RNA methylation patterns mediated by m 6 A regulators are involved in the regulation of immune microenvironment in hepatocellular carcinoma

doi:10.21203/rs.3.rs-2592778/v1

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RNA methylation patterns mediated by m 6 A regulators are involved in the regulation of immune microenvironment in hepatocellular carcinoma

https://doi.org/10.21203/rs.3.rs-2592778/v1

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Background: It has been reported that epigenetic regulation is emerging as a new regulatory pattern, especially for RNA N⁶-methyladenosine (m⁶A) modifications. It has been reported to play an important biological function in immunity. However, the role of m⁶A on the immune microenvironment in hepatocellular carcinoma (HCC) remains unclear. In this study, we systematically evaluated the RNA modification patterns mediated by 23 m⁶A modulators in HCC samples using the TCGA database.

Methods and results: The effects of m⁶A modification on the characteristics of immune microenvironment gene were investigated. Meanwhile, we characterized m⁶A phenotype-related immune genes. Our study further identified two distinct patterns of RNA modification mediated by 23 m⁶A modulators. They have different immune cell abundances, immune responses, and HLA genes.

Conclusion: In a word, our findings suggest that m⁶A modification plays a crucial role in regulating the immune microenvironment in HCC, providing a guiding significance in the selection of immunotherapy or target for treating HCC.

Hepatocellular carcinoma

RNA methylation

m6A regulators

Immune microenvironment

Hepatocellular carcinoma (HCC) is a fatal malignancies in the world, originating from oncogenic factors in primary hepatocytes [1]. It has been reported that HCC is the second leading reason for cancer-related death, and even the sixth common cancer worldwide [2]. As we known, HCC is a major health problem, and needs efficient therapeutic strategy to alleviate it [3]. Although multiple therapeutic approaches such as surgical and operative excision, radiotherapy and chemotherapy have been applied in the treatment of this disease, the treatment effect is not satisfactory, which leads a poor prognosis [4]. Recently, a promising therapeutic strategy named as immunotherapy is pointed, and proves to be effective against solid organ cancers including HCC under laboratory conditions [5, 6]. However, the well understanding of the changes in immune microenvironment during HCC is still urgent, which may contribute to identify novel and efficient Immunotherapeutic targets.

M⁶A modification is emerged as a new regulatory pattern in terms of epigenetics [6]. Increasing studies revealed that m⁶A modification sites widely occurred messenger RNA (mRNA) [7], which changes gene expression by regulating mRNAs that closely associated with different types of human cancers including HCC [8, 9]. It suggested that the m⁶A modification might also play a crucial regulatory role in the development and progression of human cancers. Along with the in-depth study, 23 regulatory genes related to m⁶A modification were known (8 Writers, 13 Readers and 2 Erasers) were identified thoroughly [9, 10]. More and more studies have demonstrated that these 23 m⁶A regulators closely participated in the cancer progression. For example, down-regulation of m⁶A reader FTO could enhance the stemness capacity in lung adenocarcinoma, and then and promoted tumor progression [11]. METTL3 was reported to enhance non-m⁶A translation of epigenetic factors and promotes tumorigenesis in various human cancers [12]. Recently, the immune microenvironment was confirmed to closely associated with cancer development [13]. More attentions have been focused on the role of m⁶A regulators in tumor immune microenvironment, and further determined their important regulatory function in cancer progression and also immunotherapy [14–16]. However, the impact of m6A regulators involved in regulation of tumor immune microenvironment in HCC has not been well studied, and our study aimed on it.

In the present study, we systematically evaluated the RNA modification patterns mediated by 23 m⁶A modulators in HCC samples, and found that the expression of m⁶A regulator could divide the disease into distinct biologically subtypes (subtype 1 and 2). Subtype 1 had higher levels of aDCs cells, iDCs, Tfh, etc. Subtype 2 has high levels of NK cells. Further, 177 DEGs in the blue module are closely related to m⁶A modification were identified. Moreover, we also screened out the top10 hub and found that ESR1 gene corresponded to the most drugs using drug gene interaction database, such as raloxifene and bexarotene. These findings suggested that raloxifene and bexarotene might be candidate drugs for treating HCC.

