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Journal of Bone Oncology logoLink to Journal of Bone Oncology
. 2025 Jun 12;53:100695. doi: 10.1016/j.jbo.2025.100695

Regulatory role of the METTL3/MALAT1 axis in multiple myeloma progression

Xiaohong Lu 1, Yafei Li 1, Ruie Li 1, Jingheng Zhang 1, Jiayu Peng 1, Yan Zhang 1,⁎
PMCID: PMC12264608  PMID: 40672991

Highlights

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    The m6A methyltransferases METTL3 and MALAT1 are highly expressed in MM;

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    Down-regulation of METTL3 can inhibit the survival of RPMI8226 and U266 cells;

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    METTL3 positively regulates the expression of MALAT1;

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    Interfering with MALAT1 inhibits the growth of RPMI8226 and U266 cells;

  • •

    METTL3/MALAT1 axis accelerates the development of MM.

Keywords: Multiple myeloma, Methyltransferase-like 3, Long noncoding RNA metastasis-associated lung adenocarcinoma transcript 1, Tumor growth, Migration, Invasion

Abstract

Objective

Methyltransferase-like 3 (METTL3) plays a crucial role in cancer progression, both in m6A modification-dependent and −independent pathways. We aimed to elucidate the mechanism by which METTL3 and the long noncoding RNA metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) contribute to the pathogenesis of multiple myeloma (MM).

Methods

Bone marrow samples were collected from 56 patients with MM and 42 healthy donors, followed by assessment of METTL3 and MALAT1 levels. An interaction between METTL3 and MALAT1 was also identified. METTL3- and MALAT1-related oligonucleotides were transfected into RPMI8226 and U266 cells to explore their role in cell growth. Apoptosis, migration, proliferation, and invasion of RPMI8226 and U266 cells were assayed.

Results

Elevated METTL3 and MALAT1 levels were observed in patients with MM. Interference with METTL3 or MALAT1 inhibited the malignant behavior of RPMI8226 and U266 cells. There was an interaction between METTL3 and MALAT1. Overexpression of MALAT1 reversed the inhibitory effects of METTL3 interference on tumor cell malignancy.

Conclusion

METTL3 augments MM development by enhancing MALAT1 expression.

1. Introduction

Multiple myeloma (MM) is a tumor of clonal plasma cells derived from the lymphoid B cell lineage after germinal center maturation, developing within the bone marrow from committed progenitor cells [1]. For most patients, the characteristic symptoms include bone involvement-induced pain, renal failure, fatigue, hypercalcemia, anemia, and weight loss [2]. Strategies for MM mainly consist of chemotherapy, autologous stem cell transplantation, and allogeneic stem cell transplantation [3]. Almost all MM patients will eventually relapse, and the treatment options largely depend on the timing of relapse, response to previous treatments, aggressiveness, and behavioral status of relapse [4]. This underscores the importance of delving deeper into the pathological mechanisms of MM.

In recent years, studies have found that RNA N6-methyladenosine (m6A) methylation modification plays a pivotal role in the occurrence and development of MM. As the most crucial RNA epigenetic modification, m6A forms a synergistic regulatory network with DNA methylation and histone modifications, constructing a multi-layered epigenetic regulatory circuit [5]. Methyltransferase-like 3 (METTL3) is the core catalytic enzyme for m6A modification. Recent research has revealed that it promotes drug resistance in MM cells by mediating m6A modification of the long non-coding RNA H19 [6]. Moreover, METTL3 can regulate mRNA translation and biological processes, including cell entry, apoptosis, migration, differentiation, and inflammation [7]. METTL3 supports the translation of oncogenes in lung cancer [8] and induces tumor proliferation in bladder cancer [9], as well as colorectal cancer metastasis [10]. In osteosarcoma (OS), reduction of METTL3 reduces m6A methylation levels and restrains the aggressive behavior of cancer cells [11]. However, the detailed mechanism of METTL3 in MM has not been completely elucidated.

