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. 2025 Sep 30;25:1463. doi: 10.1186/s12885-025-14899-y

The integration of transcriptomics and metabolomics elucidates the antitumor mechanisms of mycophenolic acid in bladder cancer cells

Shanshan Liu 1,✉, Kunyang Lei 2, Gang Li 1, Peiming Zheng 1, Yi Li 1, Lan Gao 1, Fuming Zhang 1, Qian Shi 1
PMCID: PMC12482199  PMID: 41029501

Abstract

The potential anticancer effects of mycophenolic acid (MPA), an inosine-5′-monophosphate dehydrogenase (IMPDH) inhibitor, and its ability to prolong survival have been suggested. However, the comprehensive impact of MPA on bladder cancer remains unclear. Our results demonstrate that MPA induces both ferroptosis and apoptosis in human bladder cancer cells. To investigate the underlying mechanisms, we employed integrated transcriptomic and metabolomic analyses. Our analysis revealed that differentially expressed genes (DEGs) are primarily implicated in the regulation of metabolism, inflammatory responses, and angiogenesis. Metabolomic profiling identified phosphocreatine, 2’-CMP, CDP, and others as differentially accumulated metabolites (DAMs). Integrated analysis of differentially expressed genes (DEGs) and differentially abundant metabolites (DAMs) demonstrated that MPA significantly alters the metabolism of key compounds (GMP, phenol, glutathione, NAD+, cytosine) and regulates critical genes (tyrosine, LDHA, LDHB). Notably, the dysregulation of genes and metabolites associated with reactive oxygen species (ROS)-related pathways may serve as a central mechanism by which MPA facilitates ferroptosis and apoptosis in bladder cancer.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-025-14899-y.

Keywords: Mycophenolic acid, IMPDH, Bladder cancer, Ferroptosis, Apoptosis

Highlights

  • 1. This study first demonstrates that MPA induces both ferroptosis and apoptosis in bladder cancer cells.

  • 2. A unique combination of transcriptomics and metabolomics provides a comprehensive analysis of MPA’s anti-tumor mechanisms.

  • 3. The study reveals ROS-associated gene and metabolite dysregulation as a key driver of MPA-induced ferroptosis and apoptosis.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-025-14899-y.

Introduction

Mycophenolic acid (MPA) is an immunosuppressive drug primarily used to suppress immune responses after organ transplantation, preventing graft rejection by selectively inhibiting inosine-5′-monophosphate dehydrogenase (IMPDH) [1]. In recent years, the type II isoform of IMPDH has been identified as a key factor in the pathogenesis of various cancers, prompting a re-evaluation of MPA - an IMPDH2 inhibitor first discovered over a century ago, now considered a promising candidate for anticancer therapy [2]. However, despite several clinical trials assessing its anticancer effects in humans, most results have been unsatisfactory [3]. Thus, gaining a more profound understanding of the existing evidence and the intricate mechanisms underlying it is pivotal for unlocking the full anticancer potential of MPA.

Bladder cancer is one of the most prevalent malignant tumors of the urinary system worldwide, with a notably higher incidence in men. Despite considerable advancements in early diagnostic methods in recent years, the overall prognosis remains dismal. The high recurrence rate and the complexities associated with treating metastatic bladder cancer continue to pose a substantial burden on global public health [4]. Surgical resection has remained the cornerstone of bladder cancer treatment, especially for early-stage disease. Radical cystectomy or partial cystectomy remains the most common therapeutic approach in such cases [5]. Current therapies for advanced/metastatic bladder cancer demonstrate poor clinical outcomes, highlighting the urgent need for the development of novel treatment options.

Preliminary data indicate that MPA demonstrates anti-tumor activity, especially in metastatic tumors [6–8]. Although the anti-tumor effects of MPA have been substantiated in various in vitro and animal models, its precise pharmacological mechanisms in bladder cancer remain inadequately understood. Existing research indicates that MPA may exert its effects through several mechanisms: (1) inhibition of tumor cell proliferation and migration [9–11]; (2) induction of tumor cell apoptosis [12–15]; (3) restoration of immune responses to counteract tumor immune evasion [16, 17]. Current studies are primarily focused on the potential of combining MPA with other anti-cancer agents, particularly in bladder cancer treatment, where the repurposing of drugs as a cancer therapy strategy is increasingly gaining attention for its effectiveness.

