Identification of the function, immunological role and m6A modification of MYSM1 in head and neck squamous cell carcinoma

doi:10.21203/rs.3.rs-1734482/v1

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Identification of the function, immunological role and m6A modification of MYSM1 in head and neck squamous cell carcinoma

https://doi.org/10.21203/rs.3.rs-1734482/v1

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Background:MYSM1 displays vital roles in hematopoiesis and lymphocyte development. However, the prognosis, immune infiltration and m6A modification role of MYSM1 in Head and Neck squamous carcinoma (HNSC) remains unexplored.

Methods: A comprehensive bioinformatics analysis was used to identify the expression profiles and prognosis of MYSM1 in pan-cancer through multiple databases. TIMER and CIBERSORT database were used to analyze the relationship between MYSM1 expression and immune infiltration. GSEA and R language was utilized to estimate the functional enrichment analysis of MYSM1 in HNSC. The TCGA-HNSC cohort was used to investigate the relationship between MYSM1 expression and m6A modification in HNSC.

Results: MYSM1 was differentially expressed in a variety of cancers including HNSC, and highly MYSM1 expression was associated with favorable overall survival (OS) and histological grade. CIBERSORT analysis showed that MYSM1 expression was significantly correlated with the infiltration of multiple immune cells. And the specific mechanism of MYSM1 regulating immune infiltration may be involved in the PD-L1 expression and PD-1 checkpoint pathway in cancer and TNF signaling pathway. TCGA-HNSC cohort analysis revealed that MYSM1 expression was related to 9 m6A modifier genes.

Conclusion: Our comprehensive study found that MYSM1 can serve as a prognostic biomarker for determining prognosis, and is correlated with tumor immune infiltrating cells and m6A modification.

Biological sciences/Immunology

Health sciences/Oncology/Cancer/Head and neck cancer

MYSM1

head and neck squamous cell carcinoma

m6A modification

bioinformatics analysis

immune infiltration

Head and neck squamous cell carcinoma (HNSC) ranks as the sixth most frequently lethal maglignancy and claims around 350,000 deaths every year^[1]. Despite advances has been made in screening, diagnosis and multiple treatments of head and neck diseases, the prognosis of patients with advanced HNSC is still poor. The molecular mechanism of the progression of HNSC is extremely complicated. Recent studies have shown that patients with advanced diseases treated with anti-programmed cell death (PD)-1 therapy, only a subset of metastatic patients responded^[2–4]. Therefore, an improved understanding of the molecular targets for early diagnosis and treatment of HNSC is urgently required.

Myb-like, SWIRM, and MPN domains 1 (MYSM1) is a nuclear chromatin-interacting protein that specifically deubiquitinates histone H2AK119 through transcriptional regulation^[5]. MYSM1 plays a critical role in immune response, lymphocytes, blood cells production and hematopoietic stem cells function^[6]. Patients with MYSM1 mutuation displays bone marrow failure syndromes^[7]. Accompanying evidence indicates that MYSM1-deficient mice show lymphopenia, complex immune phenotypes and hematopoietic stem cell (HSC) exhaustion, ulteriorly confirming the essential role of MYSM1 in hematopoiesis and lymphocyte development^{[8, 9]}. In human melanomas, higher MYSM1 was observed when compared with normal melanocytes, and inhibition the expression of MYSM1 impaired cell proliferation and survival of melanoma cells^[10]. Recent data identified MYSM1 as an essential regulator in immunity and other aspects of mammalian physiology. MYSM1-mediated regulation of gene expression indicates that MYSM1 as the signal transduction pathway of innate immunity in the cytosol^[11]. However, whether MYSM1 has the same cytosolic functions in other cells and regulates other signaling pathways remains unclear. MYSM1 is identified as a positive regulator of post-thymic CD8⁺ T-cell maintenance and development, showing its new role in the immune system^[12]. Intriguingly, the splenic B cells of MYSM1^f1/f1 mice have altered in vitro stimulation, including impaired survival. In addition, MYSM1^f1/f1 mice have normal antigen-specific antibody titres and serum antibody levels, indicating a negative role of MYSM1 in cell differentiation^[13]. MYSM1 has been shown to remove polyubiquitin chains from TRAF3, TRAF6 and RIP2, resulting in the inactivation of NOD2 and TLR signal pathway^[14]. MYSM1^−/− macrophages have been reported to have increased the production of pro-inflammatory cytokines, cell proliferation, induced apoptosis and enhanced melanoma tumor growth^[15]. The precise role of MYSM1 in immune cell infiltration and epigenetic landscape of different cell types requires further investigation.

The tumor microenvironment (TME) plays a critical role in tumorigenesis, metastasis, therapeutic response to immunotherapy and occurrence of cancer^[16]. In recent years, the development of immunotherapy to treat solid tumors within tumor immune microenvironment (TIME) arises, which improves the prognosis of cancer patients. Some immune-checkpoint inhibitors, anti-PD1, anti-PD-L1, as well as anti-CTLA-4 monoclonal antibodies, enhance anti-tumor immune responses and show favorable prognostic outcomes in patients^[17]. Besides, the interaction between immune microenvironment components and tumor cells have been confirmed as the possible immunotherapy response of hepatocellular carcinoma, head and neck squamous cell carcinoma, lung cancer and bladder cancer^{[18, 19]}. However, immunotherapy for tumors still has many problems unsolved, especially in terms of predicting immunotherapy biomarkers and identifying new effective therapeutic targets.

