Prognostic and immunological analysis of METTL16 in pan-cancer

doi:10.21203/rs.3.rs-4705174/v1

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Prognostic and immunological analysis of METTL16 in pan-cancer

https://doi.org/10.21203/rs.3.rs-4705174/v1

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Background

METTL16 is a methyltransferase that functions as a m6A writer. In recent years, it has been demonstrated that METTL16 can participate in tumor development, but there is no report on pan-cancer analysis of METTL16. The purpose of this study is to investigate the role and mechanism of METTL16 in pan-cancer.

Methods

Through the use of multiple bioinformatics approaches, we have elucidated the role of METTL16 at the pan-cancer level in the methylation modification, DNA repair, tumor cell stemness, genome stability, and the tumor immune microenvironment, as well as its consequent impact on cancer prognosis.

Results

We found that METTL16 is differentially expressed at the transcriptional and translational levels in pan-cancer, which correlates with tumor prognosis. The methylation of METTL16, which has multiple methylation modification activities, enhances the prognosis of cancer by promoting the infiltration of cytotoxic T lymphocytes and the expression of chemokines, receptors, MHCs, and immunostimulators. In pan-cancer, the aberrant expression of METTL16 also affects DNA repair, leading to altered tumor cell stemness and gene stability. METTL16 is an important molecule involved in the reprogramming of the tumor immune microenvironment. It impacts the expression of numerous immune checkpoint genes and prevents the infiltration of immune-suppressing cells, including M2 macrophages, regulatory T cells, and Dendritic cells. And it serves as an indicator of M1 macrophages infiltration. Furthermore, the expression level of METTL16 can also reflect the outcomes of cancer treatment after chemotherapy or immunotherapy. We also conducted a screening of drugs that target METTL16 and predicted their efficacy.

Conclusions

METTL16's differential expression in pan-cancer can result in alterations to DNA repair pathways, which can influence the stemness of tumor cells and contribute to genome instability. Methylation of METTL16 and its expression level can affect the remodelling of the tumor immune microenvironment. The prognosis of patients may be affected by these changes in tumors caused by METTL16, which implies that it could be a target for anti-tumor therapy.

METTL16

pan-cancer

immune infiltration

tumor microenvironment

By integrating extensive quantities of data (DNA, RNA, protein, and epigenetic information) generated in various types of tumors, pan-cancer analysis investigates the similarities and differences between genomic and cellular alterations in order to identify potential non-tumor-specific markers and targets [1].

Methyltransferase-like protein 16 (METTL16) is a methyltransferase that is primarily located in the nucleus and is dependent on S-adenosylmethionine (SAM). It is composed of an N-terminal RNA binding m6domain (RBD), a methyltransferase domain, and C-terminal vertebrate conserved regions (VCRs). It is a critical component of the transcriptome and is implicated in the regulation of numerous N6–methyladenosine (m6A) modifications. It has been established that METTL16 is involved in the m6A modification of the 3' end of U6 snRNA and MAT2A pre-mRNA in humans. The conserved sequence UACAGAGAA is present in both of these targets and is the site of METTL16 methylation. METTL16 also contains regions that interact with RNA substrates, including rRNAs, ncRNAs, lncRNAs, and mRNAs [2, 3]. For instance, it has the capacity to precisely bind to the triple helix structure at the 3' end of the oncogenic lncRNA MALAT1 [2, 4], an interaction that may be pertinent to tumorigenesis.

The complex and essential regulatory role of METTL16 in the cell is suggested by its multiple functions, and a significant number of studies have identified METTL16 as being closely associated with cancer. METTL16 can be subjected to R200Q or G110C mutations in colorectal cancer [2], resulting in an upregulation of its expression and the promotion of cancer progression [5, 6]. In patients with soft tissue sarcoma, an increase in the copy number of METTL16 was associated with a worse overall survival (OS) [7]. Poor patient prognosis is believed to be associated with increased expression of METTL16 in breast cancer [8]. This may be due to the fact that METTL16 can enhance the expression of GPX4 through m6A modification, which subsequently prevents iron death and promotes the development of cancer [9]. In gastric cancer, the high expression of METTL16 also facilitates the proliferation of cancer cells by increasing the level of downstream cyclin D1 through its mediated m6A modification [10]. By downregulating RAB11B-AS1, increased METTL16 expression in hepatocellular carcinoma can stimulate cancer cell proliferation, migration, and invasion, while simultaneously suppressing apoptosis [11]. Also, it has the ability to directly bind to eIF3a/b and rRNAs, which can be beneficial for the development of hepatocellular carcinoma by enhancing the efficacy of protein translation and facilitating the formation of 80S ribosomes [12]. In pancreatic cancer, METTL16 expression is downregulated, whereas the p21 signaling pathway is activated by upregulated METTL16 expression, which inhibits cancer cell growth and enhances cell cycle arrest [13, 14]. The progression of epithelial ovarian cancer is inhibited by METTL16 through the lncRNA MALAT1/β-Catenin axis [15]. The expression level of METTL16 is positively correlated with OS in endocrine system tumors [16]. Therefore, the expression of METTL16 in tumors and its impact on cancer prognosis are still subject to debate.

Methyltransferases are increasingly implicated in DNA damage repair (DDR). Proto-oncogenes can induce DNA replication stress and DDR defects in cancer cells, which leads to a higher number of DNA double-strand breaks (DSBs) in comparison to healthy cells [17]. The two primary mechanisms involved in the repair of DSBs are homologous recombination repair (HRR) and non-homologous end joining (NHEJ) [18]. Furthermore, there is a significant role for non-DSB repair mechanisms, including mismatch repair (MMR) [19]. The activation of the DDR pathway was discovered to be correlated with the stemness of cancer cells [20]. The suppression of MMR protein MSH6/MLH1 resulted in a substantial reduction in the expression of cancer stemness markers CD133, BMI-1, OCT-4, and SOX-2 in colorectal cancer cells [21]. In CD133-expressing lung cancer cells, chemotherapy incites the expression of DNA repair genes, which results in enhanced radiation resistance [22]. Increased DNA damage or malfunctioning DNA repair pathways can induce genomic instability, which, in turn, promotes tumor growth [23, 24]. Furthermore, the tumor microenvironment (TME) may be influenced by faulty DNA repair through mechanisms such as intrinsic and acquired immunity [25].

