Identification and Validation of N6-methyladenosine-related Biomarkers for Bladder Cancer: Implications for Immunotherapy

doi:10.21203/rs.3.rs-1101509/v1

Download PDF

Research Article

Identification and Validation of N6-methyladenosine-related Biomarkers for Bladder Cancer: Implications for Immunotherapy

https://doi.org/10.21203/rs.3.rs-1101509/v1

This work is licensed under a CC BY 4.0 License

Journal Publication

published 02 Mar, 2022

Read the published version in Frontiers in Oncology →

Version 1

posted

You are reading this latest preprint version

N6-methyladenosine (m6A) has emerged as one of the most important modifications of RNA. Based on the expression of 23 kinds of m⁶A regulatory factors, we identified three different m⁶A modification patterns in bladder cancer. The effects of the three kinds of m⁶A modification modes on clinicopathological characteristics, immune cell infiltration level, and gene expression level of immune checkpoint genes were comprehensively analyzed. In addition, the effects of different m⁶A modification modes on the therapeutic efficacy of anti-PD-L1 (atezolizumab) are also discussed. Our results confirm that m⁶A methylation plays an important role in the immune cell recruitment process in the tumor microenvironment of bladder cancer, which is influences the efficacy of anti-PD-L1 therapy for bladder cancer. We further confirmed the important role of FTO on the biological function of bladder cancer cells by in vitro experiments, FTO functions as an oncogene in bladder cancer cells, and after FTO knockdown, the level of m⁶A enzyme activity in bladder cancer cells was significantly increased, apoptosis was increased, and cell proliferation and cell invasion were reduced. In addition, our study also confirmed that K216H and K216E probably are important targets for regulating FTO in the future. We provide new insights into the regulatory pathways of the immune microenvironment and the methylation function of m⁶A in bladder cancer, which will help to design novel diagnostic methods, prognostic tools, and therapeutic targets.

Cancer Biology

Oncology

Bladder cancer

N6-methyladenosine

tumor immune microenvironment

anti-PD-L1

immune checkpoint inhibitors

Bladder cancer is the most common cancer of the urinary system and is characterized by a difficult early diagnosis, rapid metastasis, and resistance to treatment. Immune checkpoint therapy (ICT) targets immune pathway effectors such as cytotoxic lymphocyte antigen-4 (CTLA-4), programmed cell death protein 1 (PD-1), or programmed death-ligand 1 (PD-L1)[1]. Anti PD-L1 immunotherapy has been approved by the Food and Drug Administration (FDA) for the treatment of metastatic bladder cancer[2, 3].Immunotherapeutics that target PD-1 or PD-L1 have significantly improved the survival outcomes for some patients, showing an astonishing effect in a small number of patients with sustained efficacy. The improved clinical efficacy for the detection of cancer of the urinary system including advanced-stage bladder cancer has changed the intervention measures used [4]; however, some patients with bladder cancer undergoing immunotherapy show no response to ICT or display resistance to drugs, and this scenario does not meet the clinical needs of these patients[5, 6]. At present, multiple studies have confirmed that the immune response resulting due to many tumors, including those found in bladder cancer, is related to the level of immune cell infiltration in the tumor microenvironment, the expression of PD-1/PD-L1, and the tumor mutation burden (TMB)[7, 8].

The microenvironment in which tumor cells grow and survive is called the tumor microenvironment (TME), which includes stromal cells such as cancer-related fibroblasts and macrophages, immune infiltrating cells (myeloid cells and lymphocytes), bone marrow-derived cells (BMDCs) such as endothelial progenitor cells and hematopoietic progenitor cells, and secretory factors such as cytokines [9]. There is mounting evidence that TME plays a crucial role in tumor progression, immune escape, and has an impact on the response to immunotherapy [10, 11]. A comprehensive analysis of the heterogeneity and complexity of the TME is a key step in improving the success rate of existing ICTs and the development of new immunotherapy strategies. Further exploration of the regulatory mechanisms underlying TME cell infiltration will improve the ability to guide and predict the response to immunotherapy, and may help identify new therapeutic targets.

N6-methyladenosine (m⁶A) is the most common and abundant RNA modification in eukaryotes. As a reversible epigenetic modification, m⁶A is found in almost all types of RNAs, including mRNAs, ribosomal RNAs (rRNAs), long noncoding RNAs (lncRNAs), microRNAs (miRNAs), small nuclear RNAs (snRNAs), and circular RNAs (circRNAs), and are dynamically regulated in many physiological processes [12, 13]. The m⁶A modification is a dynamic and reversible process in mammalian cells and is regulated by various methyltransferases, demethylases, and binding proteins, also known as writers, erasers, and readers, respectively. The process of m⁶A methylation is catalyzed by methyltransferases such as RBM15, ZC3H13, METL3, METL14, WTAP, and KIAA1429, and the removal process is mediated by demethylases such as FTO and ALKBH5 [14]. Further, a group of specific RNA binding proteins, such as YTHDF1/2/3, YTHDC1/2, HNRNPA2B1, and IGF2BP1/2/3 can recognize the m⁶A motif, thus affecting the function of m⁶A [15]. Results from various studies have shown that the abnormal expression and changes to gene expression for m⁶A regulatory factors are related to the dysregulation of multiple biological processes essential for the initiation, progression, metastasis, drug resistance, recurrence, and the immune regulation of cancer [16].

Many studies have confirmed the special correlation between m⁶A modification and changes in the TME. Li et al.[17] observed that when METTL3 was depleted, the steady-state growth of T cells was blocked and was maintained at a slow rate. Further, their research showed that METTL3 deletion resulted in a decrease in the level of m⁶A, and an increase in the mRNA stability and protein levels of SOCS1, SOCS3, and CISH, thus negatively regulating the IL-7 signal production in CD4⁺T cells. Results from another study[18] revealed the role of m⁶A in dendritic cell activation, in which METTL3 mediated the m⁶A modification of the transcripts for the adaptor Tirap for CD40, CD80, and TLR4. Signal adaptors enhanced the translation of METTL3 in dendritic cells, thereby enhancing TLR4/NF-κB signal transduction, producing cytokines, and stimulating T cell activation. From the perspective of immune checkpoint therapy, the m⁶A binding protein YTHDF1 can be used to modulate antitumor immunity and improve immunotherapy, as it can regulate the expression of lysosomal proteinase in an m⁶A dependent manner. YTHDF1 can recognize the transcription of m⁶A modified transcripts, and encodes a lysosomal protease to increase translation in dendritic cells. Moreover, the deletion of YTHDF1 enhances the cross-presentation of tumor antigens and the cross initiation of CD8⁺T cells in vivo. Further, Wang et al. confirmed that the deletion of YTHDF1 enhances the therapeutic effect of the PD-L1 checkpoint blockade[19].In melanoma, elevated FTO expression levels promote tumor growth by reducing the methylation of m⁶A in the mRNA for PD-1 (PDCD1), CXCR4, and SOX10 and preventing their RNA decay mediated by YTHDF2. Knockout of FTO in melanoma cells induces tumor cells to become sensitive to interferon-gamma (IFNγ) in vitro, and promotes a sensitizing response of melanoma cells to anti-PD-1 antibody in mice[20]. Together, these data prove that the m⁶A modification plays an important role in regulating the tumor immune microenvironment， and the combined use of m⁶A modulators and PD-1/PD-L1 drugs may prove to be a new strategy to enhance the clinical efficacy of tumor immunotherapy.

In this study, we examined the effect of the m⁶A methylation modification on the invasion characteristics of immune microenvironment cells and the effect of anti-PD-L1 therapy on bladder cancer. Our study helps elucidate the molecular mechanism underlying the regulation of the immune microenvironment in bladder cancer, and provides new predictors, potential auxiliary targets, and directions for more effective immunotherapy strategies for combating bladder cancer.

2.1 Datasets

The gene expression profile was measured experimentally using the Illumina HiSeq 2000 RNA Sequencing platform at the University of North Carolina The Cancer Genome Atlas (TCGA) genome characterization center. Level 3 data were downloaded from the TCGA data coordination center. This dataset shows the gene-level transcription estimates, i.e., the log₂(x+1) transformed RSEM normalized counts, and somatic mutation data was downloaded from the site https://portal.gdc.cancer.gov/. IMvigor 210 cohort is a multicenter, single-arm phase II clinical study for evaluating the safety and efficacy of Tecentriq, a PD-L1 inhibitor, in patients with advanced urothelial carcinoma [21, 22]. The complete processed expression data，detailed clinical annotations, and somatic mutation data was obtained from IMvigor210CoreBiologies, which is a complete documentation software and data package for the R statistical computing environment. The software package is available free of charge under the Creative Commons 3.0 License.

2.2 Consensus clustering for 23 m⁶A regulators

We identified 23 m⁶A regulators from published literature[23, 24], including 2 erasers (ALKBH5, FTO), 13 readers (FMR1, HNRNPA2B1, HNRNPC, IGF2BP1, IGF2BP2, IGF2BP3, YTHDC1, YTHDC2, YTHDF1, YTHDF2, YTHDF3, ELAVL1, LRPPRC), and 8 writers (KIAA1429, METTL14, METTL3, RBM15, RBM15B, WTAP, ZC3H13, CBLL1). The consensus clustering algorithm determined the optimal number of clusters (K value) as 3. Based on the expression of the 23 m⁶A modulators, different m⁶A modification patterns were identified by using k-means(based on Euclidean distance), and the patients were classified into three groups for further analysis. We used the consusclusterplus R package to perform the above steps, and performed 1000 iterations (50 iterations, resampling rate of 80%) to ensure the stability of the classification [25].

