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N6-Methyladenosine-related alternative splicing events play a role in bladder cancer

  • Yuan Chang , Shukun Yu EMAIL logo , Miao Zhang , Xiuyun Zhu , Tianshu Jiang and Yuan Liu
Published/Copyright: October 14, 2022
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Open Life Sciences
From the journal Open Life Sciences Volume 17 Issue 1

Abstract

This study investigated the role of N6-methyladenosine (m6A) and alternative splicing (AS) in bladder cancer (BLCA). The BLCA-related RNA expression profiles and AS events were downloaded from the UCSC Xena and SpliceSeq databases, respectively. Differentially expressed AS (DEAS) was screened, and prognostic-related DEAS events were used to construct prognostic risk models based on Cox proportional hazards regression analysis. Receiver operating characteristic curves and multivariate Cox analysis were used to evaluate the predictive efficiency and independence of these models. We also constructed a protein-to-protein interaction (PPI) network and a regulation network of splicing factors (SFs) and DEAS events. In total, 225 m6A-related prognostic-related DEAS events were identified. The predictive ability of each prognostic model was good, and the alternate terminator model showed the best performance when the area under the curve was 0.793. The risk score of the model was an independent prognostic factor for BLCA. The PPI network revealed that AKT serine/threonine kinase 1, serine- and arginine-rich SF6, and serine- and arginine-rich SF2 had higher-node degrees. A complex regulator correlation was shown in the SF and DEAS networks. This study provides insights for the subsequent understanding of the role of AS events in BLCA.

Keywords: bladder cancer; N6-methyladenosine; alternative splicing; enrichment analysis; splicing factor; independent prognostic factor

1 Introduction

Bladder cancer (BLCA) is a disease where a malignant tumor develops in the mucous membrane of the bladder and is a common malignant tumor in the urinary system. The etiology of BLCA is complicated and includes both external environmental and internal genetic factors [1]. The Bacillus Calmette–Guérin vaccine is mainly used to treat and prevent recurrence after transurethral resection of bladder tumors. Patients who do not respond to the drug require additional treatments. Advances in genetic understanding of BLCA and immunotherapy have led to new treatments [2,3].

Like many diseases involving tumors, BLCA can inactivate cytotoxic T cells by downregulating tumor antigen expression. Similarly, the immune system can be avoided by updating immune checkpoints and maintaining the immune environment [4]. Many immune-based biomarkers are being studied for the development of BLCA biomarkers. These studies include predictive biomarkers for patient selection, pharmacodynamic markers of target engagement, and identification and standardization of surrogate markers/endpoints to develop immunotherapy [5].

N6-Methyladenosine (m6A) is the most common form of RNA modification, and its regulatory factors are involved in the development of many cancers [6]. The level of modification of transcript m6A is regulated by “writers,” “readers,” and “erasers” [7]. Methyltransferases are known as “writers” and include KIAA1429, methyltransferase 3, N6-adenosine-methyltransferase complex catalytic subunit (METTL3), methyltransferase 14, N6-adenosine-methyltransferase subunit (METTL14), RNA-binding motif protein 15 (RBM15), WT1-associated protein (WTAP), and zinc finger CCCH-type containing 13 (ZC3H13). Its main function is to add a methyl group to the nitrogen of the sixth carbon of the aromatic ring of an adenosine residue [8]. “Reader” m6A-binding proteins mainly include heterogeneous nuclear ribonucleoprotein C (HNRNPC), YTH domain containing 1 (YTHDC1), YTH domain containing 2 (YTHDC2), YTH m6A RNA-binding protein 1 (YTHDF1), and YTH m6A RNA-binding protein 2 (YTHDF2). It preferentially binds to RNA and regulates downstream functions [9]. Demethylase, an “eraser,” specifically targets RNA m6A, mainly AlkB homolog 5, RNA demethylase (ALKBH5), and FTO alpha-ketoglutarate-dependent dioxygenase (FTO) [10]. Dysregulation of m6A regulatory factors leads to reduced cell proliferation, loss of self-renewal ability, developmental defects, and cell death [11]. Recently, some scholars have started studying the relationship between m6A RNA methylation regulators and BLCA. Han et al. reported that METTL3 promotes bladder tumor proliferation in an m6A-dependent manner by accelerating pri-miR221/222 [12]. Ying et al. reported that BLAC development could be promoted by m6A modification of CUB domain containing protein 1 mRNA [13].

Alternative splicing (AS) refers to the production of multiple types of mRNA from a single gene and is an important mechanism of post-transcriptional regulation. AS affects the normal growth and development of the human body and plays an important role in the occurrence and development of many diseases, including cancer. Studies have found that the loss of wild-type periostin by downregulation or AS, which produces Variant I, is closely correlated with the development of BLCA [14]. A study also found that splicing mutations were more common in both BLCA and normal bladder tissues [15].

However, it is still unclear how m6A methylation-related AS affects BLCA. In this study, we investigated how AS events related to BLCA prognosis and m6A methylation play a role in BLCA to provide new possible targets for future treatment and intervention of BLCA. A flow chart of the overall study design is shown in Figure 1.

Figure 1 Research flow chart.
Figure 1

Research flow chart.

2 Materials and methods

2.1 Data collection

The cohort gene expression RNAseq-RSEM transcripts per million of BLCA was downloaded from The Cancer Genome Atlas (TCGA) database, and 407 BLCA samples were obtained (https://portal.gdc.cancer.gov/projects/TCGA-BLCA). The Genotype-Tissue Expression database was used to download the normal bladder samples’ information, and 28 normal bladder samples were obtained (https://www.gtexportal.org).

