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. 2023 Jul 18;149(14):12867–12880. doi: 10.1007/s00432-023-05164-5

A feed-forward loop based on aerobic glycolysis and TGF-β between tumor-associated macrophages and bladder cancer cells promoted malignant progression and immune escape

Chengquan Shen 1,#, Jing Liu 2,#, Wei Jiao 1, Xuezhou Zhang 1, Xinzhao Zhao 1, Xuecheng Yang 1,✉, Yonghua Wang 1,3,✉
PMCID: PMC11798317  PMID: 37462772

Abstract

Purpose

Immunotherapy with programmed cell death 1/ligand 1 (PD-1/PD-L1) checkpoint inhibitors has revolutionized the systemic treatment of solid tumors, including bladder cancer. Previous studies have shown that enhanced glycolysis, tumor-associated macrophage (TAM) infiltration, and TGF-β secretion in the tumor microenvironment (TME) are closely related to PD-1/PD-L1 inhibitor immunotherapy resistance. However, the potential mechanism of their interaction in bladder cancer has not been fully uncovered.

Methods

By coculturing bladder cancer cells and TAMs, we studied the relationship and interaction mechanism between tumor cell glycolysis, TAM functional remodeling, TGF-β positive feedback secretion, and PD-L1 mRNA m6A methylation in the bladder cancer microenvironment.

Results

Bioinformatics analysis and IHC staining found a close correlation between tumor glycolysis, M2 TAM infiltration, and the prognosis of bladder cancer patients. In Vitro experiments demonstrated that bladder cancer cells could re-educate M2 TAMs through lactate and promote TGF-β secretion via the HIF-1α signaling pathway. Reciprocally, in vitro, and in vivo experiments validated that M2 TAMs could promote glycolysis in bladder cancer cells by TGF-β via the Smad2/3 signaling pathways. Furthermore, M2 TAMs could also promote CSCs and EMT of bladder cancer cells. More importantly, we found M2 TAMs enhance PD-L1 mRNA m6A methylation by promoting METLL3 expression in bladder cancer via the TGF-β/Smad2/3 pathway in the TME.

Conclusions

Our study highlights a feed-forward loop based on aerobic glycolysis and TGF-β between M2 TAMs and bladder cancer cells, which may be a potential mechanism of malignant progression and immunotherapy resistance in bladder cancer.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00432-023-05164-5.

Keywords: Bladder cancer, Tumor-associated macrophage, TGF-β, Glycolysis, PD-1/PD-L1 inhibitor

Introduction

Bladder cancer is considered one of the most aggressive neoplasms in the urinary system, with approximately 573,000 new cases and 213,000 deaths worldwide in 2020 (Sung et al. 2021). Surgical resection is the main treatment for early bladder cancer, but for advanced or metastatic bladder cancer, radiotherapy, chemotherapy, and other traditional adjuvant treatments often have difficulty achieving satisfactory therapeutic effects. Recently, the discovery of immune checkpoint molecules and the use of PD-1/PD-L1 inhibitors have opened a new era of immunotherapy for bladder cancer. However, the first generations of PD-1/PD-L1 inhibitors are still limited by low long-term response rates and a lack of reliable prognostic predictors, highlighting a better understanding of the interaction between tumor cells and immune cells in the tumor microenvironment (TME).

Tumor metabolism plays a vital role in driving and regulating the process of tumor immune escape. Lactate secreted by metabolism-reprogrammed cancer cells contributes to the immunosuppression in the TME that favors cancer cell growth, metastasis, and immune escape (Ngwa et al. 2019). Elevated lactate suppresses the antitumor activity of T cells by causing extracellular acidification that inhibits the functions of CD8 + T lymphocytes, while neutralization of acidic TME and proton-pump inhibitors can reverse the suppression of antitumor immunity and improve immunotherapy (Ippolito et al. 2019). In addition, bladder cancer has an immunosuppressive TME characterized by the presence of tumor-associated macrophages (TAMs), which are an M2 phenotype and regulate many critical processes, including the promotion of tumor growth, metastasis, and immune suppression (Qian and Pollard 2010; Coussens and Werb 2002; Lewis and Pollard 2006). TAMs can secret several cytokines, such as IL-10 and TGF-β, that contribute to the maintenance of a strong immunosuppressive microenvironment by inhibiting CD4+ and CD8+ T cells and by inducing regulatory T cell expansion to inhibit the efficacy of immunotherapy (Cassetta and Pollard 2018). Moreover, elevated lactate levels in TME are important for the production of oncogenic factors by M2 TAMs (Zhang et al. 2019). Our previous studies indicated that enhanced glycolysis in bladder cancer cells had an important role in remodeling the function of TAMs (Yu et al. 2021; Zhao et al. 2015). Recent studies have also shown that enhanced glycolysis, M2 TAM infiltration, and TGF-β secretion in the tumor microenvironment are closely related to the epithelial-to-mesenchymal transition (EMT) process and PD-L1/PD-1 inhibitor resistance (Ding et al. 2021; Jiang et al. 2019; Choo et al. 2018; Mariathasan et al. 2018). However, the mechanism of their interaction in tumors, especially bladder cancer, is still unclear.

