METTL3 is a Potential Prognostic Biomarker in Head and Neck Squamous Cell Carcinoma

doi:10.21203/rs.3.rs-1440953/v1

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Research Article

METTL3 is a Potential Prognostic Biomarker in Head and Neck Squamous Cell Carcinoma

https://doi.org/10.21203/rs.3.rs-1440953/v1

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posted 24 Mar, 2022

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Background

Head and neck squamous cell carcinoma (HNSCC) is a common malignant tumor., and incidence and mortality of HNSCC are relatively high. Most HNSCC patients are diagnosed at an advanced stage and lack specific treatment. There is an urgent need to identify new molecular markers for prognostic evaluation. It is well known that N6-methyladenosine (m⁶A) RNA modification plays a critical role in a variety of tumors, especially HNSCC. However, the relationship between the m⁶A “writer”, METTL3, and the prognosis of HNSCC remains unknown. The present study aimed to evaluate the potential roles and prognostic value of METTL3 in HNSCC.

Methods

A total of 448 HNSCC samples with clinical information obtained from The Cancer Genome Atlas (TCGA) datasets and 72 HNSCC samples from Gene Expression Omnibus (GEO) datasets were analyzed. The expression of METTL3 across cancers was analyzed with the TIMER database. Univariate and multivariate Cox regression analyses were used to determine the independent prognostic factors for HNSCC. The Kaplan–Meier method was used for survival analysis. Differential expression analysis of METTL3-related genes was performed with DESeq2. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted to identify the potential role of METTL3-related genes in HNSCC. The TIMER database was used to analyze the association between METTL3 expression and the infiltration levels of immune cell. Immunohistochemistry was used to verify the relationship between METTL3 expression and immune cells infiltration.

Results

METTL3 expression was significantly upregulated in HNSCC (P < 0.001), and HNSCC patients had a better prognosis when METTL3 was significantly upregulated. Low METTL3 expression in HNSCC patients over 60 years old was associated with poor prognosis (P = 0.0062) with the most significant result for laryngeal carcinoma patients (P < 0.0001). Additionally, METTL3 expression led to changes in the tumor immune microenvironment. We found that the high expression of METTL3 had a positive correlation with CD4 + T cells and neutrophils but a negative correlation with B cells, CD8 + T cells, macrophages, and dendritic cells. IHC assays further confirmed this conclusion. In addition to oral cancer, METTL3 was positively correlated with CD4 expression in laryngeal and tongue cancers (P < 0.05). Finally, we constructed a prognostic nomogram in HNSCC to predict the individuals’ survival probability by age, stage, T stage, N stage, M stage, and grade. Calibration was performed for the nomogram, and the calibration curves showed that the nomogram-predicted probability matched the observed line for the 1-, 3-, and 5-year survival.

Conclusion

The present study found that upregulated METTL3 recruits CD4 + T cells around the tumor, reshapes the immune microenvironment and enables prolonged survival. In conclusion, METTL3, as an independent factor affecting the prognosis of HNSCC, may become a novel biomarker for HNSCC, playing a role in regulating the tumor immune response.

Head and neck squamous cell carcinoma

METTL3

immune infiltrating cells

prognosis

Head and neck squamous cell carcinoma (HNSCC) is the sixth most common cancer worldwide, with nearly 900,000 new cases and approximately 500,000 deaths each year[1, 2]. HNSCC is a highly malignant tumor arising from the mucosal epithelium of the nose, sinuses, oral cavity, tonsils, pharynx and larynx, accounting for 90% of all head and neck malignancies[3]. In particular, the incidence and mortality of lip/oral cancer and laryngeal cancer are higher[2] (Fig. 1A, 1B). The treatment approach is generally multimodal and systemic, consisting of surgery followed by chemotherapy plus radiation (chemoradiation or CRT) for oral cavity cancers and primary CRT for pharyngeal and laryngeal cancers. Cetuximab, an epidermal growth factor receptor (EGFR) monoclonal antibody, and immunotherapy are also approved for recurrent or metastatic disease[3]. Without a clinical history of a premalignancy, most patients with HNSCC are advanced at the time of diagnosis, and the prognosis for such patients is often poor. To date, despite advances in screening, diagnosis and multimodal treatment of HNSCC, its prognosis has not significantly improved and approximately 50% of patients die from this disease[4]. At present, scientists aim to integrate our understanding of HNSCC biology and immunobiology to identify predictive biomarkers that will provide the most effective and least toxic therapeutic strategies[3]. Therefore, it is important to identify HNSCC-specific markers and to better understand their mechanisms of action.