The landscape of m⁶A regulators

As previously reported, a total of 23 genes related to m⁶A modification (8 Writers, 13 Readers and 2 Erasers) were known [17]. Hg38 was selected as the reference genome for the analysis of chromosomal location distribution of three types of m⁶A modifier genes. String database was used to construct PPI network among m⁶A regulators. The gene expression data of HCC samples were obtained from TCGA database. Pearson method was used to analyze the correlation between the expression of 23 m⁶A regulators.

Differential expression analysis

The limma package in R was used to analyze the differential expression of m⁶A regulators between the integrated data of HCC samples and healthy controls. The threshold value was set as p < 0.05 and log₂ ^FC > = 1. T test in R was used to analyze differentially expressed genes (DEGs) in all genes among the finally confirmed m⁶A regulator-related subtypes in HCC, and the p < 0.05 of corrected Benjamini-Hochberg (FDR) was set as the threshold to obtain the DEGs among subtypes.

Correlation analysis of immune characteristics of m⁶A regulators

SSGSEA is used to estimate the number of specific infiltrating immune cells and the activity of a specific immune response, and it defines an enrichment score to represent the absolute enrichment of a gene set in each sample [18]. GSVA in R was used to realize SSGSEA, to evaluate the immune cell infiltration of 28 kinds of immune cells in different groups. The proportion of infiltrated immune cells was estimated using SSGSEA method. CIBERSORT (https://cibersort.stanford.edu/index.php) is used to characterize cell composition based on gene expression profiles of complex tissues [19]. CIBERSORT was combined with LM22 to estimate the proportion of 22 immune cell phenotypes among different groups.

The recognition of m⁶A modification related patterns

The unsupervised cluster analysis was performed to identify different patterns of m⁶A modification. Consensus clustering algorithm was used to evaluate the number and robustness of clusters. The different HCC subtypes were identified using R package "ConsensusClusterPlus". The gene sets c2.cp.kegg.v7.5.1.symbols” and “c5.go.v7.5.1.symbols were downloaded from MSigDB database to perform GSVA analysis, and then identified the functional differences of KEGG and GO among different subgroups.

Weighted gene co-expression network analysis and potential drug identification

The expression profiles of DEGs among subtypes were obtained by weighted gene co-expression network analysis (WGCNA) package in R to establish gene co-expression networks. By using a soft threshold power of β = 4, we applied a dynamic tree cutting algorithm to classify genes based on their expression patterns and merged gene modules, which included at least 30 genes. The module characteristic gene (ME) is calculated as a summary of all genes in the module. Cluster analysis was performed to combine these modules into two main modules (blue and turquoise). Pearson correlation coefficients were calculated between two modules and the clustering features of m⁶A regulators, and the most relevant modules were selected for further analysis. Gene salience (GS) was used as a correlation coefficient between transcriptome expression and modular traits. Module membership (MM) is defined by the correlation coefficient between ME and transcriptome data. The genes with GS > 0.20 and MM > 0.60 were selected as the candidate central genes for each module, and finally obtained the hub gene of the key module. The cytoHubba module in Cytoscape software was used to mine the top10 hub nodes in the network based on MCC algorithm, and DGIdb database was used to identify potential drugs based on hub node.