Bladder cancer is closely associated with the reprogramming of various metabolic pathways, with metabolic reprogramming serving as a key driver of its initiation and progression. Similar to other cancer types, the hallmark of cancer is the profound alteration of critical metabolic pathways, including glucose metabolism and nucleotide metabolism [18, 19]. Bladder cancer cells, for example, typically exhibit the Warburg effect, characterized by predominant anaerobic glycolysis despite oxygen availability [20]. Moreover, metabolomic analyses of bladder cancer have revealed numerous specific metabolic abnormalities that play crucial roles in tumor initiation, progression, metastasis, and resistance to treatment [21, 22]. Consequently, bladder cancer treatment strategies are increasingly targeting metabolic reprogramming pathways. Therapeutic strategies aimed at modulating metabolic pathways in bladder cancer cells, particularly those involving glucose metabolism, lipid metabolism, and nucleotide metabolism, have become a major area of current research. These metabolic changes provide potential biomarkers for the early diagnosis of cancer and offer a theoretical foundation for the development of new therapeutic strategies. Consequently, exploring the metabolic mechanisms underlying bladder cancer and developing targeted therapeutic agents has become one of the key directions in bladder cancer research.

Recent studies have identified ferroptosis as a critical vulnerability in bladder cancer cells, particularly due to their high dependence on glutathione metabolism and iron homeostasis. The metabolic reprogramming in bladder cancer, including elevated polyunsaturated fatty acid (PUFA) phospholipid synthesis and impaired antioxidant defenses, creates a unique susceptibility to ferroptosis induction [23, 24]. As an IMPDH inhibitor, MPA may disrupt nucleotide metabolism and redox balance, potentially triggering ferroptosis through ROS accumulation and lipid peroxidation – a hypothesis supported by preliminary evidence in other cancers [25].

At present, transcriptomic and metabolomic approaches are utilized to evaluate the effects of MPA on bladder cancer. To further explore potential transcriptomic alterations, metabolic changes, and the underlying molecular mechanisms, we conducted a systems-level correlation network analysis integrating both omics datasets. The aim of this study is to identify key metabolites involved in MPA treatment of bladder cancer, ultimately providing novel and comprehensive insights into MPA-based therapeutic strategies for bladder cancer.

Methodology

Reagents and cell line cultures

The 5637 and UMUC2 cell lines were sourced from the Chinese Cell Bank. Human bladder cancer cells were cultured in RPMI-1640 medium (Gibco, #31800-022) supplemented with 10% fetal bovine serum and incubated at 37 °C in a 5% CO2 atmosphere. The MPA concentrations used in this study (MCE, #HY-B0421) were established through prior dose-response experiments [26], with 5637 cells requiring 4 µmol/L and UMUC2 cells requiring 1.5 µmol/L for optimal treatment effects. The 5637 and UMUC2 cells treated with MPA were designated as T-5637 and T-UMUC2 cells, respectively, while the untreated control groups were labeled as C-5637 and C-UMUC2 cells.

Cell viability assay

The CCK-8 assay kit (Beyotime, #C0038) was used to assess cell viability. 5637 and UMUC2 cells were seeded in 96-well plates. After successful attachment, the cells were treated with MPA or Ferrostatin-1 (MCE, #HY-100579) for 24 h. After treatment, 10 µL of CCK-8 solution was added to each well, followed by a 1-hour incubation. The absorbance was then measured at 450 nm.

Transmission electron microscopy (TEM)

Cells were fixed in a solution of 2% paraformaldehyde and 2% glutaraldehyde in 0.1 mol/L phosphate-buffered saline (PBS, pH 7.4) for 1 h, followed by post-fixation with 1% OsO4 for 6 h. Samples were dehydrated using a graded ethanol series for 15 min each, and then embedded in epoxy resin. Thin sections were mounted on copper grids (Sigma-Aldrich, TEM-74357) and stained with 2% uranyl acetate and 1% lead citrate. The samples were then dried and analyzed under a TEM (JEOL).

Apoptosis and necrosis assay

The Apoptosis and Necrosis Kit (Beyotime, #C1056) was equilibrated to room temperature. A total of 200,000 cells from each sample were collected and resuspended in 0.8-1 mL of staining buffer. Then, 5 µL of Hoechst and 5 µL of PI staining solutions were added, and the cells were incubated at 4 °C for 30 min. The red and blue fluorescence signals were subsequently monitored using a FACS Canto II flow cytometer (BD Biosciences) and cell populations were analyzed using BD FACSDiva software v8.0.1 (BD Biosciences).

