METTL5 reprograms glycolytic metabolism and promotes non‑small cell lung cancer progression by modifying PGAM1

Introduction

Lung cancer remains a life-threatening malignancy worldwide, with >2 million new cases and ~1.76 million mortalities reported annually (1). Non-small cell lung cancer (NSCLC) is the most predominant subtype, accounting for 85% of all annual lung cancer diagnoses (2). Despite advancements in surgical techniques, precision treatment approaches and immunotherapeutic interventions, the 5-year survival rate of patients with NSCLC has exhibited limited improvement (3,4). Therefore, it is necessary to identify novel biomarkers and develop targeted pharmacological interventions to improve the prognosis of NSCLC (5,6).

A defining characteristic of malignant tumors is reprogrammed energy metabolism, which facilitates tumor development and progression. However, the precise underlying molecular mechanisms warrant further investigation (7,8). The Warburg effect, characterized by enhanced lactate production instead of mitochondrial oxidation under oxygen-rich conditions, represents a fundamental metabolic adaptation in cancer (9,10). This glycolytic pathway serves a dual purpose, as it immediately generates energy substrates while producing key biosynthetic precursors, such as glucose-6-phosphate, pyruvate derivatives and lactate, for macromolecule synthesis. Notably, the lactate accumulated in the tumor microenvironment induces extracellular acidification, creating favorable conditions for the metastatic spread and immune evasion of tumor cells (11–13). Studies have shown that lactate can impair immune cell function. In particular, it can suppress the cytotoxic function of natural killer cells and T lymphocytes, facilitating tumor immune evasion by impairing immune surveillance mechanisms (14–16). In addition, lactate can promote the proliferation and differentiation of regulatory T cells, inhibiting antitumor immune responses (17). A number of studies have shown that targeting the glycolytic pathway in cancer cells is a promising therapeutic strategy. For example, phosphoglycerate mutase 1 (PGAM1), a key glycolytic enzyme, has emerged as a key therapeutic target for cancer, and marked advancements have been made in designing PGAM1 inhibitors (18,19).

N6-methyladenosine (m6A) modification, characterized by the addition of a methyl group to the sixth nitrogen of adenine in RNA molecules, is a prevalent post-transcriptional modification in eukaryotic organisms (20,21). This dynamic modification influences the stability, localization and translational efficiency of RNA through enzymatic regulation. m6A modification is a reversible process regulated by three functional protein groups, namely methyltransferases (writers), demethylases (erasers) and methyl recognition proteins (readers). The methyltransferase complex involved in m6A modification is composed of core catalytic subunits such as methyltransferase (METTL)-3, METTL5 and Wilms' tumor 1-associating protein (WTAP) and auxiliary components such as vir-like M6A methyltransferase associated (VIRMA), RNA binding motif protein 15 (RBM15) and METTL16 (22–24). Fat mass and obesity-associated protein and α-ketoglutarate-dependent dioxygenase homolog 5 function as key m6A erasers (demethylases) by catalyzing the removal of methyl groups from modified nucleotides (25,26). YTH protein family members, including YTH N6-methyladenosine RNA binding protein (YTHD)-F1, YTHDF2 and YTHDC2, serve as primary m6A readers that modulate RNA stability and translational efficiency through selective binding to methylated transcripts (27–29).

m6A modification serves as an important regulatory mechanism in cancer initiation and progression. In gastric cancer, this epigenetic modification accelerates malignant progression by stimulating key oncogenic pathways, such as the Wnt and PI3K/AKT/mTOR pathways (30–32). METTL3 knockout can markedly inhibit the proliferation and migration of oxaliplatin-resistant gastric cancer cells and induce their apoptosis, thereby enhancing oxaliplatin sensitivity. These findings indicate that m6A modification may affect chemotherapy resistance in gastric cancer (33). Therefore, small-molecule inhibitors targeting m6A modification-related enzymes are promising therapeutic agents for cancer (34).

METTL5 is an emerging RNA methyltransferase that catalyzes m6A modification of target mRNAs, often in complex with regulatory proteins such as WTAP (35). Dysregulation of METTL5 has been associated with tumorigenesis across numerous cancer types, highlighting its context-dependent oncogenic roles. For example, in liver hepatocellular carcinoma (LIHC), the upregulation of METTL5 enhances the stability of c-Myc, which in turn activates glycolytic genes, leading to abnormal glucose metabolism and tumor growth (36). In renal clear cell carcinoma (KIRC), METTL5 participates in regulation of the tumor immune microenvironment and may promote tumor progression by facilitating the infiltration of immunosuppressive cells (37). High METTL5 expression has been shown to be negatively associated with the prognosis of patients with stomach adenocarcinoma. The mechanism involves increased stability of nuclear factor (erythroid-derived 2)-like 2 mRNA, inhibition of Fe2+ accumulation and ferroptosis and therefore, suppression of the antitumor immunity mediated by peripheral blood mononuclear cells (38). However, the functional roles and regulatory mechanisms of METTL5 in NSCLC remain poorly understood. In addition, to the best of our knowledge, the mechanisms by which METTL5-mediated m6A methylation regulates PGAM1 mRNA expression in NSCLC remain unexplored. The potential interplay among METTL5, PGAM1 and glucose transporter type 1 (GLUT1), another key protein in metabolic adaptation (39,40), has not been elucidated. Notably, to the best of our knowledge, the clinical relevance of this regulatory axis and its impact on patient prognosis remain unexplored.