The landscape analysis of m⁶A regulators

Firstly, we generated the 23 m⁶A regulators from previously studies. From Fig. 1A, we could find that the three types of m⁶A regulators were widely distributed on multiple human chromosomes. Figure 1B showed that the interaction of these m⁶A regulators could be seen as a protein-protein network, and the relationship among them was very close. Figure 1C showed the expression of 23 m⁶A regulators between HCC patients and healthy controls by heat-map. By calculating the expression correlation of m⁶A regulators intersection in the HCC group and in all samples, we found that some of the m⁶A regulators also showed the stronger correlation in expression levels (Fig. 1D), suggesting that they might work together. Meanwhile, limma was used to screen the differentially expressed m⁶A regulators between HCC samples and healthy controls, and we found that most of m⁶A regulators showed significant differences (Fig. 1E). The results of box plots also showed the significant expression differences of 23 m⁶A regulators between HCC group and control groups (Fig. 1F).

M⁶A regulators were involved in immune regulation in HCC

Subsequently, we analyzed the correlation between the expression of 23 m⁶A regulators and the expression of invasive immune cells, immune response genomes and HLA genes. By performing SSGSEA analysis, we found that the infiltration ratio of some immune cells (Fig. 2A) and immune functions (Fig. 2B) were significantly different between the HCC group and the healthy group. For example, immune cells such as B cells, CD8 T cells, NK cells, T helper cells, and immune functions such as CCR, HLA, and inflammation showed differential infiltration proportions. The Cibersort results also showed the significant differences in the infiltration ratio of some immune cells (Fig. 2C) and immune functions (Fig. 2D) between the HCC group and the healthy control. Meanwhile, correlation analysis showed that m⁶A regulators were closely related to many immune cells and immune function (Supplementary Fig. 1A and B). For example, Treg cells were positively correlated with KIAA1429, suggesting that increased infiltrating Treg cells in HCC was closely related to KIAA1429 expression. Similarly, we analyzed HLA in HCC samples and explored the expression of HLA, and we found that there were 12 HLA genes that exhibited the expression differences (Supplementary Fig. 1C). There was also a correlation between the expression of HLA and m⁶A regulator (Supplementary Fig. 1D). Of which, HLA − F gene and LRPPRC m⁶A regulator showed a strong negative correlation, HLA − DOA and WTAP m⁶A regulator showed a positive correlation.

M⁶A regulator expression divide the disease into distinct biologically subtypes

In order to study the correlation patterns of m⁶A regulator in HCC, ConsensusClusterPlus was used to identify molecular subtypes based on the expression data of HCC and m⁶A regulator. by using "KM" clustering algorithm and distance “algorithm Euclidean”, we identified two different HCC subtypes including 228 HCC samples of subtype 1 and 145 HCC samples of subtype 2 (Fig. 3A). PCA clustering in the two subtypes based on 23 m⁶A regulator also exhibited two obvious categories (Fig. 3B), and the expression of 23 m⁶A regulators was significantly different between the two patterns (Fig. 3C). To further investigate the biological responses in the two m⁶A modification patterns, we compared their respective GO and KEGG pathways. and GSVA enrichment analysis was applied to assess the activation status of the biological pathways. The results indicated that some metabolic pathways and biological processes related to protein synthesis were significantly different among different subtypes (Fig. 3D and E).

The different immune characteristics between two subtypes

To identify the differences in immune microenvironment characteristics between different m⁶A regulator patterns, we evaluated infiltrating immune cells, immune response genomes, and the expression of HLA gene (Fig. 4A and B). We found that there were many immune cells exhibited significant difference in two different subtypes. Subtype 1 had higher levels of aDCs cells, iDCs, Tfh, etc. Subtype 2 has high levels of NK cells. In terms of immune-related functions, the immune function of subtype 2 was more active. In addition, high expression of HLA genes was observed in subtype 2 compared to that in subtype 1 (Fig. 4C). These results suggested that subtype-2 mediated active immune responses and subtype 1 led to mild immune responses in HCC.

