct} method. The primers used to amplify these genes (Table 1) were designed and synthesized by Tsingke Biotech (Beijing, China).

The total protein content was isolated from HCC tissues and cultured cells using radioimmunoprecipitation (RIPA) lysis buffer (P0013B, Beyotime Biotechnology, Shanghai, China) supplemented with protease inhibitors, phosphatase inhibitors, and phenylmethylsulfonyl fluoride (KGB5303-100, KeyGEN BioTECH, Nanjing, Jiangsu, China). After 20 min ice incubation, lysates were centrifuged (12,000 g, 10 min, 4 °C) to remove debris. Protein concentrations were quantified via bicinchoninic acid (BCA) assay (KGB2101-100, KeyGEN BioTECH, Nanjing, Jiangsu, China), followed by denaturation with loading buffer at 100 °C for 10 min. Samples were separated by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and transferred to polyvinylidene fluoride (PVDF) membranes. Membranes were blocked for 1 h at room temperature with 5% BSA or skim milk, then incubated with primary antibodies overnight at 4 °C. The primary antibodies used were as follows: p-RAF1 (1:1000, 9427, Cell Signaling Technology, Danvers, MA, USA), MED10 (1:1500, DF13141, Affinity, Nanjing, Jiangsu, China), E-cadherin (1:2000, 60335-1-Ig, Proteintech, Wuhan, Hubei, China), p-ERK1/2 (1:2000, AF1015, Affinity, Nanjing, Jiangsu, China), Cyclin D1 (1:1000, ab16663; Abcam, Cambridge, Cambridgeshire, UK), RAF1 (1:1000, ab181115; Abcam, Cambridge, Cambridgeshire, UK), MEK1/2 (1:1000, 9122, Cell Signaling Technology, Danvers, MA, USA), Vimentin (1:1500, PA1-16759, Invitrogen, Carlsbad‌, CA, USA), ERK1/2 (1:3000, 11257-1-AP, Proteintech, Wuhan, Hubei, China), CDK6 (1:1000, ab222395, Abcam, Cambridge, Cambridgeshire, UK), MMP13 (1:2000, ab39012, Abcam, Cambridge, Cambridgeshire, UK), c-Myc (1:1000, 5605, Cell Signaling Technology, Danvers, MA, USA), N-cadherin (1:2000, 66219-1-Ig, Proteintech, Wuhan, Hubei, China), p-MEK1/2 (1:1000, 9154, Cell Signaling Technology, Danvers, MA, USA), CDK4 (1:1500, ab108357, Abcam, Cambridge, Cambridgeshire, UK), and $\beta$-actin (1:3000, ab213262, Abcam, Cambridge, Cambridgeshire, UK). Following three TBST (T1086, Solarbio, Beijing, China) washes, membranes were probed with Goat Anti-Rabbit IgG H&L (HRP) (1:5000, ab6721, Abcam, Cambridge, Cambridgeshire, UK) or Goat Anti-Mouse IgG H&L (HRP) (1:5000, ab205719, Abcam, Cambridge, Cambridgeshire, UK) for 1 h at room temperature. Protein bands were visualized using an enhanced chemiluminescence plus kit (PE0010, Solarbio, Beijing, China) with chemiluminescence imaging.

500 µL of DMEM supplemented with 10% FBS was placed in the lower chamber of a Costar Transwell system (Corning, New York, NY, USA), while the upper chamber received 200 µL of serum-free DMEM containing 8 $\times$ 10⁴ cells. Following 48 h incubation, non-migratory cells on the upper membrane surface were removed by gentle swabbing. Migrated cells on the lower surface were fixed with 4% paraformaldehyde (15 min), stained with 0.1% crystal violet (30 min), and imaged using an inverted fluorescence microscope (DMi8, Leica, Wetzlar, Hesse, Germany). Cell counts were quantified from five random fields per membrane using ImageJ software (version 1.8.0, National Institutes of Health, Bethesda, MD, USA).

Confluent cell monolayers in 6-well plates were wounded using a sterile 200-µL pipette tip. After gentle PBS washing to remove debris, cells were maintained in serum-free medium. Wound closure was documented at 0 and 48 hours usingan inverted fluorescence microscope (DMi8, Leica, Wetzlar, Hesse, Germany).