Given these gaps, the present study aimed to determine whether METTL5 is expressed in NSCLC and assess its prognostic importance, investigate the functional role of METTL5 in regulating glycolytic metabolism and tumor progression, elucidate the molecular mechanism by which METTL5 modulates PGAM1 expression through m6A modification, and examine the downstream effects on GLUT1 expression and metabolic reprogramming. The present investigation of this regulatory axis is considered to provide novel insights for the development of precision therapeutic strategies for NSCLC.

Materials and methods

Cell culture and cell lines

The A549 human lung adenocarcinoma (LUAD) cell line, as well as the BEAS-2B bronchial epithelial cell line, the PC9 LUAD cell line, the H520 lung squamous cell carcinoma cell line and the NCI-H1299 NSCLC cell line were obtained from ZFdows Biotech Co., Ltd. All cell lines were authenticated through short tandem repeat profiling, which determined the absence of Mycoplasma contamination. Detailed specifications of all cell lines are provided in Table I, with data derived from the Catalogue of Somatic Mutations in Cancer (Sanger Institute; http://cancer.sanger.ac.uk/cosmic) and the Cancer Cell Line Encyclopedia (American Type Culture Collection; http://www.atcc.org/) databases. All cells were cultured in DMEM/F12 (1:1 ratio; cat. no. BL1917A; Biosharp Life Sciences) enriched with 10% FBS (cat. no. BL305A; Biosharp Life Sciences) under standard conditions (37°C; 5% CO2) for optimal cell proliferation.

Table I. Cell line characteristics.

Table I.

Cell line characteristics.

Cell line	Histologic origin	Key driver mutation(s) (COSMIC/ATCC)	TP53 status	Other recurrent alterations	Doubling time, h	Tumorigenicity in mice	Typical applications
A549	Adenocarcinoma (peripheral)	KRAS G12S	Wild-type	STK11 loss, KEAP1 mutation	22-24	++ (s.c. and orthotopic)	KRAS-mutant model; chemo-/radio-/immuno-therapy studies
H1299	Large-cell carcinoma	None (EGFR/KRAS/ALK WT)	Homozygous deletion	NF1 loss, PTEN loss	20-22	++	p53-null background; high transfection efficiency; generescue assays
PC9	Adenocarcinoma	EGFR E746_A750 deletion (exon 19)	Wild-type	BIM deletion (subset)	24-26	++	Prototype EGFR/TKI-sensitive line; parental strain for resistant clones
H520	Squamous-cell carcinoma	KRAS G12C + BRAF G466V	Mutant	-	28-32	+	Squamous KRAS model; immune-checkpoint studies
BEAS-2B	Immortalized (non-cancerous) bronchial epithelium	None	Wild-type	SV40 large T-antigen	30-34	-	Normal control for transformation studies; chemical- or oncogene-induced carcinogenesis models; MSC-like properties reported

[i] COSMIC, Catalogue Of Somatic Mutations In Cancer; ATCC, American Type Culture Collection; s.c., subcutaneous; TKI, tyrosine kinase inhibitor; SV40; simian virus 40; STK11, serine/threonine kinase 11; KEAP1, Kelch-like ECH-associated protein 1; ALK, anaplastic lymphoma kinase; WT, wild-type; MSC, mesenchymal stem cell; BIM, B-cell chronic lymphocytic leukemia-lymphoma like 11 gene; NF1, neurofibromin 1. Tumorigenicity grading: +, low tumorigenic potential or slower tumor formation; ++, high tumorigenic potential or rapid tumor formation in immunodeficient mice.

Cell transfection

METTL5- and PGAM1-overexpression plasmids were purchased from Heyuan Liji (Shanghai) Biotechnology Co., Ltd. The open reading frames of METTL5 and PGAM1 were cloned into the pcDNA3.1(+) vector. Specific small interfering RNAs (siRNAs), along with a non-targeting negative control siRNA, were obtained from Guangzhou RiboBio Co., Ltd. For transient transfection, cells were seeded in 6-well plates at a density of 3×105 cells per well 24 h prior to transfection. Plasmid DNA (2 µg) or siRNA (50 nM) were transfected into cells using Lipo8000™ reagent (cat. no. C0533; Beyotime Biotechnology) according to the manufacturer's protocol. The transfection mixture was incubated with cells for 6 h at 37°C before replacing with fresh complete medium. After 48 h transfection, cell samples were collected for protein quantification.

For stable gene knockdown, short hairpin RNA (shRNA/sh)-YTHDF1 and sh-PGAM1 lentiviral particles were obtained from Guangzhou RiboBio Co., Ltd. These lentiviral particles were constructed in the pLKO.1-puro vector system, which drives shRNA expression under the human U6 promoter and contains a puromycin resistance gene for selection of transduced cells. A549 and PC9 cells were infected with lentiviral particles encoding either sh-YTHDF1 or sh-PGAM1, or corresponding control shRNA, in the presence of polybrene (8 µg/ml) to enhance infection efficiency. At 48 h post-infection, cells were selected with puromycin (2 µg/ml) for 7–10 days to establish stable cell lines. The knockdown efficiency was validated by western blot analysis 72 h post-infection and prior to use in downstream assays.

Detailed information on the plasmid constructs, siRNA sequences and shRNA sequences is provided in Tables II and III. Validation of transfection efficiency for YTHDF1 and PGAM1 modulations in A549 and PC9 cells is shown in Fig. S1.

