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. 2026 Jun 30;65(9):1077–1091. doi: 10.1002/mc.70145

Ubiquitin Specific Peptidase 48 Deubiquitinates METTL3 to Modulate Ferroptosis in Gastric Cancer Cells

Yi‐lin Xie 1,2, Tian‐xia Lei 1, Yong‐sheng Zheng 1, Ze‐lin Xu 3, Hai‐xing Wang 1,✉
PMCID: PMC13465995  PMID: 42378628

ABSTRACT

Ferroptosis, an iron‐dependent cell death, is a promising target in gastric cancer. METTL3, an m6A RNA methyltransferase, drives tumor progression, but its role in ferroptosis regulation and the involved deubiquitinating enzymes (DUBs) remain unclear. Here, we demonstrated that METTL3 expression heterogeneity in different gastric cancer cells, identified functional role of METTL3 in modulating ferroptosis sensitivity. In MKN‐7 and GCIY cells, forced METTL3 overexpression conferred resistance to ferroptotic stress by driven GPX4 and FTH1 expression, and inhibiting oxidative stress accumulation. Conversely, METTL3 depletion sensitized cells to ferroptosis, and promoted ferroptotic oxidative damage. Through a comprehensive screening of deubiquitinases, we identified ubiquitin specific peptidase 48 (USP48) as a critical stabilizer of METTL3 protein. Functional assays revealed that USP48 mimics METTL3's protective role against ferroptosis, while METTL3 overexpression rescued the ferroptosis sensitivity caused by USP48 knockdown. Mechanistically, this USP48–METTL3 axis regulated key ferroptosis markers including GPX4, FTH1, lipid peroxidation, iron accumulation, and reactive oxygen species (ROS) levels. Our findings reveal a novel regulatory pathway by which USP48 controls ferroptosis sensibility in gastric cancer cell via METTL3 stabilization. This crosstalk between the ubiquitin–proteasome system and m6A transferases highlights the USP48–METTL3 axis as a promising target to sensitize gastric cancer cells to ferroptosis‐inducing therapy.

Keywords: deubiquitinating enzymes, Ferroptosis, Gastric cancer, METTL3, USP48

1. Introduction

Gastric cancer remains a leading cause of malignancy‐related mortality worldwide, characterized by significant molecular heterogeneity and a pressing need for improved therapeutic strategies [1, 2]. Despite advances in surgical techniques and chemotherapeutic regimens, the overall prognosis for advanced gastric cancer remains poor, primarily due to late diagnosis, metastasis, and intrinsic or acquired resistance to conventional therapies [3, 4]. This underscores the need to identify novel molecular targets, which deepen our understanding of the mechanisms regulating tumor cell survival and death.

Ferroptosis, a form of regulated cell death characterized by iron‐dependent accumulation of lipid peroxides, has attracted considerable attention for its dual role in tumor suppression and therapy resistance [5, 6]. Unlike apoptosis or necrosis, ferroptosis involves distinct metabolic and redox vulnerabilities in cancer cells, making it a promising therapeutic target for gastric cancer [7, 8, 9]. Various modulators of ferroptosis, such as ACTL6A, NOX4, and GPX4, have been identified, including transporters, metabolic enzymes, and antioxidants [9, 10, 11, 12], yet the complex regulatory networks controlling ferroptosis sensitivity in gastric cancer remain incompletely defined.

One emerging area of interest is the role of RNA modifications in regulating cancer ferroptosis [13, 14, 15]. The RNA methyltransferase METTL3 is the catalytic core of the N6‐methyladenosine (m6A) methyltransferase complex responsible for installing the m6A modification on mRNA, a prevalent epi‐transcriptomic mark [16]. METTL3‐mediated m6A modifications influence multiple aspects of oncogenic processes like cell proliferation, differentiation, migration, and response to chemotherapy or immunotherapy in gastric cancer [17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27]. However, although METTL3's contribution to tumor progression is well established, its impact on ferroptosis and regulatory mechanism in gastric cancer has not been thoroughly investigated.

Protein stability and activity are also tightly controlled at the post‐translational level, chiefly through the ubiquitin–proteasome system [28]. This tightly regulated system not only governs protein degradation but also modulates signaling, localization, and interaction networks [29]. Growing evidence suggests that deubiquitinating enzymes (DUBs) play pivotal roles in cancer by fine‐tuning oncogenic and tumor‐suppressive pathways via regulating the m6A modification process [30, 31, 32]. Despite this, the specific DUBs that regulate METTL3 stability or function, especially within the context of ferroptosis in gastric cancer, remain largely unexplored. Ubiquitin specific peptidase (USP) is the largest family of DUBs, some of them regulate METTL3 ubiquitin levels affecting cancer cell malignant phenotype. Such as USP13, through stabilizing METTL3 to regulate autophagy in osteosarcoma [33].

In this study, we aimed to elucidate the role of METTL3 in gastric cancer ferroptosis and to identify how deubiquitinating enzymes regulate METTL3 stability and function to influence Erastin‐induced ferroptosis sensitivity. Initially, we profiled METTL3 expression and function across multiple gastric cancer cells. We investigated how manipulating METTL3 levels influences gastric cancer cell viability and sensitivity to Erastin‐induced ferroptosis. Furthermore, we screened the DUBs interacting and stabilizing METTL3, focusing on USP48 as a critical regulator. Loss‐of function and rescue experiment further validated key regulatory role of USP48–METTL3 axis in Erastin sensitivity. Our results reveal a novel USP48–METTL3 regulatory axis that confers some gastric cancer cell resistance to ferroptosis, which provides a foundation for developing targeted therapies aimed at disrupting this axis to enhance ferroptosis‐based cancer treatments in gastric cancer.

2. Methods

2.1. Cell Lines and Culture

Human gastric cancer cell lines NCI‐N87 (RRID: CVCL_1603), SNU‐1 (RRID: CVCL_0099), MKN‐45 (RRID: CVCL_0434), MKN‐7 (RRID: CVCL_1417), HS‐746T (RRID: CVCL_0333), GCIY (RRID: CVCL_1228) along with a normal gastric epithelial cell line GES‐1 (RRID: CVCL_EQ. 22) were obtained from Xiamen Immocell Biotechnology Co. Ltd. The cells were cultured under standard conditions in RPMI‐1640 medium (HyClone, SH30255.01) supplemented with 10% fetal bovine serum (FBS) (HyClone, SH30396.02), maintained at 37°C in a humidified atmosphere with 5% CO2.

2.2. Quantitative PCR (qPCR)

Total RNA were extracted using the Total RNA Extraction Kit (Yeasen, 19211ES60) and reverse transcribed into cDNA with the RT‐qPCR Kit (Yeasen, 11143ES50). Quantitative PCR was performed with METTL3‐specific primers (F‐5′‐ACTGCCACTGACATCAGAGTA‐3′, R‐5′‐GCTTGCGTGTCCATTACATTTAC‐3′), USP48‐specific primers (F‐5′‐ACTGCCACTGACATCAGAGTA‐3′, R‐5′‐ GCTTGCGTGTCCATTACATTTAC‐3′), GPX4‐specific primers (F‐5′‐ ACAAGAACGGCTGCGTGGTGAA‐3′, R‐5′‐GCCACACACTTGTGGAGCTAGA‐3′), FTH1‐specific primers (F‐5′‐GAACTACCACCAGGACTCAGA‐3′, R‐5′‐ TAAACGTAGGAGGCGTAGAGC‐3′), using 18S rRNA as the internal control (F‐5′‐CGACGACCCATTCGAACGTCT‐3′, R‐5′‐CTCTCCGGAATCGAACCCTGA‐3′). SYBR Green qPCR mix (Vazyme, Q431‐02) and a machine (Thermo, ABI7500) were employed as per manuals. Relative gene expression was calculated using the 2^(‐ΔΔCt) method.