There is growing evidence that m⁶A regulates gene expression and thus regulate processes such as cellular self-renewal, differentiation, invasion and apoptosis. m⁶A is installed by m⁶A methyltransferases, removed by m⁶A demethylases and recognized by reading proteins to regulate RNA metabolism, including processes such as translation, splicing, export, degradation and processing of microRNAs[5, 6]As one of the most abundant epigenetic methylation modifications of mRNAs and noncoding RNAs (ncRNAs), m⁶A modification is invertible and dynamic, and it is regulated by m⁶A regulators[7, 8]. New evidence suggests that m⁶A methylation plays a key role in cancer through various mechanisms[9–11]. Furthermore, m⁶A methylation offers additional possibilities for the early diagnosis and treatment of cancer[12]. Despite some progress in the study of m⁶A modification and tumors, the characteristics of m⁶A modification in HNSCC remain inadequate.

The m⁶A modification is installed by RNA methyltransferases (METTL3, METTL14 and WTAP; writers), cleared by the demethylases (FTO and ALKBH5; scavengers), and recognized by m⁶A-binding proteins (such as the YT521-B homologous YTH domain protein; readers)[13]. Methyltransferase 3 (METTL3), one of the “writers” of m⁶A modification, is the only catalytic subunit that binds to the methyl donor S-adenosylmethionine (SAM) and catalyzes methyl transfer. In recent years, it has been reported that METTL3 plays critical roles in various tumors, either as an oncogene or a tumor suppressor[14, 15], and it has also been reported to be a tumor suppressor in some cases[16]. METTL3 expression is lower in renal cell carcinoma (RCC) tissues than in adjacent nontumor tissues, and that RCC patients with higher METTL3 expression may have a better survival outcome[17]. Based on a new statistical model and subsequent experimental validation, it has also been shown that METTL3 is a tumor suppressor genes in bladder cancer and that somatic mutations in METTL3 may promote cancer cell growth[18].

As the complex interactions between the tumor microenvironment and immunotherapy are understood, there is increasing interest in the role of immunomodulators in the treatment of HNSCC. Activation of T cells and immune checkpoint molecules is important for the immune response to cancer[19]. The tumor microenvironment (TME) consists mainly of a mixture of tumor cells, tumor-infiltrating immune cells and stromal components. Clinical trials have shown that tumor immune cell infiltration (ICI) correlates with sensitivity to immunotherapy in patients with HNSCC[20]. Immunotherapies, particularly those targeting the PD1 receptor or its ligand (PD-L1), including nivolumab, pembrolizumab, durvalumab and atezolizumab, have shown significant efficacy in a subgroup of patients with HNSCC[21]. To overcome obstacles to targeted therapies and enable prolonged survival, molecular and immune characterization of HNSCC suggests that prognostic and predictive biomarkers should be included in clinical management.

In the present study, a total of 448 HNSCC samples from TCGA and 72 HNSCC samples from GEO datasets were analyzed. We explored the differential expression of METTL3 in HNSCC and its relationship with prognosis. GO and KEGG pathway enrichment analyses were conducted to identify the potential role of METTL3-related genes in HNSCC. The TIMER database was used to analyze the association between METTL3 expression and the infiltration levels of immune cells. Overall, the present study proposed METTL3 as a novel biomarker to evaluate prognosis in HNSCC.