Table II. Overexpression constructs generated in the present study.

Table II.

Overexpression constructs generated in the present study.

Plasmid	Backbone	Selection	5′MCS	3′MCS	Insert (codon-optimized)	Insert length, bp	Plasmid length, bp
pcDNA3.1(+)-PGAM1-HA	pcDNA3.1(+)	Ampicillin	NheI	BamHI	Homo sapiens PGAM1 (NM_002629.3)	810	6,213
pcDNA3.1(+)-METTL5-FLAG	pcDNA3.1(+)	Ampicillin	NheI	BamHI	Homo sapiens METTL5 (NM_001318165.2)	672	6,075

[i] PGAM1, phosphoglycerate mutase 1; HA, hemagglutinin; METTL5, methyltransferase 5; MCS, multiple cloning site.

Table III. Sequences of the siRNAs and shRNAs used in cell transfection.

Table III.

Sequences of the siRNAs and shRNAs used in cell transfection.

Name	Sequence (5′-3′)
si-PGAM1
Sense	CCACAUCUGUAGACAUCUU
Antisense	AAGAUGUCUACAGAUGUGG
si-METTL5
Sense	GCAUGUAUGCUCUAUACAA
Antisense	UUGUAUAGAGCAUACAUGC
si-YTHDF1
Sense	CCGCGUCUAGUUGUUCAUGAA
Antisense	UUCAUGAACAACUAGACGCGG
sh-YTHDF1	GTTCGTTACATCAGAAGGATATCAAGAGTATCCTTCTGATGTAACGAACTTTTTT
sh-PGAM1	CCCTTCTGGAATGAAGAAATATCAAGAGTATTTCTTCATTCCAGAAGGGTTTTTT

[i] PGAM1, phosphoglycerate mutase 1; METTL5, methyltransferase 5; sh/shRNA, short hairpin RNA; si/siRNA, small interfering RNA; YTHDF1, YTH N⁶-methyladenosine RNA binding protein 1.

Reverse transcription-quantitative PCR (RT-qPCR)

Total RNA was extracted from cells using the SteadyPure Mag Tissue & Cell RNA Extraction Kit (cat. no. AG21207; Hunan Accurate Bio-Medical Technology Co., Ltd.). cDNA was synthesized using the Evo M-MLV Reverse Transcription Premix Kit (cat. no. AG11728; Hunan Accurate Bio-Medical Technology Co., Ltd.) according to the manufacturer's instructions. qPCR was performed using the ChamQ Universal SYBR qPCR Master Mix (cat. no. Q711; Vazyme Biotech Co., Ltd.), a ROX reference dye-containing SYBR Green I-based fluorescent dye system. The reactions were performed using an Applied Biosystems™ PCR thermocycler (Thermo Fisher Scientific, Inc.) with three technical replicates (41). The thermal cycling conditions were as follows: Initial denaturation at 95°C for 30 sec, followed by 40 cycles of denaturation at 95°C for 5 sec, and annealing/extension at 60°C for 30 sec. GAPDH was used as the internal reference gene and the relative expression levels of target genes were calculated using the 2−ΔΔCq method. The specific oligonucleotide sequences used for PCR are shown in Table IV.

Table IV. Primer sequences used in reverse transcription-quantitative PCR.

Table IV.

Primer sequences used in reverse transcription-quantitative PCR.

Name	Primer	Sequence (5′-3′)
METTL5	Forward	TGTTAGGAGCAGGGTTGTGTG
	Reverse	AAGCACACATCACATTGAACCAT
PGAM1	Forward	ATGATGTCCCACCACCTCCGAT
	Reverse	ATCCTTCAGACTCTCACAGGAG
GAPDH	Forward	ATGAGAAGTATGACAACAGCCTCA
	Reverse	GAGTCCTTCCACGATACCAAAG

[i] METTL5, methyltransferase 5; PGAM1, phosphoglycerate mutase 1.

Western blotting

Total proteins were extracted from cells using RIPA lysis buffer (cat. no. 89901; ThermoFisher Scientific, Inc.). Protein concentration was determined using the BCA Protein Assay Kit (cat. no. 23227; Thermo Fisher Scientific, Inc.) according to the manufacturer's instructions. Equal amounts of protein (30 µg per lane) were separated by 10% SDS-PAGE and subsequently transferred onto PVDF membranes (MilliporeSigma). The membranes were blocked with 5% non-fat dry milk (cat. no. P0216; Beyotime Biotechnology) in TBST containing 0.1% Tween-20 (cat. no. ST671; Beyotime Biotechnology) for 1 h at room temperature. The membranes were incubated with specific primary antibodies at 4°C overnight. Subsequently, the membranes were incubated with HRP-conjugated goat anti-rabbit IgG (H+L) secondary antibody (1:5,000; cat. no. SA00001-2; Proteintech Group, Inc.) at room temperature for 1 h. Protein bands were visualized using sensitive ECL detection kit (cat. no. PK10002; Proteintech Group, Inc.) and the band intensity was semi-quantified using ImageJ software (version 1.54; National Institutes of Health). The experiment was performed using three independent biological replicates (n=3), with β-actin serving as the loading control. The following primary antibodies were used for western blotting: Anti-METTL5 (1:1,000; cat. no. CL488-16791; Proteintech Group, Inc.), anti-PGAM1 (1:1,000; cat. no. 16126-1-AP; Proteintech Group, Inc.), anti-YTHDF1 (1:1,000; cat. no. 17479-1-AP; Proteintech Group, Inc.), anti-GLUT1 (1:1,000; cat. no. 21829-1-AP; Proteintech Group, Inc.) and anti-β-actin (1:1,000; cat. no. 60008-1-Ig; Proteintech Group, Inc.).