Alternative splicing (AS), which is recognized as a critical contributing factor to tumorigenesis and development, can also influence the TME by influencing immune cells and mesenchymal stromal cells [26]. AS is the process of using distinct splicing patterns to produce a range of mRNA isoforms from pre-mRNAs, hence increasing the diversity of gene products [27]. Changes in AS can give tumors benefits in terms of cell invasion, angiogenesis, proliferation, and evasion of apoptosis [28]. Nevertheless, there is a dearth of research on the potential of METTL16 as an immunotherapeutic target.

In this study, we employed a diverse array of data processing methods to conduct a multi-omics pan-cancer analysis of METTL16 on samples from cancer and normal tissue databases. The objective was to elucidate the clinical significance and diversity of METTL16 in cancer. It was discovered that METTL16 exhibited a wide range of histological heterogeneity in pan-cancer, which strongly influences immunogenesis and DNA repair and is crucial for clinical prognosis. We thoroughly investigated the shared characteristics of the development of a variety of tumors and compared the sensitivity and specificity of METTL16 in a variety of malignancies using single-gene pan-cancer analysis. This research outcome not only broadens our theoretical comprehension of cancer biology, but also serves as a critical reference for the development of cancer treatment strategies.

Data acquisition

Standardized pan-cancer and normal tissue datasets were obtained from The Cancer Genome Atlas Program (TCGA, https://portal.gdc.cancer.gov/) and Genotype-Tissue Expression (GTEx, https://www.gtexportal.org/). HPA (Human Protein Atlas, https://www.proteinatlas.org/) [29], a human proteome atlas database comprising information on tissue and cellular protein distribution, was used to obtain METTL16 immunohistochemical images of normal tissues and corresponding tumor samples.

Expression and prognostic analysis of METTL16 in pan-cancer

The Open Targets Platform (https://platform.opentargets.org/) [30] was employed to generate bubble maps of the METTL16-associated disease network. Differences in METTL16 mRNA expression between tumor tissues and normal tissues adjacent to the cancer were analysed by R package (panCancer06). For cancer stages and cancer categories that lacked sufficient normal adjacent samples, GEPIA2.0 (Gene Expression Profiling Interactive Analysis, http://gepia2.cancer-pku.cn/) data were used to supplement [31]. Comparison of cancerous tissues with normal tissues was analysed using UALCAN (The University of Alabama at Birmingham Cancer data analysis Portal, http://ualcan.path.uab.edu/) [32] and HPA databases to compare METTL16 expression differences at the protein level. The correlation between the expression level of METTL16 and the prognosis of pan-cancer was assessed using the Kaplan-Meier Plotter (https://kmplot.com/) [33] in terms of OS, disease-specific survival (DSS), and progression-free interval (PFI), respectively.

The function analysis of METTL16

The R package (WGCNA16.GO) was employed to conduct a Gene Ontology (GO) functional analysis of METTL16. The expression of METTL16 in pan-cancer was assessed using UALCAN at the level of certain important pathways, including WNT pathway status, mTOR pathway status, p53/Rb-related pathway status, RTK pathway status, NRF2 pathway status, Chromatin modifiers status, MYC/MYCN status, SWI/SNF complex status, and HIPPO pathway status. The correlation between these pathway-related signatures [34] and METTL16 expression was analyzed on GEPIA 2.0.

The correlation between METTL16 and epigenetic methylation

The R package (panCancer23.geneCor) was applied to analyze the association of METTL16 with the expression of DNA methyltransferases (DNMT1, DNMT3A, DNMT3B, DNMT3L) [35] in pan-cancer. The online instrument TIDE (Tumor Immune Dysfunction and Exclusion, http://tide.dfci.harvard.edu/) [36] was employed to evaluate the methylation of the METTL16 promoter in relation to cytotoxic T lymphocytes (CTLs) and its association with the prognosis of pan-cancer patients, respectively. The STRING instrument (https://cn.string-db.org/) [37] was employed to screen proteins that are capable of interacting with METTL16 proteins. The Multiple Genes Comparison module and the Correlation Analysis module in GEPIA 2.0 were used to illustrate the differences in the expression of interacting proteins in pan-cancer and the correlation of METTL16 expression with them.

The effect of METTL16 on DNA repair and stemness in pan-cancer

Using TCGA-PanCanAtlas Publications, the relationship between homologous recombination deficiency (HRD) and METTL16 expression was examined. The R package (panCancer23.geneCor) was chosen to evaluate the association of METTL16 with the expression of MMR-related genes (MLH1, MSH2, MSH6, PMS2, EPCAM) [38] in pan-cancer. METTL16 and cell stemness were correlated using the R package (panGenes16.RNAssCor and panGenes17.DNAssCor). The expression correlation between HRR pathway-related signatures [39] and METTL16 was investigated on GEPIA 2.0.

The genomic alterations of METTL16 in pan-cancer

The genomic alterations of METTL16 were analyzed on cBioPortal (https://www.cbioportal.org/) [40]. The Cancer Types Summary and mRNA vs Study modules, respectively, examined the frequency of different METTL16 genomic alterations and the relationship between CNA (copy number alteration) and METTL16 expression in pan-cancer. In the Mutations module, the protein-level mutation sites of METTL16 were identified. The impact of the METTL16 CNA level on the Kaplan-Meier curve of pan-cancer was assessed by TIDE. The R packages (panCancer15.cor and panCancer17.MSIcor) were employed to analyze METTL16 for tumor mutation burden (TMB) and microsatellite instability (MSI), respectively. TCGA-PanCanAtlas Publications was applied to analyze the correlation between METTL16 expression and ploidy. TSNAdb v2.0 (https://pgx.zju.edu.cn/tsnadb/) was used to analyze the correlation between neoantigens and METTL16 expression.

