Exploration of biological significance of m6A-related genes in Wilms tumor

doi:10.21203/rs.3.rs-2433205/v1

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Exploration of biological significance of m6A-related genes in Wilms tumor

https://doi.org/10.21203/rs.3.rs-2433205/v1

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Background: Wilms tumor (WT) is an embryonal abdominal malignant tumor which is a common renal malignant tumor in children. N6-methyladenosine (m6A) RNA methylation can dynamically regulate the development of tumors. However, m6A-related genes in WT have not yet been identified and researched.
Methods: In this study, the RNA-seq data of TARGET-WT were extracted from the UCSC Xena for bioinformatics analysis.
Results: 296 candidate hub genes were obtained by intersecting 3 gene sets (8610 gene modules with significantly associated m6A RNA methylation score, 7774 differentially expressed genes (DEGs) between 121 WT patients and 6 control samples, 763 DEGs between high and low score groups of m6A RNA methylation). Survival analysis of the 296 genes yielded 4 hub genes (ADGRG2, CPD, CTHRC1, and LRTM2) associated with WT prognosis. Subsequently, a prediction model with the 4 hub genes was developed and the model had good predictive power for the WT prognosis. In addition, 7 immune gene sets were obtained by intersecting 2 gene sets (18 significant difference immune gene sets between the WT group and control group, 10 immune gene sets related to the hub genes). Among them, APC_co_stimulation, CCR, Macrophages, Parainflammation, Treg, and Type_II_IFN_Reponse were low expressed in the WT, and only Th1_cells were highly expressed in the WT. APC_co_stimulation, CCR, Macrophages, Parainflammation, Treg, and Type_II_IFN_Reponse are negatively correlated with LRTM2, Th1_cells are positively correlated with ADGRG2, CCR is negatively correlated with CPD, CCR is positively correlated with CTHRC1. Finally, qRT-PCR results showed that the expression levels of the 4 hub genes were up-regulated in different WT cell lines compared with 293T cell lines.
Conclusion: In conclusion, ADGRG2, CPD, CTHRC1, and LRTM2 may be m6A-related genes in WT, which have potential prognostic value and play an immunoregulation role in WT.

Wilms tumor

m6A RNA methylation

target genes

immune microenvironment

bioinformatics analysis

Wilms tumor (WT) is the most common kidney tumor in children, affecting one in 10,000 children, accounting for about 90% of all pediatric kidney tumors, and approximately 5% of all childhood cancers [1]. In the United States, the median age of onset of WT is 38 months, and the main clinical symptom is an asymptomatic abdominal mass [2, 3]. According to Children's Oncology Group (COG) staging systems, WT can be divided into stages I-IV from low to high based on the degree of tumor invasion, and stage V refers to the rare bilateral WT [4]. Although the WT survival rate increased from 30–80% with surgery in addition to radiotherapy and chemotherapy, the child has to endure this kind of long, multimodal and toxic regimen [5]. Therefore, it is still necessary to find better treatments. Understanding the pathogenesis of WT is conducive to the development of new therapies, but the pathogenesis of WT is still unknown. Mutations of WT1, CTNNB1, SIX1, SIX2, FGFR1, CHD4, and other genes have been reported in WT [6]. About 5% of WT patients have an underlying susceptibility gene syndrome, and more than 50 such syndromes have been described [7]. Although some WT susceptibility genes have been identified, there is strong evidence that many more may exist [8]. Therefore, the study of WT-related genes is of great value in determining biomarkers of WT and discovering new therapeutic targets, which is conducive to the development of personalized therapy.