Colony formation assay

Cells were seeded in 6-well plates at a density of 200 cells/well and maintained under standard conditions (37°C; 5% CO2; humidified atmosphere). After 14 days culture, during which no additional treatments were applied, cell colonies were fixed with 95% ethanol at room temperature for 15 min and stained with 0.1% crystal violet at room temperature for 20 min. The colonies were visualized using low-magnification microscopy (CKX53; Olympus Corporation) and quantified using ImageJ software (version 1.54; National Institutes of Health). Briefly, digital images were captured and analyzed using the ‘Cell Counter’ (https://imagej.nih.gov/ij/plugins/cell-counter.html) plugin to mark and count clusters containing ≥50 cells, which were defined as viable colonies. Quantification was performed in a blinded manner by two independent observers. Three independent replicates (n=3) were prepared for each sample to ensure reproducibility.

Transwell migration assay

For the migration assay, a total of 5×104 cells were cultured in serum-free DMEM/F12 (1:1) liquid medium(cat. no. BL1917A; Biosharp Life Sciences) in the upper Transwell chamber, whereas 600 µl of DMEM/F12 (1:1) medium supplemented with 10% FBS was added to the lower chamber. After 12–24 h of incubation at 37°C in a humidified atmosphere containing 5% CO2, the migrated cells were fixed with methanol at room temperature for 15 min, stained with 0.1% crystal violet for 20 min at room temperature and observed under a phase-contrast light microscope (CKX53; Olympus Corporation) at a magnification of ×400. The cells were manually counted in in five randomly selected visual fields per membrane. All experimental groups and biological replicates were seeded with an identical initial density of 5×104 cells per well. Three independent replicates were prepared for each sample to ensure reproducibility.

Analysis of the extracellular acidification rate (ECAR) and oxygen consumption rate (OCR)

The cellular metabolic parameters ECAR and OCR were measured on a Seahorse XFe96 metabolic analyzer (Agilent Technologies, Inc.). The glycolytic rate and mitochondrial function were assessed using the Agilent Seahorse XF Glycolytic Rate Assay Kit (cat. no. 103344-100) and the Seahorse XF Cell Mito Stress Test Kit (cat. no. 103015-100), respectively. The assay solution was prepared according to the manufacturer's instructions. Briefly, glucose, pyruvate, glutamine, oligomycin, rotenone/antimycin A and carbonyl cyanide 4-(trifluoromethoxy)phenylhydrazone (FCCP) were used at final concentrations of 10 mmol/l, 1 mmol/l, 2 mmol/l, 1.5 µmol/l, 0.5 µmol/l and 1 µmol/l, respectively. Injections were automatically performed by the instrument at the following timepoints: For the OCR assay, oligomycin was injected at 14 min, FCCP was injected at 35 min and Rotenone/Antimycin A was injected at 55 min. For the ECAR assay, glucose was injected at 14 min, oligomycin was injected at 35 min and 2-DG was injected at 55 min. Measurements were recorded after achieving thermal equilibrium and stable pH conditions.

RNA stability assay

RNA stability was assessed by exposing cells to 5 µg/ml actinomycin D at 37°C in a humidified incubator with 5% CO2 and extracting total RNA at specified intervals (0, 4 and 8 h). Temporal mRNA expression was quantified using RT-qPCR.

Treatment with methylation inhibitor 3-deazaadenosine (DAA)

For functional validation, METTL5-overexpressing cell lines were established and treated with the methylation inhibitor 3-deazaadenosine (DAA; cat. no. HY-W013332; MedChemExpress). Cells were seeded at a density of 1×105 cells per well in 6-well plates and allowed to adhere overnight under standard culture conditions (37°C; 5% CO2). The following day, cells were treated with 50 µM DAA, a concentration previously established to effectively inhibit methyltransferase activity without inducing significant cytotoxicity (42). DAA was dissolved in dimethyl sulfoxide (DMSO; final concentration ≤0.1%; cat. no. HY-Y0320C; MedChemExpress) and added directly to the culture medium. Control cells were treated with an equivalent volume of DMSO vehicle. Cells were incubated at 37°C in a humidified atmosphere with 5% CO2 for 48 h prior to harvest for downstream assays.

Bioinformatics analysis

Data (TCGA-LUSC, TCGA-LUAD) used for bioinformatics analysis were obtained from The Cancer Genome Atlas (TCGA) repository (https://portal.gdc.cancer.gov/). The mRNA expression patterns and prognostic importance of METTL5 and PGAM1 in NSCLC were examined using R software (https://www.r-project.org/; version 4.3.2). Differentially expressed genes (DEGs) were identified using the ‘limma’ package with a significance threshold of adjusted P-value <0.05 and log2 fold change (log2FC) >1. Gene expression correlation analysis was performed using the Gene Expression Profiling Interactive Analysis (GEPIA) platform (43). Potential targets of METTL5 were identified using the M6A2Target database (44).