The specific percentage spliced in (PSI) value for the AS event of TCGA-BLCA was downloaded from the SpliceSeq database (http://projects.insilico.us.com/TCGASpliceSeq/) [16]. To generate as reliable a set of AS events as possible, we implemented a series of stringent filters (percentage of samples with PSI value ≥75, average PSI value ≥0.05). A total of 418 samples were obtained, including 19 normal and 405 tumor samples.

2.2 Differential expression analysis

The seven AS events were alternate acceptors (AA), alternate donors (AD), exon skipping (ES), retained intron (RI), alternate promoters (AP), alternate terminators (AT), and mutually exclusive exons (ME). Each AS event detected by the PSI value in BLCA vs. normal tissue was identified and counted using the software R-3.4.3. The corresponding p values were obtained using the t-test in the generalized linear model, and the difference multiplication relations were calculated to screen the AS with significant differences. Finally, an adjusted p value of <0.05 was set as a criterion to screen the significantly differentially expressed AS (DEAS).

2.3 Screening of m6A methylation-related DEAS

The expression of m6A methylation regulators [METTL3, METTL14, METTL15, WTAP, RBM15, RNA-binding motif protein 15B, KIAA1429, ZC3H13, FTO, ALKBH5, RNA-binding motif protein X-linked, YTHDC1, YTHDC2, insulin-like growth factor 2 mRNA-binding protein 1, insulin-like growth factor 2 mRNA-binding protein 2, insulin-like growth factor 2 mRNA-binding protein 3, YTHDF1, YTHDF2, YTH m6A RNA-binding protein 3, heterogeneous nuclear ribonucleoprotein a2/b1 (HNRNPA2B1), and HNRNPC] was extracted from the BLCA samples from TCGA. The Pearson correlation coefficient between the expression levels of the DEAS-corresponding genes and the expression levels of the m6A methylation regulators was calculated, and p < 0.05 and ǀrǀ > 0.5 were set as the threshold to identify the m6A methylation-related DEAS.

In addition, we analyzed the correlation between PSI of AS events and the expression levels of m6A methylation regulators.

2.4 Prognostic analysis of m6A methylation-related DEAS

The prognosis information of BLCA, including overall survival (OS) and OS status, was downloaded from TCGA database. Prognostic-related m6A methylation-related DEAS was screened using univariate Cox hazard analysis in the survival package (version 2.41-1, http://bioconductor.org/packages/survivalr/) of R3.6.1. Statistical significance was set at p < 0.05.

2.5 Functional enrichment and pathway analysis of differentially expressed genes

The prognostic-related m6A methylation-related DEAS in the same AS type was used to perform Gene Ontology biological process (GO BP) [17] and Kyoto Encyclopedia of Genes and Genomes (KEGG) [18] enrichment analysis using clusterProfiler [19]. The significance threshold of the hypergeometric test p < 0.01 was set as the significant enrichment result.

2.6 Protein-to-protein interaction (PPI) network and module mining analysis

The interaction between proteins was analyzed using the STRING database (Version: 11.0, http://www.string-db.org/) [20]. The PPI score was set to 0.4. The network was then constructed using Cytoscape (version 3.6.1) software [21] after the PPI edges were obtained.

2.7 Relationship between splicing factors (SFs) and the prognostic-related m6A methylation-related DEAS

To study the relationship between SFs and prognostic-related m6A methylation-related DEAS, we obtained 71 SFs through a literature search [22]. Subsequently, survival analysis was performed on the genes corresponding to the SFs to determine prognosis-related SFs. Pearson’s test was used to assess the correlation between the expression levels of SFs and PSI of AS. p < 0.05 and ǀrǀ > 0.3 were set as the significant threshold.

2.8 Construction of Cox proportional hazards regression model

Based on the different AS types, prognostic-related m6A methylation-related DEAS was used to conduct multivariate Cox proportional hazards regression survival analysis and construct risk models. The Cox regression coefficients and PSI for each DEAS were then obtained.

Risk score = ∑ Coef AS × Exp AS, Coef AS represents the Cox regression coefficient, and Exp AS represents the PSI.

2.9 Survival analysis

The patients were divided into high-risk and low-risk groups based on their risk scores. Kaplan–Meier survival curves were constructed to compare whether there was a significant difference in prognosis between patients in the high- and low-risk groups. The survival receiver operating characteristic curve (ROC) package was used to perform an ROC analysis of these risk models.

2.10 Validation of the role of m6A methylation-related DEAS in BLCA prognosis

The GSE13507 BLCA dataset obtained from the GPL6102 Illumina human-6 v2.0 expression BeadChip sequencing platform was downloaded from the NCBI GEO (https://www.ncbi.nlm.nih.gov/) database on May 23, 2022. After excluding samples with missing survival information, 165 samples were retained for validation analysis.

2.11 Independent analysis

Univariate and multivariate Cox regression analyses were used to assess whether clinical characteristics (TNM stage, age, and sex) and risk score were independent prognostic factors for BLCA. Log-rank p < 0.05 was set as the threshold for screening significantly independent prognostic factors.

3 Results

3.1 AS event statistics and differential analysis

The seven types of AS patterns are shown in Figure 2a. Furthermore, we counted the number of occurrences of each AS type and the number of corresponding genes. As shown in Figure 2b, there were 2,237, 2,278, 1,900, 2,769, 3,558, 9,547, and 39 events for AA, RI, AD, AP, AT, ES, and ME, respectively. There were 1,840, 1,616, 1,549, 2,769, 3,558, 5,332, and 39 genes for AA, RI, AD, AP, AT, ES, and ME events, respectively. ES was the most frequent event among the seven AS types, whereas ME was the least frequent. We also identified 3,715 DEAS, including 1,226 downregulated and 2,489 upregulated DEAS. These 3,715 DEAS corresponded to 2,766 genes (Table S1).