Data Source and Acquisition

In total, 448 HNSCC samples with clinical information were obtained from TCGA (https://portal.gdc.cancer.gov), and 72 HNSCC samples from GEO (https://www.ncbi.nlm.nih.gov/geo/) datasets were analyzed. TCGA clinical information was downloaded from the UCSC Xena database (http://xena.ucsc.edu), and the samples without clinical information were filtered out. The dataset from GEO was processed using the chip platform (Illumina HumanHT-12 WG-DASL V4.0 R2 Expression BeadChip).

Data Processing

For microarray data (GSE136037), the robust multiarray average (RMA) algorithm of the Affy package was used to process the raw probe-level data in each CEL file. Average probe values were used as the expression values for genes that matched multiple probes. TCGA dataset was normalized by log-transformation of the fragment per kilobase per million reads (FPKM) + 1. The missing data in these two gene expression matrices were supplemented by the k-nearest neighbor (KNN) approach (k = 10). The gene expression data were transformed by the Z score to avoid systematic errors in different experiments.

Bioinformatic Analysis

The expression of METTL3 across cancers was analyzed with the TIMER database (http://timer.comp-genomics.org/) and the Wilcoxon test was used to evaluate the P values. The “edgeR” package was used to explore differentially expressed genes between different groups with the criteria of an absolute log₂-fold change (FC) > 1 and p value < 0.05. The common differentially expressed genes were obtained using the “Venn” package. Univariate and multivariate Cox regression analyses were used to assess the prognostic value of METTL3 and clinical features in TCGA. To explore the potential functions and signaling pathways of METTL3, GO and KEGG pathway enrichment analyses were performed on METTL3-related differentially expressed genes using the “clusterProfiler” package. The TIMER database was used to analyze the association between METTL3 expression and the infiltration levels of immune cells.

IHC for METTL3 and CD4 in HNSCC tissue

According to the location of the tumor, 120 cases of head and neck squamous cell carcinoma were selected, including laryngeal cancer, tongue cancer and oral cancer (40 cases per cancer type). Formaldehyde-fixed, paraffin-embedded tissue samples obtained by surgical resection were sectioned into 4 µm slices and affixed on glass slides. After being heated for more than an hour at 56 ℃, the slides were deparaffinized in xylene and rehydrated through a graded alcohol series.. Antigens were retrieved by heating in Tris/EDTA buffer (PH 8.0) for 3 minutes and quenching in endogenous peroxidase blocker for 20 minutes. The slides were incubated at 4°C overnight with anti-METTL3 [EPR18810] (ab195352, Abcam, UK) and anti-CD4 [EPR6855] (ab133616, Abcam, UK) antibodies at concentrations of 1:300 and 1:800, respectively. The antibodies were detected by an Elivision™ plus Polymer HRP (Mouse/Rabbit) IHC Kit (KIT-9903) (Maxim Biotechnologies, China). A DAB staining kit was used for the visualization of immunoreactive cells. The primary antibody was substituted with PBS for the negative control. Positive cells were stained brownish yellow in the cytoplasm or nucleus. For semiquantitative analysis, the staining rate (SR) and staining index (SI) were both used to describe the protein expression of METTL3 and CD4. The SR refers to the percentages of positive samples in all samples, and the SR ranged from 0 to 3 (0 ≤ 10% of tumor cells were stained; 1 = 11–25% were stained; 2 = 26–50% were stained; and 3 ≥ 51% were stained). The SIs were determined based on the average of at least five high-powered fields (400 ×) and ranged from 0 to 3 (0 = no staining; 1 = mild staining; 2 = moderate staining; and 3 = strong staining). The last score was the sum of the two parts above. A total score ≥ 3 was defined as positive. To analyze the relationship between METTL3 and CD4, we defined the low expression group with a score ≤ 4, and we defined the high expression group with a score > 4.

