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. 2025 Jun 13;87(8):4918–4925. doi: 10.1097/MS9.0000000000003480

METTL9 as a protein biomarker predicts the prognosis and immunotherapy response of skin-cutaneous melanoma

Song Tang a, Yiran Zhang b, Simin Luo c, Wendan Wang d, Xunxin Wu e, Xiaoxu Zhao e, Yin Leng a,*
PMCID: PMC12333701  PMID: 40787492

Abstract

The role of methyltransferase-like proteins (METTLs) in skin cutaneous melanoma (SKCM) remains unclear. This study investigates the expression of METTLs, particularly METTL9, in SKCM and its clinical significance. Through multiple database analyses and immunohistochemical experiments, we found that METTL9 is significantly upregulated in SKCM tissues, and its expression level is closely associated with tumor-node-metastasis stage, N stage, and ulceration status. Patients with high METTL9 expression have a poor prognosis and show a reduced response to immunotherapy. Furthermore, METTL9 is identified as an independent risk factor for poor prognosis in SKCM patients.

Keywords: human methyltransferase-like proteins, METTL9, prognostic biomarkers, skin cutaneous melanoma, therapeutic targets

Introduction

Skin cutaneous melanoma (SKCM) is a typical skin malignancy brought on by aberrant melanocyte hyperproliferation[1]. Excessive sun exposure may raise the risk of SKCM, and other risk factors for the disease include family history and a variety of dysplastic nevi[2]. Despite the rising incidence of SKCM each year, effective systemic treatment options remain limited[3]. Studies have shown that models based only on clinicopathological features cannot accurately assess the prognosis of patients with SKCM tumors, much less predict the response to individual therapeutic agents[4]. Therefore, further investigation into the diagnostic and prognostic biomarkers identified in patients with SKCM is imperative to assess their clinical utility and gain a deeper understanding of the underlying molecular processes and gene networks that contribute to the progression and prognosis of this disease.

HIGHLIGHTS

  • METTL9 is significantly upregulated in skin cutaneous melanoma (SKCM) tissues and is closely associated with tumor-node-metastasis stage, N stage, and ulceration status.

  • SKCM patients with high METTL9 expression have lower survival rates and a higher risk of recurrence, identifying METTL9 as an independent prognostic risk factor.

  • Patients with high METTL9 expression respond poorly to PD-1 immunotherapy, while those with low METTL9 expression show better treatment outcomes, suggesting METTL9 as a predictive biomarker for immunotherapy response.

Human methyltransferase-like proteins (METTLs) contain domains that bind to S-adenosylmethionine and belong to a large family of proteins[5]. At present, there are 34 known human METTLs, among which METTL3 and METTL14 have been studied more[6]. Since there are limited conserved domains shared among this family[5], their functions have been found to be diverse. A wealth of evidence has demonstrated that METTLs are involved in the progression of tumors, including gastric cancer[7], breast cancer[8], choriocarcinoma[9], thyroid cancer[10], hepatocellular carcinoma[11], etc. However, the role of METTLs in SKCM has not been explored, and it remains unknown whether METTLs play a similar role in SKCM and its metastatic form. Therefore, further investigation is necessary.

In our study, we searched and analyzed multiple online public databases to explore METTL expression and its interaction in SKCM. Given the importance of METTL9, we then performed immunohistochemical analysis of METTL9 expression levels in SKCM and adjacent normal tissues to further evaluate and verify the relationship between METTL9 and prognosis in SKCM patients.

Materials and methods

Differential expression analysis (ONCOMINE and UALCAN)

In this study, the ONCOMINE database (www.oncomine.org)[12] was used to analyze the expression difference of METTL genes between SKCM tissues and adjacent tissues on November 15, 2020. Then, we used the UALCAN database (http://ualcan.path.uab.edu/analysis.html)[13] to analyze the mRNA expression of METTLs in SKCM tissues and adjacent tissues.