Figure 2 Different AS. (a) Schematic representation of AS events, AA and AD sites, AP, AT, ES, RI, and ME. (b) Numbers of AS events and corresponding genes in enrolled cases. (c) Correlation between m6A methylation regulators and AS events.
Figure 2

Different AS. (a) Schematic representation of AS events, AA and AD sites, AP, AT, ES, RI, and ME. (b) Numbers of AS events and corresponding genes in enrolled cases. (c) Correlation between m6A methylation regulators and AS events.

3.2 Screening of prognostic-related DEAS

A total of 1,310 m6A methylation-related DEAS were identified, corresponding to 1,015 genes (Table S2). After the univariate Cox hazard analysis, six AS types were significantly associated with OS (p < 0.05). There were 13, 6, 38, 52, 57, and 59 AS events in AA, AD, AP, AT, ES, and RI significantly related to OS, respectively (Table S3, p < 0.05).We then calculated the Pearson correlation coefficients between m6A methylation regulators and AS events. As shown in Figure 2c, there was a strong correlation between m6A methylation regulators and AS events (Table S4).

3.3 Function and pathway enrichment analysis of prognostic-related DEAS

The genes corresponding to the RI event were enriched in 4 KEGG and 210 GO BP pathways. These terms included spliceosome, regulation of RNA splicing, Golgi to plasma membrane protein transport, sister chromatid segregation, regulation of mRNA splicing via spliceosome, and mitochondrial calcium ion transmembrane transport (Figure S1a and b). A total of 3 KEGG pathways and 152 terms were obtained from the AT enrichment analysis. These terms were mainly involved in base excision repair and the regulation of the G2/M cell cycle phase (Figure S2a and b). The genes corresponding to AA were mainly involved in the MAPK signaling pathway, VEGF signaling pathway, and histone acetylation (Figure S3a and b). The genes corresponding to AP were mainly involved in endocrine resistance and responses to prostaglandins (Figure S4a and b). A total of 6 KEGG pathways and 174 terms were obtained from the ES enrichment analysis. These terms were mainly involved in mitotic centrosome separation, cytokinesis, and centrosome separation (Figure S5a and b). As shown in Figure S6a and b, genes in AD were mainly involved in the phospholipase D signaling pathway, antigen processing, and antigen presentation by MHC II.

3.4 The correlation network among genes in different AS events

The constructed PPI network included 110 edges and 81 nodes (Table S5 and Figure 3). The ten genes with the highest degrees in the network were Akt serine/threonine kinase 1 (AKT1), serine- and arginine-rich splicing factor 6 (SRSF6), serine- and arginine-rich splicing factor 2 (SRSF2), heterogeneous nuclear ribonucleoprotein a1 (HNRNPA1), nucleoporin 62 (NUP62), HNRNPA2B1, serine- and arginine-rich splicing factor 7 (SRSF7), minichromosome maintenance complex component 7 (MCM7), heterogeneous nuclear ribonucleoprotein l (HNRNPL), and heterogeneous nuclear ribonucleoprotein D like (HNRNPDL). Their respective cutting events were AP, ES, RI, ES, RI, AA, RI, AP, ES, and ES (Table 1).

Figure 3 PPI network of prognostic-related m6A methylation-related DEAS. The blue, lavender, purple, green, yellow, and red nodes represent genes where AP, AT, ES, AD, AA, and RI have occurred, respectively. AA and AD sites, AP, AT, ES, RI. The larger the nodes, the more proteins interact with them.
Figure 3

PPI network of prognostic-related m6A methylation-related DEAS. The blue, lavender, purple, green, yellow, and red nodes represent genes where AP, AT, ES, AD, AA, and RI have occurred, respectively. AA and AD sites, AP, AT, ES, RI. The larger the nodes, the more proteins interact with them.

Table 1

TOP10 genes and corresponding alternative splicing events in protein–protein interaction network

Name as_id Betweenness Closeness Degree Splice_type
AKT1 29559 935.6667 0.032219 9 AP
SRSF6 59434 102.6048 0.031733 9 ES
SRSF2 43660 228.1071 0.031911 9 RI
HNRNPA1 22149 264.0103 0.031923 8 ES
NUP62 51123 535.3992 0.032116 7 RI
HNRNPA2B1 79038 13.77143 0.031484 7 AA
SRSF7 53279 23.4 0.031658 7 RI
MCM7 80881 470 0.031583 6 AP
HNRNPL 49699 0.333333 0.031471 6 ES
HNRNPDL 69705 0.333333 0.031471 6 ES

3.5 Network of SFs and the prognostic-related DEAS

A total of eight SFs related to prognosis were obtained. These were NOVA AS regulator 1 (NOVA1), heterogeneous nuclear ribonucleoprotein H1 (HNRNPH1), QKI, KH domain containing RNA binding (QKI), splicing factor 3b subunit 1 (SF3B1), NOVA AS regulator 2 (NOVA2), TIA1 cytotoxic granule-associated RNA-binding protein (TIA1), ELAV-like RNA-binding protein 3 (ELAVL3), and RNA-binding Fox-1 homolog 2 (RBFOX2). Furthermore, we divided the patients into high- and low-expression groups according to the expression values of these eight SFs. A Kaplan–Meier (KM) curve was used to measure the difference in OS between the groups (Figure 4). Patients with low expression levels of NOVA1, NOVA2, and QKI had a better prognosis than those with high expression levels (p < 0.05) (Figure 4a–c). Low expression of ITA1, ELAV3, HNRNPH1, and SF3B1 was correlated with a significantly worse prognosis (p < 0.05) (Figure 4d–g). There was no significant difference between the prognosis of patients in the RBFOX2 high- and low-expression groups (p = 0.05) (Figure 4h).