In addition to the mutation of the gene itself can cause tumors, abnormal expression of normal genes can also cause tumors, which involves epigenetics. In recent years, epigenetic inheritance has been studied intensively in WT [9]. Epigenetics means that the DNA sequence does not change, but the gene expression has changed heritably [10]. RNA modification is a key process in epigenetic regulation of post-transcriptional gene expression, the most common of which is RNA methylation. Methylation of the N6 position of adenine, called N6-methyladenine ( m6A ), is the most common internal modification of eukaryotic mRNAs [11]. M6A can be installed and removed by specific enzymes, classified as “ writer ”, “ eraser ” and “ reader ” [12]. In general, abnormal expression of m6A disrupts the normal RNA modification process, directly interfering with mRNA processing, transport, translation and degradation, leading to tumorigenesis and progression [13]. In childhood cancer research, the overexpression of m6A RNA methylation has been found in glioma, hepatoblastoma, nephroblastoma, neuroblastoma, osteosarcoma, medulloblastoma, retinoblastoma and acute lymphoblastic leukemia [14]. The target of m6A modification may be the key to promote the treatment of pediatric cancer. In addition, in other tumors, five m6A regulatory genes were found to be new therapeutic targets for neuroblastoma [15]. Therefore, paying attention to the overexpression of m6A RNA methylation and m6A-related genes in tumor tissues may help to further treat tumors.

The occurrence of tumors is associated with immune surveillance disability [16]. Immunosuppressive microenvironment seems to be prevalent in WT. A animal study found that anti-tumor executive lymphocytes (such as CD4 + T cells, CD8 + T cells, NK cells, and NKT cells) in the WT high-risk group responded in an immunosuppressed state compared to the low-risk subgroup [17]. Studies have shown that patients with nephroblastoma with high CD8 + T lymphocyte infiltration have better clinical prognosis [18]. These findings imply that high-risk WT patients are in an immunosuppressive tumor microenvironment. Therefore, it seems feasible to treat tumors by activating the immune activity of WT microenvironment. However, immunotherapy for most solid cancers in children has not shown an objective anti-tumor effect [19]. Lack of knowledge of the immune infiltration landscape of these tumors may be one of the reasons for the failure of immunotherapy.

Our study aimed to investigate the potential prognostic value of m6A-related genes and their relationship to the immune microenvironment in WT, To search for potential target genes for treatment of WT. In this study, RNA-seq data and clinical information from 121 WT samples and 6 normal samples were obtained from the UCSC Xena website. We analyzed the relationship between m6A RNA methylation and the occurrence and development of WT, screened m6A RNA methylation-related genes. Based on these genes, to perform WT clinical correlation analysis, immune infiltration correlation analysis, Construction of TF-miRNA-mRNA network, and prediction of targeted drugs. Finally, qRT-PCR was used to verify the expression of m6A-related genes in different WT cell lines. This study will help to deepen the understanding of the relationship between m6A and nephroblastoma, and provide some evidence for the future detection of the role of m6A RNA methylation in nephroblastoma.

Data collection

The RNA-seq data and clinical data of TARGET-WT cohort were extracted from the UCSC xena (https://xenabrowser.net/datapages/), which included 121 WT patients and 6 normal samples. M6A RNA methylation regulators including writers (METTL3, METTL14, RBM15, WTAP, ZC3H13, KIAA1429), readers (HNRNPC, YTHDC1, YTHDC2, YTHDF1, YTHDF2), and erasers (ALKBH5, FTO) were collected from previous research [20].

GSVA analysis

M6A RNA methylation score was estimated using RNA-seq data of TARGET-WT patients through GSVA package (version 3.13), and the correlation between different clinicopathological features (Stage, Race, Gender, Histologic_Classification) and m6A score was analyzed.

Identification of the module with significantly associated m6A RNA methylation score based on WGCNA analysis

Based on the median value of all gene expression matrices, the 121 WT samples were divided into high and low score groups of m6A RNA methylation, and the two groups were used as trait data to construct the co-expression network using WGCNA software. The samples initially were clustered to check the overall correlation of all the samples. And the outlier samples were excluded to ensure the accuracy of the analysis. We then determined the soft threshold of the data to assure that the genes interaction was the maximum extent conformed the scale-free distribution. The adjacency and similarity between genes were calculated, and we gained the cluster dendrogram. The modules were segmented via dynamic tree cutting algorithm, and the minModuleSize was 300. We evaluated the correlation between each module and high and low score groups of m6A RNA methylation, and screened out the module genes with the highest absolute value.