Notably, METTL3 is the main methyltransferase of m6A modification in the long noncoding RNA metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) [12], significantly upregulating MALAT1 expression levels [13]. MALAT1, located around the nuclear speckle along with the pre-RNA splicing factor, presents remarkable value in cancer diagnosis, prognosis, and treatment [14]. Clinical studies have demonstrated that MALAT1 expression is closely associated with poor prognosis and drug resistance in patients [15], and its expression level is positively correlated with the degree of bone destruction [16]. As an important oncogene, MALAT1 is inversely correlated with the progression-free survival of patients with MM [17] and is involved in extramedullary dissemination [18]. Basic research has confirmed that targeting MALAT1 can induce DNA damage and apoptosis [19,20], suggesting its therapeutic potential.

Based on these findings, we propose the hypothesis that METTL3 positively regulates MALAT1 expression through m6A modification, thereby driving MM progression. This mechanism aims to elucidate the critical role of epigenetic transcriptional regulation in MM and provides a theoretical basis for the development of novel targeted therapeutic strategies.

2. Methods and materials

2.1. Ethics statement

All experiments were conducted with the approval of the ethics committee of Luzhou People’s Hospital, and informed consent was obtained from all participants. All animal experiments were approved by the Animal Ethics Committee of Luzhou People’s Hospital.

2.2. Clinical samples and cell culture

Fifty-six patients diagnosed with MM and treated in the Hematology-Oncology Department of Suzhou Hospital Affiliated to Nanjing Medical University between January 2013 and March 2018 were enrolled in this study. Additionally, 42 healthy individuals who underwent bone marrow biopsy during the same period and were confirmed to have no abnormalities in bone marrow function were included as controls. All tissue samples were immediately frozen in liquid nitrogen and stored for further use. The human MM cell lines RPMI8226 and U266 were obtained from ATCC. Cells were grown in Roswell Park Memorial Institute (RPMI)-1640 medium supplemented with 10 % fetal bovine serum (FBS) [21].

Inclusion criteria: Patients whose bone marrow tissues were pathologically confirmed as MM or normal bone marrow by the Pathology Department of Suzhou Hospital Affiliated to Nanjing Medical University, and whose clinical diagnoses met the World Health Organization diagnostic criteria for MM [22]. Exclusion criteria: Patients with infectious diseases, a history of other hematological disorders (e.g., emolytic anemia, idiopathic thrombocytopenic purpura), other primary malignancies, a prior history of bone marrow biopsy, and pregnant women.

Specific clinical information is shown in Table 1.

Table 1.

Clinical characteristics of patients with multiple myeloma.

Features MM group (n = 56)
Age (years)
< 50 26
≥ 50 30
Gender
Male 33
Female 23
M protein
IgG 29
IgA 15
Light chain/other 12
β2 microglobulin (µg/ml)
<3.5 25
≥3.5 31
DS stage
I–II 27
III 29
ISS stage
I–II 26
III 30
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2.3. Cell transfection

Cells were grouped into small interfering (si)-METTL3 + MALAT1-negative control (NC) and si-METTL3 + overexpression (oe)-MALAT1. The corresponding plasmids were sourced from GenePharma Co. Ltd. (Shanghai, China). Prior to transfection, RPMI8226 and U266 cells were cultured to 50–60 % confluence. MM cells were seeded in 24-well plates and transfected using Lipofectamine 2000 (Invitrogen, CA, USA). The siRNA final concentration used for transfection was 50 nM, and the plasmid DNA concentration was 1.0 μg/well. Transfections were carried out in serum-free Opti-MEM for 6 h, after which the medium was replaced with regular RPMI-1640 medium containing 10 % FBS. Cells were cultured for an additional 48 h before subsequent experiments were performed.

2.4. Reverse transcription quantitative polymerase chain reaction (RT-qPCR)

Total RNA was isolated from tissues or cultured cells using TRIzol® reagent (15596–018, Solarbio, Beijing, China), following the manufacturer's guidelines. cDNA was synthesized from 1 μg of total RNA using the PrimeScript™ RT-PCR Kit (TaKaRa, Mountain View, CA, USA) in a 20 μL reaction volume. Reverse transcription conditions were: 37℃ for 15 min, followed by enzyme inactivation at 85℃ for 5 s. RT-qPCR was performed using SYBR Premix Ex TaqTM (TaKaRa) on a LightCycler 480 system (Roche Diagnostics). Each 20 μL reation contained 10 μL of SYBR mixture, 0.4 μL each of forward and reverse primers (10 μM), 2 μL of cDNA template, and 7.2 μL of ddH2O. The amplification program consisted of an initial denaturation at 95℃ for 30 s, followed by 40 cycles of denaturation at 95℃ for 5 s and annealing/extension at 60℃ for 30 s. GAPDH served as the internal control for normalization. Primers were designed and synthesized by Shanghai General Biotechnology Co., Ltd. and their sequences are listed in Table 2. Relative transcription levels of the target genes were calculated using the 2-ΔΔCT method. All reactions were performed in triplicate, and each experiment was independently repeated three times [[23], [24], [25]].