N6-methyladenosine (m6A) methylation is the most abundant chemical modification in the 3’untranslated region of mRNA^[20]. m6A RNA methylation has been recognized as a pivotal factor in T cell homeostasis^[21]. However, knowledge regarding the constitution of m6A regulators in TME and tumor-infiltrating lymphocytes (TILs) has not been clearly elucidated in HNSC, especially the relationship among MYSM1 and m6A modification.

The present study analyzed the MYSM1 expression in different cancers based on TCGA and public database. Using Kaplan-Meier plotter, UALCAN and TIMER database to evaluate the role of MYSM1 in prognosis and the immune status. We found that the MYSM1 expression was abnormally upregulated and higher MYSM1 expression correlated with favorable overall survival in HNSC. The MYSM1 expression exhibited strong correlations with the immune cell infiltration status. Our analysis provides a deep understanding of the functions of MYSM1, and emphasizes its relationship between m6A modification and tumor immunity, which provides a theoretical basis for further research to improve HNSC treatment strategies.

Difference validation of MYSM1 expression in pan-cancer

In order to uncover the role of MYSM1 in cancers, we investigated the expression of MYSM1 in tumors and normal samples of various cancer types via the Oncomine and TIMER databases. As indicated in Fig. 1A, compared with the normal samples, the expression level of MYSM1 was upregulated in brain and CNS cancer, colorectal cancer, gastric cancer and other cancer, and downregulated in breast cancer, lymphoma, and myeloma in the Oncomine database. Likewise, in the TIMER database, the expression of MYSM1 was elevated in CHOL, HNSC and HNSC HPV-positive, LIHC, LUAD and STAD, and decreased in BRCA, KICH, KIRP and THCA (Fig. 1B). To further evaluate the expression of MYSM1 in HNSC, we used TCGA-HNSC data and GEO dataset for analysis and found that MYSM1 expression was significantly increased in tumor tissues (Fig. 1C, D). Besides, to uncover the role of MYSM1 in cancer, we compared the expression of MYSM1 protein between tumor tissues and normal tissues through the Clinical Proteomic Tumor Analysis Consortium (CPTAC) database. MYSM1 expression was upregulated in BRAC and KIRC, however, there was no consistence observed in HNSC samples (Fig. 1E, F). We also used HPA database to confirm the above data analysis, and IHC staining indicated that MYSM1 staining was weaker in normal tissues, while stronger levels of MYSM1 were observed in HNSC tissues (Fig. 1G). The above results indicated that MYSM1 has a potential carcinogenic effect on the progression of HNSC.

Prognostic feature of MYSM1 in pan-cancer

A previous study has shown that loss of MYSM1 strongly inhibits oncogenes activity and represses cellular senescence in human and mice^{[22, 23]}, but the function of MYSM1 in cancers is not well studied. By means of data mining in the K-M plotter and UALCAN database, we investigated whether MYSM1 expression was associated with prognosis in patients. The high expression of MYSM1 was associated with favorable prognosis in Head-neck squamous cell carcinoma, Esophageal Squamous Cell Carcinoma, Bladder Carcinoma, Breast cancer, Lung adenocarcinoma, Rectum adenocarcinoma and poor prognosis in Liver hepatocellular carcinoma and Sarcoma (Fig. 2). UALCAN analysis indicated that the increased MYSM1 expression was remarkably associated with favorable OS in HNSC and KIRC, which was consistent with the results of Kaplan-Meier plotter analysis (Fig. 3A,B). Besides, we found higher expression of MYSM1 was associated with poor prognosis in LGG and UCS (Fig. 3C,D). To widely explore the relationship of MYSM1 expression and prognosis, we also analysed the change of RFS, DSS and DMFS in the MYSM1 high- and low-groups divided by the median in pan-cancer (Supplementary Fig. 1).

Relationship between MYSM1 expression and progression of HNSC

The HNSC patients with varying MYSM1 expression levels showed distinct clinical and pathological features in the TCGA cohort (Fig. 4A). Meanwhile, MYSM1 mRNA expression in HNSC samples was significantly correlated with advanced tumor stage, tumor N stage and tumor grade (Fig. 4B-D). We integrated clinical and pathological data to investigate the relationship between MYSM1 expression and overall survival in HNSC (Table 1). Similar results were observed that high expression of MYSM1 was statistically associated with stromal score and immune score (Fig. 4E). Of note, the expression of MYSM1 negatively correlated with ESTIMATE score (Fig. 4F), indicating that MYSM1 was positively connected with tumor purity of HNSC. In addition, univariate and multivariate cox regression indicated that gender, M1 and N2 stage were independent factors of prognosis (Fig. 4G).