2.3. Western Blot

Protein extracts were separated by SDS‐PAGE and transferred to PVDF membranes. Membranes were probed with primary antibodies against METTL3 (Proteintech, 15073‐1‐AP, RRID: AB_2142033, 1:500), GPX4 (Proteintech, 67763‐1‐Ig, RRID: AB_2909469, 1:500), FTH1 (Proteintech, 60875‐5‐Ig, 1:5000), USP48 (Proteintech 12076‐1‐AP, RRID: AB_2213840, 1:500), and Actin (Proteintech, 20536‐1‐AP, AB_10700003, 1:10000) as the internal reference. Secondary antibodies were HRP‐conjugated Anti‐Mouse IgG (Proteintech, SA00001‐1, RRID: AB_2722565, 1:10000) and HRP‐conjugated Anti‐Rabbit IgG (Proteintech, SA00001‐2, RRID: AB_2722564, 1:10000). Chemiluminescent detection was used, and band intensity was quantified by densitometric analysis using Image J software.

2.4. shRNA Knockdown and Overexpression

Lentiviral vectors encoding METTL3‐specific shRNA (pLKO.1‐METTL3‐puro or pLKO.1‐USP48‐puro) and package vectors were transfected into 293 T cells, then harvested virus after 3 days transfection. Virus delivering shRNA were infected target cells after ultracentrifugation concentration. Puromycin selection were employed to screen out stable knockdown cells.

Overexpression constructs (pcDNA3.1‐METTL3‐HA) were transiently transduced into the target cell lines via transfection reagent (Vazyme, T101). The efficiency of knockdown or overexpression was validated by qPCR.

2.5. Cell Viability Assays and IC50 Determination

Cells were treated with a range of Erastin concentrations (1–128 μM) (Beyotime, SC0224‐25mg) for 24 h. Cell viability was assessed using the MTT assay kits (Yeasen, 40201ES72), measuring absorbance at 570 nm. Dose‐response curves were generated to calculate IC50 values. Functional assays employed Erastin at IC50 comcentration (~41.6 μM for GCIY, ~49.4 μM for MKN‐7) refer to reported methods [34, 35]. All experiments were performed in triplicate.

2.6. Flow Cytometry

For cell cycle analysis, cells were fixed in 70% ethanol, stained with 7‐AAD (Elabscience, E‐CK‐A351), and analyzed for cell cycle distribution (G0/G1, S, G2/M phases). For apoptosis assay, early and late apoptosis were quantified using Annexin V‐FITC/Propidium Iodide (PI) dual staining (Vazyme, A211‐01) followed by flow cytometry.

2.7. Lipid Peroxidation Assay

Malondialdehyde (MDA) levels were quantified using a commercial TBARS assay kit (Solarbio, BC0020), normalized to total protein content, following the manufacturer's instructions. In brief, harvested 5 × 106 cells with 1 mL lysis buffer, and ultrasonicated cells in 200 W power until cells were completed homogenized. Combined detection buffer with diluted samples in recommended ratio, and measured absorbance values in 532 nm and 600 nm wavelength.

2.8. Glutathione Assay

Levels of reduced (GSH), oxidized glutathione (GSSG), and the GSH/GSSG ratio were measured by kits from Beyotime (S0053), following the manufacturer's instructions.

2.9. Intracellular Iron (Fe2+) Measurement

Cellular iron concentrations were determined by a colorimetric assay kit (Elabscience, E‐BC‐K881‐M) using total cell lysates in accordance with the instruction manual.

2.10. Reactive Oxygen Species (ROS) Detection

Intracellular ROS was detected with the fluorescent probe DCFH‐DA (Yeasen, 50101ES01) using a flow cytometry (Beckman, CytoFLEX). When indicated treatment finished, removed culture medium and digested and collected cells in tubes, loading probes at 10 μM final concentration with 1×106 cells about 30 min in dark, then washed cells twice with serum‐free medium, detected fluorescence at 488 nm excitation wavelength and 525 nm emission wavelength.

2.11. Nanoluciferase Reporter Screening

HEK293T (RRID: CVCL_0063) cells were plated in 96‐well plates at a density of 3×104 cells per well. The cells were then co‐transfected using ExFect Transfection Reagent (Vazyme, T101) with plasmids encoding METTL3 fused to Nanoluciferase and individual USP family expression vectors. After 24 to 48 h, the culture medium was removed, and 100 μL of lysis buffer was added to each well to lyse the cells (MeiLunBio, MA0522). The lysates were collected by centrifugation and transferred to a 96‐well chemiluminescent plate. Luciferase detection reagent was then added, and the firefly luciferase activity in each well was measured using a microplate reader, recording relative light units (RLU). Luminescence, which reflects METTL3 protein stabilization, was measured 48 h post‐transfection with a luminometer.

2.12. Co‐Immunoprecipitation (Co‐IP)

HEK293T cells co‐transfected with Flag‐tagged METTL3 and HA‐tagged USP48 plasmids were lysed. Immunoprecipitation used anti‐HA (Proteintech, 51064‐2‐AP) or anti‐Flag (Proteintech, 20543‐1‐AP) antibodies and magnetic beads (anti‐FLAG beads, Sigma, M8823; anti‐HA beads, Beyotime, P2121).

2.13. Ubiquitination Assay

Cells co‐expressing Flag‐METTL3, HA‐USP48, and His‐tagged ubiquitin were lysed under denaturing conditions. Ubiquitinated METTL3 was detected by Western blot using anti‐His antibody.

2.14. Cycloheximide Chase Assay

Protein stability was assessed by treating cells overexpressing USP48 or vector control with 50 μg/mL cycloheximide (CHX) (MCE, HY‐12320) to inhibit protein synthesis. Samples were collected at 0, 6, 9, and 18 h post‐treatment, analyzed by Western blot, and normalized to Actin.

2.15. Data Sources for Bioinformatics Analysis

This study leveraged multiple publicly accessible bioinformatics databases to acquire comprehensive gastric cancer datasets. Microarray expression profiles were obtained from the Gene Expression Omnibus (GEO) (RRID:SCR_005012) database, specifically datasets GSE56807, GSE63089, and GSE66229. Additionally, RNA‐sequencing data along with clinical annotation for stomach adenocarcinoma (TCGA‐STAD) were acquired from The Cancer Genome Atlas (TCGA) via the “TCGAbiolinks” R package and corroborated with clinical data from UCSC Xena. Protein expression information for USP48 was sourced from the Human Protein Atlas (HPA) database, providing immunohistochemistry‐based protein‐level validation complementary to transcriptomic data.

2.16. GEO Dataset Analysis

Raw microarray data from GEO were processed and normalized using the R environment (version 4.3.0). Differential gene expression analysis was performed primarily on the GSE66229 dataset using the “limma” package (RRID:SCR_010943), which applies linear modeling and empirical Bayes methods to detect significantly dysregulated genes between tumor and normal gastric tissue samples. For RNA‐sequencing data from TCGA‐STAD, count normalization and quality control were conducted, followed by differential expression analysis with the “edgeR” package (RRID:SCR_012802). This package uses negative binomial distribution models, suited for count‐based RNA‐seq data, to identify genes exhibiting expression differences among tumor versus normal samples and across clinical subgroups such as tumor stage.