2.3 Common molecular typing of bladder cancer

We used several published subtype classification systems for bladder cancer, including the Baylor subtype described by Mo et al. [26], who defined urothelial differentiation based on 18 gene markers, thus dividing the muscle-invasive bladder cancers (MIBC) and non-MIBCs (NMIBC) into basal and differentiated subgroups. The UNC of Damrauer et.al. [27] identified basal and luminal cancer subtypes by consensus cluster analysis, and further identified 47 genes as predictors of these subtypes. The MDA subtype [28] was analyzed by using 2252 genes (2697 probes) from 73 freshly frozen primary MIBC samples, including p53 like, luminal, and basal cancer. The Lund group[29] presented a six-class system based on global mRNA expression, including the urothelial-like, genomically unstable, epithelial infiltrated, SCC-like/Mes-like, SCC-like/UroB, and Sc/NE-like cancer. TCGA genotyping[30] used 2707 genes to conduct unsupervised cluster analysis on 129 patients with MIBC. The patients were classified into four subtypes (I, II, III, and IV)， using the Cartes d'Identitédes Tumeurs（CIT）-Curie subtypes [31]. These classifiers were combined into an R package (BLCAsubtyping) [32], which can be found at https//github.com/cit-BioInfo/BLCAsubtype. We applied these classifiers independently to the TCGA-BLCA cohort and the IMvigor210 cohort to analyze the relationship between the subtypes of different classification methods and three different m⁶A modification patterns. Further, we interpreted the differences among the three different m⁶A modified subtypes.

2.4 Estimation of TME cell infiltration

We used the single-sample gene set enrichment analysis (ssGSEA) algorithm to quantify the relative abundance of cell infiltration in TME. We obtained gene sets from the data of Qingzhu Jia et al[33] to label the different types of TME infiltrating immune cell types. As demonstrated by Jia et al[33], we calculated the antitumor-immunity-score as the sum of the immune cell scores of various antitumor cells. Further, the pro-tumor-immunity-score is the sum of the scores of tumor-promoting immune cells, and we define the tumor immunity score as the difference between the pro-tumor-immunity score and the antitumor-immunity-score for unified analysis. In addition, we also used the ESTIMATE algorithm[34] to estimate the ratio of the immune matrix components of each sample in the TME to further explore the differences of TME components including immune cells and stromal cells scores among different m⁶A modification modes. The results were presented in three forms: immune score, stromal score, and the ESTIMATE score; the higher the score from immune score or stromal score, the more immune or matrix components in TME. The ESTIMATE score is the sum of the immunescore and stromal score, which represents the level of the two components in TME.

2.5 Identification of differentially expressed genes (DEGs) among three types of m6A modified modes

DEGs were identified via the Bayesian method between different m⁶A modification modes using the limma R package[35],the absolute value of logFC>1 as the significant criteria. We used the ssGSEA method to score the samples based on the 23 m⁶A regulatory factors. Based on the median value of the m⁶A score, the patients were classified into a high m⁶A score group or a low m⁶A score group. The difference between the two groups was analyzed by the empirical Bayes method using the limma R package, taking the corrected p-value<0.05, and the absolute value of logFC>1 as the significant criteria, and 647 DEGs were obtained for subsequent signal pathway analysis.

2.6 Differential signaling pathway enrichment among the three m6A modified modes

The enrichment of signaling pathways for the DEGs expressed relative to the different phenotypes of m⁶A were analyzed using the metascape website[36] http://metascape.org. We determined the statistically rich terms, accumulated hypergeometric p values, and enrichment factors, and used these in a filtering step. Next, according to the Kappa statistical similarity between the gene members, the remaining important terms were clustered in a hierarchical structure, and the kappa score of 0.3 was used as a threshold to convert the terms to clusters[36]. We selected the genes with the best p-value in each cluster as its representative term, and displayed them in the heatmap. The databases for the KEGG pathway, GO biological processes, Reactor gene sets, Canonical pathways, and the CORE resources were used for pathway and process enrichment analysis applying a p-value <0.01, a minimum count of 3, and an enrichment factor>1.5 (the enrichment factor is the ratio between the observed count and the chance expected count). The GSEA analysis[37] evaluates the skewness of the two distributions for the selected gene set in the gene list sorted by a specific phenotype. The analysis gene set was obtained from the hallmark gene sets provided by the Molecular Signatures Database (https://www.gsea-msigdb.org/). In this study, we used the clusterProfiler R package to implement the GSEA analysis[38].

2.7 Construction of a scoring system to evaluate the level of m⁶A modification in individual samples

To quantify the m⁶A modification pattern in a single patient, we constructed a scoring system called m⁶Ascore based on the mRNA expression level of the 23 identified m⁶A regulatory molecules in each sample, and used the ssGESA method to evaluate the m⁶A modification pattern in individual bladder cancer patients.

2.8 Analysis of somatic cell copy number of different subtypes

For copy number analysis, we use GISTIC.2 to identify significantly amplified or missing genomes, the burden of copy number loss or gain was calculated as the total number of genes with copy number changes at the focal and arm levels.

2.9 Relationship between methylation of m⁶A and the core biological pathways impacted due to bladder cancer

We analyze a set of gene sets for storing genes related to certain biological processes, and further, to reveal the association between the m⁶A gene signature and some related biological pathways, including: (1) the immune-checkpoint; (2) antigen processing machinery; (3) CD8 T-effector signature; (4) epithelial-mesenchymal transition (EMT) markers including EMT1, EMT2, and EMT3; (5) the angiogenesis signature; (7) pan-fibroblast TGF-β response signature (Pan-F-TBRS); (8) WNT targets; (9) DNA damage repair; (10) mismatch repair; (11) Nucleotide excision repair; (12) DNA replication; (13) and antigen processing and presentation.

The differential gene expression of the core biological pathway genes between the different m⁶A phenotypes of the IMvigor210 cohort was displayed in the form of a heatmap, including:（A）FGFR3 gene signature;（B）CD8 Teff signature;（C）antigen-processing machinery;（D）immune checkpoint signature;（E） MKI67 and cell cycle genes;（F）DNA replication-dependent histones;（G）DNA damage-repair genes;（H）TGFβ receptor and ligand;（I） F-TBRS genes;（J）EMT markers;（K）angiogenesis signature.

2.10 Cell lines and cell culture

The human bladder cancer cell lines were obtained from the Oncology Institute of Central South University. The cell lines were incubated at 37°C under a humidified atmosphere with 5% CO2 and cultured in DMEM (Invitrogen, CA), added with 10% fetal bovine serum (FBS) (GIBCO, NY), 1 mmol/L glutamine, and 1% penicillin/streptomycin.

2.11 Cell viability, apoptosis, and invasion assay

Cell proliferation was analyzed using a commercial CCK-8 assay kit (#C0038, Beyotime). We employed fluorescence-activated Cell Sorting (FACS) to assess apoptosis with the Annexin V-FITC/PI staining kit (Mbchem). According to the manufacturer, the transwell assay assessed cell invasion with the 6-well insert device (8 μm pore size; Corning Life Sciences, Bedford, MA) and Biocoat Matrigel (BD Biosciences) ’s instructions. We calculate the middle and surrounding 5 fields of view and take the average.

2.12 Western blotting

Western blotting was performed as described previously [39]. Briefly, total protein was extracted with RIPA lysis buffer and determined using the bicinchoninic acid (BCA) Protein Assay Reagent Kit. The proteins were separated using 10% SDS-PAGE and electrophoretically transferred into a polyvinylidene fluoride (PVDF) membrane. The membranes were blocked with 5% skimmed milk powder and incubated overnight at 4°C with primary antibodies targeting FTO(27226-1-AP) and β-actin(1:2000, Ptgcn, 66009-1-Ig) and anti‐GAPDH (Abcam, ab125247), followed by incubation with the appropriate secondary antibodies for one h. Positive bands were visualized using an enhanced chemiluminescence system

2.13 Measurement of total m6A assay

Total m⁶A content was measured in 200-ng aliquots of total RNA extracted from cells using an m⁶A RNA methylation quantification kit (cat. no. P-9005; Epigentek) according to the manufacturer’s instructions

2.14 Statistical analysis

The non-paired t-test was used to compare the normally distributed variables between the two groups; the Mann-Whitney U test (also known as the Wilcoxon rank-sum test) was used to estimate the statistical significance of the non-normal distribution variables. The Kruskal-Wallis test and a one-way ANOVA were used as nonparametric and parametric analysis methods[40]. The correlation coefficient between TME infiltrating immune cells and the expression of m⁶A regulatory genes was calculated using the Pearson correlation method. We used the univariate Cox regression model to calculate the risk ratio (HR) for the m⁶A regulatory genes. The survminer R software package was used to determine the cut-off point for the correlation between the m⁶A scores and patient survival in each data set subgroup. The "surv-cut point" function of the maximum rank statistic was used to double score the m⁶A score by repeatedly testing all possible cut-off points, and further, the patients were classified into “high” and “low” subgroups, according to the maximum selected log-rank statistic, to reduce the batch effect in the calculations. The Kaplan-Meier method was used to plot the survival curve for prognosis analysis, and a log-rank test was used to determine the significance of the difference. The waterfall diagram of the maftools software package was used to show the mutations in the 23 m⁶A regulatory factors in the TCGA-BLCA cohort. The thermograms of the 23 mutations for the m⁶A regulatory factors were plotted. All statistical P-values were bilateral, and P<0.05 was considered to be statistically significant.