Table 1

Correlation of MYSM1 mRNA expression with clinicopathological features in the TCGA-HNSC cohort.
		MYSM1 expression
	levels	Low (%)	High (%)	P
Age				0.203
	> 60	81(32.7%)	44(17.7%)
	<=60	70(28.2%)	53(21.4%)
Gender				0.952
	male	110(44.4%)	71(28.6%)
	female	41(16.5%)	26(10.5%)
Grade				0.580
	I	16(6.5%)	12(4.8%)
	II	89(35.9%)	50(20.2%)
	III	40(16.1%)	27(10.9%)
	unknow	6(2.4%)	7(2.8%)
Stage				0.033
	I	7(2.8%)	9(3.6%)
	II	21(8.5%)	16(6.5%)
	III	26(10.5%)	11(4.4%)
	IV	81(32.7%)	34(13.7%)
	unknow	16(6.5%)	18(7.3%)
AJCC_T				0.207
	T0	0(0.0%)	1(0.4%)
	T1	13(5.2%)	13(5.2%)
	T2	39(15.7%)	24(9.7%)
	T3	37(14.9%)	15(6.0%)
	T4	48(19.4%)	29(11.7%)
	unknow	14(5.6%)	15(6.0%)
AJCC_M				0.367
	M0	55(22.2%)	39(15.7%)
	M1	0(0.0%)	1(0.4%)
	unknow	96(38.7%)	57(23.3%)
AJCC_N				0.177
	N0	50(20.2%)	35(14.1%)
	N1	18(7.3%)	15(6.0%)
	N2	57(23.0%)	23(9.3%)
	N3	2(0.8%)	3(1.2%)
	unknow	24(9.7%)	21(8.5%)
Bold values indicate P < 0.05.

Association of MYSM1 Expression With immune infiltration level

MYSM1 is known to participate in the signal transduction pathway of innate immunity in the cytosol, and the above results support a prognostic role of MYSM1 in HNSC. The function of MYSM1 and its impact on TME were not fully discovered. Hence, it would be meaningful to assess whether MYSM1 expression was correlated with immune infiltration level for HNSC. As shown in Fig. 5A, MYSM1 expression was correlated with B cells, CD8⁺ T cells, CD4⁺ T cells, dendritic cells, macrophages and neutrophils in HNSC, HNSC-HPVpos and HNSC-HPVneg. To further investigate the correlation between MYSM1 and various subsets of infiltrating immune cells in HNSC. We analyzed the correlations between MYSM1 expression and immune marker genes, which included B cells, CD8⁺ T, T cells (general), tumor-associated macrophages (TAMs), monocytes, M1/M2 macrophages, NK cells, neutrophils and DCs in HNSC (Table 2). After adjustments for tumor purity in TIMER database, MYSM1 expression was significantly correlated with 42 out of 57 immune cell markers in HNSC. Most notably, MYSM1 expression was significantly correlated with markers of CD8⁺ T cells and Natural killer cells in HNSC (Fig. 5B). It was reported that a large number of CD8⁺ T cells in the TME were associated with a good prognosis, and Natural killer cells had spontaneous lethality against tumor cells^{[24, 25]}. Hence, these results verified our speculation that MYSM1 expression in HNSC correlates with immune markers in different manners, which suggests that MYSM1 plays a pivotal role in tumor immune escape and explains the difference in survival. Besides, we found that MYSM1 CNV has a significant correlation with the infiltration level of each immune subset (Fig. 5C). In addition, correlation between MYSM1 and tumor immune cells were examined. MYSM1 significantly correlated with the infiltration levels of B cells, CD8⁺ T cells, T cells follicular helper, T cells regulatory, activated NK cells and Macrophages M2 in HNSC (Fig. 5D) and other cancer types (Supplementary Fig. 2). We also tried to explore the differential expression of 22 tumor-infiltrating immune cells between high- and low-MYSM1 expression groups in HNSC. The results showed that the expression of T cells follicular helper, activated NK cells, Monocytes and Macrophages M2 are different between the two groups (Fig. 5E), indicating that MYSM1 might influence the carcinogenesis of cancer through regulating various components of the TME infiltration.

Table 2

Correlation between MYSM1 and markers of various immune cells in the TIMER database
Description	Gene markers		None Purity
			Cor	P		Cor		P
CD8 + T cell	CD8A		0.115	*		0.13		*
	CD8B		0.068	0.117		0.073		0.104
T cell (general)	CD3D		0	0.991		0.011		0.8
	CD3E		0.114	*		0.134		*
	CD2		0.097	*		0.11		0.015
B cell	CD19		0.158	**		0.172		**
	CD79A		0.181	***		0.198		***
Monocyte	CD86		0.188	***		0.217		***
	CD115(CSF1R)		0.189	***		0.213		***
TAM	CCL2		0.207	***		0.22		***
	CD68		0.226	***		0.241		***
	IL10		0.24	***		0.271		***
M1 Macrophage	INOS(NOS2)		0.27	***		0.256		***
	IRF5		0.187	***		0.201		***
	COX2(PTGS2)		0.261	***		0.259		***
M2 Macrophage	CD163		0.238	***		0.267		***
	VSIG4		0.167	**		0.181		***
	MS4A4A		0.148	**		0.163		**
Neutrophils	CD66b(CEACAM8)		0.028	0.52		0.043		0.339
	CD111b(ITGAM)		0.308	***		0.329		***
	CCR7		0.208	***		0.229		***
Natural killer cell	KIR2DL1		0.023	0.592		0.041		0.366
	KIR2DL3		0.017	0.697		0.014		0.762
	KIR2DL4		0.053	0.228		0.086		0.559
	KIR3DL1		0.112	0.0102		0.118		*
	KIR3DL2		0.134	*		0.138		*
	KIR3DL3		0.037	0.403		0.046		0.305
	KIR2DS4		0.002	0.968		0.023		0.612
Dendritic cell	HLA-DPB1		0.052	0.239		0.07		0.12
	HLA-DQB1		0.081	0.0649		0.093		0.0392
	HLA-DRA		0.146	**		0.168		**
	HLA-DPA1		0.149	**		0.165		**
	BDCA-1(CD1C)		0.172	***		0.183		***
	BDCA-4(NRP1)		0.374	***		0.388		***
	CD11c(ITGAX)		0.292	***		0.34		***
Th1	T-bet(TBX21)		0.114	*		0.126		*
	STAT4		0.238	***		0.25		***
	STAT1		0.214	***		0.228		***
	IFN-γ(IFNG)		0.01	0.817		0.024		0.589
	TNF-α(TNF)		0.185	***		0.213		***
Th2	GATA3		0.137	*		0.146		*
	STAT6		0.49	***		0.478		***
	STAT5A		0.199	***		0.205		***
	IL13		0.099	0.0243		0.117		*
Tfh	BCL6		0.469	***		0.457		***
	IL21		0.29	***		0.279		***
Th17	STAT3		0.587	***		0.588		***
	IL17A		0.157	**		0.166		**
Treg	FOXP3		0.287	***		0.311		***
	CCR8		0.471	***		0.494		***
	STAT5B		0.507	***		0.505		***
	TGFβ(TGFB1)		0.157	**		0.179		***
T cell exhaustion	PD-1(PDCD1) CTLA4	0.085			0.0514		0.102		0.0243
		0.129			*		0.148		**
	LAG3	-0.013			0.766		-0.005		0.912
	TIM-3(HAVCR2)	0.158			**		0.182		***
	GZMB	-0.005			0.903		0.011		0.81
P < 0.01; P < 0.001; **P < 0.0001.