2.17. Functional Enrichment Analysis

To elucidate the biological significance of identified gene sets, Gene Set Enrichment Analysis (GSEA) was carried out using GSEA software (version 4.4.0) on TPM‐normalized TCGA‐STAD RNA‐seq data. Enrichment of pathways and biological processes was assessed through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases, highlighting cellular mechanisms related to metabolism and ferroptosis.

2.18. Correlation Analyses

Correlation analyses examining relationships between USP48, METTL3, and ferroptosis‐related genes were performed using statistical methods available in R packages like “ggpubr.” Both Spearman's and Pearson's correlation coefficients were calculated, along with visualizations, to explore gene co‐expression and potential interactions within the gastric cancer microenvironment.

2.19. Data Quantification and Statistical Analysis

Band intensities and fluorescence measurements were quantified using image analysis software Image J. All experiments were performed in triplicate. Statistical significance was determined by unpaired student's t test or other appropriate methods using Graphpad prism 8.0.

3. Results

3.1. METTL3 Expression Profile and Ferroptosis‐Related Genes Regulation in Gastric Cancer Cell Lines

To find out endogenous METTL3 levels in gastric cancer cell and normal cell, we first quantified METTL3 expression across six human gastric cancer cell lines (NCI‐N87, SNU‐1, MKN‐45, MKN‐7, HS‐746T, GCIY) and a normal gastric mucosal cell line (GES‐1). qPCR analysis identified MKN‐7 cell have higher METTL3 transcript levels cell line than GES‐1, while NCI‐87, SNU‐1, MKN‐45 and GCIY have lower METTL3 mRNA level (Figure 1A). Moreover, western blot results confirmed the relative high endogenous protein level of METTL3 in MKN‐7, the low protein level in SNU‐1, MKN‐45 and GCIY compared to GES‐1 (Figure 1B,C), indicating minor METTL3 expression heterogeneity in gastric cancer cell lines.

Figure 1.

Figure 1

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METTL3 Expression Profile and Ferroptosis‐related Genes Regulation in Gastric Cancer Cells. (A) Relative METTL3 mRNA levels in six gastric cancer cell lines and a normal gastric epithelial cell line GES‐1, measured by qPCR. (B) Representative Western blot showing METTL3 protein expression across cell lines with Actin as internal control. (C) Quantification of METTL3 protein levels normalized to Actin and GES‐1 METTL3 level. (D) Dose‐response curves for cell viability after Erastin treatment in GCIY and MKN‐7 cells, with calculated IC50 values. (E) METTL3, GPX4, FTH1 mRNA levels in GCIY cells overexpressing METTL3; and in MKN‐7 cells with METTL3 knockdown. Significance values are indicated. Data represented mean ± SD of three independent experiments.

To diminish endogenous METTL3 expression, GCIY (relative low METTL3) and MKN‐7 (relative high METTL3) were used for subsequent analysis. Dose‐response experiments determined IC50 to ensure biologically functional concentration of erastin (~41.6 µM for GCIY and ~49.4 µM for MKN‐7) used in subsequent experiments (Figure 1D).

To confirm Erastin induces biological function in ferroptosis pathway, ferroptosis inhibitor (Fer‐1), apoptosis inhibitor (Z‐VAD‐FMK), and necroptosis inhibitor (Nec‐1) were utilized with Erastin. Cells were treated with Erastin combined either Fer‐1, or Z‐VAD‐FMK, or Nec‐1. Cell viability assays showed that GCIY and MKN‐7 cell viability significantly decreased at ~60 μM Fer‐1, the same as Z‐VAD‐FMK and Nec‐1, so 40 μM concentration of each inhibitor was chosen for subsequent experiments (Figure S1A,B). Then, GCIY and MKN‐7 cells were treated with DMSO, Erastin, Erastin+Fer‐1, Erastin+Z‐VAD‐FMK, or Erastin+Nec‐1, with IC50 concentration of Erastin and 40 μM of mentioned inhibitors. The 24‐h viability assays showed Fer‐1 competed viability impairments inducing by Erastin (Figure S1C).

METTL3 overexpression (OE) in GCIY cells and knockdown (sh) in MKN‐7 cells were confirmed by qPCR (Figure 1E). Coordinately, METTL3 overexpression in GCIY cells resulted in increases of GPX4, and FTH1 mRNA levels, whereas METTL3 knockdown in MKN‐7 cells resulted in remarkable suppression of these genes (Figure 1E). Although most gastric cancer cell lines maintain relatively low basal METTL3 expression under normal conditions, forced expression of METTL3 is sufficient to enhance genes that negatively regulate ferroptosis, and relatively higher basal METTL3 conferred to higher IC50 of Erastin, suggesting that METTL3 possesses the capacity to promote ferroptosis defense. Collectovely, these results reveal the vital roles of METTL3 in modulating gastric cancer cell ferroptosis activities.

3.2. METTL3 Modulates Erastin‐Induced Ferroptosis in Gastric Cancer Cells

To determine whether METTL3 expression affects gastric cancer cells susceptibility to Erastin‐induced ferroptosis, we established METTL3 knockdown shRNAs and overexpression plasmids. After knocked down METTL3 in MKN‐7 and overexpressed METTL3 in GCIY, we treated cells with Erastin, qPCR analysis confirmed METTL3 expression changes, moreover, Erastin also affected METTL3 mRNA levels in the IC50 concentration (Figure 2A,B). Viability assays demonstrated that METTL3 overexpression counteracted Erastin's cytotoxic effect, while METTL3 deficiency aggravated the viability impairments induced by Erastin (Figure 2C,D). We further investigated the downstream cellular effects of METTL3 under ferroptosis stress. Flow cytometry analysis of cell cycle distribution showed that Erastin treatment led to an increased proportion of cells arrested in the G0/G1 phase in vector control cells. However, METTL3 overexpression attenuated this Erastin‐induced G0/G1 arrest, concomitantly increased the percentage of cells in the S phase in GCIY cells, whereas METTL3 knockdown exhibited the opposite effect on the cell cycle distribution in MKN‐7 cells (Figure S2A,B).

Figure 2.

Figure 2

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Impact of METTL3 expression on Erastin‐induced ferroptosis. (A, B) qPCR validating METTL3 mRNA levels in Vector, Vector+Era, METTL3 OE+Era GCIY cells and shNC, shNC+Era, shMETTL3+Era MKN‐7 cells. (C, D) Cell viability assessed by MTT assay in GCIY cells overexpressing METTL3 (METTL3 overexpression, OE) and MKN‐7 cells with METTL3 knockdown (METTL3 knockdown, shMETTL3), following treatment with or without Erastin. (E, F) Western blot analysis showing protein expression of METTL3, glutathione peroxidase 4 (GPX4), and ferritin heavy chain 1 (FTH1) in GCIY cells and MKN‐7 cells with METTL3 expression modulation, with or without Erastin exposure. Actin served as the internal control. (G) Malondialdehyde (MDA) levels indicating lipid peroxidation in GCIY and MKN‐7 cells, with or without Erastin exposure. (H–J) Intracellular levels of reduced glutathione (GSH), oxidized glutathione (GSSG), and GSH/GSSG ratio in GCIY and MKN‐7 cells, with or without Erastin exposure. (K) Quantification of intracellular ferrous iron (Fe2+) concentrations. (L) Representative flow cytometry histograms of 2′,7′‐dichlorodihydrofluorescein diacetate (DCFH‐DA) fluorescence as an indicator of reactive oxygen species (ROS) levels in GCIY and MKN‐7 cells, with or without Erastin exposure (upper panel). Statistical analysis of mean fluorescence intensity (MFI) of DCF for ROS quantification (lower panel). Data represent mean ± SD, with p‐values indicated on graphs.