Here, we found a close correlation between tumor glycolysis, M2 TAM infiltration, and prognosis of bladder cancer patients and highlighted a feed-forward loop based on aerobic glycolysis and TGF-β between TAMs and bladder cancer cells, which promoted the process of cancer stem cell-like properties (CSCs), EMT, and immune escape in bladder cancer. Our findings may provide potential mechanisms of PD-1/PD-L1 inhibitor immunotherapy resistance and novel therapeutic strategies in the growing field of combination immunotherapy for cancer.

Methods and materials

Bioinformatics analysis

Genes encoding proteins involved in glycolysis and gluconeogenesis were downloaded from the GSEA website (https://www.gsea-msigdb.org/gsea/msigdb/index.jsp), LC–MS/MS was used to characterize the variety of proteins in our 3 bladder cancer samples and corresponding adjacent samples. The process contained protein extraction, trypsin digestion, TMT/iTRAQ labeling, HPLC fractionation, LC–MS/MS analysis, database search, and bioinformatic analysis. The enrichment of the differentially expressed protein against all identified proteins was detected by a two-tailed Fisher’s exact test, and protein domains with a corrected p value < 0.05 were recognized as statistically significant. RNA-seq expression and clinical information of bladder cancer patients were downloaded from The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/repository) and the GEO database (https://www.ncbi.nlm.nih.gov/geo/, GSE13507). To evaluate the glycolysis levels of bladder cancer patients, we clustered bladder cancer patients into three different groups by consensus clustering analysis with “Consensus Cluster Plus” in R based on the expression levels of glycolysis genes. Heatmap was used to display the expression levels of differentially expressed genes in distinct groups. In addition, the R package “CIBERSORT” (robust enumeration of cell subsets from tissue expression profiles) was used to analyze the relative expression levels of M2 TAMs in the TME of bladder cancer. Kaplan–Meier survival analysis was performed to evaluate the prognostic values of glycolysis levels.

Immunohistochemistry (IHC)

Tissue samples were collected from 70 patients with primary bladder cancer treated at the Affiliated Hospital of Qingdao University between January 2017 and January 2022. The research protocol was approved by the Ethical Committee of the Affiliated Hospital of Qingdao University and all patients provided written informed consent before participation. No patients received adjuvant chemotherapy, radiotherapy, or immunotherapy before surgery. Follow-up information was available for all 70 patients with durations ranging from 2 to 60 months (median 29 months).

Paraffin-embedded sections were mounted on glass slides, deparaffinized, rehydrated in a graded ethanol series, and then subjected to microwave antigen retrieval. Sections were incubated overnight at room temperature with CD206, PKM2, TGF-β, and PD-L1 polyclonal antibodies (Cell Signaling Technology, Boston, USA). Immunostaining was performed by the avidin–biotin-peroxidase method and counterstained with hematoxylin. Immunohistochemical staining was reviewed independently by two pathologists and scored based on a 12-point semiquantitative scoring system (Kobierzycki et al. 2014). The staining score was evaluated by both the percentage of staining (0 = 0%, 1 = 1–25%, 2 = 26–50%, 3 = 51–75%, and 4 = 76– 100%) and the intensity of staining (0 = negative, 1 = weakly positive, 2 = moderately positive, and 3 = strongly positive). The final score was quantified as the percentage × intensity of staining and the cutoff point was the median (score ≥ 8 was considered to be a positive expression).

Cell culture and treatment

The human bladder cancer cell lines 5637 and T24 were obtained from the Cell Bank of the Chinese Academy of Sciences (Shanghai, China). The human cell line THP-1 was obtained from the Shanghai Institute of Biochemistry and Cell Biology (Shanghai, China) and differentiated into macrophages by treatment with 100 ng/ml phorbol-12-myristate-13-acetate (PMA) (MCE, Shanghai, China) for 48 h. Macrophages were then polarized toward the M2 phenotype by stimulation with 20 ng/ml IL-4 (PeproTech, San Diego, CA, USA) for 24 h (Chanput et al. 2014). All cells were incubated at 37 °C in a humidified atmosphere containing 5% CO2. Galloflavin (Solarbio, Beijing, China) was used as an inhibitor of lactic dehydrogenase (LDH) in the culture system. YC-1 was used as an inhibitor of HIF-1α and TGF-β neutralizing antibody AF-246 (R&D System, Minneapolis, MN, USA) was used to block TGF-β secretion. SIS3 was used as an inhibitor of Smad3.

Determination of lactate concentration and TGF-β secretion

Lactate concentrations in the culture system were measured by a lactate colorimetric assay kit (Wuhan, China). TGF-β levels were tested by ELISA kits (Abcam, Cambridge, England) following the manufacturer’s instructions.