Statistical analysis

All statistical analyses were performed using GraphPad Prism (version 9.5; Dotmatics) and R (https://www.r-project.org/; version 4.3). Data are presented as the mean ± standard deviation (SD). Comparisons between two groups were performed using unpaired Student's t-tests (for independent samples) or paired t-tests (for matched samples, as indicated in figure legends), whereas multi-group comparisons were performed using one-way ANOVA followed by the Bonferroni post hoc test. Survival probabilities were calculated using Kaplan-Meier analysis and differences in survival curves were assessed using the log-rank test. Gene expression correlations were quantified using Spearman's rank coefficients. All experiments were performed in triplicate. P<0.05 was considered to indicate a statistically significant difference.

Results

METTL5 is highly expressed in NSCLC and contributes to tumor progression

Analysis of transcriptomic data obtained from TCGA revealed significantly elevated METTL5 mRNA expression levels in NSCLC (Fig. 1A). Furthermore, METTL5 mRNA expression was significantly upregulated in colon adenocarcinoma, stomach and esophageal carcinoma, pan-kidney cohort, stomach adenocarcinoma, uterine corpus endometrial carcinoma, head and neck squamous cell carcinoma, kidney renal clear cell carcinoma, LIHC, kidney chromophobe and cholangiocarcinoma (Fig. 1B). RT-qPCR and western blotting revealed consistent upregulation patterns, with lung cancer cell lines (A549, PC9 and H520) exhibiting significantly higher METTL5 mRNA and protein expression levels compared with normal pulmonary epithelial cells (Fig. 1D). Prognostic evaluation through Kaplan-Meier analysis indicated that elevated METTL5 expression was associated with reduced survival rates in patients with NSCLC (Fig. 1C).

Figure 1. METTL5 is upregulated in NSCLC tissues. (A) Expression levels of METTL5 in NSCLC tissues and healthy tissues (unpaired samples) and in paired samples. (B) METTL5 expression in pan-cancer. (C) Kaplan-Meier overall survival analysis showing the survival probability of patients with NSCLC and high or low METTL5 expression (HR=1.4; log-rank P=0.014). (D) mRNA levels of METTL5 measured by reverse transcription-quantitative PCR and protein levels detected by western blot analysis in NSCLC cell lines (A549, PC9, H520 and NCI-H1299) compared with normal human lung epithelial cell line (BEAS-2B). β-actin was used as a loading control. Western blot analysis demonstrating the efficient (E) overexpression or (F) knockdown of METTL5 in A549 and PC9 cells transfected with si-METTL5 or si-NC. *P<0.05, **P<0.01, ***P<0.001 and ****P<0.0001. Data are presented as the mean ± SD. METTL5, methyltransferase 5; NSCLC, non-small cell lung cancer; HR, hazard ratio; TPM, transcripts per million; NC, negative control; oe, overexpression; ns, not significant; si, small interfering RNA.

To investigate the role of METTL5 in NSCLC pathogenesis, cell proliferation and migration were evaluated after METTL5 knockdown or overexpression. Western blotting demonstrated the successful knockdown or overexpression of METTL5 in experimental models constructed through transfection (Fig. 1E and F). The colony formation assay showed that silencing of METTL5 significantly suppressed the proliferation of A549 and PC9 lung cancer cells, whereas ectopic overexpression significantly enhanced cell proliferation (Fig. 2A and B). The Transwell migration assay showed that silencing of METTL5 significantly inhibited the migration of lung cancer cells, whereas its overexpression significantly stimulated cell migration (Fig. 2C and D). These findings collectively indicated that METTL5 serves an oncogenic role in NSCLC, positioning it as a promising diagnostic biomarker and therapeutic target.

Figure 2. Colony formation and Transwell assays. Colony formation assays were performed to detect the proliferation of (A) A549 and PC9 cells with METTL5 overexpression or (B) A549 and PC9 cells with METTL5 knockdown. Colonies were stained and quantified. Transwell assays were performed to detect the migration of A549 and PC9 cells with METTL5 (C) overexpression or (D) knockdown. Representative images and quantifications are shown. *P<0.05, **P<0.01 and ****P<0.0001. METTL5, methyltransferase 5; oe, overexpression; Ctrl, control; si, small interfering RNA.

Identification of PGAM1 as a direct target of METTL5

To investigate the molecular mechanisms via which METTL5 contributes to NSCLC progression, the M6A2Target database was used to predict the targets of METTL5. PGAM1 mRNA was identified as a key potential target (45). Subsequent analysis using the GEPIA database showed a significant positive correlation between the mRNA expression levels of METTL5 and PGAM1 (R=0.45; P=5.4×10−56; Fig. 3A). Analysis of transcriptomic data obtained from TCGA showed that the mRNA expression levels of PGAM1 were higher in NSCLC tissues compared with corresponding healthy tissues (Fig. 3B). For functional validation, METTL5-overexpressing cell lines were established and treated with the methylation inhibitor 3-deazaadenosine (DAA). Western blot analysis revealed that overexpression of METTL5 significantly increased PGAM1 protein levels compared with the negative control, while treatment with the methylation inhibitor DAA reversed this effect (Fig. 3C), indicating that PGAM1 expression is dependent on METTL5-mediated methylation. These findings collectively suggested that PGAM1 mRNA was methylated by METTL5 under physiological conditions.

