The comprehensive analysis of the prognostic and functional role of N-terminal methyltransferases 1 in pan-cancer

Data collection

To evaluate NTMT1 expression in cancer patients, we obtained mRNA expression profiles and clinical outcome data from The Cancer Genome Atlas (TCGA) pan-cancer cohort website (https://portal.gdc.cancer.gov/). Additionally, mRNA expression profiles for normal tissues were obtained from both the TCGA (https://portal.gdc.cancer.gov/) and the University of California Santa Cruz (UCSC) Xena (https://xenabrowser.net/datapages/). To ensure consistency and comparability between samples, we standardized the raw data by normalizing via the transcripts per million (TPM) method. We then applied a log₂(TPM+1) transformation for further analysis.

Expression analysis of NTMT1

We conducted a comparative analysis of NTMT1 mRNA expression levels in tumors and corresponding normal tissues using mRNA expression profiles. This analysis included 33 different types of tumors, and we also compared NTMT1 expression between patients with different characteristics. We analyzed the expression levels of NTMT1 protein in tumors using Human Protein Atlas (HPA) database (http://www.proteinatlas.org) (Colwill & Renewable Protein Binder Working, 2011) and UCLCAN databases (https://ualcan.path.uab.edu/index.html) (Chandrashekar & Karthikeyan, 2022). To further investigate NTMT1 expression, we retrieved representative immunohistochemistry and immunofluorescence images from the HPA (Colwill & Renewable Protein Binder Working, 2011).

Survival analysis

We used GEPIA2.0 to investigate the potential prognostic significance of NTMT1 expression in various cancer types by applying the Mantel–Cox test to evaluate overall survival (OS) and disease-free survival (RFS) (Tang & Kang, 2019). To perform a more comprehensive survival analysis, we divided TCGA tumor patients into low and high NTMT1 expression groups using a 50% cut-off value, and then conducted proportional hazards hypothesis testing and fitted survival regression models using the survival package (3.3.1). Finally, we used the survminer package (3.3.6) and ggplot2 package (3.3.6) to visualize the results (Liu & Lichtenberg, 2018). Additionally, we used the Kaplan–Meier plotter (http://kmplot.com/analysis) to analyze the relationship between NTMT1 expression and the survival of tumor patients by applying the log-rank test (Nagy & Munkacsy, 2021).

Immune infiltration analysis of NTMT1

TISIDB (http://cis.hku.hk/TISIDB/index.php) is a comprehensive web portal that enables analysis of the interactions between tumors and the immune system by integrating various types of heterogeneous data (Ru & Wong, 2019). In our study, we used TISIDB to explore the associations between the expression of NTMT1 and the abundance of tumor-infiltrating lymphocytes (TILs), immunomodulators, and chemokines. Moreover, we utilized the ssGSEA algorithm, which is available in the R package-GSVA (Hanzelmann & Castelo, 2013) to evaluate immune cell infiltration in HNSCC. To conduct these analyses, we utilized a panel of 24 different immune cell types (Bindea & Mlecnik, 2013), which included dendritic cells (DCs), CD8+ T cells, natural killer (NK) cells, mast cells, type 17 Th (Th17) cells, regulatory T cells (Treg), type 1 Th (Th1) cells, macrophages, among others. Furthermore, we used the estimate R package (1.0.13) (Yoshihara & Shahmoradgoli, 2013) to calculate both the StromalScore and ImmuneScore for HNSCC, providing valuable insights into the potential roles of the tumor microenvironment in HNSCC progression and treatment.

Genetic alteration analysis

We used the cBioPortal (Gao & Aksoy, 2013) (https://www.cbioportal.org/) to gather data on the frequency of alterations and mutation types of NTMT1 in all TCGA tumors. Furthermore, we downloaded and analyzed OS data for TCGA cancer patients with and without NTMT1 gene alterations using the “Comparison” module. In the “Comparison” module, we analyzed the relationship between NTMT1 gene alterations and OS of TCGA tumor patients.

Single-cell sequencing

CancerSEA (http://biocc.hrbmu.edu.cn/CancerSEA/home.jsp) is the first dedicated database that aims to comprehensively decode distinct functional states of cancer cells at single-cell resolution (Yuan & Yan, 2019). In our study, we utilized this database to analyze the correlation between NTMT1 expression and various tumor functions based on single-cell sequencing data. The T-SNE diagrams were used to demonstrate the expression profiles of NTMT1 at the single-cell level in TCGA samples.