Transcriptomic analysis

RNA integrity and concentration were evaluated using the Agilent 2100 Bioanalyzer. PolyA-tailed mRNA was enriched by binding to Oligo magnetic beads, with the purified mRNA then fragmented into short fragments (~ 200–300 nt) in fragmentation buffer containing divalent cations under elevated temperature. First-strand cDNA was synthesized using the fragmented mRNA as templates, random hexamer primers, and M-MuLV Reverse Transcriptase, after which RNA strands were degraded by RNase H and second-strand cDNA was synthesized using DNA Polymerase I, with the resulting double-stranded cDNA purified, end-repaired, adenylated at the 3’ ends, and ligated to Illumina sequencing adapters, followed by size selection of cDNA fragments (370–420 bp) using AMPure XP beads, PCR amplification, and final purification. The constructed libraries were quantified using Qubit 2.0 and diluted to 1.5 ng/µL, with insert size distribution verified by Agilent 2100 Bioanalyzer and library concentration precisely determined via qRT-PCR (effective concentration > 2 nM), after which qualified libraries were pooled based on effective concentration and target sequencing depth for paired-end sequencing (150 bp) on the Illumina platform using Sequencing-by-Synthesis (SBS) technology, where fluorescently labeled dNTPs, DNA polymerase, and adapter primers were incorporated into the flowcell for real-time fluorescence capture and conversion of signals into sequence reads.

Raw sequencing data were processed using FastQC (v0.11.9) for quality control, followed by adapter trimming using Cutadapt (v2.10) and alignment to the human reference genome (GRCh38) using HISAT2 (v2.2.1). Gene expression quantification was performed using featureCounts (v2.0.1), with differential expression analysis conducted using DESeq2 (v1.30.1) with adjusted p-value < 0.05 and |log2FC|>1 as significance thresholds.

Metabolomic analysis

Dried extracts were dissolved in 50% acetonitrile and filtered through a cellulose acetate. Metabolomics profiling was performed using a UPLC-ESI-Q-Orbitrap-MS system. LC separation was conducted on an ACQUITY UPLC® HSS T3 column, with a mobile phase consisting of A: 0.1% formic acid in water and B: 100% acetonitrile, at a flow rate of 0.3 mL/min. The gradient program began at 0% buffer B, increased to 48% over 4 min, then to 100% in 4 min, maintained for 2 min, and returned to 0% buffer B in 0.1 min, followed by a 3-minute re-equilibration. MS data were acquired using electrospray ionization in both positive and negative modes.

Multivariate statistical analysis

Multivariate statistical analysis was performed using R (version 4.3.1) with the following packages: pcaMethods (v1.94.0), ropls (v1.34.0), mixOmics (v6.24.0), pls (v2.8-1), ggplot2 (v3.4.2), and pheatmap (v1.0.12). Data preprocessing and Pareto scaling were conducted using pcaMethods, PCA and OPLS-DA analyses were performed with ropls, PLS-DA was implemented through mixOmics, model validation metrics were computed using pls, and all visualizations were generated with ggplot2 and pheatmap. Permutation testing (1000 iterations) confirmed model robustness, with perfect models demonstrating R² (cum) = 1 and Q² (cum) = 1. Permutation testing ensured that permuted models had lower R² and Q² values than non-permuted models.

Discriminating metabolites were identified using OPLS-DA and variable importance in projection (VIP) scores, with values over 1 considered significant. VIP scores indicate the contribution of variables to sample classification. Statistically significant metabolites were selected based on VIP > 1.0 and p < 0.05 (two-tailed Student’s t-test, ANOVA for multiple groups). Fold change was calculated as the logarithmic ratio of mass response between two classes. Cluster analysis was performed on identified differential metabolites using R packages.

KEGG enrichment analysis

KEGG pathway analysis was performed on differential metabolite data and differentially expressed genes using the KEGG database (http://www.kegg.jp) to identify perturbed biological pathways. KEGG enrichment was analyzed using the hypergeometric test, with FDR correction for multiple comparisons. Pathways were considered statistically significant at p < 0.05.

Statistical analysis

Statistical analysis was performed using GraphPad Prism version 8.0. Data are expressed as the mean ± standard deviation from a minimum of three independent experiments. Comparisons between two groups were made using an unpaired two-tailed Student’s t-test. Three or more groups were compared by one-way analysis of variance followed by Dunnett’s or Tukey’s post hoc multiple comparisons test. A p-value < 0.05 was considered statistically significant.

Results

MPA inhibits bladder cancer cell proliferation

To explore the impact of the MPA inhibitor on bladder cancer cell proliferation, we performed a CCK-8 assay to evaluate the viability of 5637 (HTB-9) and UMUC2 (CRL-1748) cells after 24 h of MPA treatment. The UMUC2 cell line (CRL-1748) used in this study has been authenticated using STR profiling to ensure its identity and authenticity. Our results demonstrate that the MPA inhibitor significantly decreased the viability of both 5637 and UMUC2 cells at a concentration of 2.5 µM (p < 0.001). This effect was concentration-dependent, with higher concentrations of the MPA inhibitor inducing a more pronounced suppression of cell proliferation compared to the 2.5 µM treatment (Fig. 1A, B). These findings suggest that the MPA inhibitor exerts cytotoxic effects and inhibit the growth of bladder cancer cells.