3.1 Survey of 23 m⁶A regulatory factors in bladder cancer

In this study, we analyzed 23 m⁶A regulatory factors related to bladder cancer, including 2 erasers (ALKBH5, FTO)，13 readers（FMR1, HNRNPA2B1, HNRNPC, IGF2BP1, IGF2BP2, IGF2BP3, YTHDC1, YTHDC2, YTHDF1, YTHDF2, YTHDF3, ELAVL1, LRPPRC）；and 8 writers（KIAA1429, METTL14, METTL3, RBM15, RBM15B, WTAP, ZC3H13, CBLL1）. We first mapped the network for the 23 m⁶A regulatory factors in bladder cancer (Fig.1A), and examined the interactions and their significance for the prognosis of bladder cancer patients. We found that there were significant correlations between the expression of m⁶A regulatory factors in writers, erasers, and readers，and most of the correlations were positive（Supplementary table1）. Based on these correlations, the 23 m⁶A regulatory factors were divided into four categories (Clusters A-D). Fig. 1B summarizes the somatic mutation frequency of the 23 m⁶A regulators. In 116 out of 412 patient samples, mutation in m⁶A regulators were observed, with a frequency of 28.16 %. Most of the mutations were missense mutations, and the regulators KIAA1429, METTL3, and ZC3H13 had the highest mutation frequency (4%), while HNRNPC, YTHDF3, and FMR1 did not harbor any mutations. Subsequently, we found that FMR1 and YTHDF2, YTHDF1 and KIAA1429, YTHDF2 and KIAA1429, WTAP, and METTL3, ZC3H13 and LRPPRC, and ZC3H13 and YTHDC2 had significant symbiotic relationships (Fig. 1C, P< 0.05). The differential expression analysis for the 23 m⁶A regulatory factors in bladder cancer and its adjacent tissues is shown in Fig. 1D. The expression of ALKBH5, FTO, METL14, WTAP, YTHDC1, YTHDF3, and ZC3H13 in adjacent cancer tissues was significantly higher than that in cancer tissues, while the expression of METL3, YTHDF1, YTHDF2, ELAVL1, IGF2BP1, IGF2BP3, and HNRNPA2B1 in cancer tissues were significantly higher than those in non-cancerous tissues. However, due to the small sample size of the paracancerous tissues, this result needs further verification. A univariate Cox regression analysis was performed to study the effect of the 23 m⁶A regulatory factors on the overall survival (OS) of bladder cancer patients (Fig. 1E). It was observed that six m⁶A regulatory factors had a significant effect on the prognosis for bladder cancer patients. YTHDC1 and WTAP may be protective factors (HR<1, P<0.05), while IGF2BP2, ALKBH5, IGF2BP3, and FTO are risk factors for bladder cancer (HR>1, P<0.05).

3.2 Three different m⁶A methylation modification patterns defined by the 23 m⁶A regulatory factors

Based on the gene expression of the 23 m⁶A regulatory factors, 407 patients with bladder cancer were classified using the R-package ConsensusClusterPlus. Our analysis showed that 3 clusters were optimal (Fig. 2A-C, Supplementary Fig.1 A-D), and we identified three different m⁶A modification patterns in 407 patients with bladder cancer, which we named clusters 1-3. We plotted a heatmap (Fig. 2D) and boxplot (Fig.3A) for the expression of the 23 m⁶A regulatory factors in clusters 1-3 and found that the expression of IGF2BP1, IGF2BP1, IGF2BP3, KIAA1429, and RBM15 in cluster2 was lower than in the other clusters (P<0.01), while the expression of METTL3, RBM15B, YTHDC1, YTHDC2, YTHDF1, YTHDF2, YTHDF3, and ELAVL1 was higher in cluster2 than the other clusters. Significant overall survival were found in the patients who had m⁶A modifications from cluster2 (Fig.3B). To better understand the potential differences between the three m⁶A modification patterns, we compared them with the published molecular typing of bladder cancers, including Baylor, MDA, CIT-Curie, and UNC (Fig.3C). Using the CIT classification, Cluster1 and 3 showed more MC7 subtypes, and more MC1 subtypes were seen in cluster2; Using the Baylor classification, cluster1 and cluster3 had more basal subtypes, and cluster2 contained more differentiated subtypes; based on the UNC classification, the basal subtype was more frequent in cluster1 and cluster3 than the luminal subtype, while cluster2 showed the opposite result. To further characterize the molecular differences between the RNA based subtypes, we analyzed the expression of 23 regulatory genes associated with bladder cancer [41-47], including the steroid hormone receptors ESR1/2, AR and PGR, the nuclear receptors PPARG, three RARs (A/B/G), and three RXRs (A/B/G), the receptor tyrosine kinases ERBB2/3 and FGFR1/3, and the transcription factors FOXA1, FOXM1, GATA3/6, HIF1A, KLF4, STAT3, and TP63，We found significant differences in the expression of most regulators between cluster2 and the other two m⁶A subtypes. The expression of RARG, FGFR3, RXR3, FOXA1, ERBB3, AR, ERBB2, PPARG, GATA3, ESER2, and RXRB in cluster2 was significantly higher than that in cluster1 and cluster3, while the opposite relation was found for STAT3, FOXM1, EGFR, HIF1A, GATA6, FGFR1, and RARB (Fig.3D). Further, we identified differences in the mutation of 20 genes in the three m6A modification modes as shown in the waterfall diagram (Supplementary Fig.1E). and we analyzed the difference in Copy number alterations across three m6A modification modes. (Supplementary Fig.2,3).

3.3 Difference in immune cell infiltration characteristics in the tumor immune microenvironment for different m⁶A modification modes

To reveal the role of m⁶A modification-related phenotypes in TME immunoregulation, we calculated the abundance of 28 cellular subsets in each patient sample. The heatmap shows the average difference in the infiltration level of 28 types of immune cells in the three different m⁶A modification modes (Fig. 4A, Supplementary table2-3),We found significant differences in the characteristics of cellular infiltration in the TME; cluster2 is characterized by immunosuppression, and most of the infiltrating cells are immune cells .The infiltration level was significantly lower than that of cluster1 and cluster3 (Supplementary Fig.4A). In addition, the anti-tumor immunity score and the pro-tumor immunity score of cluster2 was significantly lower than that of cluster1 and cluster3 (Supplementary Fig.4B and C), and the pro-tumor score of cluster2 is significantly higher than in cluster1 and cluster3 (Fig. 4B). The ESTIMATE algorithm can estimate the ratio of the immune and matrix components of each sample in the TME, which is presented in the form of three scores: the immune score, stromal score, and the ESTIMATE score. We found that the 3 scores in cluster2 were significantly lower than those in cluster1 and cluster3 (Fig.4C), suggesting that m⁶A methylation may be involved in regulating the type of TME infiltrating cells and play a central role in immune regulation. However, the mechanism for how m⁶A modification affects the immunophenotype is unclear. Therefore, we analyzed the correlation between the 23 m⁶A regulatory factors and the 28 types of immune cells (Fig.4D). A heatmap of the correlation showed that the m⁶A regulators were highly correlated with most of the immune cells, and WTAP, IGF2BP2, and IGF2BP3 showed the most significant correlation with most immune cells. Further, YTHDF2, YTHDC1, METTL3, andELAVL1 showed a high negative correlation with most immune cells, indicating that these regulators may play an important role in the differentiation and recruitment of immune cells. To further explore the possible role of m⁶A related phenotypes in immunotherapy, we studied the difference in the expression of the immune checkpoint related genes CD274, PDCD1LG2, CTLA4, LAG3, PDCD1, HAVCR2, TIGIT, and the CD8+T cell marker genes including CD8A, GZMB, CXCL9, CXCL10, PRF1, TBX21, and CD8B in the three different m6A modification modes (Fig. 4e, F). Our results showed that the expression of these genes in cluster2 was significantly lower than that in cluster1 and cluster3 (P< 0.0001). The results suggest that m⁶A methylation affects the type of TME infiltrating cells, and cluster2 had a significantly lower level of immune cell infiltration related to the immune checkpoint than the other two modification modes. The gene expression in cluster2 is significantly low, and we speculated that the response to immunotherapy in patients showing cluster2 type m⁶A modifications may be worse than that of cluster1 and cluster3.

3.4 Enrichment of DEGs and the signaling pathways involved in the different m⁶A modification modes

To reveal the potential biological differences among the three different m⁶A modification patterns, we undertook an expression profiling of the patient tissue samples and identified 403 DEGs (Fig.5A). The Circos diagram shows the overlap of the DEGs obtained by a pairwise comparison of the three different m⁶A modification modes. Cluster2 had more DEGs than cluster 1, and there are more gene overlaps in the list of DEGs obtained for cluster1 compared with cluster2, and between cluster3 and cluster2 (Fig. 5B). In Fig. 5B, the blue lines connect genes belonging to the same ontology term showing the number of functional overlaps between different groups.

We used Metascape[36] to analyze the enrichment of signaling pathways between the different m⁶A modification classes. The results are shown in the heatmap which is colored by p-values, and the white cells indicate a lack of enrichment of the term in the corresponding gene list (Fig. 5C, D, and Supplementary Fig. 5A, B). Additionally, a GSEA was performed for further signal pathway enrichment analysis (Figure 5E),IL2/STAT5 signaling, EMT, inflammatory response, interferon-gamma response, and TNFα signaling via NFκB are enriched in cluster1. Allograft, Rejection, Complement, EMT, Ransition, Inflammatory Response,Kras Signaling Dn are enriched in cluster3. These signaling pathways involve different core biological processes, most of which are related to the regulation of immunity, and some of them have been confirmed to have a correlation in immunotherapy [48-50], which provides a basis for further exploration of the effect of m⁶A modifications on the immunophenotype.