Protein-protein interaction network analysis and functional enrichment analysis

We utilized the BioGRID^4.4 website (https://thebiogrid.org/) to analyze the protein-protein interaction network associated with NTMT1 (Oughtred & Rust, 2021). The top 50 NTMT1-associated proteins were obtained via the STRING database (https://cn.string-db.org/), with a medium confidence cut-off of 0.4 for the minimum required interaction score. Additionally, we used GEPIA2 (Tang & Kang, 2019) (http://gepia2.cancer-pku.cn/#index) to obtain the top 100 NTMT1-correlated genes from TCGA tumor samples. We then utilized UALCAN to identify NTMT1-associated genes in HNSCC and conducted an intersecting analysis with the top 100 genes to identify HNSCC-related genes.

To identify differentially expressed genes (DEGs) between high- and low-NTMT1 expression groups, we utilized the “DESeq2” (v1.36.0) R package (Love & Huber, 2014) with |logFC| > 1 and p < 0.05 as the threshold. The volcano plots of the DEGs were visualized using the “ggplot2” (v3.3.6) R package. The “org.Hs.eg.db” R package was used to convert the Entrez ID to the gene symbol. The “ClusterProfiler” (Yu & Wang, 2012) (v4.4.4) R package was used for the functional annotation and Gene Set Enrichment Analysis (GSEA) of the DEGs between high- and low-NTMT1 expression groups. To perform GSEA, we selected the curated reference gene sets from the MgDB file: c2.cp.all.v2022.1.Hs.symbols.gmt (https://www.gsea-msigdb.org/gsea/msigdb/index.jsp) (Subramanian & Tamayo, 2005).

Promoter methylation of NTMT1 in pan-cancer

UALCAN (Chandrashekar & Karthikeyan, 2022) (http://ualcan.path.uab.edu) is a web portal that utilizes RNA sequencing and clinical data from the TCGA database across 31 different types of cancer. In our study, we utilized UALCAN to investigate the differential methylation levels of NTMT1 in various types of tumors.

Cell culture and regents

SNU1076 cell lines were purchased from the Cell Bank of the Shanghai Institute of Cells, Chinese Academy of Science (Shanghai, China). The cell was cultured in RPMI 1640 medium (Gibco, Billings, MT, USA) containing 10% fetal bovine serum (FBS, BI), 100 U/mL penicillin, and 100 μg/Ml streptomycin at 37 °C in a 5% CO₂ environment. RiboPECT^TM CP Transfection Kit was obtained from Ribobio (Guangzhou, China). RNA Quick Purification kit was purchased from ESscience. PrimeScript RT Master Mix was purchased from TAKARA. CCK8 Kit was purchased from Biosharp. SiRNA NTMT1 was synthesized from Ribobio (Guangzhou) were synthesized using sequences provided in the relevant literature (Zhang & Song, 2021). siNTMT1-1: GCAUUGGGAGGAUCACCAATT, siNTMT1-2: GCAAGAGGGTGAGGAAC TA. Anti-γ-H2AX was purchased from CST. Annexin V-FITC/PI apoptosis kit was purchased from MultiSciences.

RNA extraction and qPCR

Cell transfection was performed using the riboPECT^TM CP Transfection Kit according to the manufacturer’s protocol. Total RNA from approximately 1 × 10⁶ cells was isolated using an RNA Quick Purification kit. The primers used for qPCR, including those for NTMT1 and GAPDH, were synthesized from Shenggong (Shanghai, China). The primer sequences were (5′–3′): NTMT1 forward-CGAGGTGATAGAAGACGAGAAGC, reverse-CGGGAGCTGTTGATGTCGAT. GAPDH forward-GGAGCGAGATCCCTCCAAAAT, reverse-GGCTGTTGTCATACTTCTCATGG. Relative elevels were normalized to GAPDH and calculated according to the 2^−ΔΔCT method.

Cell proliferation

A total of 5 × 10³ cells were seeded on 96-well plates in a culture medium containing 10% FBS with four replicate wells for each group. Transfection was performed the next day. At various time points, cell viability was measured using Counting kit-8 according to the manufacturer’s instructions.

Cellular immunofluorescence

The cells were fixed with 4% paraformaldehyde for 30 min at room temperature and treated with 0.1% Triton-X for 10 min. Next, they were blocked with 5% BSA for 1 h at room temperature and incubated with anti-γ-H2AX (1:500) (CST) overnight at 4 °C. This was followed by incubation with CoraLite594-conjugated Goat Anti-Rabbit IgG (H+L) (1:500; Proteintech, Rosemont, IL, USA) at room temperature for 1 h in the dark. Nuclei were stained with DAPI in the dark for 5 min, and the fluorescence intensity was observed under a microscope.

