NSUN2–tRNA^Val−CAC-axis-regulated codon-biased translation drives triple-negative breast cancer glycolysis and progression

Abstract

Background

Epitranscriptomic data indicate that aberrant tRNA modifications in malignant diseases can promote tumor growth by facilitating oncogene translation. NSUN2, a 5-methylcytosine (m5C) methyltransferase of tRNA, is elevated in an array of solid cancers, including triple-negative breast cancer (TNBC). However, it remains unclear how NSUN2 drives aggressive behavior and if NSUN2 could be an effective therapeutic target for TNBC.

Methods

Functional experiments, including RNA interference, lentivirus transduction, and in vivo xenograft models, were conducted to evaluate the role of NSUN2 in TNBC cell proliferation, metastasis, and chemoresistance. Ribosome sequencing (Ribo-seq), tRNA m5C bisulfite sequencing, and codon usage bias analysis were employed to explore the translational mechanisms underlying NSUN2-mediated tRNA modifications. Glycolysis assays and molecular docking were used to investigate metabolic reprogramming and protein interactions.

Results

NSUN2 was significantly upregulated in TNBC and correlated with poor patient prognosis. Mechanistically, NSUN2 mediates m5C modification of tRNA^Val−CAC, enhancing the codon-frequency-dependent translation of key glycolysis-related genes, including ALDH3A2, ALDH7A1, HK1, and PFKM. Depletion of NSUN2 disrupted tRNA^Val−CAC m5C modification, impairing the translation of these metabolic enzymes and suppressing glycolysis, which ultimately inhibited TNBC cell proliferation, migration, and invasion both in vitro and in vivo. Furthermore, NSUN2 overexpression conferred resistance to docetaxel, while its inhibition sensitized TNBC cells to docetaxel treatment. Clinically, elevated expression levels of NSUN2 and glycolysis-related genes were observed in docetaxel-resistant TNBC tissues, further supporting the role of NSUN2 in chemoresistance.

Conclusions

This study identifies NSUN2 as a critical regulator of TNBC progression through tRNA^Val−CAC m5C modification and codon-biased translation of glycolysis-related mRNAs. Our findings reveal a novel NSUN2–tRNA^Val−CAC axis that orchestrates metabolic reprogramming and translational control in TNBC, offering a promising prognostic biomarker and therapeutic target.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s11658-025-00781-z.

Background

Breast cancer remains the most prevalent malignancy and the leading cause of cancer-related mortality among women globally. Among its various subtypes, triple-negative breast cancer (TNBC)—characterized by the absence of human epidermal growth factor receptor 2 (HER2), estrogen receptor (ER), and progesterone receptor (PR) expression—represents a particularly aggressive form, accounting for 15–20% of all breast cancer cases [1, 2]. TNBC disproportionately contributes to metastasis and cancer-related deaths due to its limited targeted therapeutic options and rapid development of chemoresistance. While recent advances in immune checkpoint inhibitors and PARP inhibitors have expanded treatment options, intrinsic and acquired resistance mechanisms continue to undermine clinical outcomes [3–5]. This underscores an urgent need to elucidate the molecular drivers of TNBC progression and uncover vulnerabilities that could translate into novel therapeutic strategies.

Recent breakthroughs in epitranscriptomics have highlighted RNA modifications as critical regulators of cancer biology, with tRNA chemical modifications emerging as master coordinators of translational reprogramming [6]. Alterations of tRNA modifications may lead to fluctuations in tRNA levels through either stabilizing or destabilizing tRNA, or disrupting anticodon–codon interactions, ultimately influencing the efficiency and accuracy of translation of mRNAs with specific codon biases [7, 8]. Among these modifications, methylation is regarded as the most prominent post-transcriptional modification, dynamically regulated by methyltransferases and demethylases. For instance, METTL1/WDR4-complex-mediated N7-methylguanosine (m7G) tRNA modifications can drive hepatocellular carcinoma resistance to lenvatinib treatment by selectively regulating the EGFR pathway [9]. Similarly, METTL6 catalyzes the formation of 3-methylcytidine (m3C) at C32 of specific serine tRNA iso-acceptors for maintaining stem cell self-renewal, as well as impacting tumor cell proliferation [10]. 5-Methylcytosine (m5C) emerges as one of the most abundant tRNA modifications, typically located between the T-arm and the variable loop or within the cytosine of the anticodon loop [11]. However, the landscape of tRNA m5C modifications in TNBC, their codon-specific regulatory mechanisms, and their interplay with metabolic reprogramming remain uncharted.

Notably, the metabolic demands of translation machinery intersect critically with cancer’s reliance on aerobic glycolysis. While glycolysis provides rapid ATP and biosynthetic precursors, its byproduct lactate fosters an immunosuppressive tumor microenvironment and promotes cancer cell metastasis [12–14]. Intriguingly, tRNA-modification-mediated translational control may serve as a hidden link between metabolic rewiring and oncogenic signaling. For example, tRNA m7G modifications have been shown to upregulate glycolytic enzymes in colorectal cancer, driving metabolic adaptation to support tumor growth [15]. Despite these advances, how tRNA m5C modifications coordinate codon-biased translation of metabolic regulators to sustain TNBC’s glycolytic addiction remains unexplored.

Here, we identify NSUN2 as a master tRNA m5C modifier that drives TNBC progression through the codon-biased translation of glycolytic enzymes. Integrating multi-omics analyses, functional assays, and translational profiling, we demonstrate that NSUN2-mediated m5C modification of tRNA^Val−CAC selectively enhances the decoding of GUG-valine codons, enabling hypertranslation of key glycolytic effectors (ALDH3A2, ALDH7A1, HK1, and PFKM). Depletion of NSUN2 disrupts this axis, impairing glycolysis, suppressing tumor growth, and sensitizing TNBC cells to docetaxel. Crucially, we establish NSUN2 as an independent prognostic marker in TNBC and provide preclinical evidence for targeting tRNA m5C modifications to disrupt metabolic–translational crosstalk. Our findings illuminate a novel epitranscriptomic–metabolic axis in TNBC and propose NSUN2 as a dual therapeutic target for curbing both tumor progression and chemoresistance.

Materials and methods

Bioinformatics analysis

The database of The Cancer Genome Atlas (TCGA, https://cancergenome.nih.gov/) was used for gene expression and associated clinical features. The correlation of NSUN2 expression with survival outcomes, including overall survival (OS), disease-specific survival (DSS), progression-free interval (PFI), and disease-free interval (DFI) were estimated using Kaplan–Meier (KM) survival analyses and log-rank test. The Genomics of Drug Sensitivity in Cancer (GDSC) database (https://www.cancerrxgene.org) was utilized to determine the relationship between NSUN2 and drug sensitivity on the basis of the IC50 of compounds.