Figure 3. METTL5 regulates PGAM1 through N6-methyladenosine methylation of PGAM1 mRNA. (A) Correlation analysis between METTL5 and PGAM1 expression in NSCLC tissues from The Cancer Genome Atlas database. (B) Expression levels of PGAM1 in NSCLC tissues and normal tissues and in paired samples. (C) Western blot analysis of PGAM1 protein levels in A549 and PC9 cells under different experimental conditions compared with oe-NC group. (D) PGAM1 protein expression in A549 and PC9 cells with YTHDF1 knockdown. The stability of PGAM1 mRNA was detected in YTHDF1-konckdown compared with the shNC group through reverse transcription-quantitative PCR at the indicated time after actinomycin D (5 µg/ml) treatment in (E) A549 and (F) PC9 cells. *P<0.05, **P<0.01, ***P<0.001, ****P<0.0001, ##P<0.01 and ###P<0.001. METTL5, methyltransferase 5; PGAM1, phosphoglycerate mutase 1; TPM, transcripts per million; NSCLC, non-small cell lung cancer; YTHDF1, YTH N6-methyladenosine RNA binding protein 1; oe, overexpression; NC, negative control; DAA, 3-deazaadenosine; si, small interfering RNA; sh, short hairpin RNA.

Identification of YTHDF1 as an m6A reader of PGAM1

Studies have shown that m6A readers serve an important role in methylation (46,47). Based on the predictions of the M6A2Target database and Shi et al (48) (GSE ID: GSE136433), YTHDF1 was identified as an m6A reader that recognizes and binds to methylated PGAM1 mRNA. Western blotting further confirmed these findings, indicating that PGAM1 protein expression was significantly lower in YTHDF1-silenced cells compared with control cells (Fig. 3D). These findings align with those of RNA immunoprecipitation (RIP) sequencing in previous studies (48,49), determining direct molecular interactions between YTHDF1 and PGAM1 mRNA. Furthermore, RNA stability analysis revealed that the silencing of YTHDF1 accelerated PGAM1 mRNA decay rates in NSCLC cells, indicating enhanced mRNA degradation (Fig. 3E and F) These findings collectively indicated that YTHDF1 directly recognized m6A-modified PGAM1 mRNA to regulate its stability and expression.

METTL5 and PGAM1 promote glycolysis by regulating GLUT1 expression

As an important enzyme in glycolysis, PGAM1 mediates the interconversion between 3-phosphoglycerate (3-PGA) and 2-phosphoglycerate (2-PGA), thereby enhancing cellular energy metabolism. Through its regulatory role in maintaining equilibrium between these metabolites, PGAM1 affects ancillary metabolic processes. This enzymatic activity influences a number of biosynthetic processes, not only channeling carbon flux towards biosynthetic pathways for macromolecule production but also maintaining cellular redox homeostasis. Furthermore, emerging evidence indicates that PGAM1 regulates mitochondrial function and shapes the tumor immune microenvironment, collectively driving malignant proliferation and metastatic spread in neoplastic tissues (50–52). In the present study, the functional importance of the METTL5/PGAM1 axis in NSCLC pathogenesis and energy metabolism was examined. Metabolic flux analysis showed that PGAM1-knockdown cells exhibited a decreased ECAR but an increased OCR (Fig. 4F and G).

Figure 4. Correlation analysis and metabolic flux analysis. Correlation analysis between PGAM1 expression and (A) GLUT1, (B) PFKP, (C) PFKFB3, (D) HK2 and (E) GLUT3 expression in non-small cell lung cancer tissues from The Cancer Genome Atlas database. (F) ECAR and (G) OCR profiles were measured in PGAM1-knockdown A549 cells. The metabolic inhibitors were injected sequentially at different time points as indicated. TPM, transcripts per million; PGAM1, phosphoglycerate mutase 1; SLC2A1, solute carrier family 2 member 1; GLUT, glucose transporter; PFKP, phosphofructokinase, platelet; PFKFB, 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatases; HK2, hexokinase 2; ECAR, extracellular acidification rate; OCR, oxygen consumption rate; sh, short hairpin RNA; Rot-AA, rotenone/antimycin A; 2-DG, 2-deoxy-D-glucose; FCCP, carbonyl cyanide 4-(trifluoromethoxy)phenylhydrazone.

Studies have shown that overexpression of GLUT1 and GLUT3 can promote glucose uptake in tumor cells and lead to chemoresistance by activating oncogenic signaling pathways, such as the PI3K/AKT pathway (53,54). Hexokinase 2 (HK2) is a key enzyme in glycolysis initiation, and its activation can drive tumor metabolism toward glycolysis (55,56). Phosphofructokinase, platelet (PFKP) and 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatases (PFKFB)-3 serve important roles in tumor glycolysis, inducing high glycolytic activity, which may lead to drug resistance (57). To determine the molecular mechanism of METTL5/PGAM1-mediated glycolysis, correlation analysis was performed between PGAM1 expression and GLUT1, GLUT3, HK2, PFKP or PFKFB3 expression (Fig. 4A-E). The correlation between PGAM1 and GLUT1 expression exhibited the highest significance (P=4.12ⅹ10−183; R=0.6). This finding was validated by detecting protein expression. Knockdown of PGAM1 significantly decreased GLUT1 protein expression in NSCLC cells, whereas its overexpression had the opposite effect (Fig. 5A).