Gene expression profiling interactive analysis (GEO)

The online database gene expression profile interactive analysis (GEPIA, http://gepia.cancer-pku.cn/index.html)[14] was used to analyze and compare METTLs expression levels in SKCM and normal skin tissues, and Kaplan–Meier curve to analyze the prognostic value. The P-value cutoff was 0.05. The GEO database (https://www.ncbi.nlm.nih.gov/geo/) served as the source for the SKCM dataset GSE46517. Using the limma program, the difference in expression between normal skin samples (excluding nevus, n = 8) and melanoma samples (including both primary and metastatic melanoma, n = 104) was examined. In order to investigate the impact of high and low expression on prognosis, the genes for the GSE91061 data were separated into high and low-expression groups based on the median expression levels.

Tumor immune dysfunction and exclusion and immunophenoscore

Given the importance of METTL9 established in previous analyses, we investigated the possibility that high levels of cytotoxic T cell infiltration in SKCM interacts with METTL9 expression to influence patient survival and predict the response to immunotherapy in SKCM patients using the tumor immune dysfunction and exclusion (TIDE) database[15]. The immunophenoscore (IPS) calculation was performed according to the instructions provided in a prior study[16]. According to data downloaded from TCIA (https://tcia.at/home/), patients with low METTL9 expression exhibited high TCGA-SKCM IPS scores and showed a positive response to immunotherapy.

Clinical data and follow‑up

Using a random number method, we selected 40 SKCM patients who had surgery at our hospital from January 2016 to December 2017 without having undergone any prior systemic therapy. Three centimeters from the margin of the malignant tissue, 40 samples of matched neighboring tissues and SKCM tumor tissue were taken. The inclusion criteria were as follows: admitted for surgical care at our hospital; postoperative pathology was diagnosed by our hospital’s pathology department and comprehensive clinical and follow-up information was available. The exclusion criteria were as follows: history of neoadjuvant chemotherapy and radiation therapy; the patient passed away from an unrelated illness; the patient died from an unrelated issue.

Immunohistochemical test

Obtain SKCM tissue paraffin specimens and make 4 μm thick paraffin sections. Bake the sections at 65°C for 30 minutes, remove the paraffin, block endogenous peroxidase activity with 3% H2O2 for 10 minutes. After washing twice, the slices were placed in 0.01 mol/L (pH 6.0) citrate buffer at 90°C–95°C and heated for 15 min for antigen retrieval. After two additional washes with phosphate-buffered saline, block nonspecific binding by incubating with 5% bovine serum albumin (BSA). Incubate the samples with METTL9 primary antibody diluted 1:200 in 5% BSA. Incubate the samples overnight at 4°C. After rinsing, the secondary antibody was added to the slide to cover the tissue completely. The samples were counterstained with hematoxylin at room temperature, washed and dehydrated, and then mounted with neutral glue. Observe under the microscope. The staining scores were independently assessed by two pathologists based on the proportion of positive cells and the integrated staining intensity. The final score ranged from 0 to 12. Samples with scores between 6 and 12 were defined as high expression, while samples with scores between 0 and 5 were defined as low expression.

Statistical analysis

Data analysis and charting were done using GraphPad Prism (version 8.0) and SPSS (version 22.0). Student’s t-test or one-way analysis of variance was used to evaluate the differences between groups. The associated gene expression level and common clinicopathological traits were compared using Fisher’s exact test or chi-square (χ2) test. The correlation between METTL9 expression levels and overall survival (OS) was examined using Kaplan–Meier survival curves. P-values below 0.05 were considered statistically significant. Tumor marker prognostication studies should be reported in accordance with the REMARK guidelines.

Results

Aberrant expression of METTLs in patients with SKCM

Abnormally expressed genes can be important molecular markers for diagnosing tumor pathogenesis. In this study, we preliminarily evaluated the transcript levels of METTLs in SKCM tissues and adjacent normal tissues using the Oncomine and UALCAN databases. The Oncomine database showed that the mRNA expression levels of METTL1, METTL4, and METTL9 were significantly increased in tumor tissues compared to adjacent tissues. In contrast, the mRNA expression level of METTL7A in SKCM tissues was significantly decreased (P < 0.05). Furthermore, we compared the expression patterns of various METTL genes in SKCM using the UALCAN database (including 473 samples, normal = 1, primary = 104, metastasis = 368), and METTL9 had the highest expression level. Additionally, we observed that the expression levels of METTL1, METTL3, METTL5, and METTL13 also increased.