Figure 4 KM curves of the OS difference between high and low expression of SF. (a–h) Represent the KM curves of the OS difference between the high and low expression of NOVA1, NOVA2, QKI, ITA1, ELVA3, HNRNPH1, SF3B1, and RBFOX2, respectively.
Figure 4

KM curves of the OS difference between high and low expression of SF. (a–h) Represent the KM curves of the OS difference between the high and low expression of NOVA1, NOVA2, QKI, ITA1, ELVA3, HNRNPH1, SF3B1, and RBFOX2, respectively.

The relationship between SFs expression and the PSI of AS events was analyzed. A regulatory network was constructed based on this relationship (Figure 5). This regulatory network contained 32 genes that corresponded to 34 AS events and 4 SFs (QK1, RBFOX2, TIA1, and HNRNPH1) (Table S6).

Figure 5 Correlation network between SF and the genes corresponding to AS events. Blue, lavender, purple, green, and red nodes represent AP, AT, ES, AD, and RI events, respectively. The pink diamond represents SF. AD, AP, AT, ES, RI. The red line indicates a positive correlation, and the blue line indicates a negative correlation.
Figure 5

Correlation network between SF and the genes corresponding to AS events. Blue, lavender, purple, green, and red nodes represent AP, AT, ES, AD, and RI events, respectively. The pink diamond represents SF. AD, AP, AT, ES, RI. The red line indicates a positive correlation, and the blue line indicates a negative correlation.

3.6 Cox proportional hazards regression model

Six Cox proportional hazards regression models were constructed using the above six OS-related AS types (AA, AD, AP, AT, ES, and RI). Patients were divided into two groups based on the risk scores calculated in each model. In either models, patients with low-risk scores had a better OS than those with high-risk scores (p < 0.05) (Figure 6a–f). Furthermore, we constructed an ROC curve to measure the performance of each model. The AUC of AT (0.793) was the highest among all models, while that of AD (0.624) was the lowest. The AUCs for RI, AP, ES, and AA were 0.729, 0.703, 0.786, and 0.652, respectively (Figure 6g).

Figure 6 KM and ROC of prognostic predictors in the TCGA. (a–f) Represent the KM plot depicting the survival probability over time for the prognostic predictor of six types of AS events with the high- (red) and low- (blue) risk groups, respectively. (g) ROC analysis for all prognostic predictors.
Figure 6

KM and ROC of prognostic predictors in the TCGA. (a–f) Represent the KM plot depicting the survival probability over time for the prognostic predictor of six types of AS events with the high- (red) and low- (blue) risk groups, respectively. (g) ROC analysis for all prognostic predictors.

3.7 Correlation between m6A methylation-related DEAS and BLCA prognosis in the validation dataset

Based on GSE13507, we validated that the prognosis of patients in the high-risk score group was significantly worse than that in the low-risk score group in all six models (AA, AD, AP, AT, ES, and RI) (Figure 7a–f). Further ROC analysis showed that all six models had good predictive performances. The AUCs for RI, AP, AT, AA, ES, and AD were 0.845, 0.809, 0.759, 0.738, 0.677, and 0.672, respectively (Figure 7g).

Figure 7 KM and ROC of prognostic predictors in GEO. (a–f) Represent the KM plot depicting the survival probability over time for the prognostic predictor of six types of AS events with the high- (red) and low- (blue) risk groups, respectively. (g) ROC analysis for all prognostic predictors.
Figure 7

KM and ROC of prognostic predictors in GEO. (a–f) Represent the KM plot depicting the survival probability over time for the prognostic predictor of six types of AS events with the high- (red) and low- (blue) risk groups, respectively. (g) ROC analysis for all prognostic predictors.

3.8 Screening of independent prognostic clinical factors

After performing a univariate Cox analysis, we found that TNM stage, age, and risk score were significantly associated with prognosis (data not shown, p < 0.05). Further multivariate Cox analysis showed that in the AS groups, except for AP (p = 0.148), the risk score was an independent prognostic factor of BLCA (p < 0.05) (Figure 8).

Figure 8 Forest plots for independent prognostic clinical factors based on multivariate Cox regression analysis. (a–g) Represent Forest plots for independent prognostic clinical factors in AA, AD, AP, AT, ES, and RI, respectively. AA and AD sites, AP, AT, ES, RI.
Figure 8

Forest plots for independent prognostic clinical factors based on multivariate Cox regression analysis. (a–g) Represent Forest plots for independent prognostic clinical factors in AA, AD, AP, AT, ES, and RI, respectively. AA and AD sites, AP, AT, ES, RI.

4 Discussion

BLCA is a common disease of the urinary system involving malignant tumors with a 60–70% recurrence rate after therapy [23]. Genetic factors, including AS, have been proved by many studies to play a role in the metastasis and development of BLCA [24]. m6A methylation modification is a potential target for cancer therapy and has been extensively studied in recent years [25]. However, there are few reports on combining m6A methylation modification and AS to analyze its role in BLCA species. The main purpose of this study was to explore whether the combined analysis of m6A methylation modification and AS will provide new insights into understanding the mechanism of BLCA.

We found that six AS types were significantly associated with OS. A significant correlation was also observed between m6A methylation regulators and AS events. In another study, m6A methylation regulators were found to play an important regulatory role in gene AS events [26]. Future studies will investigate the possible function of prognostic-related DEAS. Enrichment analysis revealed that they were mainly involved in RNA splicing regulation, cell cycle G2/M regulation, the MAPK signaling pathway, and prostaglandin response. These functions have been associated with various cancers [27]. Prostaglandins are lipids derived from polyunsaturated fatty acids. Recent studies suggest that it also plays a role in BLCA. Prostaglandin E2 (PGE2) is a highly expressed prostaglandin that controls a variety of normal, tissue-dependent physiological processes [28]. PGE2 has a tumor-promoting effect. It blocks cell death, enhances angiogenesis, promotes tumor inflammation, and prevents proper immune surveillance of tumors [29]. PGE2 shifts the phenotype of macrophages from antitumor M1 macrophages to pro-tumor M2 macrophages. PGE2/EP4 signaling can regulate macrophage plasticity [30]. Cyclooxygenase (COX)-2, one of the key proteins responsible for angiogenesis and tumorigenesis, is highly expressed in BLCA. Prostaglandin levels were positively correlated with COX2 expression and BLCA progression [31]. Inhibition of COX enzymes by non-vehicle anti-inflammatory drugs results in decreased prostaglandin D2 levels, ultimately reducing the occurrence of BLCA [32].