Figure 5. Western blot analysis of GLUT1 protein levels under different experimental conditions. (A) GLUT1 protein expression was significantly reduced in PGAM1-knockdown cell lines and significantly elevated in PGAM1-overexpressing cell lines. (B) GLUT1 protein expression was significantly reduced in METTL5-knockdown cell lines and significantly elevated in METTL5-overexpressing cell lines. (C) GLUT1 protein expression was upregulated in METTL5-silenced cells co-transfected with PGAM1 overexpression plasmid. β-actin was used as the loading control. ***P<0.001 and ****P<0.0001. GLUT1, glucose transporter type 1; PGAM1, phosphoglycerate mutase 1; METTL5, methyltransferase 5; NC, negative control; oe, overexpression; si, small interfering RNA.

Furthermore, the effects of METTL5 on GLUT1 expression were investigated. Overexpression of METTL5 significantly upregulated GLUT1 expression in NSCLC cells, whereas its knockdown significantly downregulated GLUT1 expression (Fig. 5B). In rescue experiments, compared with the METTL5-knockdown group, GLUT1 protein expression was elevated in cells with METTL5 knockdown combined with PGAM1 overexpression, implying that PGAM1 overexpression partially reversed the downregulation of GLUT1 caused by METTL5 deficiency. This supported the present hypothesis that PGAM1 acts downstream of METTL5 in regulating GLUT1 expression (Fig. 5C).

Discussion

As a hallmark of malignant transformation, the Warburg effect refers to tumor cells predominantly using glycolysis rather than oxidative phosphorylation for energy production, even under normoxic conditions (58). Although the underlying mechanisms are multifaceted, studies have indicated that this metabolic reprogramming supports the proliferation, metastatic spread and invasive potential of tumor cells (59–61). As a predominant RNA modification, m6A serves an important role in regulating both coding and non-coding RNAs. A previous study has highlighted its notable role in carcinogenesis, particularly through the modulation of metabolic pathways in a number of malignancies (62). For example, in colorectal carcinoma, the methyltransferase VIRMA enhances m6A-mediated methylation of HK2 mRNA, leading to elevated HK2 mRNA expression and increased transcript stability. This process ultimately promotes aerobic glycolysis in cancer cells and augments their malignant potential (63). Furthermore, in gastric cancer, the stability and expression levels of heparin binding growth factor (HDGF) mRNA are enhanced through m6A modification mediated by METTL3, followed by its interaction with insulin-like growth factor 2 mRNA-binding protein 3. Nuclear HDGF binds to the promoter regions of GLUT4 and enolase 2, leading to elevated levels of glycolytic enzymes, thereby stimulating glycolysis, tumor growth and hepatic metastasis in gastric cancer (64). Yang et al (65) demonstrated that elevated hepatitis B virus X-interacting protein (HBXIP) expression enhanced glycolytic activity in HCC cells, thereby increasing their malignant potential. This effect was mediated through HBXIP-induced upregulation of METTL3. In METTL3-overexpressing hepatocellular carcinoma (HCC) cells, elevated m6A methylation of hypoxia inducible factor 1-α was observed, and this led to the activation of downstream glycolytic enzymes and a corresponding increase in the invasiveness of HCC cells (65).

METTL5 is an 18S ribosomal RNA (rRNA)-specific m6A methyltransferase. Its main function is to regulate ribosomal translation by catalyzing m6A modification at the A1832 site in 18S rRNA (35). METTL5 has been reported to be markedly upregulated in a number of cancer types, including breast cancer and HCC (36,66,67). Furthermore, METTL5 has been shown to regulate metabolic reprogramming. In HCC, METTL5 promotes fatty acid β-oxidation and the Warburg effect by targeting acyl-coA synthetase long chain family member 4 (ACSL4). ACSL4 encodes a key metabolic enzyme that activates fatty acids by catalyzing their conversion to acyl-CoA esters, thereby supporting the energy demand of tumor cells (66). The present study demonstrated that METTL5 enhanced glycolytic activity and tumor cell proliferation in NSCLC, thereby promoting NSCLC progression.

PGAM1 serves as a key metabolic catalyst in facilitating the interconversion between 3-PGA and 2-PGA during glycolysis. By regulating the flux of these intermediates, PGAM1 supports the generation of energy and provides essential precursors for serine synthesis, the pentose phosphate pathway and phospholipid metabolism (68). In certain tumors, PGAM1 not only exhibits enzymatic activity but also participates in enzyme-independent metabolic regulation. For example, in breast cancer, PGAM1 interacts with α-smooth muscle actin through direct binding, enhancing the migration and metastatic spread of cancer cells through non-catalytic mechanisms involving protein-protein interfaces. This interaction is independent of PGAM1 metabolic activity; the enzymatically inactive H186R mutant retains ACTA2 binding, whereas a mutant lacking amino acids 201–210 fails to interact despite maintaining full enzymatic function. By acting as a structural adaptor, PGAM1 directly modulates actin filament assembly and cytoskeletal dynamics, thereby promoting cell motility (51). PGAM1 has emerged as a promising therapeutic target for cancer and marked advancements have been made in designing PGAM1 inhibitors. For example, HKB99 is a novel allosteric PGAM1-targeted compound with potent suppressive effects on both tumor progression and metastatic spread in NSCLC. In addition, it has exhibited therapeutic potential in erlotinib-resistant tumors (69).