The expression levels of METTLs in SKCM tumors and normal tissues were analyzed using the GEPIA database. The results showed that the expression levels of METTL1, METTL6, METTL7B, METTL9, and METTL13 in SKCM patients are higher than those in normal tissues. On the contrary, the expression level of METTL7A in SKCM patients is lower than that in normal tissues. To further validate the differential expression of METTLs in SKCM and normal tissues, we then used the GEO database to reanalyze METTL expression levels. Interestingly, among the eight differentially expressed genes (METTL1, METTL2B, METTL3, METTL4, METTL5, METTL7A, METTL8, and METTL9), only five showed statistically significant expression differences. Specifically, the expression levels of METTL1, METTL5, and METTL9 were upregulated, while the expression levels of METTL7A and METTL8 were downregulated. Combining the results from various databases, METTL1, METTL9, and METTL7A may serve as more useful biomarkers.

The prognostic value of METTLs in patients with SKCM

We utilized the GEPIA database to analyze the cases of SKCM patients from the TCGA database to assess the prognostic value of the differential expression of METTLs in SKCM patients. The results of our OS curve analysis revealed that low expression levels of METTL1 (P = 0.035) and METTL9 (P = 0.026) were significantly correlated with improved OS time and rates. Additionally, we observed that SKCM patients with high transcript levels of METTL5 (P = 0.043), METTL7A (P = 0.0016), METTL7B (P = 0.00025), and METTL14 (P = 0.003) had significantly higher survival rates compared to the low expression group. Furthermore, we reanalyzed patient cases from the GEO database to investigate the impact of METTLs on mortality in SKCM patients (Fig. 1). To our surprise, patient outcomes were significantly influenced by only one gene, METTL9. Therefore, we suggest that increased expression of METTL9 in SKCM significantly reduces patients’ survival chances.

Figure 1.

Figure 1.

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The prognostic value of different expressed METTLs in SKCM patients in the overall survival curve (GEO). The overall survival curve of (a) METTL1; (b) METTL2A; (c) METTL2B; (d) METTL3; (e) METTL4; (f) METTL5; (g) METTL6; (h) METTL7A; (i) METTL7B; (j) METTL8; (k) METTL9; (l) METTL14.

Prediction of immunotherapy response in SKCM

Treatment of cutaneous melanoma with the immune checkpoint inhibitor PD-1 offers durable clinical benefits, but only a small proportion of patients respond to treatment. To predict immunotherapy response, we used the TIDE database in our study and found that in the COX risk regression model, high expression of METTL9 in PD-1-treated patients was associated with poor prognosis (Fig. 2a). Kaplan–Meier analysis showed that patients with high METTL9 expression who were treated with PD-1 had a worse prognosis (P = 0.029, Fig. 2b). In addition, it was examined whether METTL9 expression could forecast the effectiveness of immunotherapy in SKCM patients using the IPS dataset obtained from TCIA. The IPS score for the low-METTL9 expression group was significantly higher, indicating that patients in that group are likely respond more favorably to immunotherapy (Fig. 3).

Figure 2.

Figure 2.

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TIDE database analysis results: (a) Association between METTL9 expression level and patient prognosis in the COX risk regression model. (b) The association of METTL9 biomarkers with overall patient survival by Kaplan–Meier curve. METTL9 top represents the patient group with high METTL9 expression, while METTL9 Bottom represents the patient group with low METTL9 expression.

Figure 3.

Figure 3.

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The IPS disparity between the categories with high and low METTL9.