Patients and tissue sample collection

A total of 30 pairs of TNBC and para-tumor tissues were collected from patients who underwent a mastectomy at the Department of Breast Surgery in Xiangya Hospital (Changsha, China) with the patients’ informed consent. The criteria for inclusion were as follows: (1) patients with a pathologically confirmed diagnosis of TNBC; (2) received radical surgery or breast-conserving surgery; and (3) patients with comprehensive clinical, pathological, and follow-up information. The freshly excised samples were either stored at −80 °C or processed for paraffin embedding. All processes were carried out in compliance with the Helsinki Declaration and were approved by the hospital’s institutional ethics review board.

Cell cultures

Human TNBC cell lines (HCC1937, MDA-MB-231, HCC1806, MDA-MB-468, and BT549) and a normal breast epithelial cell line (MCF10A) were all obtained from American Type Culture Collection (ATCC). TNBC cell lines were cultured in an appropriate medium supplemented with 10% fetal bovine serum and 1.2% streptomycin/penicillin. MCF10A was maintained in a specific epithelial culture medium (CL-0525, Procell, China). All cell lines were maintained in a humidified atmosphere with 5% CO₂ at 37 °C. Cell lines were authenticated using STR profile analysis and routinely tested for Mycoplasma contamination.

RNA interference and lentivirus transduction

Small interfering RNA (siRNA) targeting NSUN2, tRNA^Val−CAC, and a corresponding control (si-NC) were obtained from RiboBio (Guangzhou, China). Lentiviruses with NSUN2 shRNAs or wild-type and mutant NSUN2 were from Genechem (Shanghai, China). The sequence of the siRNAs and shRNAs are provided in Supplementary Table S1.

Quantitative RT-PCR (qRT-PCR)

Total RNA was isolated with AG RNAex Pro RNA extraction kit (AG21101) and first strand cDNA was achieved using a reverse transcription kit (AG11728) following the manufacturer’s instructions (Accurate Biotechnology, China). qRT-PCR was performed using the SYBR Green Pro Taq HS qPCR Kit (AG11701, Accurate Biotechnology, China). The primer sequences are listed in Supplementary Table S2.

Polysome profiling qRT-PCR

This protocol was done as described in our previous article [16]. Briefly, the TNBC cells were incubated with cycloheximide (200 μg/ml, MCE, USA) for 10 min and then lysed on ice with lysis buffer. After centrifugation (4 ℃, 10,000 r/min, 10 min), the supernatant was gathered. The 10%, 20%, 30%, 40%, and 50% (w/v) sucrose solutions were prepared and added to fill ultracentrifuge tubes according to the density. Subsequently, the supernatant was loaded into sucrose gradient solution and centrifuged at 39,000 rpm for 3 h at 4 ℃ using a Beckman SW-41Ti rotor. Samples were collected as ten fractions from the top of the sucrose gradient solution, followed by qRT-PCR.

Immunohistochemistry (IHC)

Tumor specimens were fixed with 4% paraformaldehyde solution, then subjected to a sequential dehydration process with ethanol of increasing concentrations, followed by embedding in paraffin and sectioning into 5 µm sections. The sections were treated to remove paraffin by heating at 65 °C for 1 h, cleared with xylene, and underwent a rehydration process through a series of alcohol concentrations. Sections were treated with hematoxylin and eosin (H&E) for staining, or incubated with a primary antibody at 4 °C overnight in a blocking solution containing 5% normal goat serum and 3% bovine serum albumin dissolved in phosphate-buffered saline (PBS) for immunostaining. The primary antibodies utilized in the study are listed in Supplementary Table S3. The criteria for immunohistochemical staining scoring were as follows: (1) cell staining intensity score: the evaluation of five random fields under a 200× microscope, assigning a score of 0 for no staining, 1 for light yellow staining, 2 for medium yellow or brown staining without background coloring, and 3 for deep brown staining, indicating strong positivity; and (2) positive expression cell count score: examination of the overall tissue section under a 100× microscope, assigning scores on the basis of the percentage of positively expressed cells (0 for 0%, 1 for 0–25%, 2 for 25–50%, 3 for 50–75%, and 4 for ≥ 75%). The product of scores from both criteria constituted the final score. All evaluations were performed independently by two experienced pathologists who were blinded to the experimental groups to minimize bias.

Western blotting assay

Total protein was isolated utilizing RIPA lysis buffer (Beyotime, China) supplemented with protease inhibitors and a phosphatase inhibitor cocktail (Sigma-Aldrich, USA). The protein concentration was determined by employing the BCA Protein Assay Kit (Beyotime, China). The collected lysates were separated by SDS-PAGE, transferred onto nitrocellulose membranes, incubated with primary antibodies, and then treated with horseradish-peroxidase-conjugated secondary antibodies. The immunoreactive bands were visualized using chemiluminescence (ThermoFisher Scientific, USA). The primary antibodies utilized in this research are listed in Supplementary Table S3.

RNA m5C dot blot assay

The total RNA was isolated from cells using an AG RNAex Pro RNA extraction kit (AG21101, Accurate Biotechnology, China). Following quantification using Qubits (ThermoFisher Scientific, USA), 300 ng of RNA from each sample was applied onto a Hybond-N+ nylon membrane (Cytiva, USA). Each spot was subjected to UV crosslinking for 5 min in a dark crosslinking apparatus. Subsequently, the membrane underwent blocking with 5% nonfat milk and was then treated with an anti-m5C antibody (Abcam, UK) overnight at 4 °C. The following day, a secondary antibody was applied to the membrane at room temperature, and the membrane was exposed to an imaging system using a sensitive enhanced chemiluminescence (ECL) exposure solution (Fisher Biotech, China). Post-exposure, the membrane was stained with 0.02% methylene blue (Sangon Biotech, China) to verify the uniformity of sample loading across all groups.

Cell viability, cycle, colony formation, migration, and invasion assays

The cell counting kit-8 (CCK-8) assay and colony formation assays were used to assess cell proliferation and drug resistance. Cell cycle was measured by flow cytometry (BD Biosciences, USA). Cell migration and invasion were evaluated by the Transwell assay (24-well, 8-μm pore size; Corning Costar, USA). The specific procedures were carried out in accordance with previously described protocols [17].