There was an interaction relationship between the genes corresponding to the prognosis-related DEAS, and a PPI network consisting of 81 points was formed. The top three nodes in the network are AKT1, SRSF6, and SRSF2. TIA1 is an important tumor suppressor gene involved in many aspects of cancer occurrence and development and is considered a new tumor suppressor [33]. It regulates the expression of various mRNAs involved in proliferation, apoptosis, and angiogenesis of cancer cells [34]. TIA1 regulates RAB40B to inhibit cell proliferation [35]. In addition, TIA1 can promote apoptosis by regulating Fas AS [36]. SRSF6 and SRSF2 are serine/arginine-rich SFs. SRSF6 is essential for normal growth and development. SRSF6 also protects the genome from the formation of DNA–RNA hybrids (R-loops) following transcription [37]. Mutations in SRSF2 are associated with many diseases [38].

In addition, these prognosis-related DEAS interacted with SFs. Most poor prognostic AS events were inversely associated with SF expression, consistent with previous findings [39]. Upregulated expression of oncogenic SFs increases the oncogenic potential of protein variants and promotes cancer development [40]. These regulatory axes provide important guidance for subsequent exploration of the specific functional mechanisms of DEAS events.

We constructed six Cox proportional hazards regression models for the six OS-related AS types. An ROC curve was developed to evaluate the predictive efficiency of these models. The results revealed that the AUC of the AT model (0.793) was the highest, followed by ES (0.786), RI (0.729), AA (0.652), and AD (0.624). The above results show that our model has good predictive efficiency. Similarly, we also demonstrated the good predictive performance of the model in the GEO validation cohort. There has been no research on constructing a BLCA prognostic risk model using different AS types. Interestingly, AT showed the best predictive performance in the different types of AS-based prognostic risk models constructed for other cancers, such as glioma (AUC = 0.907) [41]. In another study on head and neck cancer, AT events were the most powerful prognostic factor [42].

Independence analysis revealed that the prognostic risk model based on multiple AS types was an independent prognostic factor for BLCA. A recent study demonstrated that high-throughput sequencing analysis is a key technique for characterizing the most common genetic aberrations in spliceosomes and splice sites [43]. In the future, clinical factors independent of age and TNM stage will be used to predict the prognosis of BLCA, and a combination of multiple clinical factors may yield better results.

This study had certain limitations. First, it was a retrospective study based on public databases. In the future, multiple centers, large samples, and more clinical features are needed to verify the results of this study. Second, BLCA is also affected by external environmental factors, such as smoking, in addition to genetic factors. Therefore, the constructed model needs further stratified verification. Third, whether the AS events obtained in our research actually exist still requires further experimental verification.

In conclusion, this study screened for prognostic-related AS events based on m6A methylation modification and constructed a reliable prognostic risk model. Further research on upstream SF predicted a possible regulatory relationship between SF and DEAS. This study provides insights for the subsequent understanding of the role of AS events in BLCA.

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  1. Funding information: This study was supported by the 12th Five-Year Social Science Research Project of the Jilin Provincial Education Department (No. 104115023).

  2. Author contributions: Yuan Chang and Shukun Yu conceived and designed the study, and Miao Zhang and Tianshu Jiang participated in acquiring the data. Yuan Chang performed the analysis and interpretation of the data. Yuan Liu and Xiuyun Zhu designed the study and performed statistical analysis. Shukun Yu participated in obtaining funding. Yuan Chang and Shukun Yu conceived the study, participated in its design and coordination, helped draft the article, and revised the manuscript for important intellectual content. All authors have read and approved the final manuscript.

  3. Conflict of interest: Authors state no conflict of interest.

  4. Data availability statement: The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Received: 2021-11-28
Revised: 2022-06-30
Accepted: 2022-07-17
Published Online: 2022-10-14

© 2022 Yuan Chang et al., published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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https://doi.org/10.1515/biol-2022-0479
Keywords for this article
bladder cancer; N6-methyladenosine; alternative splicing; enrichment analysis; splicing factor; independent prognostic factor
Creative Commons
BY 4.0