The relationship between METTL9 expression and clinicopathological characteristics of SKCM patients

The expression levels of METTL9 in SKCM and adjacent normal tissues were detected by immunohistochemistry to explore whether the expression of METTL9 in the METTL family is associated with clinicopathological features. We collected clinical specimens from 40 patients with melanoma at our hospital for analysis. The results are shown in Figure 4a,b. METTL9 was highly expressed in 72.5% (29/40) of SKCM tissues, compared to 35.0% (14/40) of normal skin tissues exhibited high METTL9 expression. The expression level of METTL9 in SKCM tissues was significantly higher than in adjacent normal tissues. Additionally, the median survival time of the 40 SKCM patients was 37 months (Fig. 4c). In addition, the expression level of METTL9 in SKCM was significantly positively correlated with the tumor-node-metastasis (TNM) stage (P = 0.025), N stage (P = 0.027), and ulceration (P = 0.003) in Table 1. Furthermore, univariate and multivariate survival analyses revealed that upregulated METTL9 significantly reduced patient OS. Both METTL9 expression and TNM stage were independent prognostic factors for OS in SKCM patients (P < 0.05, Table 2). These findings suggest that METTL9 holds significant clinical value in SKCM patients.

Figure 4.

Figure 4.

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The relationship between METTL9 expression and clinicopathological characteristics in 40 cases of SKCM tissues: (a) METTL9 immunohistochemical staining of SKCM tissues showed low expression and high expression. (b) METTL9 expression level in 40 cases of SKCM tissues (P < 0.001). (c) The effect of METTL9 expression on OS in 40 cases of SKCM (P = 0.0401).

Table 1.

Relationship between METTL9 expression and clinicopathological characteristics in 40 cases of Skin melanoma cancer (n%)

Clinical characteristics Cases METTL9 expression P-value
Low High
Age (years) 0.488
 <65 21 7 (33.3) 14 (66.7)
 ≥65 19 4 (21.1) 15 (78.9)
Gender 0.498
 Male 18 6 (33.3) 12 (66.7)
 Female 22 5 (22.7) 17 (77.3)
TNM stage 0.014
 I/II 10 6 (60.0) 4 (40.0)
 III/IV 30 5 (16.7) 25 (83.3)
T stage 1
 T1/T2 13 3 (23.1) 10 (76.9)
 T3/T4 27 8 (29.6) 19 (70.4)
N stage 0.020
 N0 13 7 (53.8) 6 (46.2)
 N (1 + 2) 27 4 (14.8) 23 (85.2)
M stage 0.603
 M0 35 9 (25.7) 26 (74.3)
 M1 5 2 (40.0) 3 (60.0)
Clark level 0.418
 I–IV 30 7 (23.3) 23 (76.7)
 V 10 4 (40.0) 6 (60.0)
Ulceration 0.007
 Not identified 14 8 (57.1) 6 (42.9)
 Present 26 3 (8.0) 23 (92.0)
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Fisher’s exact test was used to assess the significance between variable groups.

Table 2.

Univariate and multivariate analyses of overall survival in skin melanoma cancer

Variable Univariate analysis Multivariate analysis
HR 95% CI P-value HR 95% CI P-value
Age 2.332 0.684–7.953 0.176 – – –
Gender 0.882 0.371–2.097 0.776 – – –
TNM stage 1.057 1.016–1.009 0.004 1.061 1.010–1.114 0.017
T stage 1.622 0.594–4.432 0.346 – – –
N stage 1.424 0.549–3.694 0.467 – – –
M stage 1.616 0.143–2.647 0.515 – – –
Clark level 1.730 0.266–2.002 0.540 – – –
Ulceration 1.109 0.446–2.758 0.823 – – –
METTL9 3.682 1.062–12.759 0.004 2.815 1.009–5.107 0.001
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HR, hazard ratio; CI confidence interval; TNM, tumor-node-metastasis.

P < 0.05 represents the P-values with significant differences.