Establishment of the prognostic nomogram in HNSCC

To construct a METTL3-related gene signature and nomogram, we filtered out 417 genes with an absolute value of METTL3 correlation greater than 0.45 (P < 0.01) and 55 genes from univariate Cox proportional hazards analysis (P < 0.05). These genes were screened for prognosis-related genes by Lasso Cox analysis. Finally, the largest combined models of AUC at 5 years were selected to calculate the risk score using the following formula: risk score = expression level of Gene₁ × β₁ + expression level of Gene₂ × β₂where Coefi +…+ expression level of Gene_n × β_n; where Coefi is the β regression coefficient obtained from univariate Cox analysis. Nomogram calibration curves were drawn to compare the predicted overall survival with the observed overall survival.

Statistical Analysis

SPSS version 25.0 (SPSS Inc., Chicago, IL, USA) and R software for Windows version and R-4.1.0 (The R Foundation for Statistical Computing, Vienna, Austria) were used for data analysis. Categorical variables were compared using the χ2 test in the groups. Univariate and multivariate Cox regression models were selected to identify independent prognostic factors for overall survival (OS). Hazard ratios (HRs) and 95% confidence intervals (95% CIs) were also determined. Kaplan–Meier curves with a two-sided log-rank test were used to analyze the association between variables and OS. The consistency test chart of the prognostic risk score-predicted survival curve and actual survival curve was constructed by STATA version 12.0 (StataCorp, College Station, TX). All tests were bilateral tests, and P < 0.05 was considered statistically significant.

Expression upregulation and survival prediction ability of METTL3 in HNSCC

We examined METTL3 gene expression in across cancers with TIMER and discovered that its expression level was significantly upregulated in various cancers, including HNSCC (P < 0.001) (Fig. 1C). To determine the survival prediction ability of METTL3, 448 HNSCC samples from TCGA dataset were downloaded for analysis, and the baseline demographic characteristics are shown in Table 1. The patients were first divided into METTL3 high and low expression groups according to the median expression level of METTL3. Kaplan–Meier survival analysis indicated that patients in the METTL3 low expression group had a significantly worse prognosis than their counterparts (P < 0.05) (Fig. 2A). Further analysis showed a similar result for laryngeal carcinoma patients (P < 0.0001) (Fig. 2D), while the METTL3 expression level had no significant effect on patients with tongue, tonsil, oral, and oropharyngeal carcinomas (Fig. 2E-H). However, high levels of METTL3 expression may be associated with poor prognosis in oral and oropharyngeal tumors (Fig. 2G, 2H).

Table 1

Baseline demographic characteristics
Variables	METTL3 Low (N = 224)	METTL3 High (N = 224)	P value
Age, n (%)			0.973
≤ 60	105 (46.9%)	106 (47.3%)
> 60	119 (53.1%)	118 (52.7%)
Gender, n (%)			0.015
female	73 (32.6%)	49 (21.9%)
male	151 (67.4%)	175 (78.1%)
Stage, n (%)			0.566
Stage I	11 (4.91%)	6 (2.68%)
Stage II	42 (18.8%)	44 (19.6%)
Stage III	51 (22.8%)	46 (20.5%)
Stage IV	120 (53.6%)	128 (57.1%)
Grade, n (%)			0.085
G1-2	174 (77.7%)	157 (70.1%)
G3-4	50 (22.3%)	67 (29.9%)
T, n (%)			1
T1-2	78 (34.8%)	78 (34.8%)
T3-4	146 (65.2%)	146 (65.2%)
M, n (%)			1
M0	221 (98.7%)	222 (99.1%)
M1	3 (1.34%)	2 (0.89%)
N, n (%)			0.925
N0	112 (50.0%)	114 (50.9%)
N+	112 (50.0%)	110 (49.1%)
Diagnoses, n (%)			0.001
Larynx	73 (27.5%)	50 (18.9%)
Mouth	60 (22.6%)	52 (19.7%)
Oropharynx	42 (15.8%)	59 (22.3%)
Tongue	65 (24.5%)	93 (35.2%)
Tonsil	25 (9.43%)	10 (3.79%)
METTL3 GenBank reference sequence: Chromosome 14 - NC_000014.9, version number: 109.20210514.