Fig. 1.

Fig. 1

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CCK-8 absorbance results for 5637 and UMUC2 bladder cancer cells treated with varying concentrations of the MPA inhibitor. (A) Concentration-response analysis of MPA-mediated proliferation inhibition in 5637 cells. (B) Changes in cell proliferation levels of UMUC2 cells after treatment with a gradient of MPA inhibitor concentrations

MPA-Induced ferroptosis and apoptosis in bladder cancer cells

To further investigate the mechanism by which the MPA inhibitor affects bladder cancer cell growth, we performed TEM. The results showed significant morphological changes in the mitochondria after MPA treatment, including a reduction in mitochondrial cristae, mitochondrial shrinkage, and increased membrane density, indicating features associated with ferroptosis in the cells (Fig. 2A). These changes may represent a potential mechanism through which the MPA inhibitor induces cell death. To further explore the effect of the MPA inhibitor on bladder cancer cell growth, we conducted a proliferation recovery assay. Compared to the control group, the addition of MPA to the culture medium significantly inhibited the proliferation rate of UMUC2 and 5637 cells (Fig. 2B, p < 0.0001). However, when the ferroptosis inhibitor ferrostatin was added to the medium, the cell proliferation rate was partially restored (Fig. 2B, p < 0.01), suggesting that the MPA inhibitor may inhibit bladder cancer cell proliferation by promoting ferroptosis. Moreover, our experiments showed that necrosis occurred at a higher rate than apoptosis in both 5637 and UMUC2 bladder cancer cells. IMPDH inhibition had no significant effect on necrosis but promoted apoptosis (p < 0.05), with apoptosis rates of 1.7% in 5637 cells and 0.3% in UMUC2 cells (Fig. 2C and D). These results indicate that IMPDH inhibition primarily induces cell death through ferroptosis, with a minor contribution from apoptosis, rather than through necrosis. Taken together, these findings suggest that the MPA inhibitor may suppress bladder cancer cell growth by modulating IMPDH activity, thereby inducing ferroptosis and apoptosis.

Fig. 2.

Fig. 2

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Effects of the MPA inhibitor on the normal function of 5637 and UMUC2 bladder cancer cells. (A) TEM images of bladder cancer cells treated with the IMPDH inhibitor. (B) Effect of combined treatment with MPA and Ferrostatin on the proliferation of UMUC2 and 5637 cells. (C) Flow cytometry was used to analyze the representative pictures and statistical results of the effect of MPA on apoptosis and necrosis of bladder cancer 5637 cells. P3: necrosis; P4: apoptosis. (D) The effects of MPA on apoptosis and necrosis in bladder cancer UMUC2 cells were analyzed by flow cytometry, including both representative images and quantitative results

MPA-Induced transcriptomic changes in bladder cancer

To investigate the molecular mechanisms of ferroptosis and apoptosis induced by MPA in 5637 and UMUC2 cells, we performed transcriptomic sequencing on untreated and MPA-treated 5637 and UMUC2 cells. After correcting for sequencing depth and gene length, we found that the gene expression levels in the MPA-treated group were higher than those in the untreated group (Supplementary Fig. 1 A). Correlation analysis of gene expression values (FPKM) and principal component analysis (PCA) confirmed high reproducibility within groups and significant differences between groups, validating the experimental design (Supplementary Fig. 1B-C). Volcano plots revealed that after MPA treatment, a total of 4,474 differentially expressed genes (DEGs) were identified in 5637 cells, with 2,516 genes upregulated and 1,958 genes downregulated (Fig. 3A). In UMUC2 cells, a total of 2,642 DEGs were identified, with 1,846 genes upregulated and 796 genes downregulated (Fig. 3B). A Venn diagram further highlighted 936 common DEGs shared between the two cell types, along with 3,538 DEGs unique to 5637 cells and 1,706 DEGs specific to UMUC2 cells, respectively (Fig. 3C).

Fig. 3.

Fig. 3

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DEGs analysis in the transcriptomes of 5637 and UMUC2 cells treated with the MPA inhibitor. (A) Volcano plot showing DEGs between the experimental and control conditions in 5637 cells. The volcano plot highlights genes with significant differences in expression across the experimental and control sets. Red dots represent upregulated genes, green dots indicate downregulated ones, and blue dots denote genes with no significant change. (B) Volcano plot of DEGs comparing the experimental and control samples in UMUC2 cells. Similar to 5637 cells, the volcano plot for UMUC2 depicts DEGs between the treatment and control groups. (C) Venn diagram depicting the shared DEGs between 5637 and UMUC2 cells