As an important glucose transporter, GLUT1 regulates cellular glucose uptake and frequently acts as a metabolic bottleneck in malignant tumors. These key functions of GLUT1 are observed in a number of malignancies. For example, GLUT1-enriched cancer-associated fibroblasts can drive metastatic niche formation through metabolic reprogramming in ovarian carcinoma (70). In LUAD, the long non-coding RNA GAS6-AS1 blocks glucose metabolic reprogramming by inhibiting GLUT1 expression, thereby inhibiting tumor progression (71). In glioblastoma, GLUT1 inhibition can reduce the excretion of lactate produced by tumor glycolysis, thereby improving the immunosuppressive tumor microenvironment (72). These findings collectively indicate that the METTL5/PGAM1 signaling axis facilitates glycolytic metabolism and tumor progression by regulating GLUT1 in NSCLC.

The present study demonstrated that METTL5 was markedly upregulated in NSCLC, with this upregulation being associated with unfavorable clinical outcomes. METTL5 promoted the proliferation, migration and glycolytic activity of NSCLC cells in vitro, regulating the expression of PGAM1 mRNA through m6A modification. PGAM1 mRNA could also be recognized and bound by YTHDF1 to inhibit its degradation. PGAM1 overexpression results in the upregulation of GLUT1, thereby enhancing glycolysis and lactate production in NSCLC cells. PGAM1 catalyzes the key reaction in glycolysis, converting 3-PGA to 2-PGA, increasing GLUT1 expression, accelerating glucose uptake efficiency and providing sufficient substrates for cells (68). Therefore, we hypothesized that the METTL5/PGAM1/GLUT1 axis promoted NSCLC progression by reprogramming glucose metabolism.

Despite notable findings, the present study had certain limitations. While the colony formation assays demonstrated the impact of METTL5 on clonal expansion, the integration of MTT analysis will be considered in future studies to validate these effects across different proliferation metrics. The colony formation assay was specifically selected to assess the self-renewal and sustained proliferation capacities of cells, which are important in understanding tumorigenicity. This approach aligned with the objective of the present study to explore the role of METTL5 in cancer stemness and metastatic potential. Furthermore, due to the established association between clonogenicity and tumor aggressiveness (73), the present study prioritized this endpoint to provide mechanistic insights into the contribution of METTL5 to NSCLC progression. Nevertheless, it should be recognized that MTT assays may complement the present findings by capturing shorter-term proliferation rates. Although the biological function of METTL5 in NSCLC cells was investigated using RT-qPCR, western blotting and cellular functional assays, further validation in animal or clinical tissue samples is required. While in vitro cell line experiments provide valuable mechanistic insights, they exhibit inherent limitations in fully recapitulating the complexity of human tumors, including tumor heterogeneity, the tumor microenvironment and systemic physiological regulation. In the future, the authors plan to validate key findings in clinical NSCLC tissue samples and establish animal models to evaluate in vivo tumorigenicity and therapeutic potential. In addition, small-molecule inhibitors targeting METTL5 or its downstream effectors (such as PGAM1) are being screened in preclinical models, with the aim of identifying potential strategies for metabolism-targeted therapy in NSCLC. These complementary approaches will allow the gap between in vitro observations and clinical reality to be closed, thereby enhancing the translational importance of the present study.

Overall, the mechanistic interactions between METTL5 and PGAM1, particularly those contributing to m6A modification, warrant further investigation. The m6A levels of PGAM1 transcripts were not directly quantified in the present study. Instead, inferences were drawn from protein expression patterns after DAA treatment. In future studies, methylated RIP sequencing may be used to directly quantify the m6A levels of PGAM1 mRNA. To assess whether YTHDF1 is an m6A reader of PGAM1, bioinformatics analysis and western blotting were used to indirectly determine the association between YTHDF1 and PGAM1. Although previous studies (48,49) have reported the use of RIP sequencing, RNA pulldown experiments may be used to elucidate the association between YTHDF1 and PGAM1. In addition, the METTL5/PGAM1 axis may target proteins other than GLUT1 to promote glycolysis. Notably, while HK2 and PFKP showed more modest but statistically significant correlations with PGAM1 expression (R=0.37 and R=0.33, respectively) compared with GLUT1 (R=0.6), these findings suggested that HK2 and PFKP may also be functionally associated with the METTL5/PGAM1 axis, potentially through indirect mechanisms such as metabolic reprogramming or transcriptional network modulation. These potential targets, especially HK2 and PFKP, should be identified to further understand the role of the METTL5/PGAM1 axis in NSCLC progression.

Supplementary Material

Supporting Data

Acknowledgements

Not applicable.

Funding

The present study was funded by the Changzhou High-Level Medical Talents Training Project (grant no. 2022CZBJ069), the Changzhou Sci & Tech Program (grant no. CZ20220025), the ‘333 Project’ of Jiangsu Province (grant no. BRA2020157), the 333 High-Level Talent Training Project (grant no. 2022-2) and the Science and Technology Development Fund of Nanjing Medical University (grant no. NMUB20250013).

Availability of data and materials

The data generated in the present study may be requested from the corresponding author.

Authors' contributions

YS, ZG and XD conceived and designed the experiments. Data collection and analysis was performed by KY, ML and QW. ZG and ML also analyzed and interpreted the data and prepared all figures for publication. KY and QW provided financial and technical support. The first draft of the manuscript was written by ML and ZG and revised by YS. All authors commented on previous versions of the manuscript. All authors have read and approved the final version of the manuscript. YS and ZG confirm the authenticity of all the raw data.

Ethics approval and consent to participate

Not applicable.

Patient consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

References