Identification of differentially expressed genes (DEGs)

The DEGs between 121 WT patients and 6 control samples were screened using the limma (version 3.42.2) package in the R platform, and genes with |log₂FC| >0.5, P < 0.05 were regarded as DEGs. The DEGs were exhibited through volcano plots via the ggplot2 package (version 3.3.5). The heatmaps of DEGs were performed using the pheatmap package (version 1.0.12). In addition, the DEGs between 61 high score sample and 60 low score sample of m6A RNA methylation were screened using the limma package (|log₂FC|>0.5, P < 0.05). The volcano plots and heatmaps of DEGs were plotted using the R package. Moreover, the Venn diagram was constructed with the model genes of significantly associated with m6A RNA methylation score, the DEGs between WT patients and control samples, and the DEGs between high and low score groups of m6A RNA methylation. And the intersection DEGs_m6A were obtained for further analysis.

Identification and analysis of hub genes

According to the DEGs_m6A median expression levels, the 121 WT samples were divided into high and low expression groups. The survival analysis was performed for both groups and the hub genes were filtered. Subsequently, the diagnostic models were constructed using the hub genes. The receiver operator characteristic (ROC) curve calculated the AUC area to investigate the effective of the diagnostic models by the pROC (version 1.17.0.1). In addition, according to the median expression levels of hub genes, 121 WT samples were divided into high and low expression groups, and all genes were analyzed by GSEA analysis. Furthermore, the boxplot was used to display the expression level of the hub genes in different clinical features (Gender, Stage, Race, and Histologic_Classification).

Immune cell infiltration landscape

The single sample gene set enrichment analysis (ssGSEA) algorithm was used to evaluate the immune infiltration degrees based on 28 immune related gene sets which included immune cell type, immune-related functions and pathways. First, the GSVA package was used to assess the relative expression between 121 WT and 6 normal samples. Then, the heat map and box plots of immune cells were plotted. Moreover, the correlation between hub genes and 28 immune gene sets were calculated by the Spearman method. The heat map and correlation scatter figure was plotted using the ggplot2 package.

Construction of TF-mRNA-miRNA regulatory network

The TFs that regulate hub genes were retrieval using the NetworkAnalyst (https://www.networkanalyst.ca/) database. The hub genes were then predicted by miRWalk (http://mirwalk.umm.uniheidelberg.de/) database, and we set parameters Score was 0.95, the Position was 3UTR, and miRDB was 1. Finally, the TF-mRNA-miRNA regulatory network was constructed by Cytoscape package (version 3.6.1) .

Potential therapeutic drugs prediction

The Comparative Toxicogenomics Database (CTD) included many precise data of interspecies chemical gene/protein interaction, the relationship of chemical and disease, and the relationship of genes and compounds. It contributed to understanding the molecular mechanisms underlying potentially variable susceptibility and environmental influences on disease. The potential therapeutic drugs of hub genes were predicted using the CTD.

Validation of the gene expression level by qRT-PCR

Twelve cultured cells line with a density of 70% − 90% were divided into four groups, among which 293T was the human embryonic kidney cell group and G-401, SK-NEP-1, and WT-CLS1 were the nephroblastoma groups. And the qRT-PCR was used to verify the expression level of key genes. First, the total RNA was extracted using the Trizol reagent provided by Ambion company. Then, we used SureScript-First-strand-cDNA-synthesis-kit First-Strand cDNA Synthesis Kit from the Servicebio company for reverse transcription reaction in S1000™ Thermal Cycler PCR instrument. The qRT-PCR was performed using the 2xUniversal Blue SYBR Green qPCR Master Mix kit by CFX96 Connect real-time quantitative fluorescence PCR instrument. Primer sequences were shown in Table 1. GAPDH was used as an internal reference gene. The qRT-PCR reactions were as follows: 95℃ pre denaturation for 1 min, and then 40 cycles of 95℃ denaturation for 20s, 55℃ annealing for 20s, and 72℃ extension for 30s. Additionally, the relative mRNA expression was calculated according to the 2^{− △△Ct} method. Eventually, the Graphpad Prism 5 was used to plot the boxplots of expression levels and calculate the P values in the four cultured cells line groups.

