m7G Methylation-Related Genes Impact Prognosis and Tumor Immune Microenvironment in bladder cancer

doi:10.21203/rs.3.rs-3348851/v1

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m7G Methylation-Related Genes Impact Prognosis and Tumor Immune Microenvironment in bladder cancer

https://doi.org/10.21203/rs.3.rs-3348851/v1

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Objective: N7-methylguanosine (m7G) is an important biological process of post-transcriptional modification. In recent years, the role of m7G in tumorigenesis and development has received more and more attention. However, the mechanism of m7G in bladder cancer and its impact on the immune microenvironment is still unclear.

Methods: M7G-related genes were screened out from TCGA database. Through the LASSO regression analysis, the m7G-score was constructed. A nomogram incorporating m7G-score and clinicopathological characteristics was also constructed. Then, we evaluated the effect of m7G-score on TME and the relevance of immune cells. We also divided the cohort into 2 m7G-related patterns using unsupervised clustering. And the effect of high and low m7G-score on the drug sensitivity of patients by the “pRRophetic” package.

Results: We established an 11-gene m7gscore based on training set and divided it into high and low-risk groups according to the median score. Further, m7Gscore also has good predictive ability in the test set and total cohort. A prognostic nomogram was constructed by combining m7gscore and clinicopathological features. The analysis of the TME showed that the high-risk group had more infiltrating immune cells and immune function, and were more sensitive to chemotherapy and immunotherapy. In addition, patients were divided into two patterns using unsupervised clustering and immune differences between the two groups were investigated.

Conclusion: This study also evaluated the role of the m7G-score in predicting patient prognosis, immune microenvironment landscape, and drug sensitivity, providing new insights into the treatment of bladder cancer from the level of post-transcriptional modifications.

Bladder cancer

m7G

Tumor Immune Microenvironment

drug sensitivity

Bladder cancer (BCa) is the most common malignant tumor of the urinary system, with high morbidity and mortality globally, accounting for about 550,000 new cases and 200,000 fatalities annually(1). BCa can be stratified into non-muscle-invasive BCa (NMIBC) and muscle-invasive BCa (MIBC), with morbidity of 75% and 25%(2). NMIBC has a better life expectancy with 5-year survival rates greater than 85% (3)Conventional treatment for NMIBC is transurethral resection of bladder tumor (TURBT) and intravesical chemotherapy, but approximately 60% of NMIBC patients will suffer a recurrence within 1 year after TURBT(4). MIBC has a poorer prognosis, with 5-year survival rates below 85%. It has a high probability of metastasis and mainly occurs as lymphatic metastases. The current general treatment modality is radical cystectomy combined with urethral reconstruction. In addition to surgical treatment, there are other treatments, such as cisplatin-based combination and immunotherapy(5, 6). Now a PD1-blocking antibody is approved for treating BCa patients(3).

Methylguanosine (m7G) is an important posttranscriptional modification of mRNA, where the seventh N position of RNA guanine (G) is complexed with a methyl group under the action of methylation transferase(7). It is widely found in tRNA, rRNA, and the 5′ cap of eukaryotic mRNA and plays an important role in modulating RNA(8, 9, 10). Most of the m7G regulators are expressed aberrantly in tumors, and they may act as novel biomarkers for diagnosis and prognostic prediction. METTL1/WDR4, as the most central regulator of m7G modification, catalyzes m7G under the action of methyltransferases(11, 12). A large number of studies indicate that METTL1 is upregulated in solid tumors, which often predict poor survival(13). Some studies also have shown that m7G-related genes(EIF3D, EIF4E1B, LARP1, NCBP1, GEMIN5, and DCP2) are associated with the progression of tumors as well as the prognosis of patients(14, 15, 16). In Bca, whether m7G can regulate the occurrence and development of tumors, and how M7G key genes and related gene expressions are still unknown

Tumor microenvironment (TME) is a complex system with various stromal cells, such as fibroblasts, vascular endothelial cells, immune cells, adipocytes, mesenchymal stem cells, and various cytokines(17). Researchers have found that the m7G of tumor cells can shape the tumor microenvironment(18). The m7G scoring system was constructed in previous studies to investigate the association between m7G with tumor immune infiltrating cells. For example, Dendritic cells activated, Dendritic cells resting, Macrophages M2 and Mast cells resting were negatively correlated with the m7G-score constructed by m7G-related genes in ccRCC(19).