Detection of apoptosis

To determine apoptosis, cells were seeded at a density of 1 × 10⁵ in 12-well plates. The following day, the cells were treated with siRNA for 72 h. After collecting the cells and washing them with 1× PBS, the pellet was gently resuspended in 500 µL of 1X binding buffer. Annexin V-FITC (5 µL) and PI (10 μL) were added to the cell suspension, which was incubated for 5 min in the dark. At the same time, the single dye tube of FITC and PI were set up. The samples were detected by Beckman CytoFLEX, and the analysis was performed using FlowJo v. 10.8.1 software from the USA.

Statistical analysis

The comparison between the two groups was performed using the Wilcoxon rank-sum test, while the correlation between the two groups was evaluated using Spearman’s correlation coefficient. Survival analysis was conducted using the Kaplan–Meier method and log-rank test. Statistical analysis was conducted using R (version 4.2.1) and GraphPad Prism 9 software (USA), ns, no significant, *p < 0.05, **p < 0.01, and ***p < 0.001.

NTMT1 mRNA and protein expression levels in pan-cancer

In this study, the expression of NTMT1 was analyzed in 33 cancer datasets obtained from the TCGA database. The findings demonstrated that NTMT1 was significantly up-regulated in bladder urothelial carcinoma (BLCA), breast invasive carcinoma (BRCA), cholangiocarcinoma (CHOL), colon adenocarcinoma (COAD), esophageal carcinoma (ESCA), head and neck squamous cell carcinoma (HNSCC), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), prostate adenocarcinoma (PRAD), rectum adenocarcinoma (READ), stomach adenocarcinoma (STAD), thyroid carcinoma (THCA), and uterine corpus endometrial carcinoma (UCEC). Conversely, NTMT1 was significantly down-regulated in kidney renal clear cell carcinoma (KIRC) and pheochromocytoma and paraganglioma (PCPG) (Fig. 1A). The analysis of uniform TCGA_GTEx data collected from UCSC also revealed a significant up-regulation of NTMT1 in various types of cancer tissues, which was largely consistent with the results obtained from TCGA (Fig. 1B). Moreover, the expression of NTMT1 in 18 types of tumors with paired samples in TCGA was examined, and NTMT1 was found to be significantly up-regulated in 12 types of cancer tissues (Fig. 1C).

We further investigated the protein expression levels of NTMT1 in both normal and tumor tissues of various human organs using the HPA. Our findings revealed high protein expression levels of NTMT1 in BRCA, COCA, LUAD, HNSCC, LUSC, and PAAD (Figs. 2A and S1A). The protein expression levels were statistically different between different subtypes (Figs. S1B and S1C) (Zhang & Chen, 2022; Chen & Chandrashekar, 2019). We also extracted representative immunohistochemistry (IHC) images of the tumor and normal tissues from HNSCC (Fig. 2B) and BRCA (Fig. 2C), suggesting that NTMT1 may play a critical role in the development and progression of these types of cancer. Furthermore, subcellular localization analysis using immunofluorescence demonstrated that NTMT1 is primarily located in the nucleus and cytoplasm of A-431 and U2OS cells (Fig. 2D). These results suggest that NTMT1 might play a crucial role in the occurrence and progression of tumors. However, further research is required to gain a better understanding of the functions of NTMT1 in cancer development and to assess the potential of NTMT1-targeted therapies for cancer treatment.

The association between NTMT1 expression and prognosis in pan-cancer

To evaluate the prognostic significance of NTMT1 in pan-cancer, we conducted a Kaplan–Meier survival analysis to investigate the correlation between NTMT1 expression and clinical outcomes. Initially, we examined the relationship between NTMT1 expression and OS in 33 different types of cancer. Our findings revealed that high expression of NTMT1 was positively associated with poor OS in HNSCC, ACC, and LAML (Figs. 3A and 3B). Moreover, high expression of NTMT1 was positively correlated with poor DSS and recurrence-free survival (RFS) in HNSCC, ACC, and READ (Figs. 3C and 3D), as well as with poor progression-free interval (PFI) in HNSCC, ACC, and UVM (Fig. 3E). Furthermore, the results of Kaplan–Meier plotter analysis indicated that high NTMT1 expression was positively associated with worse OS in BLCA, HNSCC, and STAD (Fig. S2A), and worse RFS in LIHC, LUSC, and STAD (Fig. S2B).