The identification of hub genes by WGCNA

To further understand the molecular mechanism of m⁶A regulator mediated regulation in HCC, we identified the DEGs associated with m⁶A modification, and a total of 2356 DEGs were considered to be related to m⁶A modification phenotype (Fig. 5A). GO enrichment analysis revealed that these DEGs were significantly associated with response to xenobiotic stimulus and apical part of cell signaling (Fig. 5B). The soft threshold was set to 4 to perform WGCNA analysis (Fig. 5C and D), we identified gene-gene modules associated with different phenotypes and screened out two modules, with 668 genes in turquoise module and 661 genes in blue module (Fig. 5E). Moreover, the different modification patterns matched the related genes in their modules (Fig. 5F). Genes in the blue module are closely related to m⁶A modification pattern 1, the correlation is 0.53, and the correlation between MM and GS in the blue module is as high as 0.43, hence 177 genes with MM > 0.6, GS > 0.2 were selected as hub genes in blue module for subsequent analysis (Fig. 5G).

Potential treatment strategies

In order to better explore the potential therapeutic strategies for molecular subtypes of HCC associated with m⁶A modification, a PPI network was constructed based on 177 hub genes in the blue module based on String database (Fig. 6A). The cytoHubba plugin in cytoscape was used to mine the top10 hub nodes in the network using the MCC method (Fig. 6B, Table 1). In addition, the drug gene interaction database DGIdb (http://dgidb.org/) was used to identify potential drugs associated with m⁶A modifications in HCC based on 10 hub nodes (Fig. 6C). The results showed that 8 of the 10 hub genes could be matched to the corresponding drugs, among which the ESR1 gene corresponded to the most drugs, such as raloxifene and bexarotene. Our findings provided the novel therapeutic strategy for HCC.

Table 1

Top 10 in network turquoise_hub ranked by MCC method
Rank	Name	Score
1	ESR1	12
2	UGT2B7	11
3	UGT2B10	10
4	ABCG2	9
4	CYP2A6	9
4	PCK1	9
7	AR	8
7	SRC	8
7	UGT1A4	8
10	ACADL	7

HCC can be considered as a prototype of inflammation-derived disease caused by chronic liver injury [20, 21], and brought us a huge social and health burden. In the recent years, the immunotherapy has revolutionized the management of HCC, and improve the overall survival of HCC patients in comparison to traditional sorafenib singly, which lead to the approval of this regimen by FDA [22]. However, the overall survival of five years in HCC patients is still poor. Recently, the common immunotherapy is immune-checkpoint inhibitors, and it can obviously improve HCC, and even combination therapy has achieved the satisfactory curative effect [23]. Despite of these significant development in the HCC treatment, it is still necessary for identifying the more efficient drug targets for further improving the HCC treatment.

Increasing evidences revealed that m⁶A RNA methylation, regulated by 23 m⁶A regulators, participated in the pathogenesis different types of human malignancies including HCC [21]. On the other hand, the role of m⁶A regulators in HCC has also been determined [24]. For example, FTO feedback loop, regulated by CircGPR137B/miR-4739 axis was showed to significantly suppresses the tumorigenesis and metastasis of HCC [25]. METTL3, also regulates gene expression through a m⁶A-dependent manner and is was confirmed to contribute to the carcinogenesis, tumor progression, and drug resistance in HCC [26]. METTL16 has been reported to promote HCC progression through directly inhibiting the expression of RAB11B-AS1 by an m⁶A-dependent manner [27]. PA2G4 promoted the metastasis of HCC by stabilizing FYN mRNA in a YTHDF2-dependent manner [28]. In addition, the m⁶A-related long noncoding RNAs (lncRNAs) have also been demonstrated to affect the development and progression of HCC, and even the prognosis of HCC patients [29, 30]. Recently, more researches have paid the attentions on the role of m⁶A regulator on immune microenvironment in human cancer. However, little is known about its impact on the immune microenvironment in HCC. In this study, we found that most of m⁶A regulators showed significantly expression difference between HCC samples and heathy controls. The infiltration ratio of some immune cells and immune functions were significantly different between the HCC group and the healthy group. M⁶A regulator expression could divide the HCC samples into two distinct biologically subtypes. Subtype-2 mediated active immune responses and subtype 1 led to mild immune responses in HCC. Our findings revealed the potential role of m⁶A regulator in tumor immune microenvironment in HCC, and contributed to our understanding on HCC progression.