Functional annotations and pathway enrichment analysis

To further clarify the biological significance of MYSM1 in HNSC, the LinkedOmics database was applied to evaluate its correlation with other genes at RNA level (Fig. 6A). The heatmaps show the top 50 genes which significantly correlated with MYSM1, respectively (Fig. 6B,C). Then we performed GO and KEGG enrichment analysis to examine the molecular mechanism of MYSM1 for HNSC. As shown in Fig. 6D, MYSM1 co-expression are mainly involved in neutrophil activation involved in immune response, regulation of innate immune response, cell-substrate junction, cell-cell junction, and cell adhesion molecule binding. The KEGG enrichment analysis included Human papillomavirus infection, TNF signaling pathway, P53 signaling pathway and PD-L1 expression and PD-1 checkpoint pathway in cancer, and these pathways were correlated with MYSM1 (Fig. 6E). Additionally, STRING database was used to construct a network of MYSM1 and its co-expression genes in this study. Functional enrichment analyses of the top 11 genes were selected from the PPI network and the results are visualized by MCODE plugins (Fig. 6F).

To further invertigate the potential mechanism of MYSM1 in HNSC, the TCGA cohorts RNA-seq data were implemented by GSEA. According to the median of MYSM1 expression, the sample were divided into MYSM1 high- and low-groups. Ruling out GO and KEGG terms with P < 0.05, the terms in the high MYSM1 expression group were significantly enriched in the cell cycle, ubiquitin mediated proteolysis, oocyte meiosis, endocytosis, neurotrophin signaling and spliceosome pathways (Fig. 7). KAT2B, UBA52, UBB, UBC, PSMD6/13, PSMC1, RPS27A and SMARCA2/4 were included in the network of which genes positively associated with MYSM1 constructed by STRING (Fig. 6F). Based on these results, we deduced that MYSM1 could regulate ubiquitin mediated proteolysis and spliceosome pathways in HNSC and play pivotal roles during tumorigenesis.

MYSM1 expression is associated with m6A RNA methylated regulators in HNSC

The m6A modification plays vital roles in cancer occurrence and progression since its discovery. However, the functional dynamic of m6A modification in regulation of HNSC development, and whether MYSM1 expression is related to m6A modification remain largely unknown. We used the TCGA-HNSC dataset to investigate the correlation between MYSM1 expression and 20 key m6A RNA methylation regulators. The results showed that MYSM1 expression was significantly correlated with 9 m6A regulators in HNSC (Fig. 8A,B). In addition, we divided TCGA-HNSC patients into two groups based on MYSM1 expression, and tried to detect the differential expression of 20 m6A methylation-related regulators. The result showed that the expression of HNRNPA2B1, METTL14, RBM14, VIRMA, YTHDC1, YTHDC2, YTHDF3, ZC3H13, METTL3, RBM15B, WTAP, RBMX, YTHDF1, and YTHDF2 were increased in the high MYSM1 expression group (Fig. 8C). Therefore, the above results indicate MYSM1 is closely related with m6A methylation in HNSC. Further, we tried to use TargetScan database to analyze and predict MYSM1 target miRNAs. As indicated in Fig. 8D, a total of 9 target miRNAs, including has-miR-30e-5p, has-miR-135b-5p, has-miR-30a-5p, has-miR-30b-5p, has-miR-30c-5p, has-miR-135a-5p, has-miR-30d-5p, has-miR-218-5p and has-miR-323a-3p are predicted. In addition, we analyze the correlation between MYSM1 expression and target miRNAs, and correlation analysis showed that has-miR-30e-5p and has-miR-135b-5p target miRNAs positively correlated with MYSM1 (Fig. 8E). TargetScan database predicts the potential binding site of MYSM1 to the target miRNAs (Fig. 8F).