To assess METTL3's impact on ferroptosis, we further analyzed the ferroptosis related protein (GPX4, FTH1), lipid peroxidation (MDA), Redox balance (GSH/GSSG), Iron content (Fe2+) as well as ROS level. Western blot demonstrated that METTL3 overexpression increased GPX4 and FTH1 protein levels, while knockdown decreased them upon Erastin‐induced ferroptosis treatment (Figure 2E,F). MDA was elevated by Erastin but reduced by METTL3 overexpression, METTL3 knockdown intensified MDA accumulation (Figure 2G). Glutathione assays confirmed improved redox balance (increased GSH/GSSG ratio) with METTL3 overexpression, which was reversed by knockdown (Figure 2H–J). Intracellular Fe2+ levels were elevated upon Erastin treatment. METTL3 overexpression attenuated Fe2+ accumulation, whereas knockdown of METTL3 further increased Fe2+ concentration in Erastin‐induced ferroptosis conditions (Figure 2K). Flow cytometry analysis using DCFH‐DA staining revealed that Erastin induced a significant increase in intracellular ROS in GCIY and MKN‐7 cells. METTL3 overexpression significantly reduced ROS levels, whereas METTL3 knockdown further enhanced ROS accumulation (Figure 2L). These data indicate forced METTL3 prevent gastric cancer cells from ferroptosis‐induced oxidative stress, lipid peroxidation, and iron accumulation.

3.3. USP Family Screening Identifies USP48 as a Key METTL3 Stabilizer

To find out if post‐translational modification affects METTL3 degradation, a deubiquitinases screening was used to identified deubiquitinase modulating METTL3 stability. A nanoluciferase reporter screen of 50 USP candidates identified USP33, USP40, and USP48 as candidates that stabilize METTL3 by increasing relative luminescence units (Figure 3A). Immunoprecipitation and Western blot validation confirmed that USP48 interact with METTL3 strongly (Figure 3B,C). Furthermore, immunohistochemistry staining from the HPA database revealed strong USP48‐positive signal in gastric cancer tissues compared to normal gastric mucosa, indicating upregulation of USP48 in the tumor microenvironment (Figure 3D). Analysis of TCGA‐STAD showed that both METTL3 and USP48 exhibit significantly higher mRNA expression in tumor tissues compared to matched normal controls (Figure 3E). Similar trends were observed in the GEO gastric cancer cohort, where METTL3 and USP48 mRNA levels were significantly elevated in tumor samples relative to normal tissue (Figure S3A). Correlation analysis in TCGA‐STAD dataset also showed the positive correlation between USP48 and METTL3 (Figure S3B). Additionally, correlation and expression analysis showed USP48 expression was positive correlated with ferroptosis negatively related genes (GCH1, SLC7A11) in gastric tumors tissues (Figure S3B,C). GO and KEGG enrichments showed positive correlation between USP48 and methyltransferase and RNA degradation pathways (Figure S3D). Given USP48's strong effect in METTL3 expression and ferroptosis, we focused on its functional interplay with METTL3 in modulating ferroptosis in gastric cancer.

Figure 3.

Figure 3

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Screening of USP deubiquitinases regulating METTL3 stability and validation. (A) Nanoluciferase reporter assay showing relative fold change of METTL3 stability upon co‐expression with 40 individual USP family members in HEK293T cells. (B) Western blot validation of METTL3 protein levels in HEK293T cells co‐transfected with Flag‐METTL3 and HA‐tagged USP33, USP40, or USP48. Actin served as internal control. (C) Quantification of Flag‐METTL3 protein expression normalized to Actin (mean ± SD, n = 3). Significant upregulation by USP proteins versus control is indicated with p‐values. (D) Immunohistochemistry images from the Human Protein Atlas showing USP48 protein expression in normal gastric mucosa and gastric cancer tissues. Scale bars: 400 μm. (E) Box plots depicting mRNA expression levels of METTL3 and USP48 in normal versus tumor samples from TCGA‐STAD dataset (log2 TPM + 1). Data present as mean ± SD, with p‐values indicated on graphs.

3.4. USP48 Protects Gastric Cancer Cells From Erastin Induced Ferroptosis

To investigate whether USP48 exerts ferroptosis modulation effects similar to METTL3, we examined the gastric cancer cells changes with altered USP48 expression under Erastin treatment. Knockdown capacity of each shRNA was confirmed by qPCR (Figure S4A,B). Viability assays showed that USP48 overexpression significantly increased cell viability in GCIY cells compared to vector controls when treating Erastin, while USP48 knockdown in MKN‐7 cells led to a significant reduction in cell viability under Erastin treatment (Figure 4A,B). Additionally, USP48 overexpression partially mitigated cell cycle arrest inducing by Erastin, while USP48 knockdown enhanced cell cycle arrest under Erastin treatment (Figure S5A,B). These results suggest that USP48 protects gastric cancer cells from Erastin‐induced cell damage by enhancing cell proliferation, promoting cell cycle progression.

Figure 4.

Figure 4

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USP48 protects gastric cancer cells from Erastin induced cell damage. (A, B) Cell viability assessed by MTT assay in GCIY cells overexpressing USP48 and MKN‐7 cells with USP48 knockdown, with or without Erastin exposure. (C, D) Western blot analysis of USP48, METTL3, GPX4, and FTH1 proteins in GCIY cells overexpressing USP48 and MKN‐7 cells with USP48 knockdown, treated with or without Erastin. Actin served as loading control. (E) cellular MDA levels reflecting lipid peroxidation level in GCIY and MKN‐7 cells ± Erastin. (F) Measurements of GSH, GSSG, and the GSH/GSSG ratio in GCIY and MKN‐7 cells ± Erastin. (G) Intracellular Fe2+ concentrations indicating iron accumulation in GCIY and MKN‐7 cells ± Erastin. (H) Representative flow cytometry histograms showing DCF fluorescence for intracellular ROS detection in GCIY and MKN‐7 cells ± Erastin. (I) Quantification of relative mean fluorescence intensity (MFI) of DCF for ROS levels. Data showed mean ± SD from three independent experiments, p‐values indicate statistical significance compared to corresponding control groups.

Subsequent molecular assays confirmed USP48 modulated Erastin induced ferroptosis in gastric cancer cells. Western blot analysis showed that USP48 overexpression in GCIY cells remarkedly elevated METTL3, GPX4, and FTH1 protein levels compared to vector controls under Erastin treatment (Figure 4C). In contrast, USP48 knockdown in MKN‐7 cells significantly downregulated these proteins in the presence of Erastin (Figure 4D). In biochemical levels, USP48 overexpression significantly reduced MDA accumulation and GSH consumption, with increased GSH and GSH/GSSG ratio compared to Erastin control groups, USP48 deficiency mitigated these effects (Figure 4E,F). Additionally, Fe2+ content elevated by Era was diminished in USP48‐overexpressed GCIY cells compared to Vector+Era control, whereas USP48 deficit MKN‐7 cells preserved elevated Fe2+ content relative to shUSP48+Erastin control (Figure 4G). Furthermore, ROS accumulation elevated by Era was significantly reduced in USP48‐overexpressiong GCIY cells, while USP48 knockdown further prompted ROS accumulation compared to vector controls under ferroptosis condition (Figure 4H,I). Overall, these data suggest that USP48 exerts a protective effect against Erastin‐induced ferroptosis, mimicking the protective phenotype of METTL3, possibly through stabilizing METTL3.