Herein, to investigate the predictive value of m7G in BCa, 400 RNAseq data of BCa patients were selected from the TCGA database and divided into the training group and test group. We built an 11 gene m7G-related signature base on known m7G associated genes. It has a significant correlation with multiple clinicopathological features. We also established a nomogram that had a predictive ability for the immune microenvironment and medication response of BCa.

Identification of m7G related genes and Construction the m7G Prognostic Signature

According to the MSigDB and previous literature, we sorted out 42 m7G-related mRNAs. With the use of the RNA-seq data of BLCA samples in TCGA, 1758 m7G-related RNAs were screened based on |Cor| > 0.4 and p-value < 0.001 (Fig. 2A). 231 m7G-related prognostic RNA included in the univariate Cox regression model and the top 50 were shown in Fig. 2B (p < 0.05). Then, the lasso Cox regression analysis was carried out for further screening in the training group. An 11-gene signature was acquired with the ideal λ value (Fig. 2C, D). Based on the following calculation, risk score was computed: risk score = (− 0:09660 * RAPGEF6 exp) + (-0.00036 * METTL7A exp) + (-0.01734 * TMC7 exp) + (0.00502 * TUBGCP5 exp) + (0.00524 * EIF3J exp) + (0.05632 * ASIC1 exp) + (-0.00595 * ATAT1 exp) + (0.00249 * PRPF19 exp) + (0.00123 * MORF4L1 exp) + (-0.57038 * LINC00310 exp) + (-0.00219 * MRPS6 exp). Base on median risk score, the training group was divided into high- and low set. The distributions of m7Gscore and survival status showed that the survival of the low-risk group was significantly higher than that of the high-risk group, and the Kaplan-Meier method supports this finding (Fig. 3A, B). The area under the curve (AUC) of the ROC curves were 0.720, 0.710 and 0.750, indicating that the m7Gscore performed well (Fig. 3C).

Validation of the m7G Prognostic Signature

To validate the model, the test group and the total cohort were analyzed using the same methods. The m7Gscore formula and cut point were the same as the training group. The distribution heatmap showed that the low-risk group had a longer survival time both in the test group and total cohort (Figs. 3D, G). Moreover, the OS of the low-risk patients was also significantly longer than that of the high-risk patients (Fig. 3E, test group: p < 0.001, HR = 2.26 (1.48–3.45); Fig. 3H, total cohort: p < 0.001, HR = 2.32 (1.72–3.13)). Ideal validation was provided by the ROC curves of the test group and total cohort with AUCs of 0.67, 0.63 and 0.61 (Fig. 3F) and 0.68, 0.65, and 0.67 (Fig. 3I).

Development and Evaluation of the Nomogram Model

We compared the clinical characteristics of the high and low-risk groups and presented them in the form of a heatmap (Fig. 4A). The result showed that papillary, lower grade and younger age were associated with significantly lower m7Gscore and better prognosis (Fig. 4B-E). After the univariate and multivariate Cox regression model was constructed, the m7Gscore, stage and age were confirmed as the independent prognostic factors (Fig. 4F, G). On the basis of important clinicopathological features and the m7Gscore, we have developed a nomogram model that can accurately predict OS in training group (Fig. 5A). The results of the calibration curve showed that the nomogram can effectively predict the 1-year, 3-year, and 5-year survival time of BCa patients (Figure S1A). The OS in the high-risk group was worse than that in the low-risk group by

Kaplan-Meier curves ((Figure S1B). The AUC values of ROC curves were 0.84 (1-year), 0.84 (3-year), and 0.85 (5-year) and the C-index was 0.76996 (95% CI: 0.72147–0.81845, p = 1.002E − 27) ((Figure S1C). Besides, compared with clinical features, the nomogram achieved the best clinical net benefit via DCA plot (Figure S1D).

The same method was used to construct the nomograms in the test set and in the total set (Fig. 5B, C). We used the test set and the total set to verify the effectiveness and accuracy of the model, and the calibration curves still showed a good fit (Figure S1E, I). Kaplan-Meier curves showed that patients in the high-risk group also significantly worse than those in the low-risk group, both in the test set and in the total cohort (Figure S1F, test set: HR = 2.94 (1.91–4.52), p < 0.001; Figure S1J, total cohort: HR = 3.34 (2.47–4.52), p < 0.001). The AUCs of the test set were 0.74, 0.66, and 0.63 (Figure S1G). In total cohort, the AUCs were 0.77, 0.74, and 0.74 (Figure S1K). The good predictive power of the model in both cohorts is shown by the DCA curves (Figure S1H, L).