In the present study, we conducted a comprehensive bioinformatics analysis to identify MYSM1 as a potential predictive biomarker for the prognosis of HNSC. MYSM1 was positively correlated with immune cells infiltration of CD8 + T cells, Th1 cells, DCs, Macrophage and Treg cells. And the specific mechanism of MYSM1 regulating immune infiltration may be involved in the PD-L1 expression and PD-1 checkpoint pathway in cancer and TNF signaling pathway. In addition, we found MYSM1 expression was correlated with m6A RNA methylation.

MYSM1, one of the histone H2A deubiquitination enzymes, plays a pivotal role in the development and functions of various cells. Over the past decade, researchers have provided MYSM1 as a critical transcriptional regulator of immune cell development and hematopoiesis. The recently discovered role of MYSM1 are focused on its regulation of signal transduction in innate immunity and a few cancers. However, except for recent insightful discovers, functions and mechanisms of MYSM1 beyond hematopoiesis and immunity remain poorly clarified yet. This study is the first to explore MYSM1 expression levels by comprehensive bioinformatics analysis, and to explore the prognostic value, effects on immune cells, and m6A methylation regulators in HNSC prognosis. We obtained the gene expression profiles of MYSM1 and clinical data from Oncomine database. We observed the variations of MYSM1 expression in different cancer types, and high expression of MYSM1 correlates with favorable prognosis in HNSC and BRCA. Interestingly, the expression of MYSM1 was highly expressed in metastatic SKCM compared to SKCM, indicating that MYSM1 may be used as a predictor for tumor metastasis. Our results also showed that high levels of MYSM1 expression associated with high risk of recurrence in patient survival. Functional enrichment analysis co-regulatory proteins with MYSM1 in the PPI network, and data from online database identify the hallmark pathways of HNSC carcinogenesis-related.

The tumor-infiltrating immune cells play a pivotal role in tumor progression^[26]. Due to the complexity of immune cells activity and tumor heterogeneity. The main methods for analyzing tumor-infiltrating immune cells are immunohistochemistry and flow cytometry^[27]. However, the evaluation of immune cells by these methods are vary depending on the cell types, number of tumor tissue and the observer. The bioinformatics analysis applied to gene expression profile of tumors provides an alternative option for evaluating immune cell status in tumor tissues^[28]. Despite the treatment and prognosis prediction have been improved in HNSC, it remains with high mortality rates and limitation of immunotherapy. Due to the poor mortality of HNSC, it is necessary to explore prognostic biomarkers for patients in order to choose appropriate treatment-strategy, including immunotherapy. In this study, we established an integration analysis to quantity the tumor immune milieu in HNSC, and the outcome of our study indicated that MYSM1 expression was significantly correlated with immune cell markers and immune cell infiltration levels in different manners, which proved that MYSM1 played a pivotal role in tumor immune escape and explained the difference in survival. Besides, this difference may be ascribe to the different enrichment patterns of MYSM1 in tumor microenvironment (TME). TME is composed of heterogeneous populations of cells, including malignant and non-malignant cells, which may support tumor cell proliferation, invasion and metastasis through extensive crosstalk^[29]. Intra-tumor immune landscape is a key factor affecting patient survival. To improve the efficiency of immunotherapy, it is firstly to determine immune-associated prognostic biomarkers. According to our results, higher expression of MYSM1 in HNSC predicted a better survival. However, the association between MYSM1 expression in tissues and patients overall survival need to be further studied. Previous study indicated that MYSM1^−/− macrophages increased the production of pro-inflammatory cytokines, cell proliferation, induced apoptosis and enhanced melanoma tumor growth^[15]. Consistent with the above result, we found that macrophages are the most principal infiltrating immune cells in HNSC. Further analysis such as signal-cell sequencing and the cellular and molecular mechanism need to be further clarified. It was reported that TNF family Fas signaling pathway could promote terminal differentiation of CD8⁺ T and CD4⁺ T cells, while non-apoptotic Fas induce tumor cell proliferation and impair the efficacy of T cell adoptive immunotherapy^[30]. PD-1 was reported to be significantly correlated with better OS and DFS in Ren et al.’s study^[31]. MYSM1 is involved in the TNF signaling pathway and PD-L1 expression and PD-1 checkpoint pathway in cancer. Therefore, our study provides insights into understanding the potential role of MYSM1 in tumor immunology and its use as a cancer biomarker.

Recent years, m6A modification has been recognized as a trigger of cancer biology and becoming the focus of research. However, the involvement of m6A regulators in progression of HNSC remains unexplored. In this study, we reported for the first time that MYSM1 expression was correlated with 9 of m6A RNA regulators in HNSC, including HNRNPA2B1, METTL14, IGF2BP2, RBM14, VIRMA, YTHDC1, YTHDC2, YTHDF3 and ZC3H13. We also found that the levels of HNRNPA2B1, METTL14, RBM14, VIRMA, YTHDC1, YTHDC2, YTHDF3, ZC3H13, METTL3, RBM15B, WTAP, RBMX, YTHDF1, and YTHDF2 increased in MYSM1 high expression group. However, there are still some limitations in our study. Clinical analysis with large samples are required to verify our predictive value of MYSM1. The molecular mechanisms and cellular functions of MYSM1 in HNSC are warrant for subsequent experimental verification work. As a biomarker candidate, MYSM1 should be evaluated in the context of HNSC immunotherapy. Further analysis is required to investigate the detailed mechanism between MYSM1 expression and carcinogenesis of HNSC and other carcinomas.