Discussion

SKCM is a typical malignant skin tumor caused by the abnormal proliferation of melanocytes and has become a major global public health concern[1]. Despite advances in medical care, the mortality rate of SKCM patients continues to rise in many countries[17]. Human SKCM patients today have access to a wider variety of therapeutic options than ever before, including radiation therapy, photodynamic therapy, immunotherapy, chemotherapy, and biochemotherapy[18–22]. Unfortunately, a significant number of patients receiving these treatments show a poor lasting response, increased drug resistance, and a dismal prognosis[23]. Over the past few years, several researchers have reported differentially expressed genes and mutations in patients with SKCM, identifying diagnostic and prognostic biomarkers (PSME, BRAF, CTLA-4, PD-1, etc.) for SKCM[24–27]. However, the current biomarkers identified for the patients with SKCM are still not sufficient[4]. Therefore, it is crucial to explore and validate new therapeutic targets and prognostic biomarkers, not only to improve clinical management but also to deepen our understanding of the molecular mechanisms and gene networks underlying the development, progression, and prognosis of SKCM.

METTLs contain domains that bind to S-adenosylmethionine and belong to a large family of proteins. According to previous studies, methyltransferases play significant roles in the development of genetic and metabolic diseases. The METTL family is engaged in a variety of different biological processes in various capacities. Examples of RNA methyltransferases include METTL3, METTL16, METTL2B, and METTL8. METTL7A has been suggested to play an important role in regulating MTX resistance in choriocarcinoma cells[9] METTL7A has been suggested to play an important role in regulating MTX resistance in choriocarcinoma cells[11]. However, few studies have investigated the potential benefits of METTLs for clinical diagnosis and therapeutic recommendations in SKCM. Therefore, to establish a foundation for clinical diagnosis and therapy, it is necessary to investigate the pathophysiological and molecular processes of METTLs in SKCM.

We started by doing differential expression analysis. Oncomine and UALCAN database analyses show that, compared to adjacent non-cancerous tissues, the mRNA expression levels of METTL1, METTL4, and METTL9 were significantly increased in tumor tissues, with METTL9 showing the highest expression level. In contrast, the mRNA expression level of METTL7A was significantly decreased in SKCM tissues. We then used the GEPIA database to analyze the expression levels of METTLs in SKCM tumors and normal tissues. The results showed that the expression levels of METTL1, METTL6, METTL7B, METTL9, and METTL13 were higher in SKCM patients compared to normal tissues. In contrast, METTL7A expression was lower in SKCM patients compared to normal tissues. To further validate the differential expression of METTLs in SKCM and normal tissues, we reanalyzed the expression levels using the GEO database and found that only five genes showed statistically significant differences in expression. We found that METTL, METTL5, and METTL9 were up-regulated, while METTL7A and METTL8 were down-regulated. Combining the results from these databases, METTL1, METTL9, and METTL7A may serve as more useful biomarkers. Previous studies have also demonstrated that METTL1 and METTL9 are frequently amplified and overexpressed in cancers, while METTL7A is downregulated in various cancers, which is consistent with our findings[28–31].

Then, we analyzed the METTLs prognostic value in patients with SKCM. The results from the GEPIA database and TCGA database showed that low expression levels of METTL1 and METTL9 were associated with prolonged OS time in SKCM patients, while high expression levels of METTL5, METTL7A, METTL7B, and METTL14 had the opposite effect. However, results from the GEO database indicated that the prognosis of patients of SKCM patients was significantly affected by only one gene, METTL9, with high expression levels of METTL9 significantly reducing patient survival chances. Previous study has shown that METTL9 expression is elevated in hepatocellular carcinoma, where it promotes cancer progression by inhibiting ferroptosis, and its high expression is closely associated with poor survival prognosis[32].

The analysis of predicting immunotherapy response indicated that high expression of METTL9 in PD-1-treated SKCM patients was associated with poor prognosis. Patients with high METTL9 expression after PD-1 treatment had poorer outcomes. In contrast, patients with low METTL9 expression showed a better response to immunotherapy. Previous study has confirmed in mouse models that Mettl9 deficiency not only inhibits the autonomous growth of tumor cells but also suppresses tumor burden by activating an effective anti-tumor immune response[33]. Furthermore, in most cancers, the expression level of the METTL9 gene is negatively correlated with immune scores[33]. These findings are similar to our results.