Clinical correlation analysis of METTL16 AS events

Schematic of AS for METTL16 were downloaded from NCBI (National Center for Biotechnology Information, https://www.ncbi.nlm.nih.gov/). AS events of METTL16 were queried through the OncoSplicing online tool (http://www.oncosplicing.com/) [41]. The PanPlot shows the percent spliced-in (PSI) of METTL16 occurring AS in TCGA cancers and GTEx tissues. The PanDiff offers a pan-cancer perspective on the PSI differences of the METTL16 AS events between tumor samples and adjacent normal samples or GTEx normal samples. PanCox (PanOS or PanPFI) diagrams present a pan-cancer perspective on hazard ratios for METTL16 AS events by utilizing the median and optimal PSI cutoff. And the prognostic significance of METTL16 AS events was explored by plotting Kaplan-Meier curves.

Impact of METTL16 on tumor immune infiltration and immunotherapy

Using the R package (ESTIMATE, version 1.0.13), we generated ImmuneScore and StromalScore for several malignancies and analyzed the link between METTL16 expression and ImmuneScore, StromalScore, and ESTIMATEscore in order to study the role of METTL16 in immune infiltration in TME. Furthermore, 47 immunological checkpoint markers were acquired [42], and their relationship with METTL16 expression was tested with R/limma package. The Subtype module of TISIDB (http://cis.hku.hk/TISIDB/) [43] was used to analyze the correlation between METTL16 expression and tumor immune subtypes. METTL16 expression was compared at the pan-cancer level in a range of subtypes, including wound healing (C1), IFN-γ dominant (C2), inflammatory (C3), lymphocyte deplete (C4), immunologically quiet (C5), and TGF-β dominant (C6). Heatmaps depicting the connections between methylation of METTL16 and chemokines, chemokine receptors, MHC, and immunostimulatory factors, respectively, are displayed in the Chemokine and Immunomodulator modules. At TISMO (Tumor Immune Syngeneic Mouse, http://tismo.cistrome.org/) [44], the expression levels of METTL16 in each tumor cell line were examined before and after cytokine therapy in order to study the impact of the treatment on METTL16 expression. The cytokines that were administered were TNFα, TGFb1, IFNγ, and IFNβ. The cell lines included were: Mammary cancer: 4T1, EMT6, E0771; Colorectal carcinoma: CT26, MC38; Lung carcinoma: LLC; Head and neck squamous cell carcinoma: MOC1; melanoma: B16; pancreatic ductal adenocarcinoma: KPC, Panc02; renal adenocarcinoma: Renca. The relationship between METTL16 expression and the abundance of numerous immune cell infiltrations was plotted on a heat map using the CIBERSORT algorithm. The expression levels of METTL16 in various immune cells in pan-cancer were compared with single-cell datasets collected by TISCH (Tumor Immune Single-cell Hub, http://tisch.comp-genomics.org/) [45].

Correlation of METTL16 expression with therapeutic response

In order to examine the influence of METTL16 on conventional cancer treatment, we used the ROC plotter (https://rocplot.org/) [46] to plot receiver operating characteristic (ROC) curves and compare the expression of METTL16 in cancer patients before and after chemotherapy or before and after treatment with immune checkpoint inhibitors.

Screening and molecular docking analysis of drugs targeting METTL16

RNAactDrug (http://bio-bigdata.hrbmu.edu.cn/RNAactDrug/) was used to analyze the relationship between METTL16 expression and drug sensitivity. Additionally, drugs with higher sensitivity can be analyzed for their 50% growth inhibition (GI50) of different cancer cells using the COMPARE tool from the NCI (National Cancer Institute) Development Therapeutics Programme (https://dtp.cancer.gov/databases_tools/compare.htm). Meanwhile, we implemented ROC Plotter to forecast the therapeutic impact of drugs that target METTL16 at the level of tumor cell lines.

The 3D structure of METTL16 protein was acquired from PDB (Protein Data Bank, https://www.rcsb.org/). The PubChem Chemical Database (https://pubchem.ncbi.nlm.nih.gov/) provided the 3D structures of small molecule drugs. Using the molecular modeling software AutoDock and PyMOL, the water molecules in the proteins were eliminated and hydrogen atoms were inserted. In order to make sure the small molecule drug's structure was optimized and had the correct charge state, it was additionally treated. Then the AutoDock Vina software was used to predict the active site of the protein, set the parameters of the docking algorithm, and perform docking calculations. For presentation, the binding conformation with the highest energy score will be generated using the docking software.

Statistical analysis

The Pearson's or Spearman's correlation coefficients were used to conduct correlation analysis. If there was a statistically significant correlation between the expression of METTL16 and any of the five MMR-related genes (or four methyltransferases) and no statistically significant result in the opposite direction for the other MMR-related genes (or methyltransferases), then there was either a positive or negative correlation between METTL16 and MMR-related genes (or methyltransferases). P-values less than 0.05 were regarded as substantially different (*p < 0.05, **p < 0.01, ***p < 0.001).