Animal experiments

All animal experiments were performed according to the guidelines of the Institutional Animal Care and the Ethics Committee of Xiangya Hospital Central South University. BALB/c nude mice (4 weeks of age) were obtained from the SJA Laboratory Animal Company (Hunan China) and raised in a specific pathogen-free (SPF) conditions. All mice were randomly divided into experimental (shNSUN2) and control (shCtrl) groups. An estimated 1 × 10⁶ MDA-MB-231 cells (suspended in 100 µl PBS) were injected into the armpits of the mice to observe tumor growth. The tumor weight and volume were monitored once a week. HCC1806 cells transfected with a vector or NSUN2 overexpression plasmid were injected into the tail veins of BALB/c nude mice to create a lung metastasis model.

In the drug sensitivity test, when the tumor volume reached 100 mm³, the shNSUN2 and shCtrl groups were treated with DMSO or docetaxel via intraperitoneal injection three times per week for 2 weeks, the groups were as follows: shCtrl + DMSO, shCtrl + docetaxel, shNSUN2 + DMSO, and shNSUN2 + docetaxel. The mice were sacrificed and all primary tumors were harvested. Tumor volume was measured and calculated as: (width² × length) × 0.5.

Puromycin intake assay

Puromycin intake assay was performed as previously described [8]. Cells were incubated with a solution of 1 μM puromycin at 37 °C for a duration of 30 min, followed by protein extraction and western blot analysis. The rate of new protein synthesis was assessed using an anti-puromycin antibody (ABclonal, China).

Ribosome sequencing (Ribo-seq)

Briefly, TNBC cells were treated with Dulbecco’s modified Eagle medium (DMEM) supplemented with 2 µg/ml harringtonine for 2 min, prior to the application of 100 µg/ml cycloheximide to inhibit protein synthesis, immobilizing the ribosome during translation of the mRNA or at the start site. The cells were then lysed and RNase was added to the cell lysate to digest the mRNA that was not protected by ribosomes. Single ribosomes were then isolated and the undigested short mRNA on the purified ribosomes was extracted for library sequencing and corresponding data analysis (Aksomics, China). Translation efficiency (TE) was determined by calculating the ratio of coding sequence (CDS) footprinting fragments per kilobase of transcript per million mapped reads (FPKM) to the corresponding mRNA FPKM.

Glycolysis assay

Intracellular ATP production, lactic acid production, and glucose utilization were quantified using an ATP assay kit, lactic acid assay kit (Jiancheng Corporation, China), and glucose uptake assay kit (Abcam, UK), respectively, following the manufacturer’s instructions. The extracellular acidification rate (ECAR) was determined on the Seahorse XFe96 Analyzer (Agilent, USA). All data were normalized and processed with Wave software.

tRNA m5C-BisSeq sequencing

TNBC cells were processed as required, placed on ice, and washed three times with pre-chilled PBS buffer, followed by lysis in TRIzol (AG21101, Accurate Biotechnology, China). tRNA bisulfite sequencing service was provided by CloudSeq Inc. (Shanghai, China). The total RNA was selected for small RNA fragments (< 200 nt) using the mirVana Isolation Kit (ThermoFisher, USA). The enriched small RNA was deaminated in 0.1 M Tris–HCl pH 9.0 and 1 mM EDTA at 37 °C for 30 min. The deaminated tRNA was subjected to bisulfite conversion and purification using the EZ RNA Methylation Kit (Zymo Research, USA). The GenSeq^® Small RNA Library Preparation Kit (GenSeq, Inc. China) was used to construct the tRNA library according to the instructions, and sequencing was ultimately performed on the NovaSeq platform (Illumina, USA).

tRNA sequencing (tRNA-seq)

tRNA sequencing service provided by CloudSeq Inc (Shanghai, China). The total RNA was subjected to size selection to isolate the small RNA fraction of less than 200 nucleotides using the MirVana Isolation Kit (ThermoFisher, USA). The isolated small RNAs underwent de-aminoacylation in a solution of Tris–HCl at pH 9.0 with 1 mM EDTA for 30 min at 37 °C. tRNA libraries were constructed with GenSeq^® Small RNA Library Prep Kit (GenSeq, Inc.China), adhering to the manufacturer’s guidelines.

Codon usage bias analysis

The software CodonW 1.4.2 was used for codon usage bias analysis. The calculation formula for the effective number of codons (ENC) [18] is:

$$\text{ENC}=2+\frac{9}{F_2}+\frac{1}{F_3}+\frac{5}{F_4}+\frac{3}{F_6}$$

$$\text{F}=\frac{n{\sum }_{i=1}^k{p}_i^2-1}{n-1}n>1p_i=\frac{\mathit{ni}}{n}$$

The expected value of ENC (ENC.^expected) = 2 + GC_3s + $\frac{29}{{{\mathit{GC}}_{3s}}^2+{\left(1,-,{\mathit{GC}}_{3s}\right)}^2}$

Here, n represents the total number of codons used in the gene, k indicates the number of the same codon, p denotes the codon usage frequency, and GC3s refers to the GC content of the third position of synonymous codons. If the actual ENC value is lower than the expected value, it is considered that the gene exhibits codon usage bias.

The correspondence analysis (COA) [19] is a dimensionality reduction analysis based on relative synonymous codon usage (RSCU), used to display the codon usage patterns of selected genes. The calculation formula for RSCU [20] is as follows:

$$\text{RSCU}=\frac{x_{\mathit{ij}}}{\frac{1}{n}{\sum }_{j=1}^{i}xij}$$

In the formula, $x_{\mathit{ij}}$ represents the occurrence count of the j-th codon encoding the i-th amino acid, and $n_i$ is the number of synonymous codons for the i-th amino acid. If RSCU > 1, it indicates that the codon is used more frequently.

Molecular docking

Based on AlphaFold 3, the potential binding of NSUN2 protein and tRNA^Val−CAC was predicted by the amino acid sequence of NSUN2 and the RNA sequence of tRNA^Val−CAC, and visualized by PyMol software.

Statistical analysis

All the statistical analyses were conducted with R software (version 4.3.1). Wilcoxon rank-sum test was used to investigate the difference for a continuous variable with a non-normal distribution, Student’s t-tests or analysis of variance (ANOVA) was used to compare two or multiple groups. Pearson chi-squared test or Fisher’s exact test was used for categorical variables. KM curves were analyzed using the log-rank test. P-values < 0.05 indicated statistical significance. All experiments were repeated three independent times.