In summary, by using integrated bioinformatics analysis, this is the first comprehensive study to identify MYSM1 expression with survival, immune cell infiltration levels and m6A modification in HNSC. Our study provide a new and promising insight for subsequent research to elucidate the molecular mechanisms of HNSC progression. MYSM1 may be considered as an effective biomarker for predicting prognosis and treatment of HNSC.

Data acquisition

RNA-sequencing data together with the clinical information were obtained from the TCGA. In this study, cases with the loss of MYSM1 expression, ≤ 30 days of survival and those with no survival information were eliminated. GSE138206 were retrieved from the GEO database to varify the transcription level of MYSM1. For further verification, the protein expression profiles were obtained from the CPTAC database. We used TCGA-HNSC data to analyze the correlation between the expression of MYSM1 and m6A-related regulators. Other data were obtained from public databases as described in the corresponding analyses.

Analysis of MYSM1 expression in pan-cancer

The differential expression of MYSM1 gene in various types of cancers was analyzed in the Oncomine database (https://www.oncomine.org/)^[32] and the TIMER database (https://cistrome.shinyapps.io/timer/)^[33]. The Oncomine data filtering threshold were determined as: P-value < 0.05, fold change of 1.5, and gene ranking of all. TIMER employs pathological examination-validated statistical method to estimate the tumor-infiltrating immune cells from gene expression profiles.

Analysis of Tumor-Infiltrating Immune Cells

The TIMER is a database providing a comprehensive analysis of tumor immune cell infiltrates across multiple cancers, which was used to validate the correlation between MYSM1 expression and tumor-infiltrating immune cells (B cells, CD8⁺ T cells, CD4⁺ T cells, macrophages, neutrophils, and dendritic cells). P-value < 0.05 was regarded as statistically significant. In addition, the relative proportions of 22 tumor-infiltrating immune cells in the low- and high-risk group were determined by CIBERSORT package^[34]. Besides, stromal and immune scores were measured by ESTIMATE algorithm^[35], respectively. MYSM1 expression and gene markers of tumor-infiltrating immune cells correlation modules were explored from the website of R&D systems. The gene markers included T cells (general), B cells, CD8⁺ T cells, monocytes, TAMs, M1/M2 macrophages, natural killer (NK) cells, neutrophils, dendritic cells (DCs), T-helper 2(Th2) cells, T-follicular helper T (Tfh) cells, T-helper 1 (Th1) cells, Tregs, T-helper 17 (Th17) cells and exhausted T cells.

Survival analysis

Kaplan-Meier plotter and UALCAN^[36] were applied to evaluate the prognostic value of MYSM1 expression in patients overall survival. Univariate cox analyses were applied to estimate the relationship of clinical characteristics with overall survival. The hazard ratio (HR) with 95% confidence intervals (CI) and log-rank P-value were calculated automatically.

LinkedOmics Analysis

The LinkedOmics database (http://www.linkedomics.org/login. php) is a web-based platform that can provide comprehensive multi-omics data analysis tools for the TCGA database^[37]. The Pearson correlation coefficient was used for statistical analysis of MYSM1 co-expression and displayed in the form of volcano map and heat map. The rank criterion was an FDR < 0.05.

Gene Set Enrichment Analysis

Functional enrichment analysis of MYSM1 in HNSC was applied by Gene Set Enrichment Analysis (GSEA). Based on the most relevant genes of MYSM1 and a characteristic gene list, the cluster genes were uploaded to DAVID (v6.8) for annotation, visualization and integrated analysis. The gene symbols were selected as identifier and Homo sapiens was selected as species. We performed enrichment analysis (GO term and KEGG pathway enrichment) by R package ClusterProfiler to explore the potential molecular mechanisms between low- and high-MYSM1 expression groups. The top ten results in ascending order were displayed in this study. The cut-off criteria was P < 0.05.

Statistical Analysis

The Student’s t-test and one-way analysis of variance were used to compare continuous variables. Pearson's chi-square test was employed for comparison of categorical clinicopathologic variables. The correlation of gene expression was analyzed by Spearman’s correlation analysis. P < 0.05 was considered as statistically significant.

DATA AVAILABILITY STATEMENT

The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding author.

AUTHOR CONTRIBUTIONS

NW conceived the project and wrote the manuscript. X-YH and J-SC participated in the data analysis. NW and X-YH participated in the language editing and all authors reviewed the manuscript.

FUNDING

The study was supported by Guangdong Basic and Applied Basic Research Foundation (2021A1515011026), Project of Department of Education of Guangdong Province (021KTSCX126) and Natural Science Foundation of Jiaying University (2019KJZ01).