1. METTL16 is differentially expressed in pan-cancer and is correlated with patient prognosis

In order to investigate the expression of METTL16 in pan-cancer, we conducted a search for METTL16 associated diseases on OpenTarget. The bubble plots indicated that METTL16 was associated with gastric carcinoma, osteosarcoma, Ewing sarcoma, and pancreatic carcinoma (Fig. 1A). Then, we investigated whether there were variations in the mRNA expression levels of METTL16 between 33 tumors and their respective normal tissues. The mRNA level of METTL16 was up-regulated in CHOL, COAD, ESCA, GBM, HNSC, KIRP, LIHC, and STAD, while it was down-regulated in BLCA, BRCA, CESC, KICH, LUAD, LUSC, PRAD, THCA, and UCEC (Fig. 1B). Due to the absence of normal tissue control in certain tumors, we utilized GEPIA2.0 for supplementary analysis in order to determine that METTL16 mRNA was highly expressed in DLBC and LGG but decreased in OV (Fig. 1C). Additionally, the expression level of METTL16 was negatively correlated with the pathological stage of STAD, OV, and PAAD (Fig. 1D). Besides, the protein level of METTL16 was up-regulated in GBM, LIHC, BRCA, KIRC, and HNSC, in contrast to UCEC (Fig. 1E). This finding was in accordance with the orientation of the METTL16 mRNA changes described above, with the exception of BRCA. We obtained immunohistochemical images from the HPA database to more visually evaluate the protein expression changes of METTL16 in tumors. These images demonstrated that the protein expression of METTL16 was significantly higher than that of normal tissues in breast, cervical, colorectal, endometrial, renal, liver, lung, skin, testis cancers, and lymphoma (Fig. S1A-J). Conversely, it was observed to be lower in ovarian, pancreatic, prostate, stomach, and thyroid cancer (Fig. S1K-O). This implies that METTL16 is differentially expressed at both the transcriptional and translational levels in pan-cancer.

Subsequently, we employed forest plots (Fig. S2) and Kaplan-Meier curves to investigate whether the expression level of METTL16 influences the prognosis of pan-cancer patients. The findings indicated that patients with BRCA, LIHC, and SARC endured a low OS and a low DSS, while those with BRCA, LUSC, and UVM suffered a low DSS. However, patients with ESCA, KIRC, PAAD, READ, and UCEC had a longer OS, while patients with KIRC, PAAD, READ, and THYM received a prolonged DSS. Additionally, patients with KIRC and PAAD experienced a prolonged PFI (Fig. 2). Therefore, the clinical significance of the differential expression of METTL16 in tumors is certain. There are two aspects to this gene: in BRCA, it may function as a pro-oncogene, whereas in PAAD, it serves as a protective factor.

2. Involvement of METTL16 in methylation modification

The GO functional enrichment analysis demonstrated that METTL16 possesses a variety of methylation modification activities (Fig. 3A). METTL16 expression resulted in an increase in the levels of tumor methyltransferase, with a particular emphasis on COAD, ESCA, HNSC, LGG, LUSC, OV, STAD, THCA, and UVM (Fig. 3B). A hallmark of tumorigenesis progression is abnormal methylation [47]. The methylation of gene promoters occurs in the early phases of tumorigenesis and has significant potential as a biomarker for cancer prognosis and diagnosis [48]. Therefore, we investigated whether there was a correlation between METTL16 promoter methylation and CTL and prognosis. We selected the top 10 cancer types with high CTL correlation coefficients (Fig. 3C), which include BRCA, SKCM, CHOL, KIRC, SARC, and partial subtypes. All of these cancers exhibited positive correlations between METTL16 promoter methylation and CTL infiltration. Finally, the correlation scatterplot is depicted in Fig. 3D. The survival of patients was extended in BRCA, SKCM, and their subtypes as a consequence of methylation of the METTL16 promoter (Fig. 3E).

Further, we discovered experimentally verified proteins that can interact with METTL16 using the String web tool. Figure 3F illustrates 10 of these proteins (WTAP, CBLL1, METTL14, RMB15, METTL5, RMB15B, ZC3H13, ZCCHC4, VIRMA, METTL3). Additionally, GEPIA2.0's analysis demonstrated that the mentioned proteins exhibited altered expression in a diverse array of tumors (Fig. 3G). In pan-cancer, METTL16 was positively co-expressed with WTAP, CBLL1, METTL14, RBM15, METTL5, RBM15B, ZC3H13, ZCCHC4, and METTL3 (Fig. 3H). Likewise, the methylation function of all of these genes can contribute to the development of cancer [49–53].

3. METTL16 has an influence on DNA repair and stemness in tumor cells

As a further study of the function of METTL16 participated in pan-cancer, we initially compared the expression of METTL16 in altered and unaltered pathways with UALCAN, and found that altered of mTOR pathway status was accompanied by the upregulation of METTL16 in 7 tumors (BRCA, COAD, KIRC, HNSC, GBM, LGG, PRCA), altered chromatin modifier status was accompanied by up-regulation of METTL16 in 7 tumors (BRCA, COAD, OV, KIRC, UCEC, HNSC, LGG), altered MYC/MYCN pathway status was accompanied by upregulation of METTL16 in 6 tumors (OV, LUAD, HNSC, GBM, LGG, PRCA), altered WNT pathway status was accompanied by upregulation of METTL16 in 5 tumors (COAD, KIRC, HNSC, GBM, LGG), altered SWI/SNF complex status was accompanied by upregulation of METTL16 in 5 tumors (BRCA, KIRC, HNSC, GBM, LGG), and altered HIPPO complex status was accompanied by upregulation of METTL16 in 7 tumors (COAD, KIRC, LUAD PAAD, HNSC, GBM, PRCA) (Fig. 4A). As the aforementioned results were restricted to a subset of pan-cancer, we performed a thorough analysis of all tumors using GEPIA2.0 to investigate the expression correlation between METTL16 and these pathway-related signatures. We discovered that the expression of METTL16 was positively correlated with all of these signatures (Fig. 4B). Since these pathways are associated with DDR [18, 54, 55], we believed that METTL16 plays a role in it.

We focused on the correlation between METTL16 and HRD and MMR-related genes (MLH1, MSH2, MSH6, PMS2, EPCAM). We observed that METTL16 was positively correlated with HRD in KICH, HNSC, LGG, GBM, and LIHC, and negatively correlated with HRD in THYM, PAAD, BRCA, LAML, UCEC, PRAD, TGCT, CESC, and STAD (Fig. 4C). METTL16 has a positive association with a number of MMR genes in the bulk of cancers, including BLCA, BRCA, CESC, COAD, ESCA, GBM, HNSC, KICH, KIRC, KIRP, LAML, LGG, LIHC, LUAD, LUSC, OV, PAAD, PCPG, PRAD, READ, SARC, SKCM, STAD, TGCT, THCA, UCEC, and UVM (Fig. 4D).