Results

NSUN2 is upregulated in TNBC and correlates with poor prognosis

To explore the landscape of m5C regulators in TNBC, we initially analyzed the expression of 14 m5C-modification-related genes in the TCGA-BRCA dataset. As shown in Fig. 1A, the expression level of ALYREF, DNMT1, DNMT3A, DNMT3B, NOP2, NSUN2, NSUN4, NSUN5, and YBX1 were significantly upregulated in TNBC compared with the normal samples (P < 0.05). Strikingly, only NSUN2 was significantly associated with the OS (P = 0.036) and DSS (P = 0.038) in patients with TNBC after univariable Cox regression analysis (Fig. 1B, C). KM survival curves further demonstrated that elevated NSUN2 mRNA expression in patients with TNBC was significantly associated with poorer OS, DSS, PFI, and DFI (Fig. S1A–D). Furthermore, the advanced T stage and AJCC stage were more frequently observed in patients with TNBC with high NSUN2 expression (Supplementary Table S4).

Fig. 1.

NSUN2 was upregulated in TNBC and correlated with poor prognosis. A Expression of 14 m5C-related genes in TNBC. B, C Univariable Cox analysis of ten differentially expressed m5C genes in TNBC concerning OS and DSS. D NSUN2 shows the highest expression in TNBC. E The expression levels of NSUN2 mRNA in 30 pairs of TNBC tissues and adjacent normal tissues were measured using qRT-PCR. F The expression levels of NSUN2 protein in six pairs of TNBC tissues and adjacent normal tissues were detected through western blot. G IHC analysis of NSUN2 protein expression in TNBC tissues and adjacent normal tissues. Scale bar = 200 μm, *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001

Interestingly, the expression of NSUN2 showed a gradual increase in three different subtypes of breast cancer following the degree of malignancy (luminal, HER2, and TNBC) (Fig. 1D). Subsequently, the qRT-PCR assay verified the significantly higher expression of NSUN2 in TNBC tissues (Fig. 1E). Western blotting and IHC assays also confirmed the increased NSUN2 expression in TNBC tumors compared with peri-tumor tissues (Fig. 1F, G). Overall, these findings demonstrated that NSUN2 is upregulated in TNBC and associated with poor prognosis.

NSUN2 promotes TNBC cell growth and metastasis in vitro and in vivo

To investigate the biological function of NSUN2 in TNBC, the relative expression level of NSUN2 was assessed in five TNBC cell lines and one human breast epithelial cell line (MCF10A), showing that the expression of NSUN2 was higher in TNBC cell lines than in MCF10A cell line (Fig. S2A, B). NSUN2 knockdown in MDA-MB-231 and HCC1937 cells (Fig. 2A–D) led to a decrease in colony-forming abilities (Fig. 2E, F) and cell proliferation (Fig. 2G, H) while NSUN2 overexpression in HCC1806 cells (Fig. S3A, B) showed an increase in cell proliferation (Fig. S3C–E). Flow cytometry analysis showed a significant reduction in the proportion of G2/M phase cells upon NSUN2 deficiency (Fig. S2C, D). Moreover, loss of NSUN2 suppressed the migratory and invasive abilities of TNBC cells (Fig. 2I, J) while its overexpression in those cells exhibited the opposite effects (Fig. S3F).

Fig. 2.

NSUN2 promotes TNBC cell growth and metastasis in vitro. A–D The knockdown efficiency of NSUN2 in MDA-MB-231 and HCC1937 cells was assessed using qRT-PCR and western blotting. E–H The effects of NSUN2 knockdown on the proliferation ability of MDA-MB-231 and HCC1937 cells were evaluated using colony formation and CCK-8 assays. I, J The impacts of NSUN2 knockdown on the migration and invasion abilities of MDA-MB-231 and HCC1937 cells were assessed using Transwell assays. Scale bar = 200 μm, *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001

To further determine whether the proliferation of TNBC cells induced by NSUN2 in vitro is reflected in tumor growth capacity in vivo, a xenograft mouse model was constructed. The NSUN2 depletion suppressed tumor growth, resulting in a notable reduction in both tumor size and weight. Conversely, NSUN2 overexpression markedly accelerated tumor progression, demonstrating its tumor-promoting role in vivo (Fig. 3A–E). IHC staining showed significant downregulation of NSUN2 and Ki67 expression in tumors from the shNSUN2 group, whereas the enforced overexpression of NSUN2 in vivo led to an increase in Ki67 expression. (Fig. 3F). In addition, in a lung metastasis mouse model, overexpression of NSUN2 significantly augmented the metastatic potential of TNBC cells, resulting in a remarkable increase in lung metastatic foci (Fig. 3G, H). Moreover, NSUN2 and Ki67 expression levels were notably elevated in these metastatic lesions following NSUN2 overexpression (Fig. 3I). Collectively, these findings suggest that NSUN2 plays a vital role in promoting the growth and metastasis of TNBC cells both in vitro and in vivo.

Fig. 3.

NSUN2 promotes TNBC cell growth and metastasis in vivo. A–C The effects of NSUN2 knockdown or overexpression on the in vivo growth of TNBC cells (n = 6). D–E Representative images of HE staining of subcutaneous tumors in each group. F IHC analysis of NSUN2 and Ki67 expression in subcutaneous tumors from each group. G The metastatic sites notably increased in the NSUN2 overexpression group. Metastatic nodules in the lungs are labeled by arrows. H Representative images of HE staining of lung metastases. I IHC analysis of NSUN2 and Ki67 expression in lung metastases. Scale bar = 200 μm, *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001

NSUN2 promotes TNBC progression in a catalytic-activity-dependent manner

To examine whether the oncogenic function of NSUN2 in TNBC depends on its m5C methyltransferase activity, an NSUN2 expressing vector with mutations at the two active sites (C271A and C321A) was constructed [21, 22], referred to as NSUN2-mut. The efficiency of overexpression was detected using qRT-PCR and western blot (Fig. S4A, B). As expected, overexpressing wild-type NSUN2 (NSUN2-wt) but not the catalytic inactive mutant increased the m5C modification levels in HCC1806 cells (Fig. S4C). Functionally, the overexpression of NSUN2-wt, but not the NSUN2-mut, promoted the proliferation of TNBC cells (Fig. S4D, E). Compared with the overexpression of NSUN2-wt, ectopic expression of the mutant NSUN2 did not increase the proportion of G2/M phase cells (Fig. S4F). Transwell assays also demonstrated that the exogenous expression of NSUN2-mut had no significant impact on the migration and invasion capacities of TNBC cells in vitro (Fig. S4G). In addition, the m5C modification levels were elevated in TNBC tissues in comparison to that of adjacent normal tissues (Fig. S4H). These findings strongly suggest that the m5C methyltransferase activity of NSUN2 is essential for its oncogenic function in TNBC.