Table 1

Quantitative real-time PCR primers.
Gene name	Forward primer	Reverse primer
ADGRG2	GAGGCGGTATCTTTGTTGTGGA	CAGTGTGGTGGAGTTAGTGGAG
CPD	AGGATTGTGATTGTCCCTTCTC	TTCCACTGTCTGTACTGGCTTG
CTHRC1	TGCTGTCAGCGTTGGTATTTC	CATTTCAGGGCTTCCTTGGTC
LRTM2	CAGGTCGGGGATAACCCCT	CCCTCGGTAGGAGAACCACT
GAPDH	ACAACTTTGGTATCGTGGAAGG	GCCATCACGCCACAGTTTC
A B

The associated between m6A RNA methylation with WT development

The m6A RNA methylation score was calculated by GSVA package and the correlation between different clinicopathological features (Stage, Race, Gender, Histologic_Classification) and m6A score was analyzed. The m6A RNA methylation score was significantly different at Stage and Histologic_Classification. Specifically, the m6A score was higher in Stage I/II and FHWT (Fig. 1A, B).

Identification of the module with significantly associated m6A RNA methylation score based on WGCNA analysis

The WGCNA was used to identify the module genes with significantly associated m6A RNA methylation score. Sample clustering results show that TARGET-50-CAAAAM-01A and TARGET-50-PAJNAV-01A were the abnormal outlier sample, and we eliminated this sample (Additional file:Figure S1). The sample dendrogram and trait heatmap were exhibited (Additional file:Figure S2). The soft threshold value = 4 was identified as the optimal soft threshold. When the ordinate scale-free fit index, signed R2 approached the threshold value of 0.86 (red line), the network was close to scale-free distribution and mean connectivity was close to 0 (Additional file:Figure S3). A total of 7 modules were obtained (Additional file:Figure S4). Then the correlation between modules and high and low score groups of m6A RNA methylation was evaluated. The results showed that the MEturquoise module had the most significant correlation with the m6A RNA methylation score (Cor = 0.46, P = 2e-07) (Additional file:Figure S5). Finally, we regarded MEturquoise as a key module with 8610 genes for subsequent analysis.

Identification of DEGs

In total, 7774 DEGs were retrieved between WT patients and normal samples, of which were 4338 upregulated genes and 3436 downregulated genes (Additional file:Figure S6). The heat map of Top50 DEGs was shown (Fig. 2). In addition, 763 DEGs were retrieved between high score and low score sample that 399 genes were upregulated and 364 genes were downregulated (Additional file:Figure S7). The heat map of Top50 DEGs was shown (Fig. 3). Then 296 DEGs_m6A were obtained by overlapping the 8610 MEturquoise module genes, 7774 DEGs of WT and control samples, and 763 DEGs of high and low score groups (Fig. 4).

Identification and analysis of hub gene

The survival analysis results showed that 4 genes (ADGRG2, CPD, CTHRC1, and LRTM2), which were regarded as hub genes, were significantly correlated in the high and low expression groups (P < 0.05) (Fig. 5A-D). The AUC area was 1 that indicated constructed logistic regression diagnosis model was effective (Fig. 6). Based on the median expression levels of the 4 hub genes, 121 WT patients were divided into two groups, and GSEA results indicated the 4 hub genes were enriched in ATP biosynthetic process, acute inflammatory response, cytochrome complex, alditol: NADP + 1-oxidoreductase activity, Inositol phosphate metabolism pathway, etc (Fig. 7). Furthermore, the expression levels of CPD and CTHRC1 had significant differences in stage (Fig. 8).

Immune cell infiltration landscape

The immune cell infiltration analysis was performed and the heat map exhibited the contents of 28 immune gene sets in the normal and WT groups (Fig. 9). There were 18 immune gene sets (APC_co_inhibition, APC_co_stimulation, B_cells, CCR, Cytolytic_activity, DCs, Inflammation.promoting, Macrophages, MHC_class_I, Parainflammation, pDCs, Tfh, Th1_cells, Th2_cells, TIL, Treg, Type_I_IFN_Reponse, and Type_II_IFN_Reponse) that showed significant difference between normal and WT groups (P < 0.05) (Fig. 10). Then the correlation coefficient and P value between hub genes and 28 immune gene sets were calculated (Fig. 11), and the 10 significantly correlated relationship of hub genes and immune genes were screened (P < 0.05, |cor| > 0.3). Finally, 7 immune gene set (APC_co_stimulation, CCR, Macrophages, Parainflammation, Th1_cell, Treg, and Type_II_IFN_Reponse) were obtained by overlapping the 18 significant difference immune gene sets and 10 immune gene sets of |cor| > 0.3 (Fig. 12). APC_co_stimulation, CCR, Macrophages, Parainflammation, Treg, and Type_II_IFN_Reponse are negatively correlated with LRTM2, Th1_cells are positively correlated with ADGRG2, CCR is negatively correlated with CPD, CCR is positively correlated with CTHRC1 (Fig. 13).