3.5 The m⁶A score is an important prognostic biomarker and predictor of bladder cancer

We constructed and evaluated a scoring system called the m⁶Ascore to quantify the level of m⁶A modification in each patient sample, taking into account the individual differences and complexity of the m⁶A modification. First, we conducted a survival analysis using the m⁶Ascore and found that patients with high m⁶Ascore had a poor prognosis (p=0.036, Fig. 6A). There were also significant differences in the m⁶Ascore among the three different m⁶A modification modes, with the lowest and highest m⁶Ascores being assigned to cluster3 and cluster2, respectively (Fig. 6B). Next, we analyzed the differences in m⁶Ascore in the MDA cancer subtype and found the highest score for the basal subtype, followed by the luminal subtype, with the lowest expression level observed in the p53-like subtype (Fig. 6C). To explore the potential biological relevance of m⁶Ascore, we classified 407 patients into a high m⁶Ascore group and a low m⁶Ascore group based on the expression level of the 23 m⁶A regulators. We first examined the difference in the composition of immune cells between the two patient groups (Fig. 6D). Our results showed that CD56^dim natural killer cells, central memory CD4T cells, eosinophils, mast cells, monocytes, and type 17 T helper cells showed a significantly low enrichment in the high m⁶Ascore group. Further, the activated CD4⁺ T cells, memory B cells, natural killer T cells, neutrophils, and type 2 T helper cells showed a highly significant enrichment in the low m⁶Ascore group. We analyzed the DEGs, and additionally examined the signaling pathways showing enrichment via GO and KEGG pathway analysis between the two groups. The pathways showing enrichment for molecular function (MF), cellular component (CC), and biological process (BP) via GO analysis were examined. Signaling pathways enriched for BP include the xenobiotic metabolic process, hormone metabolic process, epidermis development, etc.; pathways enriched for CC included the apical part of the cell, apical plasma membrane, cornified envelope, etc.; and pathways enriched for MF include monooxygenase activity, serine-type endopeptidase activity, and other signaling pathways (Supplementary Fig. 5C-E). A KEGG pathway analysis showed that the DEGs were mainly enriched in glutathione metabolism, ether lipid metabolism, and other signaling pathways (Supplementary Fig. 5F). A GSEA analysis (Fig. 6E-F) showed that pathways for the inflammatory response, interferon-gamma response, myc targets V1, TNFα signaling via NFκB, and other genes were concentrated in high m⁶Ascore group. These results strongly suggest a significant correlation between a high m⁶Ascore and immune activation. The results also demonstrate the central role of m⁶A methylation in different biological processes in bladder cancer, including different aspects of immune regulation. The m⁶Ascore may be used to evaluate the characteristics of immune cell infiltration in bladder cancer tissue to predict the clinical response of patients to immunotherapy. Thus, the m⁶Ascore may have the potential to be used as an independent prognostic biomarker to predict the OS rate of patients, guiding more effective clinical practice.

3.6 Effect of m⁶A methylation modification on anti-PD-L1 immunotherapy for bladder cancer

To explore the potential effect and mechanism of m⁶A methylation on immunotherapy for bladder cancer, we obtained data for the IMvigor210 cohort, which is an open-label, multicenter, single-arm phase II clinical study to evaluate the safety and efficacy of Tecentriq, an anti-PD-L1 agent, in patients with advanced urothelial cancer(Supplementary table4). Patients who responded in whole or in part were classified as responders, and were compared with non-responders who showed the incidence of stable or progressive disease. We performed an unsupervised clustering using data for mRNA expression for the 23 m⁶A regulatory factors from all patients. A consensus clustering approach based analysis indicated the optimal number of clusters as 3 (Supplementary Fig.6A-D). We identified three different m⁶A modification patterns and calculate the m⁶Ascore for subsequent analysis. Additionally, we performed a combined heatmap based analysis (Fig. 7) to find the correlation between the different m⁶A methylation modes and the Lund and TCGA molecular typing for the anti-PD-L1 based immunotherapy. Further, for the three groups of m6A methylation modification patterns, we also analyzed the differences in PD-L1 expression in immune cells (IC) and tumor cells (TC), patients' response to anti-PDL1 efficacy, TMB, m⁶Ascore, and the important gene mutations prevalent in bladder cancer. We also analyzed the differences in the expression of genes in important biological pathways for bladder cancer^[51], including the FGFR3 gene signature; CD8 Teff signature; antigen-processing machinery; immune checkpoint signature; MKI67 and cell cycle genes; DNA replication-dependent histones; DNA damage-repair genes; TGFβ receptor and ligand; F-TBRS genes; EMT markers; and the angiogenesis signature. In the comparison between the different m⁶A modification patterns and the TCGA and Lund classification using a heat map based analysis, we found that most genes in cluster1 classified as subtype I and the least as subtype IV. For the Lund classification, we observed that the UroA subtype had the highest proportion in cluster1, the Inf type has the highest proportion in cluster2, and the SCCL subtype was more enriched in cluster1. Additionally, we found that the cluster3 modification mode had higher levels of PD-L1 expression in immune cells and tumor cells, a higher proportion of CR/PR (Fig. 7, Fig. 8A). These results suggest that m⁶A methylation may be involved in the response to anti-PDL1 therapy. Additionally, among the three different m⁶A methylation modes in bladder cancer, there were differences in gene mutations[51]for TP53, RB1, FGFR3, CDKN2A, ERBB2, and PIK3CA. Patients had a greater number of FGFR3 and CDKN2A mutations in cluster1 than in cluster1 or cluster2, while the converse was true for TP53 mutations. There were significant differences among the three m⁶A methylation patterns in most gene sets. The expression of the FGFR3 gene signature (FGFR3, TP63, WNT7B) in cluster1 was significantly higher than in the other two groups, while the CD8 Teff signature (CD8A, GZMA, GZMB, PRF1, CXCL9, CXCL10, TBX21),antigen-processing machinery (TAP1, TAP2, B2M, HLA-A, HLA-B, HLA-C), and the immune checkpoint signature (CD274, PDCD1LG2, CTLA4, PDCD1, LAG3, HAVCR2, and TIGIT) are less expressed in cluster1 than the other two modification modes, and are highly expressed in the cluster3 subgroup. The CD8 Teff and the immune checkpoint signatures are often used to predict the efficacy of immunosuppression in patients with immune checkpoint suppression; a higher expression level is linked to better clinical outcomes[5, 52]. Additionally, our results indicated that the expression of MKI67 and cell cycle genes；DNA replication-dependent histones; DNA-repair genes is significantly higher in cluster3; TGFβ receptor and ligand; F-TBRS genes; EMT markers; angiogenesis signature show significantly increased gene expression in cluster2. We have observed that there are more compact types and fewer read types in cluster1. Compared with Lund typing, there are more SCCL types in cluster1, more UroA types in cluster1, and more infs types in cluster2 (Fig. 8A). We found differences in m⁶Ascore levels among the three m⁶A modification modes, with the lowest m⁶Ascore level found in cluster2, while the m⁶Ascore of cluster3 and cluster1 were the same, with no significant difference (Fig. 8B). Differences in the m⁶Ascore were also observed in immunophenotyping, with the excluded subtype having the highest m⁶Ascore, and no significant difference in m⁶Ascore found between the inflamed and desert subtypes (Fig. 8C).

Next, we analyzed the correlation between m⁶Ascore and biological pathway gene scores[39] in bladder cancer (Fig. 8D). The m⁶Ascore was positively correlated with the scores for DNA replication, mismatch repair, Fanconi, base excision repair, nucleotide excision repair, homologous recombination, cell cycle, and DDR, and negatively correlated with EMT2, EMT1, immune checkpoint, CD8 T effector, APM, etc. The effect of the m⁶Ascore on OS was observed by plotting the KM curve, which indicated that a higher m⁶Ascore had a poor prognosis (P=0.032，Fig. 8E). Using the SubMAP algorithm, we inferred the possibility of anti-PD1 and anti- cytotoxic T-lymphocyte-associated protein 4 (CTLA4) response immunotherapy in the high and low m6Ascore groups. High ICscore group may respond better to PD-1 treatment (Bonferroni-corrected P = 0.03，Fig. 8F).

3.7 FTO may be play an important role in anti-PD-L1 immune checkpoint therapy.

The results from our data analysis showed that patients with high expression of FTO had a poor prognosis in the IMvigor210 cohort (p=0.0035; Fig. 9A), and there were differences in FTO expression in the three groups (Fig. 9B). We also queried the predictive value of FTO expression in the cohort for anti-PD-L1 immunotherapy, and found that there was a significant difference in FTO expression between non-responders and objective remission groups. Non-responsive patients showed significantly higher FTO expression (Fig. 9C), suggesting that FTO may play an important role in the anti-PD-L1 treatment of bladder cancer. Upon further examination, FTO expression was positively correlated with the EMT signaling pathway and negatively correlated with the DDR/cell cycle, nucleotide excision repair, Fanconi anemia pathway, and other signaling pathways (Fig. 9D), and additionally, was negatively correlated with the protein level of PD-L1 in immune cells (Fig. 9e). FTO had no significant correlation with the total PD-L1 level in tumor cells (Fig. 9F). The analysis of data from the TCGA database showed that the prognosis of patients with a high expression of FTO was worse (p=0.0015; Fig. 9G), and FTO expression was higher in patients with a high stage of bladder cancer (Fig. 9H). Additionally, the enrichment results of FTO-related gene pathways showed that FTO may be involved in the regulation of focal adhesion, the Hippo signaling pathway, the TGF-β signaling pathway, and other signal pathways (Fig. 9I). Previous studies have confirmed that the activation of EMT and TGF-β related pathways reduces T cell migration to tumors and weakens their tumor-killing effect[5, 51]. Therefore, FTO may play an important role in the occurrence and development of bladder cancer, the efficacy of anti-PD-L1 immune checkpoint therapy, and may be useful for predicting the prognosis.

To further understand the role of FTO in bladder cancer, we explored the mutations that may affect its function. The overall mutation level of FTO was 0.97%, and included mainly missense and nonsense mutations (Supplementary Fig. 7A). We found that no correlation existed between FTO expression and the level of PDCD1 (PD-1) and CD274 (PD-L1) mRNAs (Supplementary Fig. 7B-C), and significant differences in FTO expression were found between cancerous and paracancerous tissues in multiple patients (Supplementary Fig7. D). The expression of FTO in paracancerous tissue in bladder cancer was higher than in cancer tissues, but due to the relatively small number of paracancerous tissue samples in the TCGA database, these findings need additional data to support them. An analysis of the correlation between FTO and the level of cell infiltration in the tumor immune microenvironment in 32 cancer species indicated a high correlation between FTO expression and the immune cells of COAD, THCA, and other cancer species (Supplementary Fig. 7E). Additionally, a high correlation between FTO expression and the ESTIMATE score, immune score, and stromal scores of multiple cancer species was observed (Supplementary Fig. 7F). Radar maps showing the correlation between FTO expression and the TMB in multiple cancer species (Supplementary Fig. 7G) and microsatellite instability (MSI) are shown (Supplementary Fig. 7H).