Table 2.

Reverse transcription quantitative polymerase chain reaction primers.

Genes Primers
METTL3 F: 5′-CTGGGCACTTGGATTTAAGGAA-3′
R: 5′-TGAGAGGTGGTGTAGCAACTT-3′
MALAT1 F: 5′- GCATTAATTGACAGCTGACCCA-3′
R: 5′- GCTTGCTCCTCAGTCCTAGCTT-3′
GAPDH F: 5′-GGAGCGAGATCCCTCCAAAAT-3′
R: 5′-GGCTGTTGTCATACTTCTCATGG-3′
MALAT1 for m6A-IP F: 5′-TTCCGGGGTTGTAGGTTC-3′
R: 5′-AAAAA CCCACAAACTTGC-3′
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Note: F, forward; R, reverse; METTL3, methyltransferase-like 3; MALAT1, long noncoding RNA metastasis-associated lung adenocarcinoma transcript 1; GAPDH, glyceraldehyde-3-phosphate dehydrogenase.

2.5. Western blot assay

Total protein was extracted using cell lysis buffer (Abcam, MA, USA) and quantified via the Bradford assay. Equal amounts of protein were separated by 10 % sodium dodecyl sulfate–polyacrylamide gel electrophoresis and transferred onto polyvinylidene fluoride membranes (Absin Bioscience Inc., Shanghai, China). Membranes were blocked with 5 % skim milk and incubated overnight at 4℃ with primary antibodies against METTL3 (1:1000), MALAT1 (1:1000), and GAPDH (1:1000; all from Abcam). The following day, membranes were incubated with appropriate horseradish peroxidase-conjugated secondary antibodies (1:1000; Abcam) and visualized using an enhanced chemiluminescence detection kit (Beyotime, Shanghai, China). Protein bands were quantified using ImageJ software (NIH, MD, USA) [26].

2.6. Cell counting kit (CCK)-8 assay

To assess cell proliferation, transfected RPMI8226 and U266 cells were seeded in 96-well plates at a density of 1 × 103 cells/well. The CCK-8 assay (KeyGen, Nanjing, China) was used according to the manufacturer's protocol. At 24, 48, and 72 h post-seeding, 10 μL of CCK-8 reagent was added to each well containing 90 μL of medium. Wells containing medium and CCK-8 reagent without cells served as blanks. After incubation, absorbance at 450 nm was measured using a microplate reader (Bio-Rad Laboratories, Inc., Hercules, CA, USA). Each condition was tested in triplicate [27].

2.7. Flow cytometry

After 48 h of transfection, cells were stained with 5 μL Annexin V-fluorescein isothiocyanate (BD Biosciences, NJ, USA) and 5 μL propidium iodide (BD Biosciences). Apoptotic cells were assessed using a FACScan flow cytometer and CellQuest3.0 software (BD Biosciences) [26].

2.8. Transwell assay

Migration assay: Cells were resuspended in serum-free medium and seeded into the upper chamber of a 24-well Transwell insert (Corning, NY, USA) without Matrigel coating. The lower chamber was filled with 600 μL of RPMI-1640 medium supplemented with 10 % FBS (BD Biosciences) as a chemoattractant.

Invasion assay: The procedure was identical to the migration assay, except the upper chamber was precoated with Matrigel.

After 48 h of incubation, non-migrated or non-invaded cells were gently removed with a cotton swab. The remaining cells on the lower membrane surface were fixed with methanol, stained with 0.5 % (w/v) crystal violet, and counted under an optical microscope in five randomly selected fields.