These results suggest that NTMT1 may have a critical role in the development and progression of these specific types of cancer. Furthermore, NTMT1 expression could serve as a valuable prognostic biomarker in these cancer types, which could help improve patient outcomes through earlier detection and personalized treatment approaches. However, further research is required to better comprehend the molecular mechanisms underlying the role of NTMT1 in these cancers and to evaluate the potential of NTMT1-targeted therapies for cancer treatment.

Clinical relevance of NTMT1 expression

To assess the diagnostic value of NTMT1 in pan-cancer, we performed a receiver operating characteristic (ROC) curve analysis based on data from the Cancer Genome Atlas (TCGA) database. Our analysis revealed that NTMT1 exhibited a high degree of diagnostic accuracy for eight types of cancer (AUC > 0.9), including PCPG, CHOL, COAD, READ, DLBC, LAML, TGCT, and THYM (Fig. 4A). These findings suggest that NTMT1 expression levels may be useful in predicting the clinical outcome and may help clinicians tailor treatment plans for these patients. However, the AUC value of 0.878 for HNSCC suggests that NTMT1 expression has only a moderately accurate diagnostic value for HNSCC compared to the other types of cancer. Nevertheless, our previous analysis showed a positive correlation between high NTMT1 expression and poor overall survival in HNSCC patients, indicating that NTMT1 may have prognostic value in this cancer type. Further research is needed to fully understand the clinical significance of NTMT1 in HNSCC and to identify potential therapeutic strategies targeting NTMT1 in this cancer type.

Given the significant prognostic value of NTMT1 in HNSCC, our study aimed to investigate the relationship between NTMT1 expression and clinicopathological features in HNSCC. Our results showed that NTMT1 expression was significantly associated with several clinical parameters, including pathological N stages, T stage, pathologic stage, histologic grade, primary therapy outcome, lymphovascular invasion, gender, and lymph node neck dissection (Fig. 4B). These findings suggest that NTMT1 may play a critical role in the development and progression of HNSCC and that NTMT1 expression could serve as a potential biomarker for the diagnosis and prognosis of HNSCC. However, further research is needed to validate the clinical utility of NTMT1 as a diagnostic and prognostic indicator in HNSCC and to explore the molecular mechanisms underlying the role of NTMT1 in HNSCC.

NTMT1 mutation in various tumors

Using TCGA data, we analyzed the mutation status of the NTMT1 gene through the cBioPortal platform to explore gene mutation patterns in various tumors. Pan-cancer analysis revealed NTMT1 high amplification in ESCA, PAAD, BRCA, OV, and SARC. The rate of “deep deletion” in BRCA, OV, and melanoma was the highest, while mutations mainly occur in ESCA and OV (Fig. 5A). As shown in Fig. 5B, the overall mutation frequency of NTMT1 in pan-cancer is only 2.4%. We also observed a positive correlation between NTMT1 mRNA expression and copy number (Spearman: 0.56; Fig. 5C). Finally, we investigated the potential association between NTMT1 gene alterations and overall survival in pan-cancer patients, and found that patients with NTMT1 gene alterations had significantly lower OS compared to those without NTMT1 gene alterations (p = 0.0118; Fig. 5D), as indicated by our analysis using Kaplan–Meier survival curves and log-rank tests.

Promoter methylation of NTMT1 in human cancers

Research has shown that DNA methylation plays a critical role in the development and pathogenesis of various diseases, and is often associated with transcriptional repression. These findings highlight the importance of epigenetic modifications, such as DNA methylation, in regulating gene expression and contributing to disease pathophysiology (Smith & Sen, 2020). In this study, we investigated the methylation status of the NTMT1 gene in normal and tumor tissues by comparing the methylation levels of the NTMT1 promoter region (Figs. 6 and S3). Our analysis revealed that the methylation level of the NTMT1 promoter region was significantly higher in several tumor tissues, including BRCA, CHOL, COAD, KIRP, LUAD, PAAD, READ, SARC, and THCA (Fig. S3). Conversely, the level of NTMT1 methylation was significantly lower in several tumor tissues, including BLCA, CESC, HNSCC, KIRC, LIHC, LUSC, PRAD, TGCT, and UCEC (Fig. 6). Previous studies have suggested that decreased promoter region methylation can lead to increased gene expression, which in turn could contribute to the development and progression of certain types of cancer. Therefore, the reduced NTMT1 promoter region methylation observed in these tumor tissues, including BLCA, CESC, HNSCC, KIRC, LIHC, LUSC, PRAD, TGCT, and UCEC, may suggest a potential role for NTMT1 in the pathogenesis of these cancers. However, there was no significant difference in the promoter region methylation level of NTMT1 in ESCA, GBM, PCPG, STAD, and THYM. These findings suggest that the methylation status of the NTMT1 promoter region may play a role in tumorigenesis. However, further research is needed to fully understand the functional significance of NTMT1 and its relationship with promoter region methylation in these tumors.