Cell proliferation was assessed using the Cell Counting Kit-8 (CCK-8) assay (B34304; Biomake, Beijing, China). HCC cells (2000 cells/well) were seeded in 96-well plates and cultured at 37 °C. At designated timepoints (12, 24, 48, 72, 96 h), 10 µL CCK-8 reagent in 100 µL DMEM was added per well. Following 2 h incubation, absorbance at 450 nm was measured using a microplate reader, with background subtraction from control wells.

Cells were seeded in 6-well plates at 500 cells/well and cultured for 14 days. Resulting colonies were fixed with 4% paraformaldehyde (15 min), PBS-washed, and stained with 0.1% crystal violet (30 min). Colony quantification was performed using ImageJ software (version 1.8.0, National Institutes of Health, Bethesda, MD, USA).

Cell proliferation was assessed using the BeyoClick™ EdU-555 Kit (C0075S; Beyotime, Shanghai, China). HCC cells grown in 24-well plates for 24 h were pulse-labeled with 10 µM EdU at 37 °C for 2 h. After fixation with 4% PFA (15 min) and permeabilization with 0.3% Triton X-100 (10 min), Click reaction solution was applied for 30 min in darkness. Nuclei were counterstained with Hoechst 33342 (5 min), and images acquired using an inverted fluorescence microscope (DMi8, Leica, Wetzlar, Hesse, Germany).

HCC cells were harvested, washed in cold PBS, and centrifuged at 1000 $\times$g. After supernatant removal, pellets were fixed in 70% ethanol (4 °C, 12 h) and stained using a cell cycle analysis kit (C1052; Beyotime, Shanghai, China). DNA content was subsequently quantified via flow cytometry (ACEA Biosciences, San Diego, CA, USA).

Female BALB/c-nu nude mice (4–5-week-old) were sourced from Beijing Weitong Lihua Co., Ltd. (Beijing, China) and raised under standard conditions in a specific pathogen-free environment. Twenty-four nude mice were randomly divided into four experimental groups (6 mice/group): (1) control (injected with HCCLM3^control cells), (2) ov-MED10 (injected with HCCLM3^ov-MED10), (3) shcontrol (injected with Hep3B^shcontrol), and (4) shMED10 (injected with Hep3B^shMED10) groups. Subcutaneous xenograft tumors were established by injecting 150 µL of a cell suspension containing 5 $\times$ 10⁶ respective HHC cells into the left axillary region of each mouse. On day 28 post-inoculation, all nude mice were anesthetized through intraperitoneal administration of sodium pentobarbital (3%, 50 mg/kg) and subsequently euthanized via cervical dislocation following confirmation of deep anesthesia. The volume of the removed tumor was measured, weighed, and photographed. Tumor volumes were calculated using the formula: volume = 0.5 $\times$ length $\times$ width². All animal experiments were conducted in accordance with protocols approved by the Medical Ethics Review Committee of Ningxia Medical University.

Statistical analyses were performed using SPSS (version 26.0, IBM, Armonk, NY, USA) and GraphPad Prism (version 9.4.0, San Diego, Boston, MA, USA). Normally distributed continuous variables were compared between two groups with Student’s t-test and among multiple groups with ANOVA. Associations between MED10 expression and clinicopathological features were evaluated using t-tests, Wilcoxon rank-sum, chi-square, or Fisher’s exact tests. Survival curves were generated by Kaplan-Meier analysis with log-rank testing. MED10’s diagnostic utility was assessed through receiver operating characteristic (ROC) curve analysis, while Cox proportional hazards regression identified survival predictors. A MED10-based prognostic nomogram was developed and validated using calibration curves. Statistical significance was defined as p 0.05.

Initial analysis of MED10 expression across multiple cancers using TIMER2.0 revealed significant upregulation in tumor tissues, particularly hepatocellular carcinoma (HCC), versus normal counterparts (Fig. 1A). TCGA database analysis further confirmed elevated MED10 expression in HCC tissues relative to both paired and unpaired adjacent normal liver samples (Fig. 1B,C). ROC curve assessment demonstrated strong diagnostic potential for HCC (area under the curve (AUC) = 0.897, Fig. 1D). Analysis of MED10’s clinical relevance in HCC demonstrated significant correlations between its expression and key clinicopathological features, including body mass index, alpha-fetoprotein (AFP) levels, T stage, histologic grade, and vascular invasion (Table 2). Kaplan-Meier analysis demonstrated markedly inferior overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI) in HCC patients with elevated MED10 expression compared to low-expression cohorts (Fig. 1E–G). Univariate and multivariate Cox regression analyses consistently established MED10 as an independent prognostic determinant (Fig. 1H,I). We subsequently developed a clinical nomogram incorporating MED10 expression with eight clinicopathological parameters—gender, age, Child-Pugh grade, AFP level, T stage, histologic grade, pathologic stage, and vascular invasion—to predict 1-, 3-, and 5-year OS probabilities (Fig. 1J). The model’s precision was validated through calibration curves showing exceptional concordance between predicted and observed survival rates across all timepoints (Fig. 1K), collectively confirming MED10’s clinical utility as a prognostic biomarker.