2.9. RNA-binding protein immunoprecipitation (RIP)-qPCR

RNA-RIP followed by qPCR was performed using the Magna RIPTM Kit (Millipore, USA). Briefly, cells were rinsed twice with phosphate-buffered saline (PBS) and lysed in RIP lusis buffer. The lysates were then subjected to high-speed centrifugation to obtain the precipitates. The resulting supernatant was incubated with specific antibodies and protein A/G beads overnight. The RNA-protein complexes bound to the beads were eluted using RIP washing buffer. The RNA was then extracted from the immunoprecipitates, and the enrichment of target RNA was quantified by qPCR [28].

2.10. Methylated RNA immunoprecipitation (MeRIP)-qPCR

Total RNA was extracted from cultured cells using the MolPure Cell/Tissue Total RNA Extraction Kit (Yesen Biotech, China). MeRIP was performed by incubating the total RNA with either anti-m6A antibody or control IgG overnight at 4℃. Then, DynabeadsTM Protein A magnetic beads were added and incubated for an additional 3 h at 4℃. The magnetic beads were then washed three times with RIP buffer, and m6A-enriched RNA was purified. Quantification of m6A-modified transcripts was performed by RT-qPCR as previously described [6].

2.11. Statistical analysis

SPSS software (version 21.0; IBM Corp., Armonk, NY, USA) and GraphPad Prism 9.5 (California, USA) were used for all data analysis. Measurement data are presented as mean ± standard deviation. Data with a normal distribution and homogeneity of variance between the two groups were compared using unpaired t-tests. Data analysis among multiple groups was performed using one-way analysis of variance (ANOVA), followed by Tukey's post hoc test. Data at different time points were analyzed using repeated-measures ANOVA. Statistical significance was set at P < 0.05 [29].

3. Results

3.1. High METTL3 expression predicts poor prognosis in MM

METTL3 is closely associated with cancer development and malignancy [30]. We first assessed METTL3 expression in the plasma of MM patients and healthy controls by RT-qPCR and Western blot analyses, and found higher METTL3 expression in MM patients (Fig. 1A, B). Next, we estimated the prognostic correlation between METTL3 expression and survival of patients with MM. Kaplan-Meier survival analysis revealed that higher METTL3 expression was associated with shorter overall survival in MM patients (Fig. 1C). In summary, high METTL3 expression may serve as a potential prognostic marker in patients with MM.

Fig. 1.

Fig. 1

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High METTL3 expression is predictive for the prognosis of MM. A/B. RT-qPCR/Western blot measured METTL3 expression in healthy controls (42 cases) and MM patients (56 cases); C. The correlation between MELLT3 expression and the survival and prognosis of MM patients; the data were expressed as mean ± standard deviation.

3.2. Deficiency of METTL3 restrains RPMI8226 and U266 cell viability

To determine the functional role of METTL3 in MM, we performed siRNA-mediated knockdown and plasmid-mediated overexpression of METTL3 in RPMI8226 and U266 cells. The efficiency of METTL3 silencing and overexpression was confirmed by RT-qPCR and western blotting (Fig. 2A, B). Functional assays, including CCK-8, Transwell migration/invasion, and flow cytometry, demonstrated that METTL3 knockdown impeded cell proliferation, migration, and invasion, while promoting apoptosis in both cell lines (Fig. 2C-E). These data indicate that METTL3 plays a critical role in sustaining the malignant phenotype of MM cells.

Fig. 2.

Fig. 2

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Deficiency of METTL3 restrains the survival of RPMI8226 and U266 cells. A/B. RT-qPCR/Western blot measured METTL3 expression in RPMI8226 and U266 cells after transfection; C. CCK-8 assay detected cell proliferation; D. Transwell assay detected cell migration and invasion; E. Flow cytometry detected cell apoptosis; the data were expressed as mean ± standard deviation. * P < 0.05 vs. the si-NC group; # P < 0.05 vs. the pcDNA-NC group.

3.3. METTL3 positively regulates MALAT1 expression

MALAT1 has been implicated in tumorigenesis, including in colorectal cancer (CRC) [31]. RT-qPCR experiments demonstrated that MALAT1 expression was significantly upregulated in MM samples compared to healthy controls (Fig. 3A). Meanwhile, knockdown of METTL3 in RPMI8226 and U266 cells led to a significant decrease in MALAT1 expression (Fig. 3B). Pearson correlation analysis revealed a positive correlation between METTL3 and MALAT1 in MM cells (Fig. 3C). The RIP-qPCR results revealed a strong physical interaction between METTL3 and MALAT1 in RPMI8226 and U266 cells, as evidenced by abundant coprecipitated complexes (Fig. 3D). Additionally, MeRIP-qPCR demonstrated that METTL3 knockdown markedly reduced the m6A methylation level of MALAT1 transcripts (Fig. 3E, F). Therefore, METTL3 directly binds to and promotes MALAT1 expression through m6A modification in MM cells.