Articles in the same Issue

  1. Biomedical Sciences
  2. Effects of direct oral anticoagulants dabigatran and rivaroxaban on the blood coagulation function in rabbits
  3. The mother of all battles: Viruses vs humans. Can humans avoid extinction in 50–100 years?
  4. Knockdown of G1P3 inhibits cell proliferation and enhances the cytotoxicity of dexamethasone in acute lymphoblastic leukemia
  5. LINC00665 regulates hepatocellular carcinoma by modulating mRNA via the m6A enzyme
  6. Association study of CLDN14 variations in patients with kidney stones
  7. Concanavalin A-induced autoimmune hepatitis model in mice: Mechanisms and future outlook
  8. Regulation of miR-30b in cancer development, apoptosis, and drug resistance
  9. Informatic analysis of the pulmonary microecology in non-cystic fibrosis bronchiectasis at three different stages
  10. Swimming attenuates tumor growth in CT-26 tumor-bearing mice and suppresses angiogenesis by mediating the HIF-1α/VEGFA pathway
  11. Characterization of intestinal microbiota and serum metabolites in patients with mild hepatic encephalopathy
  12. Functional conservation and divergence in plant-specific GRF gene family revealed by sequences and expression analysis
  13. Application of the FLP/LoxP-FRT recombination system to switch the eGFP expression in a model prokaryote
  14. Biomedical evaluation of antioxidant properties of lamb meat enriched with iodine and selenium
  15. Intravenous infusion of the exosomes derived from human umbilical cord mesenchymal stem cells enhance neurological recovery after traumatic brain injury via suppressing the NF-κB pathway
  16. Effect of dietary pattern on pregnant women with gestational diabetes mellitus and its clinical significance
  17. Potential regulatory mechanism of TNF-α/TNFR1/ANXA1 in glioma cells and its role in glioma cell proliferation
  18. Effect of the genetic mutant G71R in uridine diphosphate-glucuronosyltransferase 1A1 on the conjugation of bilirubin
  19. Quercetin inhibits cytotoxicity of PC12 cells induced by amyloid-beta 25–35 via stimulating estrogen receptor α, activating ERK1/2, and inhibiting apoptosis
  20. Nutrition intervention in the management of novel coronavirus pneumonia patients
  21. circ-CFH promotes the development of HCC by regulating cell proliferation, apoptosis, migration, invasion, and glycolysis through the miR-377-3p/RNF38 axis
  22. Bmi-1 directly upregulates glucose transporter 1 in human gastric adenocarcinoma
  23. Lacunar infarction aggravates the cognitive deficit in the elderly with white matter lesion
  24. Hydroxysafflor yellow A improved retinopathy via Nrf2/HO-1 pathway in rats
  25. Comparison of axon extension: PTFE versus PLA formed by a 3D printer
  26. Elevated IL-35 level and iTr35 subset increase the bacterial burden and lung lesions in Mycobacterium tuberculosis-infected mice
  27. A case report of CAT gene and HNF1β gene variations in a patient with early-onset diabetes
  28. Study on the mechanism of inhibiting patulin production by fengycin
  29. SOX4 promotes high-glucose-induced inflammation and angiogenesis of retinal endothelial cells by activating NF-κB signaling pathway
  30. Relationship between blood clots and COVID-19 vaccines: A literature review
  31. Analysis of genetic characteristics of 436 children with dysplasia and detailed analysis of rare karyotype
  32. Bioinformatics network analyses of growth differentiation factor 11
  33. NR4A1 inhibits the epithelial–mesenchymal transition of hepatic stellate cells: Involvement of TGF-β–Smad2/3/4–ZEB signaling
  34. Expression of Zeb1 in the differentiation of mouse embryonic stem cell
  35. Study on the genetic damage caused by cadmium sulfide quantum dots in human lymphocytes
  36. Association between single-nucleotide polymorphisms of NKX2.5 and congenital heart disease in Chinese population: A meta-analysis
  37. Assessment of the anesthetic effect of modified pentothal sodium solution on Sprague-Dawley rats
  38. Genetic susceptibility to high myopia in Han Chinese population
  39. Potential biomarkers and molecular mechanisms in preeclampsia progression
  40. Silencing circular RNA-friend leukemia virus integration 1 restrained malignancy of CC cells and oxaliplatin resistance by disturbing dyskeratosis congenita 1
  41. Endostar plus pembrolizumab combined with a platinum-based dual chemotherapy regime for advanced pulmonary large-cell neuroendocrine carcinoma as a first-line treatment: A case report
  42. The significance of PAK4 in signaling and clinicopathology: A review
  43. Sorafenib inhibits ovarian cancer cell proliferation and mobility and induces radiosensitivity by targeting the tumor cell epithelial–mesenchymal transition
  44. Characterization of rabbit polyclonal antibody against camel recombinant nanobodies
  45. Active legumain promotes invasion and migration of neuroblastoma by regulating epithelial-mesenchymal transition
  46. Effect of cell receptors in the pathogenesis of osteoarthritis: Current insights
  47. MT-12 inhibits the proliferation of bladder cells in vitro and in vivo by enhancing autophagy through mitochondrial dysfunction
  48. Study of hsa_circRNA_000121 and hsa_circRNA_004183 in papillary thyroid microcarcinoma
  49. BuyangHuanwu Decoction attenuates cerebral vasospasm caused by subarachnoid hemorrhage in rats via PI3K/AKT/eNOS axis
  50. Effects of the interaction of Notch and TLR4 pathways on inflammation and heart function in septic heart
  51. Monosodium iodoacetate-induced subchondral bone microstructure and inflammatory changes in an animal model of osteoarthritis
  52. A rare presentation of type II Abernethy malformation and nephrotic syndrome: Case report and review
  53. Rapid death due to pulmonary epithelioid haemangioendothelioma in several weeks: A case report
  54. Hepatoprotective role of peroxisome proliferator-activated receptor-α in non-cancerous hepatic tissues following transcatheter arterial embolization
  55. Correlation between peripheral blood lymphocyte subpopulations and primary systemic lupus erythematosus
  56. A novel SLC8A1-ALK fusion in lung adenocarcinoma confers sensitivity to alectinib: A case report
  57. β-Hydroxybutyrate upregulates FGF21 expression through inhibition of histone deacetylases in hepatocytes
  58. Identification of metabolic genes for the prediction of prognosis and tumor microenvironment infiltration in early-stage non-small cell lung cancer