3.5. USP48 Interacts With METTL3 and Enhances Its Stability By Deubiquitination

To elucidate the molecular mechanism of USP48‐mediated ferroptosis regulation and METTL3 involvement, we performed immunoprecipitation assays confirmed USP48 and METTL3 interaction. IP assays showed that Flag‐tagged METTL3 and HA‐tagged USP48 physically interact with each other in HEK293T cells (Figure 5A). Endogenous protein immunoprecipitation with anti‐USP48 and anti‐METTL3 antibodies further confirmed the physical binding between USP48 and METTL3 (Figure 5B). Furthermore, ubiquitination assay using His‐tagged ubiquitin revealed that USP48 overexpression markedly reduced the polyubiquitination of METTL3 compared to controls, indicating that USP48 deubiquitinates METTL3 (Figure 5C). Cycloheximide chase experiments showed prolonged METTL3 protein stability upon USP48 overexpression (Figure 5D,E). These data demonstrate that USP48 physically interacted with METTL3, reduces its ubiquitination, and promotes METTL3 protein stability in gastric cancer cells.

Figure 5.

Figure 5

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USP48 interacts with METTL3 and enhances its Stability by deubiquitination. (A) Immunoprecipitation assays showing interaction between Flag‐METTL3 and HA‐USP48 in HEK293T cells. Total cell lysates (TCL) served as input controls. (B) Endogenous protein Immunoprecipitation assays showing interaction between USP48 and METTL3 in gastric cancer cells GCIY and MKN‐7. (C) Ubiquitination assay detecting His‐tagged ubiquitin‐modified METTL3 immunoprecipitating by Flag‐tag. Expression levels of USP48 and METTL3 in inputs are shown. (D) Western blot analysis of METTL3 protein levels over time after cycloheximide (CHX) treatment in cells with or without USP48 overexpression. Actin serves as internal control. (E) Quantification of Flag‐METTL3 protein degradation kinetics. Data represents mean ± SD from three independent experiments, p‐value is indicated in the graph.

3.6. USP48 Modulates Ferroptosis Stress Via METTL3 Regulation

To confirm that METTL3 acts downstream of USP48, we performed USP48 overexpression or knockdown plus METTL3 regulation in both cell lines with Erastin treatment. Viability assays illustrated reduced viability when METTL3 deficient in USP48‐overexpressing GCIY cells in the presence of Era, while METTL3 overexpression elevated cell viability in USP48 knockdown MKN‐7 cells in the presence of Erastin (Figure 6A,B). In addition, cell cycle measurement demonstrated high proportion in G0/G1 arrest and low S‐phase population in shMETTL3 USP48‐overexpressing GCIY cells in the presence of Erastin, compared with USP48 OE+shNC group. Meanwhile, METTL3 overexpression promoted cell cycle progression in USP48‐overexpressing MKN‐7 cell during Erastin exposure (Figure S6A‐B). These results indicate that METTL3 plays a critical role in mediating the protective effects of USP48 against Erastin‐induced ferroptosis‐associated phenotypes.

Figure 6.

Figure 6

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METTL3 reverses the inhibitory effects of USP48 knockdown on gastric cancer cell with Erastin treatment. (A, B) MTT assays showing cell viability in GCIY and MKN‐7 cells with USP48 OE/KD and METTL3 OE/KD under Erastin exposure. (C, D) Western blot analysis and relative quantification of USP48, METTL3, GPX4, and FTH1 protein expression in GCIY cells with USP48 overexpression and METTL3 knockdown, and MKN‐7 cells with USP48 knockdown and METTL3 overexpression treated with Erastin. (E) Cellular MDA levels reflect lipid peroxidation in indicated treatment groups. (F) Intracellular GSH, GSSG, and GSH/GSSG ratios. (G) Intracellular Fe2+ concentrations measured by colorimetric assay. (H) Representative flow cytometry histograms showing DCF fluorescence for ROS measurement. (I) Quantification of relative mean fluorescence intensity (MFI) of ROS levels. Data represent mean ± SD of three independent experiments, p‐values indicate statistical significance between groups.

At molecular levels, METTL3 knockdown downregulated GPX4 and FTH1 expression in Erastin‐treated USP48‐overexpressing GCIY cells, while METTL3 overexpression rescued the expression suppression of GPX4 and FTH1 caused by USP48 knockdown in Erastin treated MKN‐7 cells (Figure 6C‐D). Additionally, METTL3 knockdown significantly enhanced Erastin‐inducing MDA accumulation in USP48‐overexpressing GCIY cells, whereas METTL3 overexpression reduced MDA levels caused by USP48 knockdown upon Erastin induction (Figure 6E). Moreover, in Erastin induced ferroptosis condition, METTL3 knockdown decreased GSH, increased GSSG, and lowered the GSH/GSSG ratio in USP48‐overexpressing GCIY cells. Conversely, METTL3 overexpression enhanced GSH, lowered GSSG, and increased the GSH/GSSG ratio in USP48 knockdown MKN‐7 cells (Figure 6F). METTL3 knockdown enhanced intracellular Fe2+ content elevated by Erastin in USP48‐overexpressing cells, while METTL3 overexpression reduced Fe2+ levels in USP48 knockdown cells (Figure 6G). Furthermore, METTL3 knockdown significantly increased ROS levels in USP48‐overexpressing cells, whereas METTL3 overexpression decreased ROS in USP48‐deficient cells in the Erastin induction condition (Figure 6H‐I). These findings establish METTL3 as a key downstream effector in USP48‐mediated regulation of ferroptosis‐related oxidative stress and iron metabolism.

4. Discussion

One of our major findings is the identification of USP48's role in gastric cancer, aligning with its involvement in various other tumor malignancies. In colorectal cancer (CRC), USP48 regulates HMGA2 stability, and DUB‐IN‐2 has been identified as a promising inhibitor for CRC treatment [36]. In acute myeloid leukemia, caspase‐3 cleaves USP48 during drug‐induced apoptosis at a conserved DEQD motif, leading to rapid degradation of the catalytic fragment, which enhances apoptosis and chemotherapy sensitivity [37]. USP48 acts as a tumor suppressor in hepatocellular carcinoma by disrupting metabolic reprogramming [38]. Additionally, USP48 promotes pyroptosis by deubiquitinating GSDME, which boosts antitumor immunity [39], while its elevated expression in ovarian cancer is associated with poor survival and therapy resistance [40]. In glioblastoma, USP48 expression is induced by Sonic Hedgehog (SHH) signaling via Gli1, forming a positive feedback loop linked to tumor malignancy [41]. In CRC, USP48 stabilizes TAK1, inhibiting NF‐κB activation and suppressing tumor growth [42]. Collectively, these findings underscore USP48 as a key regulator in cancer progression.

USP48 also counteracts BRCA1 E3 ligase activity to promote genome stability and influence DNA repair pathways, enhancing cell survival under chemotherapeutic stress [43]. Moreover, USP48 variants are common in USP8 wild‐type corticotroph adenomas, augmenting corticotropin‐releasing hormone (CRH)‐induced hormone secretion consistent with SHH pathway activation [44]. Lastly, in non‐small cell lung cancer (NSCLC), miR‐489‐3p promotes malignancy by targeting USP48, suggesting a potential therapeutic avenue [45]. Overall, USP48 emerges as a key candidate for developing novel cancer therapies.