NSUN2 regulates tRNA^Val−CAC m5C modification to promote TNBC progression

Considering the significance of NSUN2 as a key m5C methyltransferase, we investigated the role of NSUN2 in the development and advancement of TNBC through an m5C-dependent mechanism. The results of m5C-tRNA-BS-seq revealed that NSUN2 depletion significantly reduced the m5C modification level in tRNAs (Fig. 4A). The m5C modifications on the majority of tRNAs were significantly reduced after NSUN2 knockdown (Fig. 4B). These modifications mainly occurred on the T stem and variable loop of tRNAs (Fig. 4C, D). Enriched sequences of m5C modifications within the tRNAs were identified through motif analysis (Fig. 4E). To investigate the relationship between NSUN2-mediated m5C modification and tRNA levels in TNBC, tRNA-seq analyses were performed and showed that the levels of ten tRNAs were found to be significantly altered (Fig. 4F). The combined analyses of m5C-tRNA-BS-seq and tRNA-seq data further revealed that three tRNAs including tRNA^Ile−AAU, tRNA^Val−CAC, and tRNA^Leu−CAA were among the ones with significant decreases in both the m5C methylation and expression levels in the NSUN2 knockdown TNBC cells (Fig. 4G). The m5C modifications of these three tRNAs are most frequently observed at C48 and C49, mainly distributed in the variable loop and T stem (Fig. 4H). Further validation analysis using qRT-PCR demonstrated that the depletion of NSUN2 most significantly reduced the expression of tRNA^Val−CAC, but not that of tRNA^Ile−AAU and tRNA^Leu−CAA (Fig. 4I, J). Consistently, overexpression of NSUN2-wt upregulated the expression of tRNA^Val−CAC, whereas overexpression of the NSUN2-mut had no significant impact on tRNA^Val−CAC expression (Fig. 4K, L). The m5C modification sites on tRNA^Val−CAC encompass C23, C38, C48, and C49, and following NSUN2 knockdown, the m5C modifications at C48 and C49 were nearly completely abolished (Fig. 4M). AlphaFold 3 predictions suggested a potential interaction between NSUN2 and tRNA^Val−CAC (Fig. 4N). Collectively, our data indicate that NSUN2 regulates tRNA^Val−CAC level by m5C modification to facilitate TNBC progression.

Fig. 4.

NSUN2 regulates tRNA^Val−CAC m5C modification to promote TNBC progression. A The overall m5C modifications of tRNAs decreased after NSUN2 knockdown. B After the knockdown of NSUN2, the levels of m5C modifications in most tRNAs significantly decreased. The scale denotes the sum of m5C methylation rates at all cytidine residues on each individual tRNA species. C, D The distribution of m5C modification sites in tRNAs. E The motif plot of m5C modifications. F tRNAs with significantly altered expression levels following NSUN2 knockdown. The scale represents the expression level of each tRNA species measured by tRNA‑seq, reported as log2 counts per million (log2CPM). G A Venn diagram showing tRNAs with significantly reduced m5C modification and expression levels after NSUN2 knockdown. H The distribution and location of m5C modification sites in these three tRNAs. The numbers listed under the “m5C methylated sites” column indicate the position of each cytidine that carries an m5C modification within the corresponding tRNA. The right‑most column, “location,” specifies the structural region of the tRNA where that m5C site is situated. I–L The effects of changes in NSUN2 expression on the expression levels of three tRNAs were detected using qRT-PCR. M The distribution of m5C modification sites in tRNA^Val−CAC and changes in m5C modification levels. The orange dots mark the m5C modified sites on tRNA^Val−CAC, and the scale indicates the methylation rate at each corresponding site. N Prediction of the potential interaction between NSUN2 protein and tRNA^Val−CAC using AlphaFold 3. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001

NSUN2 accelerates glycolysis-related mRNA translation in a codon-frequency-dependent manner

Considering the correlation between tRNA abundance and protein synthesis, it is rational to speculate that NSUN2 may exert an influence on mRNA translation by affecting tRNA levels. Hence, we carried out a puromycin intake assay to measure the rate of new protein translation. The results showed that the depletion of NSUN2 with siRNAs led to a decrease in global mRNA translation efficiency (TE) within TNBC cells (Fig. 5A, B). Moreover, the overexpression of the wild-type NSUN2, as opposed to its catalytic inactive mutant, increased the puromycin incorporation efficiency in these TNBC cells, highlighting the essential role of NSUN2’s catalytic function in regulating mRNA translation (Fig. 5C, D).

Fig. 5.

NSUN2 accelerates glycolysis-related mRNA translation in a codon-frequency-dependent manner. A–D Puromycin intake assays were conducted to examine the impact of NSUN2 on protein translation in TNBC cells. E The RSCU values of GUG codons for TE-down and TE-up genes. F The proportion of GUG codon in TE-down and TE-up genes. G KEGG enrichment analysis of the TE-down genes. H The usage frequency of GUG codons in representative pathways of the KEGG analysis results. I Genes in the glycolysis pathway with significantly decreased TE but no obvious changes in mRNA levels. J The proportion of GUG codons among the four glycolysis-related genes. K The RSCU values of GUG codons for glycolysis-related genes. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001

To elucidate the translational mechanisms by which NSUN2-mediated tRNA m5C modifications drive TNBC progression, we performed ribosome profiling (Ribo-seq) and mRNA sequencing (mRNA-seq) in NSUN2-knockdown versus control cells. Quality control metrics for Ribo-seq libraries confirmed data robustness (Fig. S5A). We subsequently analyzed codon usage patterns to investigate potential links between m5C-modified tRNAs and translational efficiency (TE) alterations. The effective number of codons (ENC) for both TE-upregulated and TE-downregulated genes ranged predominantly between 45 and 60, with most values distributed below the expected neutrality curve (Fig. S5B). This distribution suggests codon usage in these genes is primarily shaped by natural selection [23, 24]. Correspondence analysis (COA) of relative synonymous codon usage (RSCU) values revealed distinct clustering of TE-upregulated and TE-downregulated genes (Fig. S5C, D), indicating divergent codon preferences potentially reflecting underlying translational regulatory mechanisms. Critically, concatenated analysis demonstrated significant RSCU differences between these gene cohorts, most notably for the GUG codon (decoded by m5C-modified tRNA^Val−CAC) (Fig. 5E; Supplementary Table S5). The RSCU value for GUG was substantially higher in TE-downregulated genes (1.61) versus TE-upregulated genes (0.79). Consistently, genes exhibiting reduced TE displayed significantly elevated GUG codon frequency (Fig. 5F). These findings establish an NSUN2-dependent, m5C frequency-dependent mechanism wherein tRNA modifications preferentially regulate translation of GUG-enriched transcripts.