Fig. 3.

Fig. 3

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METTL3 positively regulates MALAT1 expression. A. RT-qPCR measured MALAT1 expression in healthy controls (42 cases) and MM patients (56 cases); B. RT-qPCR detected MALAT1 expression levels in transfected cells; C. Correlation between METTL3 and MALAT1; D. RIP-qPCR experiment detected the interaction between METTL3 and MALAT1; E. Detection of the m6A level of MALAT1 after downregulating METTL3; F. Detection of m6A modification of MALAT1 by MeRIP-qPCR; the data were expressed as mean ± standard deviation. * P < 0.05 vs. the si-NC group; # P < 0.05 vs. the pcDNA-NC group.

3.4. Downregulation of MALAT1 represses RPMI8226 and U266 cell growth

To determine the functional significance of MALAT1 in MM, we transfected short hairpin (sh)-MALAT1 into RPMI8226 and U266 cells and confirmed successful transfection by RT-qPCR (Fig. 4A). Functional assays revealed that in response to interference with MALAT1, RPMI8226 and U266 cells showed decreased cell invasion, proliferation, and migration abilities while promoting cell apoptosis (Fig. 4B-D). Overall, MALAT1 downregulation represses RPMI8226 and U266 cell growth.

Fig. 4.

Fig. 4

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Downregulation of MALAT1 represses the growth of RPMI8226 and U266 cells. A. RT-qPCR measured MALAT1 expression in RPMI8226 and U266 cells after transfection; B. CCK-8 assay detected cell proliferation; C. Transwell assay detected cell migration and invasion; D. Flow cytometry detected cell apoptosis; the data were expressed as mean ± standard deviation. * P < 0.05 vs. the MALAT1-NC group.

3.5. Overexpression of MALAT1 reverses the inhibitory effects of METTL3 knockdown on tumor cell growth

Finally, we explored whether METTL3 mediates the biological effect of MALAT1 on RPMI8226 and U266 cells. RT-qPCR confirmed successful overexpression of MALAT1 (Fig. 5A). Functional analyses revealed that MALAT1 overexpression reversed the inhibitory effects of METTL3 knockdown on invasion, proliferation, and migration abilities and attenuated the pro-apoptotic effect of RPMI8226 and U266 cells (Fig. 5B-D). These findings indicate that MALAT1 is a critical downstream effector of METTL3 in regulating MM cell malignancy.

Fig. 5.

Fig. 5

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Overexpression of MALAT1 reverses the inhibitory effect of METTL3 knockdown on tumor cell growth. A. RT-qPCR measured MALAT1 expression in RPMI8226 and U266 cells after transfection; B. CCK-8 assay detected cell proliferation; C. Transwell assay detected cell migration and invasion; D. Flow cytometry detected cell apoptosis; the data were expressed as mean ± standard deviation. * P < 0.05 vs. the si-METTL3 + MALAT1-NC group.

4. Discussion

MM is a malignant neoplasm of plasma cells, accounting for approximately 10 % of all hematological tumors [32]. In this study, we evaluated the significance of METTL3 and MALAT1 in MM cellular behavior. Our data revealed that METTL3 expression was increased in the plasma of MM patients and was associated with a dismal prognosis, suggesting its potential value as a prognostic biomarker. Functionally, downregulation of METTL3 suppressed proliferation, migration, and invasion, while promoting apoptosis of RPMI8226 and U266 cells. This further reinforce the oncogenic role of METTL3 in MM, offering a theoretical basis for METTL3-targeted therapies. Subsequently, we revealed that MALAT1 expression was positively correlated with METTL3 expression. This suggests a possible synergistic effect between MALAT1 and METTL3 in MM. Mechanistic studies showed that knockdown of METTL3 significantly reduced the m6A methylation level of MALAT1, implicating METTL3 as a key regulator of MALAT1 via m6A-dependent modification. Given that m6A methylation plays a critical role in RNA metabolism, including splicing, stability, and translation [5], it is plausible that METTL3-mediated m6A modification enhances MALAT1 stability or function, thereby promoting MM progression. In agreement with this hypothesis, silencing MALAT1 also suppressed the proliferation, invasion, and migration of MM cells while enhancing apoptosis. These results highlight MALAT1 as an oncogenic lncRNA in MM and suggest it as a potential therapeutic target. Notably, MALAT1 overexpression was able to rescue the inhibitory effects of METTL3 knockdown on malignant phenotypes, indicating that MALAT1 may act downstream of METTL3 to mediate its tumor-promoting functions. This finding suggests that high MALAT1 expression could partially counteract the anti-tumor effects of METTL3 inhibition.