Our findings also extend the role of USP48 in ferroptosis within cancer. Previous studies have shown that USP48‐mediated deubiquitination stabilizes SLC1A5, thereby inhibiting ferroptosis in retinal pigment epithelium cells [46]. Our work further reveals that in gastric cancer, USP48 is regulated by METTL3, revealing a previously unrecognized axis linking m6A RNA modification and deubiquitinase activity in ferroptosis regulation during tumor progression. A similar mechanism has been reported in hepatocellular carcinoma, where USP48 acts as a tumor suppressor by stabilizing SIRT6 through Mettl14‐mediated m6A modification [38]. Together, these findings reveal the broader significance of m6A‐dependent regulation of USP48 across multiple cancer types and suggest that targeting the ferroptosis holds versatile therapeutic potential.

Our study demonstrates that METTL3 plays a pivotal role in regulating ferroptosis susceptibility in gastric cancer cells, acting as a key modulator of cell survival, cell cycle progression, and the cellular oxidative landscape under Erastin‐induced stress. Functional manipulation of METTL3 in these representative cell models revealed that elevated METTL3 confers resistance to ferroptosis inducers such as Erastin, whereas METTL3 depletion enhances susceptibility, driving cell cycle arrest. Mechanistically, we identified USP48 as a central player for METTL3 in gastric cancer. Functionally, USP48 mirrored the cytoprotective and anti‐ferroptotic effects of METTL3, including maintenance of proliferation and redox balance in Erastin‐treated cells. The protective advantage conferred by USP48 was abrogated upon METTL3 knockdown, and conversely, overexpressed METTL3 could rescue the deleterious effects of USP48 deficiency under ferroptotic stress. These results firmly position METTL3 as the principal downstream effector of USP48's anti‐ferroptotic action.

In ferroptosis pathway, METTL3 is a key regulator of iron‐related proteins and oxidation‐reduction regulators, METTL3 controls ferroptosis by catalyzing ACSL4, GPX4, SLC7A11 mRNA m6A levels [47, 48, 49]. In our study, we also clarified METTL3 controlled ferroptosis via mediating GPX4 and FTH1 mRNA levels. Moreover, identifying USP48 as a key regulator of METTL3 extends our understanding of the ubiquitin‐proteasome system's involvement in ferroptosis regulation. The ability of USP48 to phenocopied METTL3's protective effects, maintaining proliferation, redox homeostasis, and invasive potential, emphasizes its central role as a mediator of the anti‐ferroptotic response. Therapeutically, targeting the USP48‐METTL3 axis could enhance the efficacy of ferroptosis‐inducing agents like Erastin in gastric cancer [50]. Disrupting this pathway may overcome resistance mechanisms and sensitize tumor cells to oxidative cell death, offering a promising strategy for treatment development [51]. Future studies should explore how this regulatory axis interfaces with other ferroptosis modulators and the tumor microenvironment, as well as its potential prognostic and predictive value in clinical settings.

Although we have found that the USP48–METTL3 axis regulates ferroptosis and promotes cell survival in gastric cancer cells, our study has many limitations. First, this study is established rely on mere 2 cell lines validation, depends on high heterogeneity among gastric cancer cells and tumor, our study may not fully represent gastric cancer specificity. This study draw a conclusion from in vitro assays, much more in vivo validation need to be conducted. Further, METTL3 serves as a N6‐methyladenosine (m6A) transferase regulating translational efficiency of many genes, including USPs. For instance, METTL3 regulates stability of USP15 mRNA via its m6A transferase activity, improves USP15 expression [52]. In our study, the broader regulatory network involving USP48 and METTL3 remains unclear, and further research is needed to clarify their interactions, whether USP48 is an upstream or downstream effector of METTL3, or it conserves bidirectional regulation systems in ferroptosis signaling, need to be explored in the future study. Additionally, validating USP48 and METTL3 expression in patient sample is an important evidence supporting our study, the clinical relevance and prognostic value of this axis require validation in internal or larger patient cohorts. Finally, the therapeutic potential and safety of targeting USP48 or METTL3 have yet to be investigated in preclinical models.

Collectively, our findings uncover a novel USP48–METTL3 regulatory axis that protects gastric cancer cells against ferroptotic death by stabilizing METTL3. These findings not only advance our understanding of gastric cancer biology and ferroptosis regulation, but also present METTL3 and USP48 as promising therapeutic targets.

Author Contributions

Yi‐lin Xie, Tian‐xia Lei and Yong‐sheng Zheng conducted the methodology and wrote the manuscript. Ze‐lin Xu conducted formal analysis and validations. Hai‐xing Wang supervised this study and review this manuscript.

Ethics Statement

The authors have nothing to report.

Conflicts of Interest

The authors declare no conflicts of interest.

Supporting information

Supporting File

MC-65-1077-s001.docx (5.3MB, docx)

Acknowledgments

This study is supported by Xiamen's Guiding Project for Medical and Health Care (3502Z20244ZD1006). We would like to thank the TCGA, GEO, HPA databases for providing the available data.

Xie Y.‐l., Lei T.‐x., Zheng Y.‐s., Xu Z.‐l., and Wang H.‐x., “Ubiquitin Specific Peptidase 48 Deubiquitinates METTL3 to Modulate Ferroptosis in Gastric Cancer Cells,” Molecular Carcinogenesis 65 (2026): 1077‐1091, 10.1002/mc.70145.

Yi‐lin Xie, Tian‐xia Lei, and Yong‐sheng Zheng contributed equally to this work.

Data Availability Statement

The data analyzed in this study are publicly available in Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/) at GSE56807, GSE63089, GSE66229 and in TCGA‐STAD dataset (https://www.cancer.gov/ccg/research/genome-sequencing/tcga). The immunohistochemistry images from patients are publicly available in the Human Protein Atlas (HPA) database (https://www.proteinatlas.org/).