3.8 FTO may be involved in the occurrence and development of bladder cancer.

We examined the nuclear, cytoplasmic, and overall expression levels in nine human bladder cancer cell lines: BIU87 cells, 5637 cells, T24 cells, EJ cells, RT4 cells, J82 cells, UM-UC-3 cells, TCCSUP cells, and human bladder epithelial immortalized SV-huv-1 cells (Fig. 10A). IF immunofluorescence detected FTO protein localization in two FTO high expressing cell lines (EJ and T24 cells) and two FTO low expressing cell lines (BIU87 and RT4). We found that FTO was expressed in both cytosol and cytosol of these cell lines (Fig. 10B). Subsequently, we knocked down the FTO of EJ and RT4 by siRNA. CCK-8 examination revealed that cell proliferation was reduced in the si-FTO group (Fig. 10C), and transwell assays observed reduced cell invasion in the si-FTO group (Fig. 10D). Flow cytometry revealed increased apoptosis after knockdown of FTO (Fig. 10E). The level oftotal cellular FTO-M6A enzyme activity was significantly increased (Fig. 10F), and total FTO protein was decreased in the si-FTO group (Fig. 10G). These results confirm the critical role of FTO in bladder cancer.

According to our previous study[53], we confirmed the existence of important modification sites K216 for FTO protein, including K216R, K216H, K216S, and K216E. We constructed plasmids and transfected them into BIU87 and RT4 cells, as well as detected the expression of FTO protein in each group by WB (Fig. 11A). The apoptosis was increased in the FTO-MT(K216H) and MT(K216E) groups (Fig. 11B), and the cell invasion was significantly reduced in the FTO-MT(K216H) and MT(K216E) groups as observed by transwell assay (Fig. 11C), and the total FTO-M6A enzyme activity level was significantly increased in these two groups (Fig. 11D). The above results suggest that K216H and K216E may be important targets for regulating FTO in the future.

More than 150 RNA modifications have been identified in all organisms, including 5-methylcytosine (m⁵C), N6-methyladenosine (m⁶A), and N1-methyladenosine (m¹A), of which the m⁶A RNA methylation is considered to be the most prominent and abundant form of internal modification in eukaryotic cells[54]. The study of m⁶A modification in cancer is a new field of cancer research, which may reveal a new layer of epigenetic regulation in cancer, and provide new insights into the tumorigenesis, immune response, and the molecular mechanisms underlying drug resistance during therapy. Additionally, it may lead to the development of new, effective treatments[55] through the use of effective inhibitors targeting maladjusted m⁶A regulatory factors alone (or editing targeted mutant or dysfunctional m⁶A sites through targeted transcriptomics), or in combination with other therapies. Targeting the m⁶A modification may have a strong therapeutic potential for the treatment of all types of cancers, especially those that are resistant to existing treatments[56].

Due to technical and resource constraints, most studies have focused on a single TME immune cell type or a single m⁶A. The overall infiltration characteristics of the TME mediated by the combined effects of multiple m⁶A regulators have not been widely analyzed[57, 58]. The modification of m⁶A is a reversible process that is affected by various writers, erasers, and readers. In this study, we explored the effects of multiple m⁶A regulators on the infiltration of immune cells in the TME of bladder cancer, the molecular mechanisms underlying m⁶A modification in bladder carcinogenesis, the immune response, and the drug resistance of immune checkpoint inhibitors. We also provide new insights into the role of m⁶A modification modes in TME cell infiltration and developing more effective immunotherapy strategies.

We classified patients with bladder cancer based on the expression of 23 m⁶A regulatory molecules, and identified three different m6A methylation patterns. We found that cluster2 showed unique specificity compared with the other two types. A quantification of 28 immune cells in the immune microenvironment of bladder cancer using the ssGSEA algorithm showed that cluster2 had a low level of immune cell infiltration characterized by immune inhibition, which corresponds to the immune desert phenotype. Furthermore, an analysis performed using the ESTIMATE algorithm showed that the stromal and immune scores in cluster2 were lower than the other two subtypes, which corroborates our previous results. The expression of immune checkpoint and CD8⁺T cell markers were lower in cluster2 than in the other two groups, and studies have shown that the expression of these markers is highly sensitive to immune checkpoint inhibitors[52]. Thus, different m⁶A methylation modification patterns are significantly related to immune activation, and a comprehensive evaluation of the m⁶A modification pattern will enable the understanding of the characteristics of TME cell infiltration. An analysis of DEGs between the different m⁶A modification modes uncovered m⁶A-related signaling pathway genes. These were mainly enriched in the biological pathways related to the matrix and immune activation. In the anti-PD-L1 treatment cohort, we identified three different m⁶A methylation patterns, and found significant differences in gene expression and mutations in biological pathway genes associated with the three subtypes of bladder cancer. We constructed a scoring system to quantify and evaluate the m⁶A modification level in a single tumor, and found that a high m⁶Ascore was related to poor prognosis in the TCGA and anti-PD-L1 cohorts. Patients with a high or low m⁶Ascore showed differences in immune cell infiltration in the tumor immune microenvironment. A GSEA signaling pathway analysis indicated that the high and low m⁶Ascore are mainly enriched in the matrix and immune-related signaling pathways. Together, these results indicate that the m⁶A score is a reliable tool for the comprehensive evaluation of m⁶A modification patterns in tumors in individuals, and to determine the mode of infiltration in the TME. The expression and gene changes for m6A regulatory factors are related to a variety of biological processes, especially the matrix and immune activation. Our findings may provide new impetus for improving the clinical response of patients with bladder cancer to immunotherapy, the identification of different immunophenotypes in bladder cancer, and for promoting individualized immunotherapy.

The role of FTO in cancer has recently garnered increasing attention. Previous studies conducted by our group have shown that FTO-mediated m⁶A modification plays an important role in hepatocellular carcinoma, and the SIRT1 deacetylase can play a carcinogenic role by down-regulating FTO[59]. FTO is an RNA demethylase which can remove the methyl of m⁶A in mRNA both in vitro and in vivo[60, 61].FTO has catalytic demethylation activity for both cap-m⁶Am and internal m⁶A. As the abundance of m⁶A in mRNA is much higher than that of its preferred binding target m⁶Am, the main target of FTO is m⁶A[62]. Reports indicate that the FTO-mediated demethylation of cap-m⁶Am leads to mRNA degradation[63], but the evidence for this is inconsistent. PCIF1 is the cap-m⁶Am methyltransferase, which processes cap-m⁶A alone but not internal m⁶A. Reports indicate that the cap-m⁶Am added by PCIF1 does not change the level of gene expression or the stability of transcripts[64-66]. Additionally, studies[67] have found that the spatial distribution of FTO can also play a regulatory role. The N-terminus of FTO has an NLS, which can be partially distributed in the nucleus and the cytoplasm, and the distribution of FTO is different in different cell lines, its role and regulation being affected by the environment. Yang et al[20] have shown that the induction of FTO can be used as an adaptive mechanism to combat metabolic stress in melanoma cells, thus increasing their proliferation, invasion, and migration, and promoting the tumorigenesis and development of melanoma in mice. Further, the authors showed that an FTO knockout can increase m⁶A methylation in key oncogenic melanoma cells, including the loci for PD-1 (PDCD1), CXCR4, and SOX10. The inhibition of FTO makes melanoma cells sensitive to interferon-γ (IFN-γ) and anti-PD-1 therapy in mice, indicating that FTO plays an important role in promoting the occurrence of melanoma and anti-PD-1 drug resistance. The role of m⁶A involvement with FTO in bladder cancer had not been reported, and we found that in the TCGA and IMvigor210 cohorts, patients with high FTO expression had a poor prognosis, and the expression of FTO was higher in patients with a higher stage of bladder cancer.
In addition, we analyzed the predictive value of FTO expression in the cohort subjected to anti-PD-L1 immunotherapy, and found that there was a significant difference in FTO expression between the non-responders and the objective remission group. The level of FTO expression in the patients non-responsive to anti-PD-L1 therapy was significantly higher than in patients in remission. The data showed that FTO expression was negatively correlated with the protein level of PD-L1 in immune cells (Fig. 9e), was positively correlated with the EMT signaling pathway, and negatively correlated with DDR, cell cycle, nucleoside exercise repair, fanconi anemia pathway, and other signaling pathways. The enrichment of FTO related gene pathways indicated that FTO might be involved in the regulation of focal adhesion/Hippo signaling pathway/TGF-β signaling, these results suggest that FTO plays an important role in bladder cancer. The combination of FTO targeted regulation and anti-PD-L1 blockers may have great therapeutic potential for reducing the resistance of bladder cancer to immunotherapy. Thus, this study furthers the understanding of the regulation of m⁶A modification in the tumor immune microenvironment of bladder cancer, and contributes to the potential development of new predictive indicators, drug combination strategies, and new immunotherapeutic strategies for cancer immunotherapy. However, its specific role and mechanism need further experimental study.

In this study, we systematically analyzed the mutation and correlation of 23 kinds of m⁶A regulatory factors in bladder cancer, and their effects on OS and immune invasion, We found three different patterns of m⁶A modification and compared them with other important molecular types of bladder cancer, such as MDA, Lund and Baylor，the effects of the three kinds of m⁶A modification modes on the mutation characteristics, clinicopathological characteristics, gene expression, immune cell infiltration level, and gene expression level of immune checkpoint regulators were comprehensively analyzed. Further, we investigated the effect of m⁶A modification mode on the therapeutic efficacy of bladder cancer immune checkpoint inhibitor anti-PD-L1. Our results confirmed that m⁶A methylation is involved in the process of immune cell recruitment in the TME of bladder cancer, and may affect the efficacy of anti-PD-L1 therapy. In addition, we established a method to quantify the level of m⁶A modification (m6Ascore), which we found to be an important and powerful prognostic biomarker and predictor for bladder cancer. Thus, this study furthers the understanding of the regulation of m⁶A modification in the tumor immune microenvironment of bladder cancer, and contributes to the potential development of new predictive indicators, drug combination strategies, and new immunotherapeutic strategies for cancer immunotherapy.