CONFLICT OF INTERESTS

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

Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin (2018) 68 (6): 394–424. doi: 10.3322/caac.21492
Ferris RL, Blumenschein G, Fayette J, Guigay J, Colevas AD, Licitra L. et al. Nivolumab for recurrent squamous-cell carcinoma of the head and neck. N Engl J Med (2016) 375 (19):1856-67. doi: 10.1056/NEJMoa1602252
Chow LQM, Haddad R, Gupta S, Mahipal A, Mehra R, Tahara M. et al. Antitumor activity of pembrolizumab in biomarker-unselected patients with recurrent and/or metastatic head and neck squamous cell carcinoma: results from the phase Ib KEYNOTE-012 expansion cohort. J Clin Oncol(2016) 34 (32):3838-45. doi: 10.1200/JCO.2016.68.1478
Seiwert TY, Burtness B, Mehra R, Weiss J, Berger R, Eder JP. et al. Safety and clinical activity of pembrolizumab for treatment of recurrent or metastatic squamous cell carcinoma of the head and neck (KEYNOTE-012): an open-label, multicentre, phase 1b trial. Lancet Oncol (2016) 17 (7) :956-65. doi: 10.1016/S1470-2045(16)30066-3
Yoneyama M, Tochio N, Umehara T, Koshiba S, Inoue M, Yabuki T, et al. Structural and functional differences of SWIRM domain subtypes. J Mol Biol (2007) 369 (1): 222-38. doi: 10.1016/j.jmb
Nijnik A, Clare S, Hale C, Raisen C, McIntyre RE, Yusa K, et al. The critical role of histone H2A-deubiquitinase Mysm1 in hematopoiesis and lymphocyte differentiation. Blood (2012) 119 (6): 1370-9. doi: 10.1182/blood-2011-05-352666
Alsultan A, Shamseldin HE, Osman ME, Aljabri M, Alkuraya FS. MYSM1 is mutated in a family with transient transfusion-dependent anemia, mild thrombocytopenia, and low NK- and B-cell counts. Blood (2013) 122 (23): 3844-5. doi: 10.1182/blood-2013-09-527127
Guen TL, Touzot F, Andre-Schmutz I, Lagresle-Peyrou C, France B, Kermasson L, et al. An in vivo genetic reversion highlights the crucial role of Myb-Like, SWIRM, and MPN domains 1 (MYSM1) in human hematopoiesis and lymphocyte differentiation. J Allergy Clin Immunol (2015) 136 (6): 1619-1626.e5. doi: 10.1016/j.jaci.2015.06.008
Bahrami E, Witzel M, Racek T, Puchalka J, Hollizeck S, Greif-Kohistani N, et al. Myb-like, SWIRM, and MPN domains 1 (MYSM1) deficiency: Genotoxic stress-associated bone marrow failure and developmental aberrations. J Allergy Clin Immunol (2017) 140 (4): 1112-9. doi: 10.1016/j.jaci.2016.10.053
Wilms C, Kroeger CM, Hainzl AV, Banik I, Bruno C, Krikki I, et al. MYSM1/2A-DUB is an epigenetic regulator in human melanoma and contributes to tumor cell growth. Oncotarget (2017) (40):67287-99. doi:10.18632/oncotarget.18617
Panda S, Nilsson JA, Gekara NO. Deubiquitinase MYSM1 Regulates Innate Immunity through Inactivation of TRAF3 and TRAF6 Complexes. Immunity (2015) 43 (4): 647-59. doi: 10.1016/j.immuni.2015.09.010
Forster M, Boora RK, Petrov JC, Fodil N, Albanese I, Kim J, et al. A role for the histone H2A deubiquitinase MYSM1 in maintenance of CD8(+) T cells. Immunology (2017) 151 (1): 110-21. doi: 10.1111/imm.12710
Jiang XX, Liu Y, Li H, Gao Y, Mu R, Guo J, et al. MYSM1/miR-150/FLT3 inhibits B1a cell proliferation. Oncotarget (2016) 7 (42): 68086-96. doi: 10.18632/oncotarget.11738
Panda S, Gekara NO. The deubiquitinase MYSM1 dampens NOD2-mediated inflammation and tissue damage by inactivating the RIP2 complex. Nat Commun (2018) 9 (1): 4654. doi: 10.1038/s41467-018-07016-0
Zhao X, Huang XH, Dong XH, Wang YH, Yang HX, Wang Y, et al. Deubiquitinase Mysm1 regulates macrophage survival and polarization. Mol Biol Rep (2018) 45, (6): 2393-401. doi: 10.1007/s11033-018-4405-3
Zeng D, Zhou R, Yu Y, Luo Y, Zhang J, Sun H, et al. Gene expression profiles for a prognostic immunoscore in gastric cancer. Br J Surg (2018) 105(10): 1338–48. doi: 10.1002/bjs.10871
Hu B, Yang XB, Sang XT. Liver graft rejection following immune checkpoint inhibitors treatment: a review. Med Oncol (2019) 36, (11): 94. doi: 10.1007/s12032-019-1316-7
Cariani E, Missale G. Immune landscape of hepatocellular carcinoma microenvironment: Implications for prognosis and therapeutic applications. Liver Int (2019) 39 (9): 1608-21. doi: 10.1111/liv.14192
Chen YP, Zhang Y, Lv JW, Li YQ, Wang YQ, He QM, et al. Genomic Analysis of Tumor Microenvironment Immune Types across 14 Solid Cancer Types: Immunotherapeutic Implications. Theranostics (2017) 7 (14): 3585-94. doi: 10.7150/thno.21471
Yue Y, Liu J, Cui X, Cao J, Luo G, Zhang Z, et al. VIRMA mediates preferential m(6)A mRNA methylation in 3’UTR and near stop codon and associates with alternative polyadenylation. Cell Discov (2018); 4:10. doi: 10.1038/s41421-018-0019-0