We then analyzed the correlation between the prognosis of HNSCC patients and METTL3 expression as well as various clinical features using Cox regression analysis (Table 2). The results showed that low expression of METTL3, age over 60 years old, and metastasis were associated with poor prognosis for HNSCC patients. Moreover, we discovered that low expression of METTL3 was associated with poor prognosis for HNSCC patients over 60 years old (Fig. 2B, 2C).

Table 2

Cox regression analysis of prognostic factors for overall survival in head and neck squamous cell carcinoma.
	Univariate analysis		Multivariate analysis
	HR^† (95% CI^‡)	P value	HR (95% CI)	P value
METTL3 expression
Low	Reference		Reference
High	0.76 (0.59–0.99)	0.047	0.76 (0.58–0.99)	0.042
Age
≤ 60	Reference		Reference
> 60	1.34 (1.03–1.74)	0.028	1.41 (1.08–1.84)	0.011
Gender
female	Reference
male	0.77 (0.59–1.03)	0.075
Stage
Stage I Stage II Stage III Stage IV	Reference 1.38 (0.62–3.06) 1.37 (0.63–3.02) 1.44 (0.67–3.08)	0.428 0.427 0.352
Grade
G1-2	Reference
G3-4	0.79 (0.59–1.07)	0.123
T
T1-2	Reference
T3-4	1.18 (0.89–1.55)	0.254
M
M0	Reference		Reference
M1	4.41 (1.63–11.96)	0.003	4.80 (1.75–13.17)	0.002
N
N0	Reference
N+	1.09 (0.84–1.41)	0.508
Diagnoses
Larynx	Reference
Mouth	1.37 (0.93–2.01)	0.113
Oropharynx	1.20 (0.82–1.75)	0.340
Tongue	1.08 (0.75–1.57)	0.664
Tonsil	0.68 (0.34–1.38)	0.288
^†HR: hazard ratio; ^‡95% CI: 95% confidence intervals. METTL3 GenBank reference sequence: Chromosome 14 - NC_000014.9, version number: 109.20210514.

The potential role and function of METTL3 in HNSCC

To study the potential role and function of METTL3, HNSCC samples from TCGA dataset and GSE136037 dataset were divided into METTL3 high and low expression groups according to the median expression level. Differential expression analysis showed that the expression levels of 3017 and 495 genes were significantly dysregulated in TCGA dataset and GSE136037 dataset, respectively (Fig. 3A). We then selected 128 common differentially expressed genes for GO and KEGG analyses (Fig. 3B). GO analysis revealed that the functions of METTL3 mainly focused on regulating malignant biological behavior, including the regulation of mRNA metabolic processes, autophagy, cell growth, cell cycle arrest, and platelet aggregation (Fig. 3C). KEGG analysis indicated enrichment in several cancer-associated pathways, such as the VEGF, Th1/Th2, T cell, PD-L1 expression, PD-1 checkpoint, ErbB, Ras, PI3K/Akt, NF-kappa B, and HIF-1 signaling pathways (Fig. 3D). Therefore, these findings suggested that the effect of METTL3 tumor growth is related to the activity of these pathways.