Genes exhibiting significantly downregulated translational efficiency (TE) (|log₂FC| > 2, P < 0.05) were identified and subjected to functional enrichment analysis. GO analysis revealed enrichment of these TE-downregulated genes in nucleotide and protein processing pathways (Fig. S5E). KEGG pathway analysis further identified several tumorigenesis-associated pathways, including glycolysis/gluconeogenesis, RIG-I-like receptor signaling, sphingolipid signaling, and FoxO signaling (Fig. 5G). Notably, among these pathways, glycolysis/gluconeogenesis contained the highest proportion of GUG codons (Fig. 5H), suggesting its heightened susceptibility to NSUN2 depletion. Strikingly, four key glycolytic enzymes—ALDH3A2, ALDH7A1, HK1, and PFKM—displayed both elevated GUG codon frequency and significantly reduced TE in NSUN2-knockdown cells (Fig. 5I, J). These genes occupy critical positions within the glycolytic pathway (Fig. S5F), with established roles in regulating glycolytic flux [25–28]. Consistent with their functional importance, RSCU values for GUG in all four genes significantly exceeded 1 (Fig. 5K), confirming preferential usage of this codon. Taken together, these findings suggest that NSUN2 enhances the translation of glycolysis-related mRNAs in a codon-frequency-dependent manner, highlighting its pivotal role in regulating glucose metabolic processes in TNBC cells.

The NSUN2–tRNA^Val−CAC axis modulates metabolic reprogramming to promote TNBC progression

To further validate the finding that NSUN2 knockdown led to translation impairment of mRNAs enriched in glycolysis pathways, we assessed the metabolic activity in TNBC cells with NSUN2 knockdown or overexpression. NSUN2 deficiency led to reduced glucose uptake, lactate production, ATP levels, and ECAR (Fig. 6A–E), while overexpression of NSUN2 led to increased glucose uptake, lactate production, ATP levels, and ECAR (Fig. S6A–D). As expected, compared with the vector and NSUN2-mut controls, overexpression of wild-type NSUN2 significantly enhanced glycolysis in HCC1806 cells, which indicated that the methyltransferase activity of NSUN2 is important for glycolysis in TNBC cells (Fig. 6F–I).

Fig. 6.

The NSUN2–tRNA^Val−CAC axis modulates metabolic reprogramming to promote TNBC progression. A–E The impact of NSUN2 knockdown on glycolysis in MDA-MB-231 and HCC1937 cells. F–I Changes in glycolysis of HCC1806 cells after transfection with NSUN2-wt or NSUN2-mut plasmids. J–M qRT-PCR analysis of the expression levels of ALDH3A2, ALDH7A1, PFKM, and HK1 mRNA in MDA-MB-231 and HCC1937 cells after NSUN2 knockdown. N The mRNA expression levels of these four glycolysis-related genes in HCC1806 cells after transfection with NSUN2-wt or NSUN2-mut plasmids. O–R Polysome profiling qRT-PCR analysis of the TE of glycolysis-related genes in MDA-MB-231 cells following NSUN2 knockdown. S–V Polysome profiling qRT-PCR of the TE of glycolysis-related genes in HCC1806 cells after transfection with either NSUN2-wt or NSUN2-mut plasmids. W–X The protein expression levels of glycolysis-related genes were assessed. Y The correlation between NSUN2 expression and the expression levels of ADLH3A2, ALDH7A1, PFKM, and HK1 in TNBC tissues. Z IHC analysis of the effect of NSUN2 expression levels on the expression of glycolysis-related genes in subcutaneous tumors in nude mice. Scale bar = 200 μm, *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001

Furthermore, we observed that neither knockdown nor overexpression of NSUN2 altered the mRNA levels of HK1, PFKM, ALDH7A1, and ALDH3A2 (Fig. 6J–N; Fig. S6E). However, polysome profiling coupled with qRT-PCR revealed that NSUN2 knockdown significantly reduced the association of these transcripts with polysomes, indicating impaired TE (Fig. 6O–R). Conversely, overexpression of NSUN2-wt, but not its catalytically inactive mutant, enhanced polysome loading and translation of these target genes (Fig. 6S–V). Consistent with these translational changes, NSUN2 knockdown decreased, while NSUN2-wt overexpression increased, the protein levels of all four glycolytic enzymes (Fig. 6W; Fig. S6F). Critically, overexpression of the mutant NSUN2 failed to elicit any significant effect on protein expression (Fig. 6X). We further examined the relationship between NSUN2 expression and the protein levels of ALDH3A2, ALDH7A1, HK1, and PFKM in TNBC tissues, revealing that higher NSUN2 expression correlated with increased levels of these glycolysis-related proteins (Fig. 6Y). In addition, NSUN2 overexpression in nude mice xenografts increased the protein levels of ALDH3A2, ALDH7A1, HK1, and PFKM in tumors, while NSUN2 depletion significantly decreased their expression in both the xenograft tumor model (Fig. 6Z) and the lung metastasis tumor model (Fig. S6G).

To further clarify whether NSUN2’s role in promoting the progression of TNBC is mediated by tRNA^Val−CAC, we designed and transfected tRNA^Val−CAC interference plasmids into NSUN2-overexpressing cell lines and their corresponding controls. The enhancement of TNBC cell proliferation, migration, and invasion caused by NSUN2 overexpression could be significantly mitigated by the knockdown of tRNA^Val−CAC (Fig. S7A–E). Also, the NSUN2-mediated increase in glucose uptake, lactate production, ATP levels, and ECAR in HCC1806 cells could be alleviated by the knockdown of tRNA^Val−CAC (Fig. S7F–I). The downregulation of tRNA^Val−CAC was shown to decrease the elevated protein expression levels of ALDH3A2, ALDH7A1, HK1, and PFKM meditated by NSUN2 overexpression (Fig. S7J). Together, these results indicate that NSUN2 promotes the glycolysis of TNBC cells by m5C modification and stabilization of tRNA^Val−CAC, thereby increasing the key enzyme levels involved in glycolysis.