This study comprehensively recognized the role of m⁶A methylation modification on the invasion characteristics of bladder cancer immune microenvironment cells, and the effect on the anti-PD-L1 treatment for bladder cancer. The difference in m⁶A modification mode is an important factor indicating the heterogeneity and complexity of the tumor microenvironment, and the immunotherapy impact. The m⁶A modification mode helps decipher the molecular mechanisms underlying the immune microenvironment regulation in bladder cancer, and provide new predictive indicators, possible auxiliary targets, and directions for guiding more effective immunotherapy strategies in the future.

m⁶A: N6-methyladenosine;

TCGA: The Cancer Genome Atlas;

GEO: Gene-Expression Omnibus;

KEGG: Kyoto encyclopedia of gene and genome

GO: Gene ontology

GSEA: Gene set enrichment analysis

SsGSEA: The single-sample gene set enrichment analysis

TME: Tumor microenvironment

TMB: Tumor mutation burden

ICT: The immune checkpoint therapy

CTLA-4: Cytotoxic lymphocyte antigen-4

PD-1: Programmed cell death protein 1

PD-L1: Programmed-death ligand 1

DCs: Dendritic cells;

DEGs: Differentially expressed genes;

EMT: Epithelial-mesenchymal transition;

ICB: Immunological checkpoint blockade;

TGFb: Transforming growth factor beta;

Authors contributions

XYC and KC designed the study, XYC,DH and MQX analyzed and interpreted the data, XYC,YXZ,LG,HL AND YXC wrote this manuscript. YJ,ZWW,XML and QT edited and revised the manuscript.All aythors have seen and approved the final version of the manuscript.

Acknowledgements

We thank our colleagues in the department of laboratory medicine for helpful discussions and valuable assistance.

Conflicts of interest

The authors have declared that no conflict of interest exists.

Consent for publication

All authors have agreed on the contents of the manuscript.

Funding

This work was supported by Supported by the science and technology innovation Program of Hunan Province(2020RC2066), Hunan Provincial Natural Science Foundation of China(2020JJ5335), Science and Technology Foundation of Changsha City(kq1907127), Health Commission Science Foundation of Hunan Province(20200957), Hunan Cancer Hospital Climb Plan(QH201904),the science and technology innovation Program of Hunan Province (2021RC204),the National Science Foundation of China (81874137).

Authors' contributions

HYD, XYC and KC designed the study, HYD and FQT analyzed and interpreted the data, HYD, FQT and MZ wrote this manuscript. HYD, FQT, MZ and DYS edited and revised the manuscript. All authors have seen and approved the final version of the manuscript.

Ethics approval and consent to participate

The experimental protocol was established, according to the ethical guidelines of the Helsinki Declaration and was approved by the Human Ethics Committee of Ethical Review Committee of the Third Xiangya Hospital of Central South University. Written informed consent was obtained from individual or guardian participants.

Availability of data and materials

All data used in this work can be acquired from the GDC portal (https://portal.gdc.cancer.gov/) and the IMvigor210CoreBiologies（http://research-pub.gene.com/IMvigor210CoreBiologies）

1. Herbst RS, Soria JC, Kowanetz M, Fine GD, Hamid O, Gordon MS, Sosman JA, McDermott DF, Powderly JD, Gettinger SN, et al: Predictive correlates of response to the anti-PD-L1 antibody MPDL3280A in cancer patients. Nature 2014, 515:563-567.

2. Inman BA, Longo TA, Ramalingam S, Harrison MR: Atezolizumab: A PD-L1-Blocking Antibody for Bladder Cancer. Clin Cancer Res 2017, 23:1886-1890.

3. Powles T, Eder JP, Fine GD, Braiteh FS, Loriot Y, Cruz C, Bellmunt J, Burris HA, Petrylak DP, Teng SL, et al: MPDL3280A (anti-PD-L1) treatment leads to clinical activity in metastatic bladder cancer. Nature 2014, 515:558-562.

4. Davis AA, Patel VG: The role of PD-L1 expression as a predictive biomarker: an analysis of all US Food and Drug Administration (FDA) approvals of immune checkpoint inhibitors. Journal for Immunotherapy of Cancer 2019, 7.

5. Gomez de Liano Lista A, van Dijk N, de Velasco Oria de Rueda G, Necchi A, Lavaud P, Morales-Barrera R, Alonso Gordoa T, Maroto P, Ravaud A, Duran I, et al: Clinical outcome after progressing to frontline and second-line Anti-PD-1/PD-L1 in advanced urothelial cancer. Eur Urol 2020, 77:269-276.

6. Wolacewicz M, Hrynkiewicz R, Grywalska E, Suchojad T, Leksowski T, Rolinski J, Niedzwiedzka-Rystwej P: Immunotherapy in Bladder Cancer: Current Methods and Future Perspectives. Cancers (Basel) 2020, 12.

7. Bellmunt J, Powles T, Vogelzang NJ: A review on the evolution of PD-1/PD-L1 immunotherapy for bladder cancer: The future is now. Cancer Treat Rev 2017, 54:58-67.

8. Schneider AK, Chevalier MF, Derre L: The multifaceted immune regulation of bladder cancer. Nat Rev Urol 2019, 16:613-630.

9. Tang H, Wang Y, Chlewicki LK, Zhang Y, Guo J, Liang W, Wang J, Wang X, Fu YX: Facilitating T Cell Infiltration in Tumor Microenvironment Overcomes Resistance to PD-L1 Blockade. Cancer Cell 2016, 29:285-296.

10. Binnewies M, Roberts EW, Kersten K, Chan V, Fearon DF, Merad M, Coussens LM, Gabrilovich DI, Ostrand-Rosenberg S, Hedrick CC, et al: Understanding the tumor immune microenvironment (TIME) for effective therapy. Nat Med 2018, 24:541-550.

11. Tsukamoto H, Fujieda K, Miyashita A, Fukushima S, Ikeda T, Kubo Y, Senju S, Ihn H, Nishimura Y, Oshiumi H: Combined Blockade of IL6 and PD-1/PD-L1 Signaling Abrogates Mutual Regulation of Their Immunosuppressive Effects in the Tumor Microenvironment. Cancer Res 2018, 78:5011-5022.

12. Dai D, Wang H, Zhu L, Jin H, Wang X: N6-methyladenosine links RNA metabolism to cancer progression. Cell Death Dis 2018, 9:124.

13. Tuncel G, Kalkan R: Importance of m N(6)-methyladenosine (m(6)A) RNA modification in cancer. Med Oncol 2019, 36:36.

14. Malla S, Melguizo-Sanchis D, Aguilo F: Steering pluripotency and differentiation with N(6)-methyladenosine RNA modification. Biochim Biophys Acta Gene Regul Mech 2019, 1862:394-402.

15. Zhang Z, Luo K, Zou Z, Qiu M, Tian J, Sieh L, Shi H, Zou Y, Wang G, Morrison J, et al: Genetic analyses support the contribution of mRNA N(6)-methyladenosine (m(6)A) modification to human disease heritability. Nat Genet 2020.

16. Wang S, Sun C, Li J, Zhang E, Ma Z, Xu W, Li H, Qiu M, Xu Y, Xia W, et al: Roles of RNA methylation by means of N(6)-methyladenosine (m(6)A) in human cancers. Cancer Lett 2017, 408:112-120.

17. Li HB, Tong J, Zhu S, Batista PJ, Duffy EE, Zhao J, Bailis W, Cao G, Kroehling L, Chen Y, et al: m(6)A mRNA methylation controls T cell homeostasis by targeting the IL-7/STAT5/SOCS pathways. Nature 2017, 548:338-342.

18. Wang H, Hu X, Huang M, Liu J, Gu Y, Ma L, Zhou Q, Cao X: Mettl3-mediated mRNA m(6)A methylation promotes dendritic cell activation. Nat Commun 2019, 10:1898.

19. Han D, Liu J, Chen C, Dong L, Liu Y, Chang R, Huang X, Liu Y, Wang J, Dougherty UJN: Anti-tumour immunity controlled through mRNA m6A methylation and YTHDF1 in dendritic cells. 2019, 566:270-274.

20. Yang S, Wei J, Cui YH, Park G, Shah P, Deng Y, Aplin AE, Lu Z, Hwang S, He C, He YY: m(6)A mRNA demethylase FTO regulates melanoma tumorigenicity and response to anti-PD-1 blockade. Nat Commun 2019, 10:2782.

21. Mariathasan S, Turley SJ, Nickles D, Castiglioni A, Yuen K, Wang YL, Kadel EE, Koeppen H, Astarita JL, Cubas R, et al: TGF beta attenuates tumour response to PD-L1 blockade by contributing to exclusion of T cells. Nature 2018, 554:544-+.

22. Rosenberg JE, Hoffman-Censits J, Powles T, van der Heijden MS, Balar AV, Necchi A, Dawson N, O'Donnell PH, Balmanoukian A, Loriot Y, et al: Atezolizumab in patients with locally advanced and metastatic urothelial carcinoma who have progressed following treatment with platinum-based chemotherapy: a single-arm, multicentre, phase 2 trial. Lancet 2016, 387:1909-1920.

23. Shi HL, Wei JB, He C: Where, When, and How: Context-Dependent Functions of RNA Methylation Writers, Readers, and Erasers. Molecular Cell 2019, 74:640-650.

24. Yang Y, Hsu PJ, Chen YS, Yang YG: Dynamic transcriptomic m(6)A decoration: writers, erasers, readers and functions in RNA metabolism. Cell Res 2018, 28:616-624.

25. Wilkerson MD, Hayes DN: ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking. Bioinformatics 2010, 26:1572-1573.