Fig. 1.

MED10 overexpression in HCC correlates with poor prognosis. (A) Expression levels of MED10 across pan-cancer. The red box indicates higher expression of MED10 in liver hepatocellular carcinoma (LIHC) compared to adjacent normal tissues. (B,C) Elevated MED10 in HCC versus adjacent normal liver tissues. (D) Diagnostic receiver operating characteristic (ROC) curve for MED10 in HCC (area under the curve (AUC) = 0.897). (E–G) Survival analyses showing reduced overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI) in high-MED10 expressors. (H,I) Univariate and multivariate Cox regressions identifying MED10 as independent prognostic factor. (J) Prognostic nomogram predicting 1-/3-/5-year OS. (K) Nomogram calibration curves. (L) Representative Immunohistochemistry (IHC) of MED10 in HCC and normal tissues. Scale bars: 200 μm (top) and 50 μm (bottom). (M) Quantitative IHC scoring in 41 paired samples. (N) Western blotting confirmation of MED10 overexpression. *p 0.05; **p 0.01; ***p 0.001.

This retrospective study validated public database findings through experimental analysis. IHC confirmed significantly elevated MED10 expression in HCC versus adjacent liver tissues (Fig. 1L–N). Western blotting corroborated these results, demonstrating increased MED10 levels in HCC specimens consistent with TCGA data (Fig. 1L–N). Among 215 HCC cases stratified by median immunohistochemistry scores, high MED10 expression correlated significantly with elevated aspartate aminotransferase levels, larger tumor size, microvascular tumor thrombi, and advanced T stage (Table 3). These findings establish MED10 as a key pathogenic driver in HCC, where overexpression predicts aggressive disease and poor prognosis.

Analysis of TCGA data and clinical cohorts identified significant associations between elevated MED10 expression and microvascular invasion/tumor thrombi in HCC patients (Tables 2,3). GSEA revealed marked enrichment of cell migration and EMT pathways in high-MED10 expressors (Fig. 2A), implicating MED10 in metastatic progression. Supporting this observation, both transcriptional and translational analyses revealed MED10 dysregulation in HCC cell lines (MHCC97L, MHCC97H, SK-Hep-1, Hep3B), showing significantly elevated mRNA and protein expression relative to normal hepatocytes (LO2) (Fig. 2B,C). To functionally characterize MED10, we engineered stable HCC cell lines with modulated expression: MED10 was overexpressed in low-expressing HCCLM3 cells and knocked down in high-expressing Hep3B cells. Successful overexpression (ov-MED10) and knockdown (shMED10) were validated by RT-qPCR and Western blotting (Fig. 2D,E). Functional assessments via Transwell and scratch assays established MED10’s pro-migratory role in HCC: forced expression accelerated cellular motility, whereas genetic ablation attenuated migration (Fig. 2F,G). To investigate MED10’s role in EMT, we assessed EMT marker expression via RT-qPCR and Western blotting. MED10 overexpression elevated mesenchymal markers (N-cadherin, vimentin) while suppressing the epithelial marker E-cadherin. Conversely, MED10 knockdown increased E-cadherin and reduced mesenchymal markers (Fig. 2H,I). Protein levels of the metastatic regulator MMP13 mirrored these changes, increasing with MED10 overexpression and decreasing upon knockdown.

Fig. 2.

MED10 promotes migration and EMT in HCC. (A) Gene Set Enrichment Analysis (GSEA) showing significant enrichment of cell migration and EMT pathways. (B,C) MED10 mRNA/protein expression in normal hepatocytes (LO2) versus HCC cell lines. (D,E) Validation of MED10 (over)expression (ov-MED10) and knockdown (shMED10) models. (F,G) Enhanced migration capacity after MED10 overexpression versus suppression in knockdown models (Transwell/wound-healing). Scale bars: 200 μm (F) and 500 μm (G). (H,I) EMT marker shifts: MED10 overexpression downregulated E-cadherin while upregulating N-cadherin, vimentin, and MMP13; knockdown reversed these effects. ns: p $\ge$ 0.05; **p 0.01; ***p 0.001.