Correlation between METTL3 expression and the tumor-immune microenvironment

Interestingly, KEGG pathway analysis indicated that METTL3 may regulate several immune pathways, including natural killer cell-mediated cytotoxicity, neutrophil extracellular trap formation, Th1 and Th2 cell differentiation, the T-cell receptor signaling pathway, and the B-cell receptor signaling pathway (Fig. 3D). Therefore, we next determined whether METTL3 expression in HNSCC is associated with the infiltration of immune cells. After conducting relevant studies with TIMER datasets, we found that the high expression of METTL3 had a positive correlation with CD4 + T cells and neutrophils but a negative correlation with B cells, CD8 + T cells, macrophages, and dendritic cells (Fig. 4A), indicating that METTL3 may affect the immune microenvironment of tumor cells. Finally, we found that high levels of CD4 + T cells but low levels of B cells, CD8 + T cells, macrophages, dendritic cells, and neutrophils were associated with poor prognosis for HNSCC patients with high METTL3 expression (Fig. 4B).

Considering laryngeal cancer, tongue cancer, and oral cancer, we further analyzed the relationship between METTL3 and CD4 + T cells by IHC assay. Except for two cases of oral cancer, all other cases of HNSCC showed high expression of METTL3, especially in laryngeal cancer. Furthermore, there was a positive correlation between the expression of METTL3 and CD4 in laryngeal cancer and tongue cancer (P < 0.05) (Fig. 5A 5B), but there was no significant difference in oral cancer (Fig. 5C). This result is consistent with our conclusion through TCGA database analysis. Together, these results suggested that METTL3 may promote the remodeling of the immune microenvironment, resulting in a good prognosis, but the specific mechanism needs to be thoroughly studied in the future.

Construction of the Nomogram

The number of generations of lasso Cox analysis was 1000. In 1000 iterations, 15 gene expression characteristics were adapted to the best survival prediction of more than 999 times in the learning series. The combined risk score models predicted the 5-year survival rate of HNSCC, and we selected the best combined mode to construct a nomogram. Based on the above prognostic factors of HNSCC, a prognostic risk prediction nomogram was constructed to predict the overall survival probability of patients at 1 year, 3 years, and 5 years. Finally, we constructed a prognostic nomogram in HNSCC to anticipate the individuals’ survival probability by age, stage, T stage, N stage, M stage, and grade. Calibration was performed for the nomogram, and the calibration curves showed that the nomogram-predicted probability matched the observed line for the 1-, 3-, and 5-year survival (Fig. 6).

As the most common head and neck malignancy, the incidence and mortality of HNSCC are higher, especially in Asia[2]. Although the etiology is not clear, many studies have shown that the initiation and development of HNSCC is a multistep process. Both genetic and epigenetic changes can lead to uncontrolled growth and proliferation of tumor cells[3]. Therefore, it is important to clarify the potential molecular events leading to HNSCC tumorigenesis. METTLE3 regulates gene expression through chemical modifications of m⁶A to regulate the processes of cell self-renewal, differentiation, invasion, and apoptosis, which have been demonstrated to regulate carcinogenesis in many tumor types. However, the clinical value and potential biological significance of METTLE3 in HNSCC are unclear.