NSUN2 as a potential mediator of docetaxel resistance in TNBC

Currently, chemotherapy remains the principal treatment modality for TNBC, but its efficacy is greatly restricted owing to the emergence of resistance to cytotoxic agents in cancer cells. Upon examination of the Genomics of Drug Sensitivity in Cancer (GDSC) database, a positive correlation between the expression level of NSUN2 and the IC50 values of gemcitabine, oxaliplatin and epirubicin in TNBC cells was observed (Fig. S8A–C). Among all the tested compounds, the IC50 of docetaxel exhibited the most significant association with NSUN2 expression levels (Fig. 7A). This finding prompted our hypothesis that NSUN2 could be a mediator of TNBC cell resistance to docetaxel.

Fig. 7.

NSUN2 as a potential mediator of docetaxel resistance in TNBC. A The expression level of NSUN2 is positively correlated with the IC50 values of docetaxel in TNBC cell lines. B–C Dose–response curves of docetaxel in MDA-MB-231 and HCC1937 cells after knocking down NSUN2. D–E Colony formation assays were used to assess changes in the inhibitory effects of docetaxel on TNBC cells after NSUN2 knockdown. F The expression of NSUN2, ALDH3A2, ALDH7A1, PFKM, and HK1 in docetaxel-sensitive and docetaxel-resistant TNBC tissues was examined through IHC staining. G A schematic representation of the xenograft animal model. H–J Tumor weight and volume in nude mice after NSUN2 knockdown and/or docetaxel treatment. K IHC analysis of changes in NSUN2 and Ki67 expression levels after the respective treatments. Scale bar = 200 μm, *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001

To validate this hypothesis, we tested if inhibition of NSUN2 could regulate docetaxel response in TNBC cells. The CCK-8 assay demonstrated that NSUN2 knockdown substantially decreased the IC50 value of docetaxel in MDA-MB-231 and HCC1937 cells (Fig. 7B, C; Supplementary Table S6). In addition, TNBC cells treated with docetaxel formed significantly fewer colonies than those treated with DMSO, and this reduction in colony-forming capacity was further exacerbated by NSUN2 knockdown (Fig. 7D, E). Conversely, the overexpression of NSUN2 mitigated the inhibitory effect of docetaxel on the proliferation of HCC1806 cells (Fig. S8D, E). In clinical settings, we collected TNBC tissues that were either resistant or sensitive to docetaxel. IHC analysis showed that NSUN2 was significantly upregulated in docetaxel-resistant TNBC tissues compared with docetaxel-sensitive TNBC. Interestingly, the key glycolytic genes ALDH3A2, ALDH7A1, HK1, and PFKM were also highly expressed in docetaxel-resistant TNBC tissues (Fig. 7F), suggesting that NSUN2 may mediate TNBC cell resistance to docetaxel by regulating the metabolic reprogramming process.

Moreover, the influence of NSUN2 deletion on docetaxel responsiveness was examined in vivo using a xenograft tumor model (Fig. 7G). Compared with the control group, the mice implanted with NSUN2-knockdown TNBC cells showed a markedly reduced rate of tumor progression, as demonstrated by decreased tumor volumes and mass (Fig. 7H–J). Subsequent IHC analysis revealed that NSUN2 depletion, in tandem with docetaxel administration, significantly dampened Ki67 expression (Fig. 7K). Collectively, these findings indicate that targeting NSUN2 enhances TNBC sensitivity to docetaxel both in vitro and in vivo.

Discussion

TNBC is the most malignant subtype of breast cancer with a poor prognosis, and the therapeutic options available for patients with advanced TNBC remain limited, highlighting the urgent need to identify novel effective molecular diagnostics and therapeutic targets for TNBC. The present study used a multiomics screening strategy including m5C tRNA MeRIP-seq, Ribo-seq, and RNA-seq and identified the tRNA methyltransferase NSUN2 as a prognostic biomarker that correlates with poor prognosis of TNBC. Functionally, NSUN2 enhanced TNBC cell growth and metastasis in vitro and in vivo. Knockdown of NSUN2 enhances the sensitivity of TNBC cells to docetaxel chemotherapy. Mechanistically, NSUN2-mediated tRNA^Val−CAC m5C modification promotes TNBC progression by modulating metabolic reprogramming through codon-biased translation (Fig. 8). This study revealed a novel molecular mechanism whereby TNBC progresses and resists chemotherapeutics through NSUN2-mediated m5C modification of some tRNAs that promote translation of the key enzymes to enhance glycolysis.

Fig. 8.

Schematic summary of key findings presented in this study

Epigenetic alterations, including chromatin remodeling, histone modifications, DNA methylation, oncogenic mutations, and RNA modifications, play a crucial role in cancer development and progression [29, 30]. Thus far, over 170 different post-transcriptional RNA modifications have been identified, including various RNA molecules, with an average of 13 modifications for each tRNA molecule [31]. tRNAs exhibit the greatest diversity of chemical modifications and the most extensive level of modification compared with all other RNA types [32]. Of these, m5C modifications widely exist in all kinds of RNA and are associated with various biological processes, such as mRNA stability, DNA damage repair, differentiation, proliferation, and reprogramming of stem cells [33–35]. NSUN2, as a major m5C tRNA methyltransferase, modulates substrate concentrations through the catalysis of m5C modifications on target RNA, thereby influencing tumor development and cancer progression. For example, NSUN2 was found to confer ferroptosis resistance to endometrial cancer cells by regulating the m5C modification of SLC7A11 and enhancing the stability of its mRNA via a YBX1-dependent mechanism [36]. NSUN2-mediated m5C RNA methylation dictated retinoblastoma progression by promoting PFAS mRNA stability and expression [37]. Furthermore, NSUN2 was reported to promote hepatocellular carcinoma proliferation and invasion through the modulation of the Wnt signaling pathway in an m5C-dependent manner [38]. Collectively, these studies demonstrate that abnormal RNA modifications can influence tumor initiation and progression. Surprisingly, there are few well-defined molecular mechanisms that connect specific tRNA modifications to human cancer. Unlike previous studies [39, 40], our work focuses on dysregulated tRNA modifications to selective mRNA translational regulation in TNBC and demonstrates that NSUN2-regulated tRNA^Val−CAC m5C modification at positions 48 and 49 promote TNBC progression, which offers a comprehensive molecular insight by which m5C tRNA modifications can promote cancer progression.