Variations in the expression of DNA repair genes can also influence the self-renewal capacity, tumorigenicity, and invasiveness of cancer stem cells [56]. Additionally, a growing body of evidence has linked the poor prognosis of tumors to the stemness properties of cancer cells [57]. The HIPPO pathway is also known to be associated with DNA damage repair and to maintain the stemness of tumor stem cells [58]. The expression of METTL16 is correlated with the alteration of HIPPO complex status in pan-cancer (Fig. 4A, B). That is why we subsequently conducted an investigation into the potential impact of METTL16 on the stemness of cancer. Figure 4E indicates that METTL16 is positively correlated with stemness in GBM, HNSC, LUSC, and UVM, and negatively correlated with it in PRAD and THYM. It is intriguing that the same trend was observed when HRR signatures were conducted on the aforementioned cancers (Fig. 4F). We therefore hypothesize that METTL16 may affect the stemness of cancer that is mediated by DNA repair.

4. METTL16 can cause genome instability in pan-cancer

Effective DNA damage repair is essential for the preservation of genomic stability and the prevention of carcinogenesis. Mutations or recombination that disrupt DNA damage repair can activate proto-oncogenes and deactivate oncogenes [19]. Thus, we conducted an extended investigation of the potential impact of METTL16 on genomic stability. The cBioPortal online tool was used to analyze the CNA levels of METTL16 in pan-cancers, which included 32 studies and 10,967 samples. The results indicated that DLBC, ACC, and LIHC exhibited high levels of deep deletion of METTL16, while DLBC, UCEC, SKCM, and COAD exhibited high levels of mutation of METTL16 (Fig. 5A). The most frequently altered location is R200Q, with 59 missense variants, 7 truncation mutations, 1 splice mutation, and 7 fusion mutations within the 562 amino acids of METTL16 (Fig. 5B). In all but LAML, KIRP, THCA, and UVM, shallow deletion of METTL16 was the most prevalent among the 32 cancers (Fig. 5C). This suggests that METTL16 expression can impact genome stability in pan-cancer. The survival of patients with LGG, UCEC, and PAAD improved when METTL16 CNA levels were elevated, while the survival of patients with DLBC was decreased (Fig. 5D). It reveals that changes in the METTL16 genome can influence the prognosis of cancer.

Since TMB and MSI are indicators of the frequency of genomic mutations, we then examined the correlation between METTL16 and these two in pan-cancer. METTL16 was positively correlated with TMB in COAD, LGG, MESO, OV, and UCEC, and negatively correlated with TMB in BLCA, BRCA, ESCA, PAAD, PRAD, SKCM, and THCA. In addition, METTL16 was positively correlated with MSI in COAD, DLB, HNSC, LUAD, and LUSC, while negatively correlated with MSI in PRAD and THCA (Fig. 5E). Given that genetic mutations cause tumor cells to create proteins called neoantigens, which differ in amino acid sequence, we also explored the relationship between METTL16 and tumor neoantigens. Figure 5F showed a positive correlation between the expression of neoantigen and METTL16 in COAD, KICH, and UCEC. In addition to simple deoxyribonucleic acid sequence alterations, genomic instability encompasses chromosomal aberrations [59]. METTL16 was positively correlated with MESO, OV, LIHC, and LUSC in ploidy analysis, and negatively correlated with THYM, DLBC, PRAD, and UCEC (Fig. 5G). The results presented above demonstrate that METTL16 can result in genome instability, which is particularly evident in COAD and UCEC.

5. Differential expression of METTL16 AS events affect cancer prognosis

Maintaining genomic instability requires AS [59]. By conducting an analysis of the NCBI database, we discovered that METTL16 contains numerous variable splices (Fig. 6A). We conducted PSI analysis using OncoSplicing to identify variations in the expression of METTL16 AS events in pan-cancer. The results showed that PSI was increased in CESC and KICH compared to normal tissues, and decreased in BLCA, COAD, and PAAD (Fig. 6B). Similarly, Fig. 6C indicates that the PSI of cancers is significantly different from that of normal or GTEx tissues, as evidenced by the PSI of KICH, SKCM, BRCA, LUSC, and LUAD. Furthermore, PanCox (PanOS or PanPFI) plots demonstrate that the PSI distribution of METTL16 AS events was negatively correlated with OS in ACC, KIRC, and STAD, and positively correlated with OS in GBM, KIRP, SARC, UCS, and LGG. It was also negatively correlated with PFI in ACC, LIHC, THYM, TGCT, and STAD, and positively correlated with PFI in UCS, DLBC, LUAD, and LGG (Fig. 6D, E). Kaplan-Meier curves were employed to determine whether METTL16 AS events influence cancer prognosis. Elevated PSI levels were associated with shorter OS in CHOL, GBM, KIRP, LGG, SARC, and UCS (Fig. 6F). In contrast, the OS of STAD, KIRC, and ACC patients and the PFI of TGCT and THYM patients were extended by a high level of PSI (Fig. 6F, G). As a result, we predicted that METTL16 AS events are crucial for cancer prognosis.

6. METTL16 is involved in tumor immune infiltration and remodeling of TME

As DNA repair defects and AS have been implicated in the regulation of the TME, we examined the immune function of METTL16 in the TME. In BLCA, BRCA, GBM, KIRP, LAML, LGG, MESO, SARC, and THCA, METTL16 was negatively correlated with ESTIMATEScore, ImmuneScore, and StromalScore. Conversely, in COAD, PAAD, and STAD, METTL16 was positively correlated with ESTIMATEScore, ImmuneScore, and StromalScore. The negative correlation between METTL16 and ImmuneScore was observed in LAML and THCA, as well as with several immune checkpoint genes (Fig. 7A, B). In addition, immune checkpoint genes can also be positively correlated with METTL16 in a diverse array of cancers. For instance, in ACC, BLCA, KIRP, LGG, LIHC, OV, SARC, SKCM, TGCT, and UVM, METTL16 expression was positively correlated with CD80, a mark for M1 macrophages. In ACC, BLCA, GBM, KIRP, LGG, LIHC, MESO, OV, SARC, SKCM, and TGCT, the expression of METTL16 was also positively correlated with CD86, another marker of M1 macrophages. Up to 28 immune check genes were positively correlated with METTL16 in SARC (Fig. 7B). The results presented above imply that METTL16 participates in the effects of immune checkpoints.