Gene correlation and functional enrichment analysis

The correlation analysis of 11 m7G-related genes in BCa indicated that EIF3J was the center node significantly associated with other genes (Fig. 6A). Previous evidence suggests that eukaryotic translation initiation factors (EIFs) is an important oncogene and its multiple subunits are highly expressed in BCa and promote tumor proliferation and metastasis. In addition, five different gene sets were used to explore the underlying mechanisms of m7G (Figs. 6B-F). The results showed that the high m7Gscore phenotypes were significantly associated with several immune function and cancer pathways, including the antigen processing and presentation, CD8⁺ TCR downstream pathway, IL6/JAK/STAT3, MYC targets etc.

Tumor Immune Microenvironment of the m7Gscore-Defifined Groups

The ESTIMATE algorithm revealed that estimate, immune and stromal scores were significantly higher in the high-risk group than in the low-risk group, while tumor purity showed the opposite result (Fig. 7A). Figure 8B shows that high-risk patients have higher expression of CD4 + T cells, macrophages and eosinophils, but lower infiltration of B cells, plasma cells and regulatory T cells (Treg), suggesting that high-risk groups are more sensitive to immunotherapy. In addition, the high-risk group had higher scores for multiple immune functions, especially the expression of immune HLA family and checkpoints (Fig. 7C-E). These findings further suggest that immunotherapy may become an important treatment for patients in the high-risk group. To ensure the stability of these results, we analyzed the correlation between different tumor-infiltrating immune cells using several algorithms (Fig. 7F) and showed part of the correlation scatter diagram in Figure S2A-B.

Drug Sensitivity of the m7Gscore Defined Groups

To fully explore the potential of this signature in targeted drugs, the “pRRophetic” was used to predicted IC50 values of various drugs in the two groups. Consequently, patients in the high-risk group are less sensitive to chemotherapy drugs and bladder infusion drugs, such as Cisplatin, Gemcitabine, Docetaxel, Doxorubicin, Mitomycin.C, Camptothecin, Epothilone.B, Bleomycin, Vinblastine, paclitaxel (Fig. 8). In addition, there is evidence that the targeted drugs sorafenib and sunitinib may be more sensitive in the high-risk group.

Identification of m7G Related Patterns by Consensus Clustering

We categorized the TCGA cohort into distinct patterns with consensus clustering on the basis of the expression of the 11 hub genes (Fig. 9A). According to CDF and delta area, we divided the cohort into C1 and C2 groups (Fig. 9B, C). The result of 3dPCA revealed a substantial difference distribution in two patterns (Fig. 9D). The Sankey diagram showed that most samples of C1 belonged to the high-risk group, indicating a worse prognosis (Fig. 9E). Kaplan-Meier curves also found that the survival rate of the C1 group was significantly lower than that of the C2 group (Fig. 9F).

Tumor Immune Microenvironment of m7G Related Patterns

To discover the TME difference among the two patterns, we evaluated the TME score and found that C1 group have higher estimate score, immune score and tumor purity, but lower stromal score (Fig. 10A). We also looked at tumor-infiltrating immune cells and found that T cells, macrophages and DC cells were upregulated in C1 (Fig. 10B). Multiple immune functions were significantly higher in the C1 group than in the C2 group, similar to the M7G-related signature (Fig. 10C). The immune cell heatmap based on various algorithms showed that the level of immune cells in the C1 group was significantly higher than in the C2 group (Figure S2B). We further explore the HLA expression and BCa related immune checkpoint. The result showed the level of multiple HLA in C1 group was higher than C2 (Fig. 10D) and some checkpoint like PD-1, PD-L1, PD-L2 and TIGIT were higher in C1 group (Fig. 10E).

Data Collection and Processing

RNA sequencing data (RNA-seq) harmonized to the fragments per kilobase million (FPKM) of BLCA patients and the relevant clinical information were acquired from TCGA (https://portal.gdc.cancer.gov/). 400 BLCA tissues were involved after removal of normal tissues and tissues lacking clinical information. A total of samples was divided into the training group and test group according to 1:1 by using the “caret” package in R.

Altogether, 29 m7G-related genes were retrieved from the Molecular Signatures Database (MSigDB) (http://www.gsea-msigdb.org/gsea/msigdb/index.jsp) and the preview reviews. (Supplementary Table S1) Then 1758 m7G-related RNAs were screened according to the criteria of |Cor| > 0.4 and p-value < 0.001. The co-expression network of m7G-related genes was constructed by the “igraph” package in R. The overview design of this study is displayed in Fig. 1.