Zhang C, Fu J, Zhou Y. A Review in Research Progress Concerning m6A Methylation and Immunoregulation. Front Immunol (2019) 10:922. doi: 10.3389/fimmu.2019.00922
Tian MF, Huang YQ, Song YT, Li W, Zhao PY, Liu WY, et al. MYSM1 Suppresses Cellular Senescence and the Aging Process to Prolong Lifespan.Adv Sci (Weinh) (2020) 7 (22):2001950. doi: 10.1002/advs.202001950
Lin YH, Wang HC, Fiore A, Förster M, Tung LZ, Belle JI, et al. Loss of MYSM1 inhibits the oncogenic activity of cMYC in B cell lymphoma.J Cell Mol Med (2021) 25 (14):7089-94. doi: 10.1111/jcmm.16554
Lee N, Zakka LR, Mihm MC, Jr, Schatton T. Tumour-infiltrating lymphocytes in melanoma prognosis and cancer immunotherapy. Pathology (2016) 48 (2): 177-87. doi: 10.1016/j.pathol.2015.12.006
Konjevic GM, Vuletic AM, Mirjacic Martinovic KM, Larsen AK, Jurisic VB. The role of cytokines in the regulation of NK cells in the tumor environment. Cytokine (2019) 117:30-40. doi: 10.1016/j.cyto.2019.02.001
Binnewies M, Roberts EW, Kersten K, Chan V, Fearon DF, Merad M, et al. Understanding the tumor immune microenvironment (TIME) for effective therapy. Nat Med (2018) 24 (5): 541-50. doi: 10.1038/s41591-018-0014-x
Senbabaoglu Y, Gejman RS, Winer AG, Liu M, Van Allen EM, de Velasco G, et al. Tumor immune microenvironment characterization in clear cell renal cell carcinoma identifies prognostic and immunotherapeutically relevant messenger RNA signatures. Genome Biol (2017) 18 (1): 46. doi: 10.1186/s13059-017-1180-8
Finotello F, Trajanoski Z. Quantifying tumor-infiltrating immune cells from transcriptomics data. Cancer immunol Immunother (2018) 67 (7): 1031-40. doi: 10.1007/s00262-018-2150-z
Zuo S, Wei M, Wang S, Dong J, Wei j. Pan-Cancer Analysis of Immune Cell Infiltration Identifies a Prognostic Immune-Cell Characteristic Score (ICCS) in Lung Adenocarcinoma. Front Immunol (2020) 11: 1218. doi: 10.3389/fimmu.2020.01218
Yi F, Frazzette N, Cruz AC, Klebanoff CA, Siegel RM. Beyond cell death: new functions for TNF family cytokines in autoimmunity and tumor immunotherapy. Trends Mol Med(2018) 24 (7):642–53. doi: 10.1016/j.molmed.2018.05.004
Ren X, Wu H, Lu J, Zhang Y, Luo Y, Xu Q, et al. PD1 protein expression in tumor infiltrated lymphocytes rather than PDL1 in tumor cells predicts survival in triple-negative breast cancer. Cancer Biol Ther (2018) 19 (5):373–80. doi: 10.1080/15384047.2018.1423919
Rhodes DR, Yu J, Shanker K, Deshpande N, Varambally R, Ghosh D, et al. ONCOMINE: a cancer microarray database and integrated data-mining platform. Neoplasia (2004) 6 (1): 1-6.doi: 10.1016/s1476-5586(04)80047-2
Li T, Fan J, Wang B, Traugh N, Chen Q, Liu JS, et al. TIMER: A Web Server for Comprehensive Analysis of Tumor-Infiltrating Immune Cells. Cancer Res (2017) 77 (21): e108-e110.doi: 10.1158/0008-5472.CAN-17-0307
Newman AM, Liu CL, Green MR, Gentles AJ, Feng W, Xu Y, et al. Robust enumeration of cell subsets from tissue expression profiles. Nat Methods (2015) 12,(5):453–7. doi: 10.1038/nmeth.3337
Yoshihara K, Shahmoradgoli M, Martinez E, Vegesna R, Kim H, Torres Garcia W, et al. Inferring tumour purity and stromal and immune cell admixture from expression data. Nat Commun (2013) 4:2612. doi: 10.1038/ncomms3612
Chandrashekar DS, Bashel B, Balasubramanya SA, Creighton CJ, Ponce-Rodriguez I, Chakravarthi BV, et al. UALCAN: A Portal for Facilitating Tumor Subgroup Gene Expression and Survival Analyses. Neoplasia (2017) 19 (8): 649-58. doi: 10.1016/j.neo.2017.05.002
Vasaikar S, Straub P, Wang J, Zhang B. LinkedOmics: analyzing multi-omics data within and across 32 cancer types. Nucleic Acids Res (2018) 46(D1):D956-D963. doi: 10.1093/nar/gkx1090

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Identification of the function, immunological role and m6A modification of MYSM1 in head and neck squamous cell carcinoma

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Abstract

Figures

Introduction

Results

Difference validation of MYSM1 expression in pan-cancer

Prognostic feature of MYSM1 in pan-cancer

Relationship between MYSM1 expression and progression of HNSC

Association of MYSM1 Expression With immune infiltration level

Functional annotations and pathway enrichment analysis

MYSM1 expression is associated with m6A RNA methylated regulators in HNSC

Discussion

Materials And Methods

Data acquisition

Analysis of MYSM1 expression in pan-cancer

Analysis of Tumor-Infiltrating Immune Cells

Survival analysis

LinkedOmics Analysis

Gene Set Enrichment Analysis

Statistical Analysis

Declarations

References

Additional Declarations

Supplementary Files

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