26. Mo Q, Nikolos F, Chen F, Tramel Z, Lee YC, Hayashi K, Xiao J, Shen J, Chan KS: Prognostic Power of a Tumor Differentiation Gene Signature for Bladder Urothelial Carcinomas. J Natl Cancer Inst 2018, 110:448-459.

27. Damrauer JS, Hoadley KA, Chism DD, Fan C, Tiganelli CJ, Wobker SE, Yeh JJ, Milowsky MI, Iyer G, Parker JS, Kim WY: Intrinsic subtypes of high-grade bladder cancer reflect the hallmarks of breast cancer biology. Proceedings of the National Academy of Sciences of the United States of America 2014, 111:3110-3115.

28. Choi W, Porten S, Kim S, Willis D, Plimack ER, Hoffman-Censits J, Roth B, Cheng TW, Tran M, Lee IL, et al: Identification of Distinct Basal and Luminal Subtypes of Muscle-Invasive Bladder Cancer with Different Sensitivities to Frontline Chemotherapy. Cancer Cell 2014, 25:152-165.

29. Marzouka NA, Eriksson P, Rovira C, Liedberg F, Sjodahl G, Hoglund M: A validation and extended description of the Lund taxonomy for urothelial carcinoma using the TCGA cohort. Scientific Reports 2018, 8.

30. Robertson AG, Kim J, Al-Ahmadie H, Bellmunt J, Guo G, Cherniack AD, Hinoue T, Laird PW, Hoadley KA, Akbani R, et al: Comprehensive Molecular Characterization of Muscle-Invasive Bladder Cancer. Cell 2017, 171:540-556 e525.

31. Rebouissou S, Bernard-Pierrot I, de Reynies A, Lepage ML, Krucker C, Chapeaublanc E, Herault A, Kamoun A, Caillault A, Letouze E, et al: EGFR as a potential therapeutic target for a subset of muscle-invasive bladder cancers presenting a basal-like phenotype. Science Translational Medicine 2014, 6.

32. Kamoun A, de Reynies A, Allory Y, Sjodahl G, Robertson AG, Seiler R, Hoadley KA, Groeneveld CS, Al-Ahmadie H, Choi W, et al: A Consensus Molecular Classification of Muscle-invasive Bladder Cancer. Eur Urol 2020, 77:420-433.

33. Jia Q, Wu W, Wang Y, Alexander PB, Sun C, Gong Z, Cheng JN, Sun H, Guan Y, Xia X, et al: Local mutational diversity drives intratumoral immune heterogeneity in non-small cell lung cancer. Nat Commun 2018, 9:5361.

34. Newman AM, Liu CL, Green MR, Gentles AJ, Feng W, Xu Y, Hoang CD, Diehn M, Alizadeh AA: Robust enumeration of cell subsets from tissue expression profiles. Nat Methods 2015, 12:453-457.

35. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK: limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 2015, 43:e47.

36. Zhou Y, Zhou B, Pache L, Chang M, Khodabakhshi AH, Tanaseichuk O, Benner C, Chanda SK: Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun 2019, 10:1523.

37. Castro MA, de Santiago I, Campbell TM, Vaughn C, Hickey TE, Ross E, Tilley WD, Markowetz F, Ponder BA, Meyer KB: Regulators of genetic risk of breast cancer identified by integrative network analysis. Nat Genet 2016, 48:12-21.

38. Yu G, Wang LG, Han Y, He QY: clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 2012, 16:284-287.

39. Zeng Q, Liu J, Cao P, Li J, Liu X, Fan X, Liu L, Cheng Y, Xiong W, Li J, et al: Inhibition of REDD1 Sensitizes Bladder Urothelial Carcinoma to Paclitaxel by Inhibiting Autophagy. Clin Cancer Res 2018, 24:445-459.

40. Hazra A, Gogtay N: Biostatistics Series Module 3: Comparing Groups: Numerical Variables. Indian J Dermatol 2016, 61:251-260.

41. DeGraff DJ, Cates JM, Mauney JR, Clark PE, Matusik RJ, Adam RM: When urothelial differentiation pathways go wrong: Implications for bladder cancer development and progression. Urologic Oncology-Seminars and Original Investigations 2013, 31:802-811.

42. Choi W, Czemiak B, Ochoa A, Su XP, Siefker-Radtke A, Dinney C, McConkey DJ: Intrinsic basal and luminal subtypes of muscle-invasive bladder cancer. Nature Reviews Urology 2014, 11:400-410.

43. Eriksson P, Aine M, Veerla S, Liedberg F, Sjodahl G, Hoglund M: Molecular subtypes of urothelial carcinoma are defined by specific gene regulatory systems. Bmc Medical Genomics 2015, 8.

44. Breyer J, Wirtz RM, Laible M, Schlombs K, Erben P, Kriegmair MC, Stoehr R, Eidt S, Denzinger S, Burger M, et al: ESR1, ERBB2, and Ki67 mRNA expression predicts stage and grade of non-muscle-invasive bladder carcinoma (NMIBC). Virchows Archiv 2016, 469:547-552.

45. Dadhania V, Zhang M, Zhang L, Bondaruk J, Majewski T, Siefker-Radtke A, Guo CC, Dinney C, Cogdell DE, Zhang SZ, et al: Meta-Analysis of the Luminal and Basal Subtypes of Bladder Cancer and the Identification of Signature Immunohistochemical Markers for Clinical Use. Ebiomedicine 2016, 12:105-117.

46. Jones RT, Felsenstein KM, Theodorescu D: Pharmacogenomics: Biomarker-Directed Therapy for Bladder Cancer. Urologic Clinics of North America 2016, 43:77-+.

47. Lim S, Koh MJ, Jeong HJ, Cho NH, Choi YD, Cho DY, Lee HY, Rha SY: Fibroblast Growth Factor Receptor 1 Overexpression Is Associated with Poor Survival in Patients with Resected Muscle Invasive Urothelial Carcinoma. Yonsei Medical Journal 2016, 57:831-839.

48. Liu RY, Zeng YY, Lei Z, Wang LQ, Yang HP, Liu ZY, Zhao J, Zhang HT: JAK/STAT3 signaling is required for TGF-beta-induced epithelial-mesenchymal transition in lung cancer cells. International Journal of Oncology 2014, 44:1643-1651.

49. Gu GY, Gao TH, Zhang L, Chen XY, Pang Q, Wang YN, Wang D, Li J, Liu Q: NKAP alters tumor immune microenvironment and promotes glioma growth via Notch1 signaling. Journal of Experimental & Clinical Cancer Research 2019, 38.

50. Karn T, Denkert C, Weber KE, Holtrich U, Hanusch C, Sinn BV, Higgs B, Jank P, Huober J, Blohmer JU, et al: Tumour mutational burden and immune infiltration as independent predictors of response to neoadjuvant immune checkpoint inhibition in early TNBC in GeparNuevo. Annals of Oncology 2020, 31:S58-S58.

51. TGF beta attenuates tumour response to PD-L1 blockade by contributing to exclusion of T cells %J Nature. 2018, 554:544-548.

52. Walshaw RC, Honeychurch J, Illidge TM, Choudhury A: The anti-PD-1 era - an opportunity to enhance radiotherapy for patients with bladder cancer. Nat Rev Urol 2018, 15:251-259.

53. Liu X, Liu J, Xiao W, Zeng Q, Bo H, Zhu Y, Gong L, He D, Xing X, Li R, et al: SIRT1 Regulates N(6) -Methyladenosine RNA Modification in Hepatocarcinogenesis by Inducing RANBP2-Dependent FTO SUMOylation. Hepatology 2020, 72:2029-2050.

54. Liu J, Dou X, Chen C, Chen C, Liu C, Xu MM, Zhao S, Shen B, Gao Y, Han D, He C: N (6)-methyladenosine of chromosome-associated regulatory RNA regulates chromatin state and transcription. Science 2020, 367:580-586.

55. Zhou Z, Lv J, Yu H, Han J, Yang X, Feng D, Wu Q, Yuan B, Lu Q, Yang H: Mechanism of RNA modification N6-methyladenosine in human cancer. Mol Cancer 2020, 19:104.

56. Huang H, Weng H, Chen J: m(6)A Modification in Coding and Non-coding RNAs: Roles and Therapeutic Implications in Cancer. Cancer Cell 2020, 37:270-288.

57. Zhang B, Wu Q, Li B, Wang D, Wang L, Zhou YL: m(6)A regulator-mediated methylation modification patterns and tumor microenvironment infiltration characterization in gastric cancer. Mol Cancer 2020, 19:53.

58. He L, Li H, Wu A, Peng Y, Shu G, Yin G: Functions of N6-methyladenosine and its role in cancer. Mol Cancer 2019, 18:176.

59. Liu X, Liu J, Xiao W, Zeng Q, Bo H, Zhu Y, Gong L, He D, Xing X, Li R, et al: SIRT1 regulates N(6) -methyladenosine RNA modification in hepatocarcinogenesis by inducing RANBP2-dependent FTO SUMOylation. Hepatology 2020.

60. Mauer J, Luo X, Blanjoie A, Jiao X, Grozhik AV, Patil DP, Linder B, Pickering BF, Vasseur JJ, Chen Q, et al: Reversible methylation of m(6)Am in the 5' cap controls mRNA stability. Nature 2017, 541:371-375.

61. Fu Y, Jia G, Pang X, Wang RN, Wang X, Li CJ, Smemo S, Dai Q, Bailey KA, Nobrega MA, et al: FTO-mediated formation of N6-hydroxymethyladenosine and N6-formyladenosine in mammalian RNA. Nat Commun 2013, 4:1798.

62. Wei J, Liu F, Lu Z, Fei Q, Ai Y, He PC, Shi H, Cui X, Su R, Klungland A, et al: Differential m(6)A, m(6)Am, and m(1)A Demethylation Mediated by FTO in the Cell Nucleus and Cytoplasm. Mol Cell 2018, 71:973-985 e975.