Construction and Assessment of m7G-Related Gene Prognostic Model

Univariate Cox regression analysis and the least absolute shrinkage and selection operator (LASSO) Cox regression were performed to evaluate the relationship between m7G-associated genes expression and overall survival (OS) in training group by the “glment” package. Eventually, 11 key genes were determined for establishing signature.The formula for risk score was m7Gscore = \(\sum \left(Expi*Coefi\right)\). According to the median risk score, the samples were divided into high- and low-risk groups. The Kaplan–Meier method was used for survival analysis, and the ROC curve was depicted to validate the prognostic power of signature at

1, 3, and 5 years using the “survivalROC” package. To validate the model, the test group and total group were analyzed with the same cutoff value and analysis methods.

Prognostic nomogram construction and calibration

The differences of clinicopathological variables between the low- and high-risk groups were visualized by a heatmap and examined by Chi-square test. Subsequently, univariate and multivariate Cox regression analyses were performed to assess whether the risk scores were independent prognostic indicators of BLCA. A nomogram model was constructed by combining the m7Gscore and clinicopathological factors to estimate the 1-, 3-, and 5-year OS by the “rms” R package. The calibration analysis and decision curve analysis (DCA) were used to assess the accuracy of nomogram using “ggDCA” package. The Kaplan–Meier and ROC curve evaluated the ability of predicting OS.

Unsupervised Consensus Clustering

The “ConsensusClusterPlus” package in R was used to unsupervised cluster the patients for evaluating the association between the expression of 11 m7G-related key genes and BLCA prognosis. Finally, the clustering results were selected based on the cumulative distribution function (CDF) curve and relative change in the area. The principal component analysis (PCA) was performed with the “scatterplot3d” package in R software.

Enrichment analysis and Drug Response

All Functional and pathway enrichment analyses using the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis and Gene Ontology terms Enrichment analysis (GO) and gene set enrichment analysis (GSEA). These analyses were used to find the correlation between the differences in m7G-score and different biological functions and signaling pathways.The CellMiner database was used to explore the correlation between drug sensitivity and RNA. Pearson correlation analysis was used to evaluate the relationship between the obtained m7G-related gene expression levels and drug responses (chemotherapeutic drugs and immune drugs) in the database.

Tumor Microenvironment Analysis

We evaluated the association of m7G-related 11 genes with immune cell infiltration in the bladder cancer microenvironment.ESTIMATE and single-sample GSEA (ssGSEA) were used to assess and analyze the proportion of immune cell subsets in different m7G-score.

Statistical Analysis

R software (version 4.1.2) was used for data analytics and mapping throughout the study. ggplot2, limma, GSVA and other R packages were used. Pearson and Spearman correlation analysis was used to analyze correlations between variables. p < 0.05 was considered statistically significant.

In this study, we collected complete sequencing information of 400 patients with BCa through the TCGA database. We constructed a prognostic model of m7G-score including 11 related genes (ASIC1, PRPF19, TUBGCP5, EIF3J, MORF4L1, LINC00310, MRPS6, RAPGEF6, ATAT1, METTL7A, and TMC7) by bioinformatics analysis such as the lasso regression model. This score can significantly evaluate the prognosis of patients with BCa, and can preliminarily show the immune microenvironment landscape of patients with BCa. We further constructed a nomogram model including m7G-score and other clinicopathological features and proved that this nomogram model can well predict the prognosis of patients. Secondly, we predicted the drug sensitivity of BCa patients by m7G-score, and we found that the level of the m7G-score can also well evaluate the drug sensitivity of patients.

At present, our understanding of the regulators of m7G modification is not comprehensive enough. Only the METTL1/WDR4 complex and the WBSCR22/TRMT112 complex have been found to be validated m7G modifiers(13, 20). These factors act as m7G methyltransferases to add m7G modifications to target RNAs(21). In tumors, this m7G modification seems to be abnormally activated, which in turn affects downstream RNA production, maturation, and structural changes, and ultimately mediates changes in a variety of key biological processes in tumorigenesis and development(22, 23). Pathway analysis shows that a high m7G-score is often positively correlated with tumor progression-related pathways, such as the EMT pathway, JAK/STAT pathway, etc However, it has also been reported that m7G modification may play opposite roles in tumor development, which is a "double-edged sword"(24). In BCa, Ying et al. found that METTL1 can mediate the m7G methylation of EGFR/EFEMP1 to promote the proliferation and invasion of cancer cells(25). In this study, we tried to find genes with differences upstream or downstream based on validated m7G modifiers. These differential genes may be unconfirmed m7G modifiers or indirectly involved in the regulation of m7G modification. Therefore, we believe that this m7G modification-related gene obtained by high-throughput analysis of BCa tissues has prognostic and therapeutic potential. At the same time, we also confirmed this result through the validation group.