To further explore the function of DEGs, we analyzed their distribution across chromosomes and annotated the gene types. As shown in the figure, the DEGs are primarily concentrated on chromosomes 1–22, indicating a widespread distribution of these genes. Additionally, the majority of the DEGs are coding genes, suggesting that they are mainly responsible for protein synthesis in the cell and play a role in regulating cellular functions and structures. This indicates that the DEGs are predominantly associated with fundamental physiological processes in the cell (Fig. 4A). Subsequently, we examined the changes in key pathways in MPA-treated 5637 and UMUC2 cells using GO and KEGG enrichment analyses. The results of the GO enrichment analysis highlighted the involvement of important biological processes such as metabolic regulation, inflammatory response, and angiogenesis. Angiogenesis was significantly enriched in both groups, suggesting its central role in the context of the study. Additionally, pathways related to metabolism, such as lipid metabolic regulation, steroid metabolic processes, NAD metabolism, and glutamine metabolism, indicated the impact of metabolic activities on the research subjects. Immune and inflammatory processes, including humoral immune response, acute inflammatory response, and negative regulation of the immune system, were also significantly enriched, potentially reflecting the role of immune regulation in pathological or physiological processes (Fig. 4B-C). The KEGG enrichment analysis reveals that the study involves multiple key biological processes and signaling pathways. In terms of cellular processes, apoptosis and ferroptosis are repeatedly highlighted, indicating the critical role of cell death mechanisms. In environmental information processing, the calcium signaling pathway and cGMP-PKG signaling pathway are significantly enriched, suggesting their importance in signal transduction. Additionally, metabolism-related pathways such as tyrosine metabolism and glutathione metabolism, as well as immune-related pathways like T cell receptor signaling and IL-17 signaling, point to potential mechanisms related to metabolic regulation, immunity, and disease (Fig. 4D-E). Notably, despite the absence of direct enrichment in the canonical ferroptosis pathway (hsa04216), key upstream regulators of ferroptosis were significantly altered, including: SLC7A11(central ferroptosis suppressors) and HMOX1 ​​(iron release regulator). These changes collectively create a pro-ferroptotic cellular state. In addition, we analyzed the top five key genes involved in iron dysregulation and apoptosis pathways (Figs. 4F-G). Concurrently, we examined ferroptosis-related protein alterations at the translational level. Notably, SLC3A2 exhibited enhanced ubiquitination following MPA treatment (Supplementary Fig. 2). All of which showed abnormal expression (mainly upregulation) after MPA treatment, promoting the occurrence of ferroptosis.

Fig. 4.

Fig. 4

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Distribution and functional analysis of DEGs. (A) Chromosomal distribution of DEGs in each sample and the heatmap of gene type annotation. (B) GO enrichment analysis results of DEGs in 5637 cells. (C) GO enrichment analysis results of DEGs in UMUC2 cells. (D) KEGG enrichment analysis results of DEGs in 5637 cells. (E) KEGG enrichment analysis results of DEGs in UMUC2 cells. (F) Top five significant genes associated with ferroptosis and apoptosis pathways in MPA-treated 5637 cells. (G) Top five significant genes associated with ferroptosis and apoptosis pathways in MPA-treated UMUC2 cells

MPA-induced metabolic profile changes

Since metabolic abnormalities are one of the critical aspects of DEGs in the transcriptomes of cells treated with MPA, we conducted a systematic analysis of all differentially abundant metabolites (DAMs) in MPA-treated cells using Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA). The results revealed a positive correlation between the metabolic differences and the distances between points on the OPLS-DA score plot. With the progression of MPA treatment, significant alterations were observed in the major biological components (Fig. 5A and D). OPLS-DA score plots revealed a clear distinction between the two groups. In the OPLS-DA model for 5637 cells, the R²Y and Q² values were 0.999 and 0.899, respectively; in the OPLS-DA model for UMUC2 cells, the R²Y and Q² values were 1 and 0.888, respectively. These data suggest that there are substantial differences in the metabolic profiles of 5637 cells treated with MPA compared to untreated controls. The reliability of the OPLS-DA model was assessed through permutation testing. The x-axis represents the number of permutations, while the y-axis shows the R²Y or Q² values from the model validation. The permutation test results indicated that the model exhibits excellent stability and reliability, with all Q² values falling below the original blue Q² point (Fig. 5B and E).

Fig. 5.