TF-mRNA-miRNA network

The TF-mRNA-miRNA regulatory network was constructed. The 39 TFs that regulate hub genes were first predicted by NetworkAnalyst. The miRWalk predicted 107 miRNA that targeted with hub genes. Then, the regulatory network was constructed by the 4 hub genes, 39 TFs, and 107 miRNA (Additional file:Figure S8). The network indicated CPD had the greatest number of interactions with TF and miRNA, and CPD was modulated by HMG20A, ZNF501, GATA4, hsa-miR-15b-5p, hsa-miR-370-5p, and hsa-miR-152-3p. CTHRC1 was only modulated by TFs, including KDM5B, SAP30, ZBTB26, etc. ADGRG2 was only modulated by hsa-miR-589-5p, hsa-miR-34c-5p, and hsa-miR-519e-5p.

Potential therapeutic drugs prediction

The potential therapeutic drugs for WT were predicted by the 4 hub genes (Additional file:Table S1). The results showed the ADGRG2 predicted 82 therapeutic drugs, including Benzo(a)pyrene, Aflatoxin B1, and Tetrachlorodibenzodioxin, etc. The CPD predicted 91 therapeutic drugs, including bisphenol A, Testosterone, and Tetrachlorodibenzodioxin, etc. The CTHRC1 predicted 81 therapeutic drugs, including Valproic Acid, 4-(5-benzo(1,3)dioxol-5-yl-4-pyridin-2-yl-1H-imidazol-2-yl)benzamide, Benzo(a)pyrene, and dorsomorphin, etc. The LRTM2 predicted 35 therapeutic drugs, including Aflatoxin B1, Benzo(a)pyrene, and bisphenol A, etc.

Validation of the gene expression level by qRT-PCR

In order to further verify the expression level of the 4 hub genes (ADGRG2, CPD, CTHRC1, and LRTM2), we used qRT-PCR to compare gene expression levels in the four cells line. The qRT-PCR results showed that the expression level of four genes in 293T cells line were all lower, while the expression level of ADGRG2 were higher in WT-CLS1 cell line, and the expression levels of CTHRC1 were higher in G-401 cell line and WT-CLS1 cell line (Fig. 14).

At present, the pathogenesis of WT is still unclear, and the related epigenetic mechanism remains to be explored. As a model of post-transcriptional gene expression regulation, m6A RNA methylation regulates gene expression by affecting multiple aspects of mRNA metabolism, including mRNA pre-processing, nuclear export, decay and translation [21]. Therefore, the effect of m6 A on gene expression is extensive. In other tumor studies, it was found that m6A in peripheral blood of patients with gastric cancer increased with the progression and metastasis of gastric cancer, but decreased significantly after surgery, suggesting that m6A level in peripheral blood is a promising noninvasive diagnostic biomarker for gastric cancer patients [22]. In the study of m6A RNA methylation in glioma, it was found that high expression of m6A was associated with poor prognosis and tumor grade [23]. In the study of urological malignancies, high levels of m6A RNA methylation in mRNA were found to promote prostate cancer progression by regulating subcellular protein localization, and patients with high m6A RNA methylation had poor survival benefits [24]. Our study found that m6A is up-regulated in nephroblastoma, and high expression of m6A is associated with poor prognosis and is associated with the grade of nephroblastoma, because the expression of m6A in grade III and IV is stronger than that in grade I and II. These findings suggest that abnormal methylation of m6A may show certain diagnostic biomarkers and prognostic value in nephroblastoma. In addition, m6A is reversible, and reversing high m6A expression may contribute to cancer treatment [25]. However, the basic question of what factors cause m6A deposition remains unanswered [26], which means that overexpression of m6A RNA methylation is observed in WT tumor patients. However, what factors cause m6A deposition remains unclear and needs further study.