The potential mechanisms by which NTMT1 could contribute to the development and progression of these cancers are not yet fully understood and require further investigation. However, previous studies have suggested that NTMT1 may play a role in modulating various cellular processes, including mRNA splicing, translation, and protein stability (Bade & Cai, 2021). Moreover, recent studies have shown that NTMT1 can also modulate the activity and stability of several tumor suppressor proteins, including p53 (Sathyan & Fachinetti, 2017), which are commonly mutated or silenced in various types of cancer.

The relationship between NTMT1 expression and the tumor immune microenvironment

We performed a co-expression analysis on NTMT1 and immune-related genes (Ru & Wong, 2019) associated with immunoinhibitors, immunostimulators, MHC molecules, tumor-infiltrating lymphocytes, chemokines, and chemokine receptors in pan-cancer (Fig. 5). Our analysis revealed that, in the majority of cancers, there were negative correlations between NTMT1 and chemokine receptors (Figs. 7A–7F). However, in BLCA, PCPG, SARC, and LGG, the majority of immune-related genes displayed a positive correlation with NTMT1, suggesting that NTMT1 expression may be a useful biomarker for identifying patients who could benefit from immunotherapy. Additionally, targeting NTMT1 may enhance the effectiveness of immunotherapies by modulating the immune response in these cancers. Conversely, we observed negative correlations between NTMT1 and immune-related genes in HNSCC, KICH, and COAD, suggesting that immunotherapy may not be as effective in these cancers. However, further research is needed to fully understand the clinical implications of these findings and to determine the potential utility of NTMT1 as a therapeutic target in these cancers. However, further research is needed to fully understand the clinical implications and to determine the potential utility of NTMT1 as a therapeutic target in these cancers.

The immune microenvironment plays a crucial role in the development and progression of tumors. Given the crucial role of NTMT1 expression in HNSCC, we investigate the relationship between NTMT1 and the immune microenvironment in HNSCC, we utilized the ssGSEA algorithm to examine the correlation between NTMT1 expression and immune cells in HNSCC. Our analysis revealed that elevated NTMT1 expression was associated with reduced infiltration of aDC, iDC, cytotoxic cells, CD8 T cells, B cells, mast cells, and T cells (Fig. S4A). Furthermore, the high NTMT1 expression group displayed lower stromal score, immune score, and ESTIMATE score (Fig. S4B). We also observed reduced expression of multiple immune markers in the high NTMT1 expression group (Figs. S4C–S4G). The reduced expression of multiple immune markers in the high NTMT1 expression group could indicate a weakened immune response against cancer cells, suggesting that NTMT1 may be involved in the evasion of immune surveillance and thus promoting the progression and metastasis of HNSCC. This finding highlights the potential of NTMT1 as a therapeutic target for enhancing the immune response and improving the clinical outcomes of HNSCC patients. These findings suggest that NTMT1 may play a role in immune evasion and suppression in HNSCC and that targeting NTMT1 may enhance the effectiveness of immunotherapies in these cancers.

Correlation and enrichment analyses

To gain further insight into the molecular mechanisms underlying NTMT1’s role in tumorigenesis, we aimed to identify proteins that interact with NTMT1 and genes that are associated with NTMT1 expression for pathway enrichment analyses. We utilized the BioGRID database to uncover novel proteins and pathways associated with NTMT1. By constructing a network of 68 NTMT1-associated proteins through BioGRID (Fig. 8A), we gained valuable insights into the molecular interactions and pathways involving NTMT1. We also utilized the STRING tool to identify 50 NTMT1 binding proteins supported by experimental evidence, as shown in Fig. 8B. Obtain the top 100 genes (Table S1) associated with NTMT1 through GEPIA2, and perform an intersection analysis with genes related to HNSCC, resulting in 28 genes. The correlation between HNSCC and NTMT1 expression of these genes is shown in Fig. 8C. This information can help elucidate the function of NTMT1 in biological processes and diseases and potentially identify new therapeutic targets for cancer treatment.