To further validate and evaluate the relationship between METTL9 and prognosis, a total of 40 pairs of fresh SKCM tissues and adjacent tissues were collected from the hospital, and we performed immunohistochemical analyses on these tissue samples. The results showed that the expression level of METTL9 in SKCM was significantly positively correlated with the TNM stage, N stage, and ulceration status. Moreover, patients with high METTL9 expression had a shorter survival time, were more likely to relapse, and had a worse prognosis compared to patients with low METTL9 expression, which were independent risk factors for OS in patients with SKCM.

Mechanistically, previous studies confirmed that METTL9 is a protein N1-histidine methyltransferase required for cell proliferation and tumor growth[33,34]. METTL9 catalyzes the methylation of histidine residues in the zinc transporter SLC39A7 and regulates cytoplasmic zinc homeostasis and cell proliferation[33]. Additionally, METTL9 is associated with cancer metastasis. Study has shown that in metastatic cells, METTL9 protein is predominantly localized in the mitochondria, and its knockdown significantly reduces the activity of mitochondrial complex I[35].

These findings suggest that METTLs, especially METTL9, have significant clinical value in patients with SKCM. However, our study has some limitations. While the analysis of transcript levels reflects specific aspects of immune status, it may not capture global changes. Therefore, to fully understand the role of METTLs in SKCM and confirm our results, additional cell and animal experiments are necessary.

Conclusion

This study reveals significant expression differences in the METTL gene family, particularly METTL9, in patients with SKCM and their close association with prognosis. Through comprehensive analysis of multiple databases, we found that high METTL9 expression is closely related to reduced OS and poor response to immunotherapy in SKCM patients. Moreover, METTL9 expression in SKCM tissues is significantly positively correlated with TNM stage, N stage, and ulceration status, and METTL9 is identified as an independent risk factor for poor prognosis in SKCM patients. Although our findings suggest that METTL9 has significant clinical value, these results need to be further validated through cell or animal experiments to provide a more robust foundation for clinical diagnosis and treatment.

Acknowledgements

Not applicable.

Footnotes

S.T., Y.Z., S.L., W.W. contributed equally to this work.

Sponsorships or competing interests that may be relevant to content are disclosed at the end of this article.

Published online 13 June 2025

Contributor Information

Song Tang, Email: tangsong0802@163.com.

Yiran Zhang, Email: zhangyivity@sina.com.

Simin Luo, Email: luo950711@163.com.

Wendan Wang, Email: wangwendan@bupt.edu.cn.

Xiaoxu Zhao, Email: zhaoxiaoxu1733@163.com.

Yin Leng, Email: 89045940@qq.com.

Ethical approval

The Institutional Review Board of the First Affiliated Hospital of Jinan University evaluated and approved the investigations involving human volunteers. Patients/participants gave their informed consent in writing to participate in this research.

Consent

Not applicable.

Sources of funding

This research was supported by the Medical Science and Technology Research Foundation of Guangdong Province (20221181410149), the Clinical Frontier Technology Program of the First Affiliated Hospital of Jinan University (JNU1AF-CFTP-2022-a01223), Natural Science Foundation of Guangdong Province (2019A1515011763; 2020A1515110639; 2021A1515010994; 2022A1515011695), Guangzhou Science and Technology Plan City-School Joint Funding Project (202201020084; 202201020065), the Fundamental Research Business Expenses of Central Universities (21620306), Guangdong Basic and Applied Basic Research Foundation (2020A1515110639).

Author contributions

Y.Z. and W.W. conceived and carried out the experiments. S.T., Y.Z., and X.W. wrote the manuscript. S.T., S.L., and Y.L. made the revision. X.W. made the data collection. X.Z. and W.W. made the data analysis. X.W. perform the figure making. Y.L. designed study and guide the experiments.

Conflicts of interest disclosure

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Research registration unique identifying number (UIN)

Not applicable.

Guarantor

Yin Leng.

Provenance and peer review

Not commissioned, externally peer-reviewed.

Data availability statement

The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors.

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Data Availability Statement

The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors.

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