The oncogenic role of METTL3 is not limited to MM. Numerous studies have confirmed that METTL3 promotes cancer development in various tumor types. For instance, Zhou et al. have stated that METTL3 expression is upregulated in OS while loss of METTL3 retards OS cells to proliferate, migrate, and invade and induces cellular apoptosis, partly via downregulation of ATPase family AAA domain-containing protein 2 [11]. In gastric cancer, high METTL3 expression correlates with poor prognosis, and its overexpression stimulates cancer angiogenesis and glycolysis [33], epithelial-mesenchymal transition, as well as metastasis [34]. Similarly, in breast cancer, METTL3 upregulation supports tumor cell proliferation and anti-apoptotic activity, and METTL3 deficiency suppresses tumorigenesis [35]. In CRC, an abnormal increase in METTL3 expression suggests a relationship with accelerated tumor metastasis, migration, and invasion [36]. In bladder cancer, METTL3 is overexpressed in tumor tissues, and its knockdown impairs cell viability and tumor-forming capacity, while reintroduction of METTL3 restores aggressive behavior [37]. In ovarian cancer, METTL3 expression is associated with tumor size, metastasis, and pathological grade, and its inhibition suppresses proliferation and promotes apoptosis [38]. Likewise, in prostate cancer, METTL3 expression is elevated, and knockdown of METTL3 reduces cancer cell proliferation and invasiveness [39]. Collectively, these findings underscore the oncogenic function of METTL3 across a broad spectrum of cancers.

MALAT1 expression is positively mediated by METTL3-mediated m6A modification [13], consistent with our findings. In line with its role in MM, several previous studies have reported observations similar to ours. For instance, Liu et al. measured increased MALAT1 levels in the serum of MM patients, and depleting MALAT1 in MM cells resulted in limited viability, invasion, and glycolysis [40]. Moreover, the delivery of MALAT1-targeting antisense oligonucleotides has show potentials in reducing MM cellular growth both in vitro and in vivo [41]. In addition, Liu et al. have confirmed the oncogenic role of MALAT1, and inhibition of MALAT1 blocks cell proliferation and induces apoptosis [42]. In addition to MM, MALAT1 is also abnormally expressed in several solid tumors, including colon cancer [43] and lung cancer [44], where MALAT1-silenced cancer cells exhibit cellular growth impairment. These findings collectively support MALAT1′s role as a pro-tumorigenic lncRNA across multiple cancer types.

In this study, we confirmed the oncogenic function of MALAT1 in MM. Silencing MALAT1 significantly suppressed proliferation, migration, and invasion of RPMI8226 and U266 cells while enhancing apoptosis. However, the precise molecular mechanisms by which MALAT1 regulates MM cell cycle progression remain to be fully elucidated. Previous research has shown that MALAT1 depletion induces cell cycle arrest, with an increased proportion of cells in the G1 and G2/M phases and a concomitant reduction in S phase cells [45,46]. This cell cycle arrest has been closely associated with activation of the p53 signaling pathway, a key downstream effector of MALAT1 [47]. Additionally, MALAT1 silencing has been reported to reduce the expression of B-MYB (Mybl2), an oncogenic transcription factor that plays a critical role in G2/M phase transition [48]. The downregulation of B-MYB may disrupt alternative splicing by altering the recruitment of splicing factors to its pre-mRNA, ultimately affecting cell cycle progression. These findings suggest a multifaceted role for MALAT1 in modulating cell proliferation via both transcriptional and post-transcriptional mechanisms. Therefore, future studies should aim to further elucidate the downstream molecular pathways through which MALAT1 regulates the MM cell cycle. In particular, exploring its regulatory effects on the p53 signaling pathway and B-MYB expression may provide valuable insights into the mechanisms of MM progression and identify novel targets for therapeutic intervention.