Additionally, we conducted a correlation analysis between NTMT1 and all other mRNAs using TCGA data to explore the function and pathways influenced by NTMT1. We analyzed the differentially expressed genes between the high and low expression groups of NTMT1 in HNSCC, and selected genes with |logFC| ≥ 1 for further analysis (Fig. 8D). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses revealed that these genes are associated with various immune-related processes (Fig. 8F), such as immune response-activating cell surface receptor signaling pathway, phagocytosis, activation of the immune response, external side of the plasma membrane, intermediate filament cytoskeleton, T cell receptor complex, antigen binding, calcium-dependent phospholipase A2 activity, C-C chemokine receptor activity, cytokine-cytokine receptor interaction, estrogen signaling pathway, and vascular smooth muscle contraction.

Further analysis using GSEA revealed that the genes correlated with NTMT1 expression are functionally related to various pathways, such as FCERI_MEDIATED_MAPK_ACTIVATION, FCERI_MEDIATED_NF_KB_ACTIVATION, ROLE_OF_PHOSPHOLIPIDS_IN_PHAGOCYTOSIS, ADAPTIVE_IMMUNE_SYSTEM, and INNATE_IMMUNE_SYSTEM (Fig. 8E). These findings suggest that NTMT1 may modulate the immune response and phagocytosis in HNSCC and may also regulate various signaling pathways. The identified pathways could offer potential targets for therapeutic intervention in HNSCC.

The expression pattern of NTMT1 at single-cell levels

ScRNA-seq provides an unprecedented opportunity to explore the functional heterogeneity of cancer cells. NTMT1 expression is dysregulated in various types of cancer, and its overexpression or underexpression has been associated with different types of cellular functions. The results suggest that NTMT1 expression is positively associated with various cellular functions, such as cell cycle, DNA damage, DNA repair, invasion metastasis, and apoptosis in ALL, AML, CML, CRC, BRCA, AST, GBM, Glioma, HGG, ODG, HNSCC, LUAD, NSCLC, OV, and MEL (Fig. 9A). The expression of NTMT1 is positively correlated with cell proliferation and DNA repair in HNSCC (Figs. 9B and 9C), which is consistent with previous literature reports. NTMT1 expression is upregulated in several cancers, including HNSCC, and its overexpression has been associated with increased tumor growth and invasion. Therefore, the positive correlation between NTMT1 expression and cell proliferation in HNSCC may indicate that NTMT1 could be a potential therapeutic target for this cancer type.

However, it is primarily negatively associated with the cellular functional states of RCC, RB, and UM. In particular, NTMT1 expression is significantly and negatively correlated with 14 functional states in UM (Fig. 9A), suggesting that it may act as a potential tumor suppressor in UM. Further studies are needed to elucidate the underlying mechanisms of NTMT1 in UM. Additionally, the T-SNE plot displays the single-cell level expression profiles of NTMT1 in HNSCC (Fig. 9D), indicating that there are differences in NTMT1 expression among these cancer types at the single-cell level. However, the exact nature of these differences is not specified in the text provided.

Overall, these findings suggest that NTMT1 expression may play a complex and context-dependent role in cancer biology, potentially affecting multiple cellular processes that contribute to the development and progression of cancer. However, further research is needed to fully elucidate the functional significance of NTMT1 expression in cancer and its potential as a therapeutic target.

NTMT1 knockdown promoted tumor cell apoptosis

To further explore the role of NTMT1 in HNSCC, we knocked down NTMT1 expression by siRNA (Fig. 10A). The results showed that NTMT1 knockdown significantly inhibited the proliferation of HNSCC cell SNU1076 (Fig. 10B). Since knockdown of siNTMT1-2 could more effectively inhibit the expression of NTMT1 and the proliferation of tumor cells, siNTMT1-2 was used for further study in subsequent experiments. Furthermore, the immunofluorescence assay showed that the expression of γ-H2AX was significantly increased after NTMT1 knockdown (Fig. 10C) indicating that the cells had significant DNA damage. Flow cytometry showed that NTMT1 knockdown promoted tumor cell apoptosis (Figs. 10D and 10E). These results suggest that knockdown of NTMT1 may promote cell apoptosis by inducing DNA damage response in HNSCC. This is consistent with the single-cell analysis results. However, further specific mechanisms need to be explored.