This study also found that the m7G-score can predict immune cell infiltration in the microenvironment of BCa. The activation of m7G modification in tumor cells may promote cancer cells to secrete or release some cytokines into the microenvironment, which can regulate the recruitment of immune cells(19). In our study, we found that a higher m7G-score often has lower regulatory T cells (Treg), which may benefit patients from immunotherapy. Treg is a class of cell subsets with significant immunosuppressive effects. Foxp3, CD25, and CD4 are the main markers(26). Treg can inhibit the immune response of other cells, promote the immune escape of tumor cells, indirectly accelerate the proliferation of tumor cells, and enhance the infiltration ability of tumor cells(27). We can also see that patients with high m7G-score often express immunotherapy targets such as PD-L1 and TIGIT in tumor cells, which is beneficial for tumor immunotherapy. Therefore, in the future, patients with high m7G-score may be preferentially treated with PD-1 inhibitors compared with patients with low m7G-score after professional evaluation. Wang et al. 's study also showed that high m7GRiskscore is associated with immune benefits in lung cancer(28), which is consistent with our results. However, how m7G modification regulates the infiltration of Treg cells still needs further study.

The methylation of m7G is also associated with drug resistance. Studies have found that Ribavirin can inhibit cell reproduction as an m7G cap analog and has a more significant effect in tumors with high eIF4E levels(20, 29). Decreased resistance to cisplatin and docetaxel has been reported to be associated with decreased levels of METTL1 in cervical cancer(30). In this study, we found that a high m7G-score may reduce the sensitivity of patients to gemcitabine, cisplatin, and docetaxel, which often means that patients with a high m7G-score may be resistant to mainstream treatment of BCa. However, patients with high scores may be more likely to benefit from immunotherapy, which is an interesting point we found. Chen et al. show that METTL1 upregulates the WNT/β-catenin signaling pathway in vitro and in vivo, promotes epithelial-mesenchymal transition and chemoresistance to cisplatin and docetaxel in nasopharyngeal carcinoma cells(31). However, another study in colon cancer showed that METTL1 expression was decreased in cisplatin-resistant cells(32). Therefore, the use of the m7G-score to predict drug susceptibility requires individualized and accurate evaluation.

There are still some limitations in this study that must be noted. First, the study was validated based on TCGA databases, lack of independent cohorts, or multicenter results. In the future, we will conduct prospective multi-center studies to make m7G-score's already relevant nomogram more universal and clinically applicable. Secondly, the molecular mechanism of m7G regulating BCa still needs to be further explored, and our next step will be to carry out in vivo experiments to verify.

In conclusion, the m7G-score is an independent marker for predicting the prognosis of BCa patients, and can also provide effective guidance for clinicians to evaluate patients and choose treatment strategies by constructing nomograms. At the same time, the m7G-score can also predict the invasion of the bladder cancer microenvironment, providing a preliminary choice for the medication (chemotherapy or immunotherapy) of bladder cancer patients.

BCa Bladder cancer

TCGA The Cancer Genome Atlas

m7G N7-methylguanosine

TME Tumor microenvironment

OS overall survival

Disclosure statement

No potential conflict of interest was reported by the authors.

Funding

This work was supported in part by a grant from the National Natural Science Foundation of China, Youth Project (#82203150); Zhejiang Medical Association Clinical Medicine Special Fund Project (2023ZYC-Z12).

Authors’ Contributions

(I) Conception and design: LLS; (II) Provision of study materials or patients: YZ; (III) Collection and assembly of data: HTC, ZJZ and YQT; (IV) Data analysis and interpretation: YFC and WHH (V) Manuscript writing: XHW and KYW; (VI) Final approval of manuscript: All authors.

Availability of data and materials

The data could be acquired from the TCGA dataset ( https:// portal.gdc.cancer.gov/ ) and The data on drug sensitivity could be acquired from the CellMiner database ( https://discover.nci.nih.gov/cellminer ). The code used in the study is available from the corresponding author upon reasonable request.

Conflict of interest

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.

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Supplementary Table S1 is not available with this version.

No competing interests reported.

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m7G Methylation-Related Genes Impact Prognosis and Tumor Immune Microenvironment in bladder cancer

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