In recent years, more and more studies have confirmed that m6A-related genes are closely related to malignant tumors. For example, ZC3H13, CBLL1, ELAVL1 and YTHDF1 are differentially expressed in lung cancer tissues, which is of great significance for the prediction and treatment of lung cancer [27]. Nine m6A-related genes were identified in glioma. The mRNA levels of these genes were highly correlated with the clinicopathological features of glioma and may be involved in the progression of glioma [28]. In the study of head and neck squamous cell carcinoma, m6A-related gene changes were found to be significantly correlated with tumor grade and tumor stage [29]. Although the abnormal expression of m6A-related genes is involved in tumorigenesis in many solid tumors, the prognostic value of m6A-related genes in nephroblastoma and its correlation with clinicopathological features still need further study. We screened four highly expressed m6A-related genes ( ADGRG2, CPD, CTHRC1, LRTM2 ) in nephroblastoma, and constructed an effective diagnostic model based on these genes. In addition, we used qRT-PCR to verify the expression of m6A-related genes in different WT cell lines and normal 293T cell lines. The results showed that the expression of these four genes in normal 293T cell lines was lower than that in different WT cell lines. It is suggested that these four m6A-related genes can be used as biomarkers for the diagnosis of WT and potential therapeutic targets. Although our study focused on m6A-related genes, we found in many results of our studies that the DEGs between WT tissues and normal tissues partially overlapped with another study. A study by Li et al. compared the DEGs between WT and normal tissues from the GEO (Gene Expression Omnibus) database by bioinformatics methods, and 10 DEGs ( ALB, CDH1, EGF, AQP2, REN, SLC2A2, SPP1, UMOD, NPHS2 and FOXM1 ) were screened [30]. The AQP2, SPP1 and UMOD in the results of this study coincided with the DEGs we screened. Differences in AQP2, SPP1 and UMOD between WT and normal tissues were found in two different databases, and our study provides additional evidence to support AQP2, SPP1 and UMOD as DEGs of WT.

We reviewed the research status of these four highly expressed m6A-related genes. The correlation between CTHRC1 and tumor is the most studied, followed by ADGRG2, while the correlation between CPD and LRTM2 and tumor is less studied. CTHRC1 is an important oncogene, its expression of collagen triple helix repeat containing protein 1 is a cancer-related protein. The up-regulated expression of this protein is closely related to many cancers proved by many studies, such as gastric cancer, pancreatic cancer, hepatocellular carcinoma, breast cancer, colorectal cancer, epithelial ovarian cancer, esophageal squamous cell carcinoma, cervical cancer, non-small cell lung cancer, melanoma, etc. [31]. CTHRC1 is overexpressed in most tumors, and CTHRC1 expression is significantly correlated with the prognosis of tumor patients [32]. Qi et al.first confirmed the high expression of CTHRC1 in WT tumors and further studies have found that the survival time of patients with high expression of CTHRC1 is shorter than that of patients with low expression of CTHRC1, and CTHRC1 can be regarded as an independent prognostic factor for WT [33]. However, in the study of Huang et al., we noted that CTHRC1 is also highly expressed in WT compared to normal kidney tissue and is a key gene associated with WT prognosis, but it is a protective factor ( HR = 0.489 ) [34]. Our study also confirmed the high expression of CTHRC1 in WT, and found that CTHRC1 was significantly different in different stages of WT. The expression of CTHRC1 in grade I and II was higher than that in grade III and IV. Next, we performed a survival analysis of CTHRC1, and the results showed that patients with high expression of CTHRC1 in WT patients had a better prognosis than patients with low expression. These results suggest that the difference in CTHRC1 expression may be related to the prognosis of WT patients. CTHRC1 is a protective factor or a risk factor for WT, and further research is needed in the future. ADGRG2 is considered to be a new pathogenic gene, and most studies have shown that it is closely related to congenital absence of vas deferens [35, 36], and there are currently no studies on WT. The research on CPD is limited to plant research, and there is no research involving CPD and WT [37]. At present, there are few studies on the correlation of LRTM2, and its function needs further study.