Fig. 5

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OPLS-DA analysis of metabolic changes in MPA-treated cells. (A) OPLS-DA score plot for the comparison between T-5637 and C-5637. (B) OPLS-DA permutation test plot for the T-5637 vs. C-5637 comparison. R²X and R²Y denote the proportion of variance explained by the X and Y matrices, respectively. Q² reflects the model’s predictive performance. The closer R² and Q² values are to 1, the more robust and consistent the model. A Q² exceeding 0.5 indicates strong predictive capability, while a Q² below 0.5 suggests weaker performance. (C) OPLS-DA loading plot for the T-5637 vs. C-5637 group. (D) OPLS-DA score plot for T-UMUC2 vs. C-UMUC2. (E) OPLS-DA permutation test plot for T-UMUC2 vs. C-UMUC2. (F) OPLS-DA loading plot for T-UMUC2 vs. C-UMUC2

Furthermore, the OPLS-DA loadings plot revealed that in 5637 cells, the main metabolites based on VIP (Variable Importance in Prediction) values include 3-Keto-scyllo-inosamine, 2-Ethyl-3-hydroxy-2,4,4-trimethyloxazolidine, Dopachrome, 3’-Hydroxy-3,4,5,4’-tetramethoxystilbene, Perfluorobutanol, LysoPE, Xanthorin trimethyl ether (Fig. 5C). In UMUC2 cells, the top ten metabolites based on VIP (Variable Importance in Projection) values were: tawiyclamide B, N-Acetylaspartic acid, Ribothymidine, Aspartylglutamine, Glucosamine hydrochloride, Phenylalanine, Cissacapine, Phosphocreatine, Nonactinic Acid, and Phthalic anhydride (Fig. 5F). These findings provide potential biomarkers and offer crucial metabolomic insights for further investigation into the biological effects of MPA.

To investigate the changes in the levels of key metabolites and their impact on relevant pathways following MPA treatment, we generated heatmaps and pathway bubble plots. The heatmap results revealed a distinct pattern of metabolite level alterations in MPA-treated 5637 cells. Some metabolites showed reduced levels, including 2-Hydroxy-3-(2-hydroxyphenyl) propanoic acid, (3,5-dimethoxyphenyl)-N-(2-furylmethyl) carboxamide, and kainic acid. In contrast, other metabolites exhibited increased levels, such as Echimidine N-oxide, LPG (18:2), and CDP (Fig. 6A). These changes were also observed in UMUC2 cells, where metabolites like phosphocreatine, 2’-CMP, and CDP were elevated, while 8-oxodeoxycoformycin and pro-CDCA levels decreased (Fig. 6D-H). Critical ferroptosis-related metabolites exhibited marked changes, including glutathione and phosphocreatine, these shifts align with the transcriptomic profile of redox dysregulation. Correlation analysis of these metabolites showed that 25 pairs (56%) were negatively correlated, while 20 pairs (44%) were positively correlated, indicating a predominant antagonistic interaction between metabolites (Fig. 6B). The DAMs mainly affect the following aspects: Environmental information processing (E), genetic information processing (G), human diseases (H), metabolism (M), and organismal systems (O), with the most significant impact on metabolic processes. Specifically, environmental information processing was associated with ABC transporters, calcium signaling, and cGMP-PKG signaling pathways; Genetic information Processing primarily involved aminoacyl-tRNA biosynthesis. Human diseases were mainly linked to central carbon metabolism in cancer. Metabolism-related pathways included nucleotide metabolism, pyrimidine metabolism, oxidative phosphorylation, purine metabolism, alanine, aspartate, and glutamate metabolism, pantothenate and CoA biosynthesis, beta-alanine metabolism, caffeine metabolism, arginine biosynthesis, and glycine, serine, and threonine metabolism. Additionally, organismal systems were impacted by pathways such as protein digestion and absorption, mineral absorption, melanogenesis, aldosterone synthesis and secretion, olfactory transduction, longevity regulation, and parathyroid hormone synthesis and action (Fig. 6C). In conclusion, MPA treatment induces significant metabolic alterations and impacts a variety of biological pathways, particularly those associated with metabolism and disease, providing insights into the cellular mechanisms influenced by MPA.

Fig. 6.

Fig. 6

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Enrichment analysis of differentially expressed metabolites. (A) Heatmap of typical differentially expressed metabolites in 5637 cells. (B) Lower triangular correlation heatmap of key metabolites. (C) Bubble plot of metabolite enrichment analysis. (D-H) Bar plot of representative differentially expressed metabolites in UMUC2 cells

Integrated analysis of metabolomics and transcriptomics

To further investigate the mechanisms by which MPA promotes ferroptosis and apoptosis in bladder cancer cells, we integrated metabolomics and transcriptomics data to analyze the pathways commonly affected in 5637 and UMUC2 cells. A total of 101 significant pathways were identified (Fig. 7A). Among the top ten significantly enriched pathways were: hsa05208, hsa00230, hsa01240, hsa04022, hsa04918, hsa04974, hsa01230, hsa01232, hsa05230, and hsa00240. The number of genes involved in each pathway is shown in the figure, with hsa01232 exhibiting the most significant gene changes (Fig. 7C).