  59. BTBD10 inhibits glioma tumorigenesis by downregulating cyclin D1 and p-Akt
  60. Mucormycosis co-infection in COVID-19 patients: An update
  61. Metagenomic next-generation sequencing in diagnosing Pneumocystis jirovecii pneumonia: A case report
  62. Long non-coding RNA HOXB-AS1 is a prognostic marker and promotes hepatocellular carcinoma cells’ proliferation and invasion
  63. Preparation and evaluation of LA-PEG-SPION, a targeted MRI contrast agent for liver cancer
  64. Proteomic analysis of the liver regulating lipid metabolism in Chaohu ducks using two-dimensional electrophoresis
  65. Nasopharyngeal tuberculosis: A case report
  66. Characterization and evaluation of anti-Salmonella enteritidis activity of indigenous probiotic lactobacilli in mice
  67. Aberrant pulmonary immune response of obese mice to periodontal infection
  68. Bacteriospermia – A formidable player in male subfertility
  69. In silico and in vivo analysis of TIPE1 expression in diffuse large B cell lymphoma
  70. Effects of KCa channels on biological behavior of trophoblasts
  71. Interleukin-17A influences the vulnerability rather than the size of established atherosclerotic plaques in apolipoprotein E-deficient mice
  72. Multiple organ failure and death caused by Staphylococcus aureus hip infection: A case report
  73. Prognostic signature related to the immune environment of oral squamous cell carcinoma
  74. Primary and metastatic squamous cell carcinoma of the thyroid gland: Two case reports
  75. Neuroprotective effects of crocin and crocin-loaded niosomes against the paraquat-induced oxidative brain damage in rats
  76. Role of MMP-2 and CD147 in kidney fibrosis
  77. Geometric basis of action potential of skeletal muscle cells and neurons
  78. Babesia microti-induced fulminant sepsis in an immunocompromised host: A case report and the case-specific literature review
  79. Role of cerebellar cortex in associative learning and memory in guinea pigs
  80. Application of metagenomic next-generation sequencing technique for diagnosing a specific case of necrotizing meningoencephalitis caused by human herpesvirus 2
  81. Case report: Quadruple primary malignant neoplasms including esophageal, ureteral, and lung in an elderly male
  82. Long non-coding RNA NEAT1 promotes angiogenesis in hepatoma carcinoma via the miR-125a-5p/VEGF pathway
  83. Osteogenic differentiation of periodontal membrane stem cells in inflammatory environments
  84. Knockdown of SHMT2 enhances the sensitivity of gastric cancer cells to radiotherapy through the Wnt/β-catenin pathway
  85. Continuous renal replacement therapy combined with double filtration plasmapheresis in the treatment of severe lupus complicated by serious bacterial infections in children: A case report
  86. Simultaneous triple primary malignancies, including bladder cancer, lymphoma, and lung cancer, in an elderly male: A case report
  87. Preclinical immunogenicity assessment of a cell-based inactivated whole-virion H5N1 influenza vaccine
  88. One case of iodine-125 therapy – A new minimally invasive treatment of intrahepatic cholangiocarcinoma
  89. S1P promotes corneal trigeminal neuron differentiation and corneal nerve repair via upregulating nerve growth factor expression in a mouse model
  90. Early cancer detection by a targeted methylation assay of circulating tumor DNA in plasma
  91. Calcifying nanoparticles initiate the calcification process of mesenchymal stem cells in vitro through the activation of the TGF-β1/Smad signaling pathway and promote the decay of echinococcosis
  92. Evaluation of prognostic markers in patients infected with SARS-CoV-2
  93. N6-Methyladenosine-related alternative splicing events play a role in bladder cancer
  94. Characterization of the structural, oxidative, and immunological features of testis tissue from Zucker diabetic fatty rats
  95. Effects of glucose and osmotic pressure on the proliferation and cell cycle of human chorionic trophoblast cells
  96. Investigation of genotype diversity of 7,804 norovirus sequences in humans and animals of China
  97. Characteristics and karyotype analysis of a patient with turner syndrome complicated with multiple-site tumors: A case report
  98. Aggravated renal fibrosis is positively associated with the activation of HMGB1-TLR2/4 signaling in STZ-induced diabetic mice
  99. Distribution characteristics of SARS-CoV-2 IgM/IgG in false-positive results detected by chemiluminescent immunoassay
  100. SRPX2 attenuated oxygen–glucose deprivation and reperfusion-induced injury in cardiomyocytes via alleviating endoplasmic reticulum stress-induced apoptosis through targeting PI3K/Akt/mTOR axis
  101. Aquaporin-8 overexpression is involved in vascular structure and function changes in placentas of gestational diabetes mellitus patients
  102. Relationship between CRP gene polymorphisms and ischemic stroke risk: A systematic review and meta-analysis
  103. Effects of growth hormone on lipid metabolism and sexual development in pubertal obese male rats
  104. Cloning and identification of the CTLA-4IgV gene and functional application of vaccine in Xinjiang sheep
  105. Antitumor activity of RUNX3: Upregulation of E-cadherin and downregulation of the epithelial–mesenchymal transition in clear-cell renal cell carcinoma
  106. PHF8 promotes osteogenic differentiation of BMSCs in old rat with osteoporosis by regulating Wnt/β-catenin pathway
  107. A review of the current state of the computer-aided diagnosis (CAD) systems for breast cancer diagnosis
  108. Bilateral dacryoadenitis in adult-onset Still’s disease: A case report
  109. A novel association between Bmi-1 protein expression and the SUVmax obtained by 18F-FDG PET/CT in patients with gastric adenocarcinoma
  110. The role of erythrocytes and erythroid progenitor cells in tumors
  111. Relationship between platelet activation markers and spontaneous abortion: A meta-analysis
  112. Abnormal methylation caused by folic acid deficiency in neural tube defects
  113. Silencing TLR4 using an ultrasound-targeted microbubble destruction-based shRNA system reduces ischemia-induced seizures in hyperglycemic rats
  114. Plant Sciences
  115. Seasonal succession of bacterial communities in cultured Caulerpa lentillifera detected by high-throughput sequencing