Understanding the immune status of tumor microenvironment will help us to deepen the understanding of anti-tumor immune response and develop more effective immunotherapy methods. In this study, we found that APC _ co _ stimulation, CCR, Macrophages, Parainflammation, Th1 _ cells, Treg and Type _ II _ IFN _ Reponse were significantly decreased in WT compared with normal tissues. This suggests that overall immunosuppression in WT tumor microenvironment, which is consistent with many current findings [38]. Some studies have compared the differences of immune cells in tumor microenvironment between WT high-risk group and low-risk group. In the high-risk group, it was found that except for B cells and macrophage M1 type, other types of cells were lower than those in the low-risk group, including CD8 + naive T cells, CD8 + T cells, macrophage M2 type, mast cells, neutrophils, NKT cells and Treg cells [39]. This suggests that the high-risk WT tumor microenvironment as a whole exist immune inactivation and lack of T cells. In addition, We found that APC_co_stimulation, CCR, Macrophages, Parainflammation, Treg, and Type_II_IFN_Reponse were negatively correlated with LRTM2, Th1_cells were positively correlated with ADGRG2, CCR was negatively correlated with CPD, CCR was positively correlated with CTHRC1. These findings indicate that the 4 m6A-related genes could play an immunoregulation role in WT. In particular, LRTM2 may be involved in immunosuppressive microenvironment of WT. Althought the fact that there are few studies on LRTM2 in WT, it has potential research value in the future.

Enhancing the immune activity of WT tumor microenvironment may contribute to the treatment of WT, but it should be noted that different target cells have different therapeutic effects on WT. Immune checkpoint inhibitors ( PD1 or PD-L1 ) have completely changed the treatment of many adult tumors because they can activate tumor infiltrating lymphocytes to exert anti-tumor effects. People have placed similar hopes on the treatment of recurrent or refractory solid tumors in children. However, current clinical trials have shown that this immunotherapy is little effective in treating WT, and childhood cancers are likely to follow a unique immune pathway [40]. This means that immune checkpoint inhibitors may not be suitable for the treatment of WT. Other immunotherapy methods for WT are being explored, such as CAR-T cell therapy and cytotoxic T lymphocyte therapy. These immunotherapy for T cells shows a certain prospect and may be successfully applied to WT immunotherapy in the future [41].

In summary, four m6A-related genes were screened out by bioinformatics analysis of RNA-seq data of WT patients. After experimental verification, we found that ADGRG2, CPD, CTHRC1 and LRTM2 were highly expressed in WT and could have potential prognostic value and play an immunoregulation role in WT. These findings help to deepen the understanding of the molecular mechanism of WT and provide potential targets for clinical treatment in the future.

Acknowledgements

Not applicable.

Author contributions

YZ, WZ and HG contributed to the design of the study. JD, contributed to the data collection. HJ, XS and YC contributed to the statistical analysis. MN and ZX contributed to make diagrams and finish manuscript. All authors read and approved the final version of the manuscript.

Funding

The article was supported by Science and Technology Program of Guizhou Province (ZK-2022-418). The funding has no conflicts of interest with this article or all the authors.

Availability of data and materials

Publicly available data sets were analyzed in this study. The data can be found below: https://xenabrowser.net/datapages/.

Ethics approval and consent to participate

Ethical approval was waived since we used only publicly available data in this study.

Consent for publication

Not applicable (public data).

Competing interests

The authors have no conflicts of interest to declare.

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Exploration of biological significance of m6A-related genes in Wilms tumor

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Abstract

Figures

Introduction

Methods And Materials

Data collection

GSVA analysis

Identification of the module with significantly associated m6A RNA methylation score based on WGCNA analysis

Identification of differentially expressed genes (DEGs)

Identification and analysis of hub genes

Immune cell infiltration landscape

Construction of TF-mRNA-miRNA regulatory network

Potential therapeutic drugs prediction

Validation of the gene expression level by qRT-PCR

Results

The associated between m6A RNA methylation with WT development

Identification of the module with significantly associated m6A RNA methylation score based on WGCNA analysis

Identification of DEGs

Identification and analysis of hub gene

Immune cell infiltration landscape

TF-mRNA-miRNA network

Potential therapeutic drugs prediction

Validation of the gene expression level by qRT-PCR

Discussion

Conclusion

Declarations

References

Additional Declarations

Supplementary Files

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