Following this, we conducted an investigation using TISIDB and discovered that METTL16 expression varied among cancer immune subtypes, with the highest levels of significance observed in BRCA, LUAD, PAAD, and UCEC (Fig. 7C). Tumor immune subtypes have been demonstrated to be correlated with patient prognosis in prior research. The OS of patients with the C3 immune subtype is significantly higher than that of the other immune subtypes. Conversely, the prognosis of patients with C1 and C2 is markedly worse, and that of patients with C4 and C6 is the worst [42]. Figure 7D illustrates the METTL16 expression of each immune subtype in the top four cancers with the highest correlation factor. The expression of METTL16 was higher in the C3 immune subtype of LUAD and PAAD and lower in the C4 and C6 immune subtypes. This indicates that METTL16 may have a tumor-suppressive effect in LUAD and PAAD. Significant differences in METTL16 expression were also observed in the immune subtypes of BLCA, ESCA, HNSC, KIRC, LGG, LIHC, LUSC, OV, PRAD, SARC, STAD, STAD, and TGCT in the other 26 cancers (Fig. S3).

Previously, we discovered that the methylation of METTL16 could influence the prognosis of pan-cancer, and that m6A modification could influence the recruitment, activation, and polarization of immune cells, thereby remodeling the TME [60]. Therefore, in order to determine whether the methylation of METTL16 could play a role in the TME, we examined it in relation to chemokines, chemokine receptors, and immunomodulatory factors. As illustrated in Fig. 7E, the methylation of METTL16 is positively correlated with a variety of chemokines, receptors, MHCs, and immunostimulators, and it is particularly significant in OV and TGCT. Moreover, we conducted a comparison of the expression levels of METTL16 in tumor cell lines prior to and following cytokine treatment. Increased expression of METTL16 was observed in both B16 and LLC cell lines following IFN-β treatment (Fig. 7F). Hence, the expression of METTL16 is a critical factor in reprogramming the TME and can serve as an indicator of the efficacy of tumor immunotherapy.

7. METTL16 is a potential marker of M1 macrophages infiltration

In order to further investigate the impact of METTL16 on TME, we carried an analysis of its expression in immune cells. We identified 22 immune cells that were associated with METTL16 by employing the CIBERSORT algorithm. Our findings indicated that METTL16 expression were predominantly negatively correlated with immunosuppressive cells in pan-cancer. For instance, METTL16 was negatively correlated with M2 macrophages in CESC, CHOL, KICH, KIRP, LAML, LGG, LIHC, PRAD, TGCH, and THCA; METTL16 was negatively correlated with regulatory T cells (Tregs) in BRCA, KIRC, LUAD, PAAD, SKCM, STAD, and THCA; and METTL16 was negatively correlated with Dendritic cells in BLCA, BRCA, CESC, DLBC, LUAD, LUSC, PAAD, UCEC, and UCS (Fig. 8A).

Macrophages are capable of participating in coordinated immunosuppression due to their status as the most prevalent cell type in the TME [61]. So, we concentrated on macrophages and discovered that METTL16 expression was positively correlated with M1 macrophage infiltration in COAD, ESCA, KIRC, SKCM, STAD, and TGCT (Fig. 8B). The METTL16 expression in M1 and M2 macrophages was investigated using the cancer single cell database from TISCH. In contrast to M2 macrophages, METTL16 was predominantly expressed in M1 macrophages, which included CLL, CRC, Glioma, NET, NHL, PAAD, PBMC, SKCM, and UVM (Fig. 8C). The expression of METTL16 in various immune cells in the above tumors is illustrated in Fig. 8D. It follows that METTL16 has the potential to serve as a marker for M1 macrophage infiltration in pan-cancer.

8. METTL16 expression correlates with therapeutic response and its target drugs

In addition, we assessed the impact of METTL16 expression on the efficacy of routine chemotherapeutic agents and immune checkpoint inhibitors in a clinical cancer cohort. We found that in colorectal cancer, responders of Irinotecan had lower levels of METTL16 expression, whereas in Melanoma or pan-cancer, patients who responded to Pembrolizumab had higher levels of METTL16 expression (Fig. 9A). Also, we conducted a search on RNAactDrug to identify drugs that are linked to the expression of METTL16 mRNA. In pan-cancer, Tanespimycin was the only drug with a positive correlation with METTL16 expression among the top 28 drugs with FDR < 0.05. The remaining drugs were negatively correlated with METTL16 expression, and Irinotecan and Topotecan were more sensitive in targeting METTL16 (Fig. 9B). The GI50 of pan-cancer cell lines treated with Irinotecan and Topotecan was compared using the COMPARE tool. The mean -log10(GI50) for Irinotecan was − 4.94, while the mean -log10(GI50) for Topotecan was − 7.51 (Fig. 9C). In order to further investigate the potential of these drugs to bind to the METTL16 protein, we performed molecular docking studies. Figure 9D illustrates the three-dimensional structures of the docking pockets of Irinotecan and Topotecan with the METTL16 protein, which have binding affinities of -10.6 kal/mol and − 8.7 kal/mol, respectively.