References

  • 1. Park J. Y., Georges D., Alberts C. J., Bray F., Clifford G., and Baussano I., “Global Lifetime Estimates of Expected and Preventable Gastric Cancers Across 185 Countries,” Nature Medicine 31 (July 2025): 3020–3027, 10.1038/s41591-025-03793-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2. Albrecht P., Karabati E., Ebert M. P., and Betge J., “Gastric Cancer: From Biomarkers to Functional Precision Medicine,” Trends in Molecular Medicine 31 (June 2025): 1089–1102, 10.1016/j.molmed.2025.05.007. [DOI] [PubMed] [Google Scholar]
  • 3. Guan W. L., He Y., and Xu R. H., “Gastric Cancer Treatment: Recent Progress and Future Perspectives,” Journal of Hematology & Oncology 16, no. 1 (May 2023): 57, 10.1186/s13045-023-01451-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4. Mamun T. I., Younus S., and Rahman M. H., “Gastric Cancer—Epidemiology, Modifiable and Non‐Modifiable Risk Factors, Challenges and Opportunities: An Updated Review,” Cancer Treatment and Research Communications 41 (January 2024): 100845, 10.1016/j.ctarc.2024.100845. [DOI] [PubMed] [Google Scholar]
  • 5. Chen Z., Wang W., Abdul Razak S. R., Han T., Ahmad N. H., and Li X., “Ferroptosis as a Potential Target for Cancer Therapy,” Cell Death & Disease 14, no. 7 (July 2023): 460, 10.1038/s41419-023-05930-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6. Zhang Q., Fan X., Zhang X., and Ju S., “Ferroptosis in Tumors and Its Relationship to Other Programmed Cell Death: Role of Non‐Coding Rnas,” Journal of Translational Medicine 21, no. 1 (July 2023): 514, 10.1186/s12967-023-04370-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7. Chen F., Kang R., Tang D., and Liu J., “Ferroptosis: Principles and Significance in Health and Disease,” Journal of Hematology & Oncology 17, no. 1 (June 2024): 41, 10.1186/s13045-024-01564-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8. Gu R., Xia Y., Li P., et al., “Ferroptosis and Its Role in Gastric Cancer,” Frontiers in Cell and Developmental Biology 10 (2022): 860344, 10.3389/fcell.2022.860344. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9. Yang Z., Zou S., Zhang Y., et al., “ACTL6A Protects Gastric Cancer Cells Against Ferroptosis Through Induction of Glutathione Synthesis,” Nature Communications 14, no. 1 (July 2023): 4193, 10.1038/s41467-023-39901-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10. Lu Y., Sun J., Yang M., et al., “Myricetin Induces Ferroptosis and Inhibits Gastric Cancer Progression by Targeting Nox4,” Journal of Agricultural and Food Chemistry 72, no. 12 (March 2024): 6178–6188, 10.1021/acs.jafc.3c05243. [DOI] [PubMed] [Google Scholar]
  • 11. Wang Y., Zheng L., Shang W., et al., “Wnt/Beta‐Catenin Signaling Confers Ferroptosis Resistance by Targeting GPX4 in Gastric Cancer,” Cell Death and Differentiation 29, no. 11 (November 2022): 2190–2202, 10.1038/s41418-022-01008-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12. Yang W. S., SriRamaratnam R., Welsch M. E., et al., “Regulation of Ferroptotic Cancer Cell Death by GPX4,” Cell 156, no. 1–2 (January 2014): 317–331, 10.1016/j.cell.2013.12.010. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13. Liu Y., Zhai E., Chen J., et al., “m(6) A‐Mediated Regulation of PBX1‐GCH1 Axis Promotes Gastric Cancer Proliferation and Metastasis by Elevating Tetrahydrobiopterin Levels,” Cancer Communications 42, no. 4 (April 2022): 327–344, 10.1002/cac2.12281. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14. Shi J., Zhang Z., Yin H., et al., “RNA m6A Modification in Ferroptosis: Implications for Advancing Tumor Immunotherapy,” Molecular cancer 23, no. 1 (September 2024): 213, 10.1186/s12943-024-02132-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15. Zhao Y.‐P., Liu J.‐L., Wang S., and Li X., “Role of Non‐Coding Rna‐Regulated Ferroptosis in Colorectal Cancer,” Cell Death Discovery 11, no. 1 (July 2025): 315, 10.1038/s41420-025-02606-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16. Yu Z. and Yang Y., “METTL3 as a Potential Therapeutic Target in Gastric Cancer,” Frontiers in Oncology 14 (2024): 1483435, 10.3389/fonc.2024.1483435. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17. Fang M., Li Y., Wang P., et al., “METTL3 Inhibition Restores PD‐L1 Expression and CD8+ T‐Cell Cytotoxic Function in Immunotherapy‐Treated Gastric Cancer,” Cancer Immunology Research 13, no. 7 (July 2025): 1037–1052, 10.1158/2326-6066.Cir-24-1179. [DOI] [PubMed] [Google Scholar]
  • 18. Zhang J. Y., Du Y., Gong L. P., et al., “ebv‐circRPMS1 Promotes the Progression of Ebv‐Associated Gastric Carcinoma via Sam68‐dependent Activation of METTL3,” Cancer Letters 535 (2022): 215646, 10.1016/j.canlet.2022.215646. [DOI] [PubMed] [Google Scholar]
  • 19. Li H., Wang C., Lan L., et al., “METTL3 Promotes Oxaliplatin Resistance of Gastric Cancer Cd133+ Stem Cells by Promoting PARP1 Mrna Stability,” Cellular and Molecular Life Sciences 79, no. 3 (February 2022): 135, 10.1007/s00018-022-04129-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20. Nie K., Zheng Z., Li J., et al., “AGAP2‐AS1 Promotes the Assembly of m6A Methyltransferases and Activation of the IL6/STAT3 Pathway by Binding With Wtap in the Carcinogenesis of Gastric Cancer,” FASEB Journal: Official Publication of the Federation of American Societies for Experimental Biology 37, no. 12 (December 2023): e23302, 10.1096/fj.202301249R. [DOI] [PubMed] [Google Scholar]
  • 21. Chen S., Su X., Wang J., et al., “Identification and Validation of METTL3‐Related Molecules for Predicting Prognosis and Efficacy of Immunotherapy in Gastric Cancer Based on m6A Methylome and Transcriptome Sequencing Analysis,” Frontiers in Oncology 12 (2022): 935239, 10.3389/fonc.2022.935239. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22. Wang N., Huo X., Zhang B., et al., “METTL3‐Mediated ADAMTS9 Suppression Facilitates Angiogenesis and Carcinogenesis in Gastric Cancer,” Frontiers in Oncology 12 (2022): 861807, 10.3389/fonc.2022.861807. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23. Wang Y., Hong Z., Song J., Zhong P., and Lin L., “METTL3 Promotes Drug Resistance to Oxaliplatin in Gastric Cancer Cells Through Dna Repair Pathway,” Frontiers in Pharmacology 14 (2023): 1257410, 10.3389/fphar.2023.1257410. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24. Hu F., Zhang S., and Chai J., “METTL3 Promotes Gastric Cancer Progression via Modulation of FNTA‐Mediated KRAS/ERK Signaling Activation,” Molecular Cancer Research 23, no. 8 (August 2025): 724–738, 10.1158/1541-7786.Mcr-24-1168. [DOI] [PubMed] [Google Scholar]
  • 25. Zang Y., Tian Z., Wang D., et al., “METTL3‐mediated N(6)‐methyladenosine Modification of STAT5A Promotes Gastric Cancer Progression by Regulating Klf4,” Oncogene 43, no. 30 (July 2024): 2338–2354, 10.1038/s41388-024-03085-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26. Liu T., Feng Y. L., Wang R. Y., et al., “Long‐Term MNNG Exposure Promotes Gastric Carcinogenesis by Activating METTL3/m6A/miR1184 Axis‐Mediated Epithelial‐Mesenchymal Transition,” Science of the Total Environment 913 (February 2024): 169752, 10.1016/j.scitotenv.2023.169752. [DOI] [PubMed] [Google Scholar]