63. Zhang X, Wei LH, Wang Y, Xiao Y, Liu J, Zhang W, Yan N, Amu G, Tang X, Zhang L, Jia G: Structural insights into FTO's catalytic mechanism for the demethylation of multiple RNA substrates. Proc Natl Acad Sci U S A 2019, 116:2919-2924.

64. Sendinc E, Valle-Garcia D, Dhall A, Chen H, Henriques T, Navarrete-Perea J, Sheng W, Gygi SP, Adelman K, Shi Y: PCIF1 Catalyzes m6Am mRNA Methylation to Regulate Gene Expression. Mol Cell 2019, 75:620-630 e629.

65. Boulias K, Toczydlowska-Socha D, Hawley BR, Liberman N, Takashima K, Zaccara S, Guez T, Vasseur JJ, Debart F, Aravind L, et al: Identification of the m(6)Am Methyltransferase PCIF1 Reveals the Location and Functions of m(6)Am in the Transcriptome. Mol Cell 2019, 75:631-643 e638.

66. Akichika S, Hirano S, Shichino Y, Suzuki T, Nishimasu H, Ishitani R, Sugita A, Hirose Y, Iwasaki S, Nureki O, Suzuki T: Cap-specific terminal N (6)-methylation of RNA by an RNA polymerase II-associated methyltransferase. Science 2019, 363.

67. Sanchez-Pulido L, Andrade-Navarro MA: The FTO (fat mass and obesity associated) gene codes for a novel member of the non-heme dioxygenase superfamily. BMC Biochem 2007, 8:23.

SupplementaryFig.1.pdf
Three m6A modification patterns were identified in the TCGA-BLCA cohort through unsupervised clustering of 23 m6A regulatory factors. A-D. Consensus matrices of the TCGA-BLCA cohort for k=2-5. E. Waterfall diagram showing the influence of the three m6A modification modes on the mutation of the top 20 genes in the TCGA-BLCA cohort. Each column represents the patient, each color represents the mutation type, and gray indicates that there is no mutation in the gene in this sample. The bar graph above shows the TMB, and the number on the right shows the mutation frequency of each gene.
SupplementaryFig.2.pdf
GISTIC analysis of foci of recurrent amplification and deletion Detailed cytoband with focal amplification (A) and focal deletion (B) in the Cluster 1, Cluster 2 and Cluster 3 generated with GISTIC 2.0 software. The q value of each locus is plotted horizontally.
SupplementaryFig.3.pdf
Copy number alterations across three m6A modification modes Copy number gistic score(A) and Copy number profiles(B) for the Cluster 1, Cluster 2 and Cluster 3, with gains in dark red and losses in midnight blue. Gene segments are placed according to their location on chromosomes, ranging from chromosome 1 to chromosome 22.
SupplementaryFig.4.pdf
Difference of immune cell infiltration characteristics in tumor immune microenvironment for the three m6A modification modes in the TCGA-BLCA cohort A. The box plot shows the difference for 28 immune cell infiltration levels in the three m6A modification modes; the asterisk represents the statistical p-value (*P<0.05; **P<0.01; ***P<0.001, ****P<0.0001). B-C. The differences in the antitumor immunity score and pro-tumor immunity score among three kinds of m6A modification patterns; the asterisk represents the statistical p-value.
SupplementaryFig.5.pdf
Signaling pathway enrichment analysis A-B. In the TCGA-BLCA cohort, there are three different m6A modification patterns, and different gene signaling pathways are enriched; the same enrichment network has its nodes displayed as pies. Each pie sector is proportional to the number of hits that originated from a gene list. Color code for the pie sector represents a gene list, and is consistent with the colors used for the table rows in the slide. The same enrichment network has its nodes colored by p-value, as shown in the legend. Darker color indicates more statistically significant nodes (see legend for p-value ranges). C-E． The enrichment analysis of Gene Ontology (GO) signaling pathways in the high and low m6A groups was divided into three parts: C, cellular component (CC); D, biological process (BP); E, molecular function (MF). F．Enrichment analysis of KEGG signaling pathways of DEGs in high and low m6A groups.
SupplementaryFig.6.pdf
Unsupervised clustering of 23 m6A regulatory factors in the IMvigor 210 cohort A-D. Consensus matrices of the IMvigor210 cohort for k=2-5.
SupplementaryFig.7.pdf
Analysis of the correlation between FTO and immunotherapy A. Total mutation level of FTO in the TCGA-BLCA cohort B. The correlation between FTO levels and the PDCD1 and CD274 mRNA levels in the TCGA-BLCA cohort was studied using Pearson correlation analysis. C-D. The expression of FTO was different in cancer tissues and paracancerous tissues of several cancer species in the TCGA database; the asterisk represents the statistical p-value (*P<0.05; **P<0.01; ***P<0.001, ****P<0.0001). E. Analysis of the correlation between FTO and the cell infiltration level in the tumor immune microenvironment in 32 kinds of cancer. Red represents a positive correlation, purple represents a negative correlation, darker color indicates a higher correlation. Asterisk represents statistical p-value (*P<0.05, **P <0.01, ***P<0.001). F. Circos diagram showing the correlation between FTO and ESTIMATE score, immune score, and stromal score in 32 kinds of cancer. Red represents a positive correlation, purple represents a negative correlation. The deeper the color, the higher the correlation. G. The correlation between the expression of FTO and TMB in several kinds of cancer is shown by radar images. H. The correlation between the expression of FTO and microsatellite stability (MSI) is demonstrated using radar images.
Supplementarytable.xlsx

Download PDF

Journal Publication

published 02 Mar, 2022

Read the published version in Frontiers in Oncology →

Version 1

posted

You are reading this latest preprint version

Identification and Validation of N6-methyladenosine-related Biomarkers for Bladder Cancer: Implications for Immunotherapy

Status:

Journal Publication

Version 1

Abstract

Figures

1. Introduction

2. Material And Methods

2.1 Datasets

2.2 Consensus clustering for 23 m⁶A regulators

2.3 Common molecular typing of bladder cancer

2.4 Estimation of TME cell infiltration

2.5 Identification of differentially expressed genes (DEGs) among three types of m6A modified modes

2.6 Differential signaling pathway enrichment among the three m6A modified modes

2.7 Construction of a scoring system to evaluate the level of m⁶A modification in individual samples

2.9 Relationship between methylation of m⁶A and the core biological pathways impacted due to bladder cancer

2.10 Cell lines and cell culture

2.11 Cell viability, apoptosis, and invasion assay

2.12 Western blotting

2.13 Measurement of total m6A assay

2.14 Statistical analysis

3. Results

3.1 Survey of 23 m⁶A regulatory factors in bladder cancer

3.2 Three different m⁶A methylation modification patterns defined by the 23 m⁶A regulatory factors

3.3 Difference in immune cell infiltration characteristics in the tumor immune microenvironment for different m⁶A modification modes

3.4 Enrichment of DEGs and the signaling pathways involved in the different m⁶A modification modes

3.5 The m⁶A score is an important prognostic biomarker and predictor of bladder cancer

3.6 Effect of m⁶A methylation modification on anti-PD-L1 immunotherapy for bladder cancer

3.7 FTO may be play an important role in anti-PD-L1 immune checkpoint therapy.

3.8 FTO may be involved in the occurrence and development of bladder cancer.

4. Discussion

Conclusion

Abbreviations

Declarations

References

Supplementary Files

Status:

Journal Publication

Version 1

Identification and Validation of N6-methyladenosine-related Biomarkers for Bladder Cancer: Implications for Immunotherapy

Status:

Journal Publication

Version 1

Abstract

Figures

1. Introduction

2. Material And Methods

2.1 Datasets

2.2 Consensus clustering for 23 m6A regulators

2.3 Common molecular typing of bladder cancer

2.4 Estimation of TME cell infiltration

2.5 Identification of differentially expressed genes (DEGs) among three types of m6A modified modes

2.6 Differential signaling pathway enrichment among the three m6A modified modes

2.7 Construction of a scoring system to evaluate the level of m6A modification in individual samples

2.9 Relationship between methylation of m6A and the core biological pathways impacted due to bladder cancer

2.10 Cell lines and cell culture

2.11 Cell viability, apoptosis, and invasion assay

2.12 Western blotting

2.13 Measurement of total m6A assay

2.14 Statistical analysis

3. Results

3.1 Survey of 23 m6A regulatory factors in bladder cancer

3.2 Three different m6A methylation modification patterns defined by the 23 m6A regulatory factors

3.3 Difference in immune cell infiltration characteristics in the tumor immune microenvironment for different m6A modification modes

3.4 Enrichment of DEGs and the signaling pathways involved in the different m6A modification modes

3.5 The m6A score is an important prognostic biomarker and predictor of bladder cancer

3.6 Effect of m6A methylation modification on anti-PD-L1 immunotherapy for bladder cancer

3.7 FTO may be play an important role in anti-PD-L1 immune checkpoint therapy.

3.8 FTO may be involved in the occurrence and development of bladder cancer.

4. Discussion

Conclusion

Abbreviations

Declarations

References

Supplementary Files

Status:

Journal Publication

Version 1

2.2 Consensus clustering for 23 m⁶A regulators

2.7 Construction of a scoring system to evaluate the level of m⁶A modification in individual samples

2.9 Relationship between methylation of m⁶A and the core biological pathways impacted due to bladder cancer

3.1 Survey of 23 m⁶A regulatory factors in bladder cancer

3.2 Three different m⁶A methylation modification patterns defined by the 23 m⁶A regulatory factors

3.3 Difference in immune cell infiltration characteristics in the tumor immune microenvironment for different m⁶A modification modes

3.4 Enrichment of DEGs and the signaling pathways involved in the different m⁶A modification modes

3.5 The m⁶A score is an important prognostic biomarker and predictor of bladder cancer

3.6 Effect of m⁶A methylation modification on anti-PD-L1 immunotherapy for bladder cancer