Fig. 7.

Fig. 7

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Integrated analysis of metabolomics and transcriptomics. (A) Venn diagram of the pathways activated by transcriptomics and metabolomics in MPA-treated 5637 and UMUC2 cells. (B) Interaction network of metabolites and genes in representative pathways. Hsa05208: Chemical carcinogenesis - reactive oxygen species; hsa00230: Purine metabolism; hsa01240: Biosynthesis of cofactors; hsa04022: cGMP-PKG signaling pathway; hsa04918: Thyroid hormone synthesis; hsa04974: Protein digestion and absorption; hsa01230: Biosynthesis of amino acids; hsa01232: Nucleotide metabolism; hsa05230: Central carbon metabolism in cancer; hsa00240: Pyrimidine metabolism. (C) The number of DEGs and metabolites in the top ten activated pathways based on transcriptomics and metabolomics in MPA-treated 5637 and UMUC2 cells

To better elucidate the interactions between these pathways, we constructed a pathway-gene-metabolite network diagram, illustrating the regulatory relationships between pathways, genes, and metabolites. For example, hsa00230 influences hsa04022 and hsa04918 through the regulation of cAMP, GMP, and GUCY1A1, which in turn modulate the activity of hsa05208. hsa05208 further regulates the expression of key genes such as tyrosine, LDHA, and LDHB (Fig. 7B). Overall, the integrated analysis of transcriptomics and metabolomics not only reveals the complex interconnections between different biological pathways but also provides new perspectives for exploring the cellular mechanisms of MPA, thereby deepening our understanding of cellular biological processes.

Revealing the mechanism of MPA-induced ferroptosis in bladder cancer cells from the perspectives of metabolomics and transcriptomics

The integrated analysis of metabolomics and transcriptomics revealed one of the mechanisms by which MPA promotes ferroptosis and apoptosis in bladder cancer cells. ROS and metabolites such as tyrosine, along with genes like LDHA, LDHB, and others, are key factors or signature markers in the processes of ferroptosis and apoptosis. In the transcriptomic analysis, we identified DEGs associated with ROS-related pathways and ferroptosis and apoptosis pathways in both 5637 and UMUC2 cells, including NPPB, CAMK2G, TSPO, NGF, ADM, MALAT1, FOS, TUBB3, CXCL8, and NPPC (Fig. 8A, B). In the metabolomic analysis, metabolites related to ROS and ferroptosis, such as GMP, phenol, glutathione, NAD+, and cytosine, also showed differential expression in 5637 or UMUC2 cells (Fig. 8C-H). Taken together, MPA treatment significantly affected the ferroptosis and apoptosis processes in bladder cancer cells, and through the regulation of ROS-related pathways and the metabolites involved in ferroptosis and apoptosis pathways, further altered the metabolic state of the cells. These findings provide new insights and important clues for potential mechanisms in bladder cancer therapy.

Fig. 8.

Fig. 8

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Differential analysis of metabolites and genes related to ferroptosis and apoptosis. (A) Genes associated with ROS generation. (B) Genes involved in the activation of ferroptosis and apoptosis. (C-H) Expression of metabolites associated with ROS, ferroptosis, and apoptosis pathways

Discussion

As an IMPDH inhibitor, MPA suppresses purine synthesis by inhibiting the activity of IMPDH, thereby inhibiting the proliferation of T cells and B cells and reducing the activity of the immune system. In recent years, MPA has garnered attention not only for its applications in immunosuppression but also as one of the focal points in cancer research due to its potential antitumor activity [27, 28]. By inhibiting IMPDH, MPA restricts purine synthesis, thereby affecting cell proliferation, DNA synthesis, and the progression of the cell cycle [29]. Studies have shown that MPA exhibits potential antitumor activity across various cancer types, including bladder cancer [30], liver cancer [31], gastric cancer [9], and lung cancer [7]. However, its specific mechanisms remain incompletely understood. Traditional research has primarily focused on specific molecular targets, which has hindered a comprehensive explanation of its antitumor mechanisms. To further elucidate the antitumor effects of MPA, the application of multi-omics approaches can provide a global perspective. IMPDH includes the IMPDH1 and IMPDH2 isoforms, which play a crucial role in regulating the human purine nucleotide synthesis pathway and are essential in tumor progression [32–34]. In our previous research, we observed that the levels of IMPDH1 and IMPDH2 were increased in bladder cancer, which promoted the synthesis of guanine nucleotides in bladder cancer cells, thus promoting cell proliferation, migration and invasion [26]. Therefore, targeting IMPDH may represent a potential strategy for the treatment of bladder cancer. A comprehensive understanding of the impact of IMPDH inhibition on cells is essential for the development of clinical therapeutics. Initially, we observed that MPA inhibited the proliferation of 5637 and UMUC2 cells, inducing ferroptosis in the mitochondria and a small amount of apoptosis, rather than necrosis.