Analysis of TCGA data and HCC clinical cohorts revealed significant correlations between MED10 expression and advanced T stage/tumor size (Tables 2,3). Building on these clinical associations, we investigated MED10’s functional role in proliferation. Colony formation, CCK-8, and EdU assays consistently demonstrated enhanced proliferative capacity upon MED10 overexpression, whereas MED10 knockdown markedly suppressed HCC cell growth (Fig. 3A–C). To validate these findings in vivo, nude mouse xenograft models showed significantly reduced tumor burden in shMED10 groups versus controls, while ov-MED10 xenografts exhibited increased tumor volume and mass (Fig. 3D–F). IHC analysis of proliferation marker Ki-67 further confirmed these effects: diminished staining in shMED10 tumors and intensified staining in ov-MED10 tumors compared to respective controls (Fig. 3G).

Fig. 3.

MED10 promotes HCC cells proliferation. (A–C) Enhanced proliferative capacity in ov-MED10 cells versus suppression in shMED10 models across colony formation, CCK-8, and EdU assays. Scale bar = 200 μm. (D) Representative xenograft tumors from experimental groups post-excision (n = 6). (E,F) Significant reduction in tumor volume/weight in shMED10 cohort versus increased burden in ov-MED10 group relative to controls. (G) Immunohistochemical validation of proliferation marker Ki-67: diminished expression in shMED10 tumors versus intensified staining in ov-MED10 xenografts. Scale bar = 200 μm. **p 0.01; ***p 0.001.

To investigate MED10’s role in proliferation, we analyzed TCGA data through functional enrichment. GO/KEGG pathway analysis revealed pronounced enrichment of MED10-correlated DEGs in cell cycle regulatory pathways (Fig. 4A). GSEA further implicated MED10 in proliferation and cell cycle processes including checkpoint control, G1/S transition, cyclin D1/CDK4 targets, cyclin D-associated G1 events, and RB1-mediated pathways (Fig. 4B). Protein-protein interaction network analysis (STRING) identified predicted binding partners CDK4, CDK6, CDK8, and CCNC (Fig. 4C). These findings establish MED10 as a regulator of cell cycle progression in HCC. Flow cytometry analysis revealed that MED10 overexpression in HCCLM3 cells significantly reduced G0/G1 phase occupancy while increasing S-phase fraction. Conversely, MED10 knockdown in Hep3B cells increased G0/G1 phase cells and decreased S-phase populations, with no significant G2/M phase alterations (Fig. 4D), indicating MED10’s critical role in G1/S transition. Immunofluorescence of HCC tissues demonstrated co-localization of MED10 with key G1/S regulators CDK4, CDK6, and cyclin D1 (Fig. 4E). Western blotting confirmed MED10-dependent regulation: overexpression upregulated while knockdown suppressed CDK4, CDK6, and cyclin D1 expression (Fig. 4F). Taken together, MED10 enhances HCC proliferation by driving the G1/S cell cycle transition, possibly via regulation of CDK4, CDK6, and Cyclin D1.

Fig. 4.

MED10 orchestrates cell cycle progression in HCC. (A) Functional enrichment of MED10-associated genes (Gene Ontology (GO)/Kyoto Encyclopedia of Genes and Genomes (KEGG)) in TCGA-LIHC, such as cell cycle, etc. (B) GSEA of differentially expressed mRNAs stratified by MED10 expression. (C) Protein-protein interaction network linking MED10 to cell cycle regulators. (D) Flow cytometry quantification of cell cycle phase distribution following MED10 modulation. (E) Immunofluorescence co-localization of MED10 (green) with CDK4, CDK6, and cyclin D1 (red) in HCC tissues. Scale bar = 40 μm. (F) Western blotting analysis of CDK4/6 and cyclin D1 expression under MED10 modulation. ns: not significant (p $\ge$ 0.05); *p 0.05, **p 0.01, ***p 0.001.