The dynamic interplay between tRNA modifications and metabolic reprogramming represents a fundamental mechanism in cancer biology, with profound implications for cellular adaptation and therapeutic resistance [41]. Tumors exploit tRNA modifications to gain selective advantages by precisely regulating the translation of metabolic genes, thereby sustaining the anabolic demands of proliferation and survival [42, 43]. This paradigm is exemplified by METTL1-mediated m7G tRNA modifications, which enhance oxidative phosphorylation (OXPHOS) in treatment-resistant cells through global translational upregulation [44], demonstrating how tRNA epitranscriptomics directly orchestrates metabolic plasticity. Moreover, codon usage bias (CUB) refers to the nonrandom utilization of synonymous codons encoding the same amino acid within a genome exhibiting a preference [45]. It is commonly held that optimized codons correspond to superior translation efficiency, potentially due to the higher tRNA abundance corresponding to optimized codons, which facilitates faster translation elongation [46, 47]. In our study, we found that m5C modifications at the wobble position of tRNA^Val−CAC play a crucial role in maintaining tRNA stability and ensuring accurate codon–anticodon pairing, which is particularly important for the translation of glycolysis–related genes. In addition, the depletion of NSUN2 results in reduced levels of m5C-modified tRNAs and global translation impairments. Ribo-seq revealed that the TEs of mRNAs with a higher frequency of m5C-modified codons are inhibited in NSUN2-depleted TNBC cells. Further analysis of codon usage in these pathway-associated genes showed that the glycolysis/gluconeogenesis pathway contains the highest proportion of GUG codons. Notably, within the glycolysis pathway, genes such as ALDH3A2, ALDH7A1, HK1, and PFKM display a higher-than-average proportion of GUG codons, with RSCU values for GUG significantly exceeding 1. Overall, this finding uncovers a coordinated system of tRNA modifications and translation of codon-biased transcripts that enhance the expression of glycolysis-related proteins in a codon-frequency-dependent manner. Although we attempted to rescue the cellular phenotypes by expressing codon-altered versions of the target genes, this approach resulted in significant cellular toxicity and widespread cell death, preventing successful cell line generation or phenotypic analysis. This limitation highlights the complexity of manipulating multiple genes simultaneously and underscores the need for further optimization of strategies to achieve multi-gene rescue in future studies.

At present, chemotherapy remains the principal treatment modality for TNBC. However, its efficacy is significantly limited by the development of resistance to cytotoxic agents in cancer cells, creating an urgent need for more effective strategies to enhance TNBC adjuvant therapy. Previous bioinformatics analyses indicated a significant correlation between NSUN2 expression levels and the sensitivity of TNBC cells to docetaxel treatment (Fig. S8A–C). This compelling evidence led us to hypothesize that NSUN2 may mediate TNBC cell resistance to docetaxel. Furthermore, numerous studies [48, 49] have demonstrated that cancer cell metabolic adaptations, such as enhanced glycolysis, are closely linked to chemotherapy resistance. For instance, Muramatsu et al. showed that targeting LDHA enhances docetaxel-induced cytotoxicity, primarily in castration-resistant prostate cancer cells [50]. Similarly, Jiang et al. reported that glycolytic metabolism mediates docetaxel resistance in prostate cancer cells [51]. Therefore, this study aims to investigate whether NSUN2-mediated glycolysis contributes to docetaxel resistance in TNBC. Our findings demonstrate that NSUN2 mediates TNBC cell resistance to docetaxel by regulating metabolic reprogramming, suggesting that NSUN2 may represent a novel potential therapeutic target for the clinical management of TNBC.

Conclusions

Our research reveals that NSUN2 drives TNBC progression and chemotherapy resistance by regulating tRNA^Val−CAC m5C modification and the translation of glycolysis-related mRNAs. This study identifies NSUN2 as a potential prognostic biomarker and therapeutic target for TNBC, providing new insights for developing effective TNBC treatment strategies.

Abbreviations

ATCC

American Type Culture Collection

CCK-8

Cell counting kit-8

COA

Correspondence analysis

CUB

Codon usage bias

DFI

Disease-free interval

DSS

Disease-specific survival

ECAR

Extracellular acidification rate

ENC

Effective number of codon

ER

Estrogen receptor

GDSC

Genomics of drug sensitivity in cancer

HER2

Human epidermal growth factor receptor 2

IHC

Immunohistochemistry

KM

Kaplan–Meier

m3C

3-Methylcytidine

m5C

5-Methylcytosine

m7G

N7-methylguanosine

OS

Overall survival

PFI

Progression-free interval

PFKM

Phosphofructokinase, muscle

PR

Progesterone receptor

Ribo-seq

Ribosome sequencing

RSCU

Relative synonymous codon usage

siRNA

Small interfering RNA

SPF

Specific pathogen-free

TCGA

The Cancer Genome Atlas

TE

Translation efficiency

TNBC

Triple-negative breast cancer

Author contributions

W.W., Y.D., S.W., J.H., and L.S. designed this study. Y.D. and W.W. conducted the experiments. Y.D., W.W., and H.Z. analyzed the data. S.W., J.H., and L.S. supervised the study. All authors have read, edited, and approved the manuscript.

Funding

This study was supported by the National Natural Science Foundation of China (no. 82403230), the China Postdoctoral Science Foundation (no. 2024M763714 and no. 2024M763707), the Postdoctoral Fellowship Program of CPSF (GZC20233156 and GZC20233167), the Science and Technology Program Foundation of Changsha City (kq2403019), the Special Funding for the Construction of Innovative Province in Hunan (2022SK2041), and the Natural Science Foundation of Hunan Province of China (2024JJ6664 and 2024JJ9133).

Data availability

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

Declarations

Ethics approval and consent to participate

Informed consent was obtained from all participants, and the study was approved by the Ethics Committee of Xiangya Hospital, Central South University, on 1 November 2023 (approval number: 2023110146), in accordance with the principles of the Declaration of Helsinki. All animal experiments were approved by the Institutional Animal Care and Ethics Committee of Xiangya Hospital, Central South University, on 9 January 2024 (approval number: XY20240109003). The Local Ethics Committee operates in accordance with the guidelines of the International Council for Laboratory Animal Science.

Consent for publication

All authors have agreed with publishing this manuscript.

Competing interests

The authors declare no conflicts of interest.