METTL16 is a m6A methyltransferase that, as its name implies, can catalyze the m6A modification of mRNA in the nucleus and cytoplasm. It can directly bind to translation initiation factors and ribosomal RNA to enhance the translation efficiency of proteins. A substantial amount of research reports also suggest that METTL16 is closely associated with cancer, and certain studies have discovered that m6A-related proteins play a significant role in tumor development. At present, METTL16 has been investigated in a variety of cancers, including colorectal cancer, soft tissue sarcoma, breast cancer, gastric cancer, hepatocellular carcinoma, pancreatic cancer, ovarian cancer, and endocrine tumors. However, there are fewer comparative analyses of METTL16 function in pan-cancer. For the first time, this paper explores the connection between METTL16 and pan-cancer. Furthermore, it offers a thorough overview of METTL16, including its clinical significance, multi-omics characterization, and function in tumor immunity, as well as the screening of potential targeted cancer drugs.

Initially, we discovered that the expression of METTL16 varied at the mRNA and protein levels in pan-cancers. The trends were essentially consistent, but there were also inconsistent or even opposing results. For example, in BRCA, the changes in METTL16 mRNA and protein were in opposite orientations. This may have been due to factors such as low mRNA translation efficiency or protein degradation barriers, which resulted in high mRNA or protein levels. Additionally, there was a correlation between the expression of METTL16 and the prognosis of cancers. The survival of patients was prolonged in ESCA, KIRC, PAAD, READ, UCEC, and THYM, whereas a poor prognosis was predicted by elevated expression of METTL16 in BRCA, LIHC, SARC, LUSC, and UVM.

METTL16 may regulate tumor progression by influencing DNA damage repair-related pathways (e.g., mTOR pathway status, chromatin modifier status, MYC/MYCN status, WNT pathway status, SWI/SNF complex status, HIPPO complex status) and cell stemness-related pathways (HIPPO complex status), according to functional analyses. In malignancy, DNA repair mechanisms are a significant factor in maintaining genetic stability. Based on pan-cancer analysis, METTL16 is characterized by a significant number of amplifications, mutations, and deep deletions at the genomic level. This genetic instability is closely associated with the prognosis of cancer. The AS events of METTL16 was substantially altered in pan-cancer at the gene transcription level, and this change also had a significant impact on the prognosis of cancer.

Moreover, METTL16 has a multitude of methylation modification functions, and its expression level is positively correlated with numerous methylation function-related proteins. In pan-cancer, methylation of the METTL16 promoter was also positively correlated with CTL and prognosis. Recent studies have emphasized the significance of immune cell infiltration in tumor prognosis; however, there has been relatively little research conducted on the role of METTL16 in cancer immunity. Methylation of METTL16 can be positively correlated with a variety of chemokines, receptors, MHC, and immunostimulators. Notably, the expression of METTL16 was increased in the Melanoma cell line B16 that was treated with IFN-β and in Melanoma patients who were effectively treated with the anti-PD-1 drug Pembrolizumab. METTL16 was capable of preventing the infiltration of immunosuppressive cells, including Tregs, Neutrophils, and Dendritic cells. Besides, METTL16 was positively correlated with the expression of CD80 and CD86, which are markers of M1 macrophages, and was more frequently expressed in M1 macrophages than in M2 macrophages. Therefore, METTL16 could be employed as a marker of M1 macrophages infiltration. Because we discovered that METTL16 has a dual impact on pan-cancer prognosis, it tends to be a protective gene in PAAD and a pro-carcinogenic gene in BRCA. We attempted to determine the cause from the perspective of TME and discovered that the infiltration of immune cells in PAAD was positively correlated with the expression of METTL16, which was higher in C3 and lower in C4 and C6. Furthermore, the expression of METTL16 in PAAD suppressed the infiltration of Tregs and Dendritic cells, while simultaneously inducing the infiltration of .CD8 + T cells and CD4 + memory cells. So, the immunomodulatory functionality of METTL16 in PAAD may be a significant contributing factor to the improved prognosis that PAAD patients may experience as a result of increased METTL16 expression. Nevertheless, the infiltration of immune cells reduced by METTL16 expression in BRCA, which included not only Tregs and Dendritic cells, but also M1 macrophages, CD8 + T cells, and activated CD4 + memory cells. In addition, we employed ROC Plotter to predict drugs targeting METTL16 that are effective against PAAD and BRCA based on the drugs screened in Fig. 9B. When Tanespimycin was used to promote the elevated expression of METTL16 in pancreatic cancer cell lines, the area under the curve (AUC) value could reach 0.864; when Foretinib and Navitoclax were used to inhibit the level of METTL16 in breast cancer cell lines, the AUC value was as high as 0.875 and 0.891, respectively (Fig. S4). This research implies that METTL16 has the capacity to influence the development and prognosis of cancer by regulating TME. Moreover, it has the prospective to be utilized in the development of new targeted medicines for cancer treatment.

In summary, in this study, we investigated the role and mechanism of METTL16 in pan-cancer and elaborated on METTL16 from the perspectives of expression differences, survival analysis, function analysis and immune infiltration. Secondly, the utilization of METTL16 in conjunction with immune cells showed that the expression of METTL16 in pan-cancer could influence immune infiltration and further illustrated that METTL16 could reprogramme the TME. Lastly, we conducted a screening of potential compounds that target METTL16 as a novel therapeutic approach. We believe that this study reveals its functional mechanism in cancer and provides promising therapeutic approaches for patients with poor treatment outcomes.

Although the study employs a variety of analytical methods and conducts a thorough examination of numerous databases to guarantee the precision of its findings, it is essential to acknowledge its limitations. Initially, the conclusions may be uncertain due to the insufficient sample size of certain cancers or controls. Second, this study mainly relied on bioinformatics analyses, so the conclusions obtained need to be further validated by in vivo and in vitro experiments. In order to further solidify and verify our findings, it is imperative that these problems be investigated and resolved in subsequent investigations.

In general, METTL16 plays a dual function in pan-cancer, and its ability to regulate the tumor microenvironment is one of the significant factors that influences the prognosis of patients. It has potential as a novel target in tumor therapy.