Ferroptosis is a type of iron-dependent cell death mainly characterized by the buildup of lipid peroxides inside the cell, which eventually results in the rupture of the cell membrane [35, 36]. Cell apoptosis is a process in which cells actively self-destruct in response to internal or external signaling stimuli [37]. The mechanisms of ferroptosis and apoptosis induced by MPA in 5637 and UMUC2 cells were explored through transcriptomic and metabolomic analyses. Changes in gene expression and metabolites were observed following MPA treatment in both cell lines. After MPA treatment, 101 metabolic pathways were significantly altered in both 5637 and UMUC2 cells. Among these, the most prominent pathway changes occurred in Chemical carcinogenesis - reactive oxygen species, Purine metabolism, Biosynthesis of cofactors, cGMP-PKG signaling pathway, Thyroid hormone synthesis, Protein digestion and absorption, Biosynthesis of amino acids, Nucleotide metabolism, Central carbon metabolism in cancer, and Pyrimidine metabolism. A total of 141 genes, including LDHA, LDHB, and IKBKB, were involved, along with 69 metabolites such as Glutathione, reactive oxygen species, and tyrosine.

Metabolites such as ROS [38, 39] and tyrosine, along with genes like LDHA [40–42] and LDHB [43, 44], are critical determinants or hallmark indicators of ferroptosis or apoptosis. ROS are byproducts of cellular metabolism, particularly superoxide anions, hydrogen peroxide, and hydroxyl radicals. These species can react with intracellular free iron through the Fenton reaction, generating hydroxyl radicals, which further promote lipid peroxidation. The accumulation of these lipid peroxidation leads to cellular membrane damage and activates ferroptosis and apoptosis pathways [45, 46]. Tyrosine and its metabolites are also closely associated with both ferroptosis and apoptosis. Oxidized products of tyrosine can enhance oxidative stress by generating ROS, thereby increasing lipid peroxidation and contributing to cellular damage [47, 48]. Moreover, the LDHA and LDHB genes regulate the conversion between lactate and pyruvate, influencing cellular energy metabolism and redox balance. Overexpression of LDHA may decrease the cellular redox capacity, promote the generation of ROS and thereby exacerbating ferroptosis and apoptosis [49]. Therefore, the accumulation of ROS, the oxidative effects of tyrosine metabolites, and dysregulation of lactate metabolism collaboratively contribute to the onset of ferroptosis and apoptosis. Modulating these factors may offer new therapeutic targets for treating related diseases.

The innovation of this study lies in the discovery that MPA not only induces apoptosis in 5637 and UMUC2 cells, but also triggers ferroptosis in these cells. Additionally, the potential mechanisms underlying MPA-induced ferroptosis and apoptosis in 5637 and UMUC2 cells are elucidated. Linking MPA to ferroptosis and apoptosis provides a novel perspective for its application in bladder cancer treatment. By exploring the detailed mechanisms through which MPA induces ferroptosis and apoptosis, this study not only deepens the understanding of MPA’s antitumor effects but also provides a theoretical foundation for the development of new strategies for ferroptosis and apoptosis induction. However, the study is limited by the use of a limited number of bladder cancer cell lines and the lack of patient validation. Therefore, future research is needed to validate the effects of MPA on metabolism and transcription in vivo, as well as to develop personalized treatment regimens.

Conclusion

This study reveals the antitumor potential of MPA inhibitors in bladder cancer cells through a comprehensive set of experiments, including cell viability assays, transcriptomic analysis, and metabolite profiling. MPA significantly alters the gene expression and metabolite profiles of the cells, which may have potential therapeutic implications for bladder cancer. These DEGs and metabolites provide valuable insights for further investigation into the anticancer mechanisms of MPA, as well as potential targets for precision therapy in bladder cancer. Furthermore, the in-depth exploration of MPA’s antitumor mechanisms using multi-omics technologies offers a more comprehensive theoretical foundation for its development as a cancer therapeutic agent.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary Material 1 (14.8MB, zip)

Acknowledgements

Not applicable.

Author contributions

SL performed experiments, analyzed data and wrote the manuscript. KL performed experiments and analyzed data. GL analyzed and interpreted data. PZ and YL confirm the authenticity of all the raw data. LG, FZ and QS designed the experiments. All authors have read and approved the final manuscript.

Funding

No funding.

Data availability

Data is provided within the supplementary files.

Declarations

Ethics approval and consent to participate

Not applicable.

Patient consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Supplementary Materials

Supplementary Material 1 (14.8MB, zip)

Data Availability Statement

Data is provided within the supplementary files.

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