In the present study, we discovered that METTL3 was significantly upregulated in various cancers, including HNSCC. At the same time, we found for the first time that METTL3 reshaped the immune microenvironment and recruited CD4 + T cells near the tumor. Previous studies have found that the expression levels of m⁶A RNA methylases, including METTL3, YTHDF1, and YTHDF2, are upregulated in tumor samples[22, 23]. Among all m⁶A gene regulators analyzed, METTL3 is the only gene negatively correlated with lymph node stage and N stage in HNSCC[23], further suggesting that METTL3 plays a favorable prognostic role in HNSCC. Studies have shown that METTL3 is either an oncogene or a tumor suppressor and that its overexpression may have an opposite prognosis in various tumors[14, 15, 24]. For example, METTL3 acts as a tumor suppressor gene in endometrial cancer and glioblastoma but functions as an oncogene in bladder cancer[25, 26]. In addition, METTL3 also affects tumor progression by changing signaling pathways. Previous studies have shown that METTL3 increases hepatoma cell proliferation via the p53 and Ras/Raf/ERK pathways or the YTHDF2-dependent pathway[27, 28]. METTL3 increases the activation of AKT signaling and mTORC1, leading to malignant transformation in digestive system tumors[29, 30]. In contrast, it has been shown that higher expression of METTL3 may predict better survival in renal cell carcinoma[17]. Similarly, METTL3 suppresses cell proliferation, migration, and invasion in colorectal cancer via p38/ERK pathways with a longer survival time[31]. In the present study, we discovered that HNSCC patients with low METTL3 expression generally had a poor prognosis. For HNSCC patients over 60 years old, lower METTL3 expression indicated a worse prognosis. Because METTL3 is a m⁶A modification writer, it regulates the expression of various genes, and it has different roles in various tumors as well as various stages of tumorigenesis and tumor progression. In the present study, METTL3 was significantly overexpressed, and the prognosis was better in HNSCC with METTL3 overexpression. Thus, METTL3 may be formed as a passenger gene with the occurrence of tumors but not as an oncogene or tumor suppressor gene. Once a tumor occurs, the expression of METTL3 is upregulated, which affects various signaling pathways, such as the VEGF signaling pathway, as well as the immune microenvironment and tumor progression, resulting in a good prognosis. However, further studies are needed to clarify the function of METTL3 in HNSCC.

Emerging studies suggest that m⁶A modification participates in both innate and adaptive immune responses[32, 33], but the specific mechanism is unclear. In the present study, KEGG pathway analysis indicated that METTL3-related differentially expressed genes were involved not only in common oncogenic pathways but also in immune pathways. Several studies have confirmed the characterization of the immune microenvironment involved in the occurrence and development of HNSCC[34–37]. Studies have shown that the risk score composed of the m⁶A gene is related to the infiltration of dendritic cells, neutrophils, CD4 + T cells, CD8 + T cells, and B cells, and upregulated m⁶A regulators are positively correlated with PD-L1 in the HNSCC tumor immune microenvironment[38]. Ai Y et al. found that highly expressed METTL3 promotes the progression of oral squamous cell carcinoma by inhibiting the activation of CD8 + T cells and modulating the m⁶A levels of PRMT5 and PD-L1[39]. Other researchers have found that METTL3 is upregulated and associated with poor prognosis in oral squamous cell carcinoma[40], which was consistent with our study although the difference was not significant (Fig. 3G). In the present study, we further discovered that high METTL3 expression was positively correlated with the CD4 + T cells and neutrophils but negatively correlated with B cells, CD8 + T cells, macrophages, and dendritic cells. Moreover, we discovered that high levels of CD4 + T cells but low levels of B cells, CD8 + T cells, macrophages, dendritic cells, and neutrophils were associated with poor prognosis in HNSCC patients with high METTL3 expression. These results not only indicated that METTL3 regulates immune cell infiltration but also suggested that METTL3 may gain a better prognostic value associated with immune cells for HNSCC. However, the specific mechanism between METTL3 and the immune microenvironment needs to be further studied.

The present study had several limitations. First, this was a retrospective study, and some selection bias was inevitable in the analysis; therefore, further prospective clinical studies are needed to validate the results of this study. Second, we only used two sets of data from TCGA and GEO databases for the analysis, which was insufficient, and more data from other databases are needed to validate the results. Third, this study was performed using databases. Further in vitro and in vivo experiments are required to study the potential functions of METTL3. METTL3 may not be an ideal therapeutic target due to its irreplaceable role, but METTL3-related risk genes may be potential alternatives.