METTL16 Methyltransferase-like protein 16

Tregs regulatory T cells

m6A N6–methyladenosine

CTL cytotoxic T lymphocyte

TME tumor microenvironment

SAM S-adenosylmethionine

RBD RNA binding domain

VCR vertebrate conserved region

OS overall survival

DSS disease-specific survival

PFI progression-free interval

DDR DNA damage repair

DSB DNA double-strand break

HRR homologous recombination repair

NHEJ non-homologous end joining

MMR mismatch repair

AS alternative splicing

TCGA The Cancer Genome Atlas

GTEx Genotype-Tissue Expression

HPA Human Protein Atlas

GEPIA Gene Expression Profiling Interactive Analysis

UALCAN The University of Alabama at Birmingham Cancer data analysis Portal

TIDE Tumor Immune Dysfunction and Exclusion

HRD homologous recombination deficiency

CNA copy number alteration

TMB tumor mutation burden

MSI microsatellite instability

NCBI National Center for Biotechnology Information

PSI percent spliced-in

TISMO Tumor Immune Syngeneic Mouse

TISCH Tumor Immune Single-cell Hub

GI50 50% growth inhibition

NCI National Cancer Institute

PDB Protein Data Bank

AUC area under the curve

ROC receiver operating characteristic

GO Gene Ontology

ACC adrenocortical carcinoma

AEL acute erythroid leukemia

ALL acute lymphoblastic leukemia

AML acute myeloid leukemia

BLCA bladder urothelial carcinoma

BRCA breast invasive carcinoma

CESC cervical squamous cell carcinoma and endocervical adenocarcinoma

CHOL cholangiocarcinoma

CLL chronic lymphocytic leukemia

COAD colon adenocarcinoma

CRC colorectal cancer

DLBC lymphoid neoplasm diffuse large B-cell lymphoma

ESCA esophageal carcinoma

GBM glioblastoma multiforme

HNSC head and neck squamous cell carcinoma

KICH kidney chromophobe

KIRC kidney renal clear cell carcinoma

KIRP kidney renal papillary cell carcinoma

LAML acute myeloid leukemia

LGG brain lower-grade glioma

LIHC liver hepatocellular carcinoma

LUAD lung adenocarcinoma

LUSC lung squamous cell carcinoma

MESO mesothelioma

NET neuroendocrine tumor

NHL non-hodgkin lymphoma

NSCLC non-small cell lung cancer

OV ovarian serous cystadenocarcinoma

PAAD pancreatic adenocarcinoma

PBMC peripheral blood mononuclear cell

PCPG pheochromocytoma and paraganglioma

PRAD prostate adenocarcinoma

READ rectum adenocarcinoma

SARC sarcoma

SKCM skin cutaneous melanoma

STAD stomach adenocarcinoma

TGCT testicular germ cell tumors

THCA thyroid carcinoma

THYM thymoma

UCEC uterine corpus endometrial carcinoma

UCS uterine carcinosarcoma

UVM uveal melanoma

Ethics approval and consent to participate

Not applicable.

Consent for publication

All authors consent for publication.

Competing interests

All the authors declare that they have no conflicts of interest.

Funding

This work is supported by grants from the Yancheng Municipal Health Commission Medical Research Key Project (YK2023039) to Ji-Heng Cai.

Author Contribution

Cheng Zhang drafted the figures and wrote the manuscript. Chao Zhou collected the data. N.H.H., S.J.X. and K.F.Z. performed the statistical analysis. J.H.C. designed and supervised the study. All authors read and approved the submitted version of the manuscript.

Acknowledgements

Not applicable.

Availability of data and materials

The datasets used in the study are included in this article. Other data and materials are available from the corresponding author on reasonable request.

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No competing interests reported.

figureS1.jpg
Figure. S1 The IHC images of METTL16 in normal and tumor tissues extracted from the HPA.
figureS2.jpg
Figure. S2 The hazard ratios of METTL16 in pan-cancer. (A-C) The forest plots of METTL16 hazard ratios in OS, DSS, and PFI, respectively.
figureS3.jpg
Figure. S3 Correlations between METTL16 expression and immune subtype in 12 cancers. (A) BLCA, (B) ESCA, (C) HNSC, (D)KIRC, (E) LGG, (F) LIHC, (G) LUSC, (H) OV, (I)PRAD, (J) SARC, (K) STAD, (L) TGCT.
figureS4.jpg
Figure. S4 METTL16 can be used as a novel target for cancer therapy. (A) ROC curve predicts the efficacy of METTL16-targeting drug Tanespimycin for pancreatic cancer cell lines. (B, C) ROC curve predicts the efficacy of METTL16-targeting drugs Foretinib and Navitoclax for breast cancer cell lines.

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Prognostic and immunological analysis of METTL16 in pan-cancer

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Abstract

Background

Methods

Results

Conclusions

Figures

Background

Methods

Data acquisition

Expression and prognostic analysis of METTL16 in pan-cancer

The function analysis of METTL16

The correlation between METTL16 and epigenetic methylation

The effect of METTL16 on DNA repair and stemness in pan-cancer

The genomic alterations of METTL16 in pan-cancer

Clinical correlation analysis of METTL16 AS events

Impact of METTL16 on tumor immune infiltration and immunotherapy

Correlation of METTL16 expression with therapeutic response

Screening and molecular docking analysis of drugs targeting METTL16

Statistical analysis

Results

1. METTL16 is differentially expressed in pan-cancer and is correlated with patient prognosis

2. Involvement of METTL16 in methylation modification

3. METTL16 has an influence on DNA repair and stemness in tumor cells

4. METTL16 can cause genome instability in pan-cancer

5. Differential expression of METTL16 AS events affect cancer prognosis

6. METTL16 is involved in tumor immune infiltration and remodeling of TME

7. METTL16 is a potential marker of M1 macrophages infiltration

8. METTL16 expression correlates with therapeutic response and its target drugs

Discussion

Conclusions

Abbreviations

Declarations

Competing interests

Funding

Author Contribution

Acknowledgements

Availability of data and materials

References

Additional Declarations

Supplementary Files

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