Then we identified top 10 DEGs that influenced by m⁶A regulator, and found that 8 of the 10 hub genes could be matched to the corresponding drugs, among which the ESR1 gene corresponded to the most drugs. These findings suggested that ESR1 might be a potential drug target for treating HCC. The important role of ESR1 in HCC has also been partially studied. Zhang et al., found that ESR1 could inhibit the occurrence and development of HCC by regulating the gene MMAA, an obesity and metabolism differential gene [31]. In addition, Hu et al., revealed that ESR1 also could promote HCC progression via transcriptionally regulating HS1BP3 [32]. These evidences all suggested that ESR1 was a potential therapeutic target in HCC. Our findings found that raloxifene and bexarotene could target ESR1, as the candidate drugs for treating HCC. Our study contributed us to understand the important regulatory role of m⁶A regulator in HCC progression, and even suggested two potential treatment drugs targeting ESR1, providing some guidance for clinical treatment in HCC.

In conclusion, our study identified two distinct patterns of RNA modification mediated by 23 m⁶A modulators, and screened out a series of patterns related hub genes associated with potential treatment drugs. Moreover, our findings provided the novel treatment strategies and even candidate drugs in HCC. However, the effect of raloxifene and bexarotene in HCC, as well as the potential mechanisms between these two drugs and ESR1 needed to be further investigated in the subsequent experiments.

Ethics approval and consent to participate

This study was approved by the Research Ethics Committee of Henan Provincial People’s Hospital, People’s Hospital of Zhengzhou University and was in line with the Helsinki Declaration.

Consent for publication

Not applicable.

Availability of data and materials

The datasets used and/or analyzed during the current study available from the corresponding author on reasonable request.

Competing interests

The authors declare they have no conflict of interest.

Funding

Not applicable.

Authors' contributions

YG and YL make the literature research; HZ, MZ help data analysis. This work was performed by YG. The manuscript was written by YG and edited by YL, HZ, MZ.

Acknowledgments

Not applicable.

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No competing interests reported.

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Version 1

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You are reading this latest preprint version

RNA methylation patterns mediated by m 6 A regulators are involved in the regulation of immune microenvironment in hepatocellular carcinoma

Status:

Version 1

Abstract

Figures

Background

Materials And Methods

The landscape of m⁶A regulators

Differential expression analysis

Correlation analysis of immune characteristics of m⁶A regulators

The recognition of m⁶A modification related patterns

Weighted gene co-expression network analysis and potential drug identification

Results

The landscape analysis of m⁶A regulators

M⁶A regulators were involved in immune regulation in HCC

M⁶A regulator expression divide the disease into distinct biologically subtypes

The different immune characteristics between two subtypes

The identification of hub genes by WGCNA

Potential treatment strategies

Discussion

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1

RNA methylation patterns mediated by m 6 A regulators are involved in the regulation of immune microenvironment in hepatocellular carcinoma

Status:

Version 1

Abstract

Figures

Background

Materials And Methods

The landscape of m6A regulators

Differential expression analysis

Correlation analysis of immune characteristics of m6A regulators

The recognition of m6A modification related patterns

Weighted gene co-expression network analysis and potential drug identification

Results

The landscape analysis of m6A regulators

M6A regulators were involved in immune regulation in HCC

M6A regulator expression divide the disease into distinct biologically subtypes

The different immune characteristics between two subtypes

The identification of hub genes by WGCNA

Potential treatment strategies

Discussion

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1

The landscape of m⁶A regulators

Correlation analysis of immune characteristics of m⁶A regulators

The recognition of m⁶A modification related patterns

The landscape analysis of m⁶A regulators

M⁶A regulators were involved in immune regulation in HCC

M⁶A regulator expression divide the disease into distinct biologically subtypes