To elucidate MED10’s cell cycle regulatory mechanism, GSEA identified pronounced mitogen-activated protein Kinase (MAPK) pathway enrichment in high-expressing cohorts (Fig. 5A). Building on established MAPK/ERK functions in proliferation control [23], we interrogated pathway activity. Western blotting confirmed MED10 gain-of-function selectively enhanced RAF1-MEK-ERK cascade phosphorylation without affecting total protein abundance. Conversely, MED10 knockdown suppressed phosphorylation of these kinases (Fig. 5B), indicating MED10-dependent MAPK pathway activation. Since nuclear translocation of p-ERK1/2 is essential for transcriptional activation, immunofluorescence confirmed MED10’s regulation of this process: nuclear p-ERK1/2 accumulation increased with MED10 overexpression and decreased upon knockdown (Fig. 5C). The transcription factor c-Myc, a key MAPK/ERK downstream effector, regulates cyclin and CDK transcription (e.g., CDK4, CDK6, cyclin D1) to control cell cycle progression [24, 25]. GSEA linked MED10 to Myc pathway activation (“Myc active”, “Myc targets up”, “HCC up”; Fig. 5D), while TRRUST analysis confirmed c-Myc’s enrichment in mitotic G1/S transition (Fig. 5E). Western blotting demonstrated MED10-dependent c-Myc regulation: overexpression upregulated and knockdown downregulated c-Myc expression (Fig. 5F). Integrated analysis of TRUST, CHEA, ENCODE, and ChIP Atlas databases predicted eight c-Myc target genes involved in G1/S control, including CDK4, CDK6, and CCND1 (Fig. 5G). JASPAR identified high-score c-Myc binding motifs in these genes’ promoters (Fig. 5H). Collectively, these findings demonstrate that MED10 modulates both the RAF/MEK/ERK signaling pathway activation and c-Myc expression, potentially representing a key mechanism through which MED10 regulates cell cycle progression and proliferation in HCC cells.

Fig. 5.

MED10 regulates activation of the MAPK signaling axis. (A) GSEA linking MED10 to MAPK signaling. (B) Western blotting analysis of MAPK pathway components under MED10 modulation. (C) Subcellular localization of p-ERK1/2 by immunofluorescence. Scale bar = 10 μm. (D) GSEA enrichment of Myc-related pathways. (E) TRRUST-predicted c-Myc involvement in G1/S transition. (F) MED10-dependent c-Myc regulation via Western blotting. (G) Venn intersection of c-Myc target genes (TRUST/CHEA/ENCODE/ChIP Atlas) and G1/S regulators. (H) JASPAR-predicted c-Myc binding motifs in CDK4/CDK6/CCND1 promoters.

To determine if MED10’s pro-proliferative and cell cycle effects require MAPK signaling, we treated HCC cells with RAF inhibitor LY3009120. Proliferation assays (CCK-8, colony formation, EdU) demonstrated that MED10 overexpression-enhanced growth was partially reversed by LY3009120 (Fig. 6A–C). Flow cytometry confirmed MED10-mediated G0/G1 reduction and S-phase increase were similarly attenuated by RAF inhibition (Fig. 6D). Western blotting analysis revealed LY3009120 partially reversed MED10-driven phosphorylation of RAF1, MEK1/2, ERK1/2, and c-Myc (Fig. 6E), and correspondingly blunted MED10-induced upregulation of CDK4, CDK6, and cyclin D1 (Fig. 6F). Thus, MED10 regulates proliferation and cell cycle progression primarily through RAF, and related to MEK/ERK/c-Myc signaling. Conversely, MED10-promoted migration and EMT remained unaffected by RAF inhibition (Supplementary Fig. 1A–C), indicating these effects occur independently of MAPK pathway activation.

Fig. 6.

RAF inhibition reverses MED10-driven proliferation and cell cycle progression in HCC. (A–C) CCK-8, colony formation, and EdU assays collectively demonstrated that the RAF inhibitor LY3009120 blocks MED10-driven proliferation. Scale bar = 200 μm. (D) Furthermore, flow cytometry analysis revealed that LY3009120 rescues the promoting effect of MED10 overexpression on cell cycle progression. (E) Western blotting assessed MAPK pathway protein expression following RAF1 phosphorylation inhibition in MED10-overexpressing HCCLM3 cells. (F) Additionally, Western blotting evaluated levels of cell cycle regulators CDK4, CDK6, and cyclin D1 in these cells after LY3009120 treatment. “ns” denotes p $\ge$ 0.05; *p 0.05, **p 0.01, ***p 0.001.