  116. Cloning and prokaryotic expression of WRKY48 from Caragana intermedia
  117. Novel Brassica hybrids with different resistance to Leptosphaeria maculans reveal unbalanced rDNA signal patterns
  118. Application of exogenous auxin and gibberellin regulates the bolting of lettuce (Lactuca sativa L.)
  119. Phytoremediation of pollutants from wastewater: A concise review
  120. Genome-wide identification and characterization of NBS-encoding genes in the sweet potato wild ancestor Ipomoea trifida (H.B.K.)
  121. Alleviative effects of magnetic Fe3O4 nanoparticles on the physiological toxicity of 3-nitrophenol to rice (Oryza sativa L.) seedlings
  122. Selection and functional identification of Dof genes expressed in response to nitrogen in Populus simonii × Populus nigra
  123. Study on pecan seed germination influenced by seed endocarp
  124. Identification of active compounds in Ophiopogonis Radix from different geographical origins by UPLC-Q/TOF-MS combined with GC-MS approaches
  125. The entire chloroplast genome sequence of Asparagus cochinchinensis and genetic comparison to Asparagus species
  126. Genome-wide identification of MAPK family genes and their response to abiotic stresses in tea plant (Camellia sinensis)
  127. Selection and validation of reference genes for RT-qPCR analysis of different organs at various development stages in Caragana intermedia
  128. Cloning and expression analysis of SERK1 gene in Diospyros lotus
  129. Integrated metabolomic and transcriptomic profiling revealed coping mechanisms of the edible and medicinal homologous plant Plantago asiatica L. cadmium resistance
  130. A missense variant in NCF1 is associated with susceptibility to unexplained recurrent spontaneous abortion
  131. Assessment of drought tolerance indices in faba bean genotypes under different irrigation regimes
  132. The entire chloroplast genome sequence of Asparagus setaceus (Kunth) Jessop: Genome structure, gene composition, and phylogenetic analysis in Asparagaceae
  133. Food Science
  134. Dietary food additive monosodium glutamate with or without high-lipid diet induces spleen anomaly: A mechanistic approach on rat model
  135. Binge eating disorder during COVID-19
  136. Potential of honey against the onset of autoimmune diabetes and its associated nephropathy, pancreatitis, and retinopathy in type 1 diabetic animal model
  137. FTO gene expression in diet-induced obesity is downregulated by Solanum fruit supplementation
  138. Physical activity enhances fecal lactobacilli in rats chronically drinking sweetened cola beverage
  139. Supercritical CO2 extraction, chemical composition, and antioxidant effects of Coreopsis tinctoria Nutt. oleoresin
  140. Functional constituents of plant-based foods boost immunity against acute and chronic disorders
  141. Effect of selenium and methods of protein extraction on the proteomic profile of Saccharomyces yeast
  142. Microbial diversity of milk ghee in southern Gansu and its effect on the formation of ghee flavor compounds
  143. Ecology and Environmental Sciences
  144. Effects of heavy metals on bacterial community surrounding Bijiashan mining area located in northwest China
  145. Microorganism community composition analysis coupling with 15N tracer experiments reveals the nitrification rate and N2O emissions in low pH soils in Southern China
  146. Genetic diversity and population structure of Cinnamomum balansae Lecomte inferred by microsatellites
  147. Preliminary screening of microplastic contamination in different marine fish species of Taif market, Saudi Arabia
  148. Plant volatile organic compounds attractive to Lygus pratensis
  149. Effects of organic materials on soil bacterial community structure in long-term continuous cropping of tomato in greenhouse
  150. Effects of soil treated fungicide fluopimomide on tomato (Solanum lycopersicum L.) disease control and plant growth
  151. Prevalence of Yersinia pestis among rodents captured in a semi-arid tropical ecosystem of south-western Zimbabwe
  152. Effects of irrigation and nitrogen fertilization on mitigating salt-induced Na+ toxicity and sustaining sea rice growth
  153. Bioengineering and Biotechnology
  154. Poly-l-lysine-caused cell adhesion induces pyroptosis in THP-1 monocytes
  155. Development of alkaline phosphatase-scFv and its use for one-step enzyme-linked immunosorbent assay for His-tagged protein detection
  156. Development and validation of a predictive model for immune-related genes in patients with tongue squamous cell carcinoma
  157. Agriculture
  158. Effects of chemical-based fertilizer replacement with biochar-based fertilizer on albic soil nutrient content and maize yield
  159. Genome-wide identification and expression analysis of CPP-like gene family in Triticum aestivum L. under different hormone and stress conditions
  160. Agronomic and economic performance of mung bean (Vigna radiata L.) varieties in response to rates of blended NPS fertilizer in Kindo Koysha district, Southern Ethiopia
  161. Influence of furrow irrigation regime on the yield and water consumption indicators of winter wheat based on a multi-level fuzzy comprehensive evaluation
  162. Discovery of exercise-related genes and pathway analysis based on comparative genomes of Mongolian originated Abaga and Wushen horse
  163. Lessons from integrated seasonal forecast-crop modelling in Africa: A systematic review
  164. Evolution trend of soil fertility in tobacco-planting area of Chenzhou, Hunan Province, China
  165. Animal Sciences
  166. Morphological and molecular characterization of Tatera indica Hardwicke 1807 (Rodentia: Muridae) from Pothwar, Pakistan
  167. Research on meat quality of Qianhua Mutton Merino sheep and Small-tail Han sheep
  168. SI: A Scientific Memoir
  169. Suggestions on leading an academic research laboratory group
  170. My scientific genealogy and the Toronto ACDC Laboratory, 1988–2022
  171. Erratum
  172. Erratum to “Changes of immune cells in patients with hepatocellular carcinoma treated by radiofrequency ablation and hepatectomy, a pilot study”
  173. Erratum to “A two-microRNA signature predicts the progression of male thyroid cancer”
  174. Retraction
  175. Retraction of “Lidocaine has antitumor effect on hepatocellular carcinoma via the circ_DYNC1H1/miR-520a-3p/USP14 axis”
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