In summary, METTL3, as an independent factor affecting the prognosis of HNSCC, has a high expression level in HNSCC. The upregulation of METTL3 recruits CD4 + T cells around the tumor, reshapes the immune microenvironment and enables prolonged survival. Once METTL3 is upregulated, various signaling pathways, such as the VEGF signaling pathway, and the immune microenvironment may be modified in HNSCC, resulting in a good prognosis. Our results suggested that METTL3 may play a role in regulating the tumor immune response and may be a novel biomarker for the prediction of prognosis in HNSCC.

m⁶A

N6-methyladenosine RNA modification

HNSCC

head and neck squamous cell carcinoma

TCGA

The Cancer Genome Atlas

GEO

Gene Expression Omnibus

Gene Ontology

KEGG

Kyoto Encyclopedia of Genes and Genomes

TIMER

Tumor Immune Estimation Resource

ncRNAs

non-coding RNAs

SAM

the methyl donor S-adenosylmethionine

RCC

renal cell carcinoma

TME

the tumor microenvironment

ICI

immune cell infiltration

GSEA

Gene Set Enrichment Analysis

RMA

Robust Multiarray Average

FPKM

Fragment Per Kilobase Per Million Reads

KNN

k-Nearest Neighbor

staining rate

SI staining index

AUC

Area under the ROC curve

overall survival

HRs

Hazard ratios

VEGF

vascular endothelial growth factor

Ras, PI3K/Akt

Phosphatidylinositol-4,5-bisphosphate 3-kinase/ protein kinase B

NF-kappa B

nuclear factor-kappa B

HIF-1

hypoxia-inducible factor-1.

Acknowledgments

Not applicable.

Authors' contributions

LJL and YZ designed this work. LJL, YZ, XKH, and GJZ integrated and analyzed the data. LJL and YZ wrote the original manuscript. XKH, WJH, NK and ZWZ edited and revised the manuscript. LJL, PFG and YL performed the immunohistochemistry assays. LML made the pathological diagnosis. MG, XHR, and XQZ obtained the funding and managed the project. All authors read and approved the final manuscript.

(LJL, YZ, and XKH are the co-authors. XHR and XQZ are the co-corresponding authors.)

Funding

This work was supported by grants from the National Natural Science Foundation of China (82103386, 81872169, 82172821), Tianjin Municipal Science and Technology Project (19JCYBJC27400) and Beijing-Tianjin-Hebei Basic Research Cooperation Project (20JCZXJC00120).

Availability of data and material

All data used in this work can be acquired from the Gene-Expression Omnibus (GEO; https://www.ncbi.nlm.nih.gov/geo/), the GDC portal (https://portal.gdc.cancer.gov/) , the TIMER database (http://timer.comp-genomics.org/) and the GLOBOCAN website mapping tool (https://gco.iarc.fr/today/online-analysis-map).

Ethics approval and consent to participate

The study was performed in accordance with the Declaration of Helsinki. Institutional ethics approval was required, and patients provided written informed consent. All methods were performed in accordance with the relevant guidelines and regulations. Some of patient’s data in this work were acquired from the publicly available data sets.

Consent for publication

Not applicable.

Competing interests

All authors declare no conflicts of interest regarding this work.

Author details

Department of Thyroid and Neck Tumor, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for Cancer, Tianjin, 300060, China.
The Sixth Affiliated Hospital of Guangzhou Medical University, Qingyuan People's Hospital, Qingyuan, 510180, China.
Department of Pathology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for Cancer, Tianjin, 300060, China.
Department of Thyroid and Breast Surgery, Tianjin Union Medical Center, Tianjin, 300121, China.

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No competing interests reported.

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posted 24 Mar, 2022

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METTL3 is a Potential Prognostic Biomarker in Head and Neck Squamous Cell Carcinoma

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Abstract

Background

Methods

Results

Conclusion

Figures

Introduction

Materials And Methods

Data Source and Acquisition

Data Processing

Bioinformatic Analysis

IHC for METTL3 and CD4 in HNSCC tissue

Establishment of the prognostic nomogram in HNSCC

Statistical Analysis

Results

Expression upregulation and survival prediction ability of METTL3 in HNSCC

The potential role and function of METTL3 in HNSCC

Correlation between METTL3 expression and the tumor-immune microenvironment

Construction of the Nomogram

Discussion

Abbreviations

Declarations

References

Competing Interests

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