  • 27. Yan T., Wang Q., and Liu Y., “METTL3 Stabilizes SERPINE2 via the m6A Modification to Drive the Malignant Progression of Gastric Signet Ring Cell Carcinoma,” Translational Cancer Research 14, no. 1 (January 2025): 78–92, 10.21037/tcr-24-896. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28. Ravid T. and Hochstrasser M., “Diversity of Degradation Signals in the Ubiquitin‐Proteasome System,” Nature Reviews Molecular Cell Biology 9, no. 9 (September 2008): 679–689, 10.1038/nrm2468. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29. Zhao L., Zhao J., Zhong K., Tong A., and Jia D., “Targeted Protein Degradation: Mechanisms, Strategies and Application,” Signal Transduction and Targeted Therapy 7, no. 1 (April 2022): 113, 10.1038/s41392-022-00966-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30. Xu P., Liu K., Huang S., et al., “N(6)‐methyladenosine‐modified MIB1 Promotes Stemness Properties and Peritoneal Metastasis of Gastric Cancer Cells by Ubiquitinating Ddx3x,” Gastric Cancer 27, no. 2 (March 2024): 275–291, 10.1007/s10120-023-01463-5. [DOI] [PubMed] [Google Scholar]
  • 31. Wang Y., Wang Y., Patel H., et al., “Epigenetic Modification of m(6)A Regulator Proteins in Cancer,” Molecular Cancer 22, no. 1 (June 2023): 102, 10.1186/s12943-023-01810-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32. Yu Y., Hu J., Wang W., et al., “Targeting PSMD14 Combined With Arachidonic Acid Induces Synthetic Lethality via FADS1 m6A Modification in Triple‐Negative Breast Cancer,” Science Advances 11, no. 19 (2025): eadr3173, 10.1126/sciadv.adr3173. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33. Wang C., Meng Y., Zhao J., et al., “Deubiquitinase USP13 Regulates Glycolytic Reprogramming and Progression in Osteosarcoma by Stabilizing METTL3/m(6)A/ATG5 Axis,” International Journal of Biological Sciences 19, no. 7 (2023): 2289–2303, 10.7150/ijbs.82081. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34. Qiu H., Liu Y., Zhou H., et al., “USP5 Regulates Ferroptosis in Colorectal Cancer by Targeting the YBX3/SLC7A11 Axis Through Lysosomal Degradation,” Cell Death & Disease 16, no. 1 (November 2025): 822, 10.1038/s41419-025-08146-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35. Li M., Wang X., Lu S., et al., “Erastin Triggers Autophagic Death of Breast Cancer Cells by Increasing Intracellular Iron Levels,” Oncology Letters 20, no. 4 (October 2020): 57, 10.3892/ol.2020.11918. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36. Cheng C., Yao H., Li H., et al., “Blockade of the Deubiquitinating Enzyme USP48 Degrades Oncogenic HMGA2 and Inhibits Colorectal Cancer Invasion and Metastasis,” Acta Pharmaceutica Sinica B 14, no. 4 (April 2024): 1624–1643, 10.1016/j.apsb.2024.01.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37. Zhang Z., Lin X., Yang Y., et al., “Caspase 3‐specific Cleavage of Ubiquitin‐Specific Peptidase 48 Enhances Drug‐Induced Apoptosis in AML,” Cancer Biology & Therapy 26, no. 1 (December 2025): 2459426, 10.1080/15384047.2025.2459426. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38. Du L., Li Y., Kang M., et al., “USP48 Is Upregulated by Mettl14 to Attenuate Hepatocellular Carcinoma via Regulating SIRT6 Stabilization,” Cancer Research 81, no. 14 (July 2021): 3822–3834, 10.1158/0008-5472.Can-20-4163. [DOI] [PubMed] [Google Scholar]
  • 39. Ren Y., Feng M., Hao X., et al., “USP48 Stabilizes Gasdermin E to Promote Pyroptosis in Cancer,” Cancer Research 83, no. 7 (April 2023): 1074–1093, 10.1158/0008-5472.Can-22-1812. [DOI] [PubMed] [Google Scholar]
  • 40. Lei X., Li X., Chen H., and Liu Z., “USP48 Sustains Chemoresistance and Metastasis in Ovarian Cancer,” Current Cancer Drug Targets 20, no. 9 (2020): 689–699, 10.2174/1568009620666200503045400. [DOI] [PubMed] [Google Scholar]
  • 41. Zhou A., Lin K., Zhang S., et al., “Gli1‐induced Deubiquitinase USP48 Aids Glioblastoma Tumorigenesis by Stabilizing Gli1,” EMBO Reports 18, no. 8 (August 2017): 1318–1330, 10.15252/embr.201643124. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42. Zhang X., Zhao J., Qi G., et al., “USP48 Inhibits Colorectal Cancer Progression and Promotes M1‐like Macrophage Polarization by Stabilizing Tak1,” Experimental Cell Research 446, no. 1 (March 2025): 114469, 10.1016/j.yexcr.2025.114469. [DOI] [PubMed] [Google Scholar]
  • 43. Uckelmann M., Densham R. M., Baas R., et al., “USP48 Restrains Resection by Site‐Specific Cleavage of the BRCA1 Ubiquitin Mark From H2a,” Nature Communications 9, no. 1 (January 2018): 229, 10.1038/s41467-017-02653-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44. Sbiera S., Perez‐Rivas L. G., Taranets L., et al., “Driver Mutations in USP8 Wild‐Type Cushing's Disease,” Neuro‐Oncology 21, no. 10 (October 2019): 1273–1283, 10.1093/neuonc/noz109. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45. Zhang P., Li L., Wang B., et al., “miR‐489‐3p Promotes Malignant Progression of Non‐Small Cell Lung Cancer Through the Inactivation of Wnt/β‐catenin Signaling Pathway via Regulating Usp48,” Respiratory Research 23, no. 1 (April 2022): 93, 10.1186/s12931-022-01988-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46. Zhang G., Yu J., and Wan Y., “USP48 Deubiquitination Stabilizes SLC1A5 to Inhibit Retinal Pigment Epithelium Cell Inflammation, Oxidative Stress and Ferroptosis in the Progression of Diabetic Retinopathy,” Journal of Bioenergetics and Biomembranes 56, no. 3 (June 2024): 311–321, 10.1007/s10863-024-10008-z. [DOI] [PubMed] [Google Scholar]
  • 47. Shen C., Jiang Y., Lin J., Guo Q., and Fang D., “METTL3 Silencing Inhibits Ferroptosis to Suppress Ovarian Fibrosis in PCOS by Upregulating m6A Modification of GPX4,” Journal of Molecular Histology 55, no. 6 (December 2024): 1163–1175, 10.1007/s10735-024-10257-7. [DOI] [PubMed] [Google Scholar]
  • 48. Wang Y., Zhang G., Gao Y., Zhang X., and Qi H., “METTL3 Promotes Trophoblast Ferroptosis in Preeclampsia by Stabilizing the ACSL4 m(6)A Modification,” Experimental Cell Research 437, no. 1 (April 2024): 113990, 10.1016/j.yexcr.2024.113990. [DOI] [PubMed] [Google Scholar]
  • 49. Dai Z., Lin B., Qin M., et al., “METTL3‐mediated m6A Modification of SLC7A11 Enhances Nasopharyngeal Carcinoma Radioresistance by Inhibiting Ferroptosis,” International Journal of Biological Sciences 21, no. 4 (2025): 1837–1851, 10.7150/ijbs.100518. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50. Lei G., Zhuang L., and Gan B., “Targeting Ferroptosis as a Vulnerability in Cancer,” Nature Reviews Cancer 22, no. 7 (July 2022): 381–396, 10.1038/s41568-022-00459-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51. Chen D., Guo Z., Yao L., et al., “Targeting Oxidative Stress‐Mediated Regulated Cell Death as a Vulnerability in Cancer,” Redox Biology 84 (July 2025): 103686, 10.1016/j.redox.2025.103686. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52. Fang R., Jia Z., Xin Y., et al., “N6‐methyladenosine‐modification of USP15 Regulates Chemotherapy Resistance by Inhibiting LGALS3 Ubiquitin‐Mediated Degradation via AKT/mTOR Signaling Activation Pathway in Hepatocellular Carcinoma,” Cell Death Discovery 11, no. 1 (January 2025): 3, 10.1038/s41420-024-02282-y. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

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

Supplementary Materials

Supporting File

MC-65-1077-s001.docx (5.3MB, docx)

Data Availability Statement

The data analyzed in this study are publicly available in Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/) at GSE56807, GSE63089, GSE66229 and in TCGA‐STAD dataset (https://www.cancer.gov/ccg/research/genome-sequencing/tcga). The immunohistochemistry images from patients are publicly available in the Human Protein Atlas (HPA) database (https://www.proteinatlas.org/).

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