4.1. Limitations and future prospects

This study reveals the critical role of METTL3 in positively regulating MALAT1 expression through m6A methylation, contributing to the pathogenesis of MM and offering a novel theoretical framework for understanding MM progression. However, several limitations should be acknowledged. First, the study is primarily based on in vitro cell line experiments and lacks validation in animal models and clinical tissue samples. Second, although the regulatory relationship within the METTL3/MALAT1 axis has been confirmed, the downstream target genes and associated signaling pathways remain largely unexplored. Third, the involvement of m6A reader proteins in this regulatory mechanism has not been systematically investigated. Additionally, the effect of MALAT1 on cell cycle distribution and its specific downstream molecular targets are yet to be fully characterized. Lastly, the influence of the METTL3/MALAT1 axis on MM cell sensitivity to therapeutic agents has not been assessed. To address these limitations, future studies should focus on the following aspects: constructing appropriate animal models to validate the biological role of the METTL3/MALAT1 axis; systematically screening and validating downstream effector molecules and signaling networks regulated by this axis; elucidating the role of m6A reader proteins in mediating the regulatory interaction between METTL3 and MALAT1; investigating the mechanisms by which MALAT1 regulates the cell cycle and identifying key targets involved; and evaluating the impact of targeting the METTL3/MALAT1 axis on the efficacy of existing MM therapies. These future directions will help to comprehensively delineate the molecular landscape governed by METTL3 and MALAT1 in MM and provide a solid foundation for the development of precision therapeutic strategies targeting m6A modifications.

5. Conclusion and clinical translation

In conclusion, our study highlights that METTL3 overexpression promotes MM progression by upregulating MALAT1 expression via m6A-dependent mechanisms, thereby expanding our understanding of the molecular basis of MM. Based on these findings, several translational implications are proposed. First, the development of specific inhibitors targeting METTL3 or MALAT1—such as small-molecule compounds or antisense oligonucleotides—holds considerable clinical potential and may offer new targeted strategies for MM therapy. Second, the quantification of METTL3 and MALAT1 expression could serve as valuable molecular biomarkers for the diagnosis and prognosis of MM. Establishing standardized detection protocols may enable more precise disease classification and risk stratification. Third, further mechanistic investigation of this regulatory axis will facilitate the optimization of treatment regimens, including: clarifying the involvement of m6A reader proteins in METTL3-mediated regulation of MALAT1; mapping the cell cycle-related gene networks downstream of MALAT1, such as B-MYB and p53; and evaluating potential synergistic effects between this axis and current therapies, including proteasome inhibitors. In the future, large-scale, multi-center clinical studies are warranted to validate the clinical utility of METTL3 and MALAT1 as biomarkers and therapeutic targets and to assess their correlation with treatment response. These translational efforts will help bridge the gap between basic research and clinical application, ultimately improving therapeutic outcomes and prognosis for patients with MM.

6. Ethical approval and consent to participate

With the approval of the ethics committee of Luzhou People’s Hospital, all experiments were conducted, and informed consent was obtained from all participants. All animal experiments were approved by the Animal Ethics Committee of Luzhou People’s Hospital.

7. Consent for publication

The participant has consented to the submission of the manuscript to the journal.

8. Authors' contributions

Xiaohong Lu and Yafei Li finished the study design, Ruie Li finished the experimental studies, Jingheng Zhang finished the data analysis, and Jiayu Peng and Yan Zhang finished manuscript editing. All authors have read and approved the final version of the manuscript.

CRediT authorship contribution statement

Xiaohong Lu: Conceptualization. Yafei Li: Conceptualization. Ruie Li: Investigation. Jingheng Zhang: Formal analysis, Data curation. Jiayu Peng: Writing – review & editing. Yan Zhang: Writing – review & editing.

Funding

This work was not supported by any funding.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgement

We would like to give our sincere gratitude to the reviewers for their constructive comments.

Availability of data and materials

The experimental data used to support the findings of this study are available from the corresponding authors upon request.

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Associated Data

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

Data Availability Statement

The experimental data used to support the findings of this study are available from the corresponding authors upon request.

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