Real-time PCR

Total RNA was extracted and reverse transcribed into cDNA using a PrimeScript™ RT reagent kit (Perfect Real Time) (Takara, Japan). All primers were synthesized by Huada Gene (Beijing, China) and are shown in Table 1. RT–qPCR was performed using a Roche LightCycler 480II real-time PCR detection system (Roche, Basel, Switzerland).

Table 1.

Sequences of the primers used for real-time quantitative PCR

Name of primer Sequence of primer (5’–3’)
HIF-1α-F TATTGCACTGCACAGGCCACATTC
HIF-1α-R TGATGGGTGAGGAATGGGTTCACA
GLUT1-F GGCATTGATGACTCCAGTGTT
GLUT1-R ATGGAGCCCA GCAGCAA
HK2-F TCACGGAGCTCAACCATGAC
HK2-R CTGCAGTAGGGTGAGTGGTG
PKM2-F AGGATGCCGTGCTGAATG
PKM2-R TAGAAGAGGGGCTCCAGAGG
LDHA-F TGGGAGTTCACCCATTA
LDHA-R AGCACTCTCAACCACCTGCT
PD-L1-F TATGGTGGTGCCGACTA
PD-L1-R TGCTTGTCCAGATGACTTC
β-Actin-F GGCATCGTCACCAACTGGGAC
β-Actin-R CGATTTCCCGCTCGGCCGTGG
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Western blot analysis

Total protein was extracted by SDS buffer, and then the protein concentrations were measured using a bicinchoninic acid (BCA) kit (Thermo Fisher Scientific). Subsequently, the proteins (20 µg) were separated by 10% SDS–PAGE, transferred to polyvinylidene fluoride membranes (Millipore, Billerica, USA), and incubated with 1:1000 diluted primary antibodies against GLUT1, HK2, PKM2, LDHA, HIF-1α, Smad2/3, p-Smad2/3, CD44, E-cadherin, Vimentin, METTL3, m6A, and PD-L1 (Cell Signaling Technology, Boston, USA) overnight at 4 °C. The membranes were then incubated with HRP-conjugated secondary antibodies (Jackson ImmunoResearch, West Grove, USA; dilution 1:10,000) for another 1 h at room temperature. Immunoreactivity was determined by chemiluminescence according to the manufacturer’s instructions.

Colony formation, migration, and invasion assay

A total of 500 cells/well were seeded in a 6-well culture dish. After 14 days, the cells were fixed in 4% paraformaldehyde, stained with crystal violet (Beyotime Biotechnology, CAT#C0121), and counted microscopically. Migration and invasion assays were performed in the chamber coated with or without Matrigel matrix (24 well, 8 μm pore size, Corning, USA) according to the manufacturer’s instructions as previously described (Zheng et al. 2013).

Methylated RNA immunoprecipitation

Total RNA was isolated using Trizol and fragmented by RNA fragmentation reagents (Thero, AM88740). Then, we used a Magna MeRIP m6A kit to analyze the PD-L1 m6A level according to the manufacturer’s instructions. The relative enrichment of the m6A level for PD-L1 was analyzed by qPCR with specific primers and data were normalized to input.

Co-Immunoprecipitation (Co-IP)

For the Co-IP assay, T24 and 5637 cells were seeded in 10 cm-dishes and were scraped and lysed in IP buffer (20 mM Tris–HCl (pH 7.5), 150 mM NaCl, 1 mM Na2EDTA, 1 mM EGTA, 1% Triton, 2.5 mM sodium pyrophosphate, 1 mM beta-glycerophosphate, 1 mM Na3VO4, 1 µg/ml leupeptinNote, and 1 mM PMSF) on ice for 30 min, and then centrifuged at 13000 g for 15 min. The supernatant of samples was incubated with the protein A/G beads with the indicated antibodies for 5 h at 4 ℃. After washing three times with lysis buffer, co-immunoprecipitates were detected by immunoblotting with the indicated antibodies.

Animal experiments

For the subcutaneous implantation model, 2 × 106 MB49 cells (n = 6 for each group) were subcutaneously injected into groin regions of 4–5 weeks female C57BL/6N mice. Tumor volume was calculated as 0.5 × W2 × L (where W and L represent a tumor’s width and length, respectively). After xenografts were generated, DMSO and SB431542 (#301836-41-9, SANTA CRUZ BIOTECHNOLOGY, 10 mg/Kg), were administrated every day. All animal experiments were approved by The Affiliated Hospital of Qingdao University Committee on Animal Care.

Statistical analysis

Statistical tests were performed using R version 3.6.0 and GraphPad Prism 8.0. Data are represented as the mean ± SD of a representative point from triplicate experiments and were analyzed using GraphPad Prism 8 software (San Diego, CA, USA). Comparisons between the two groups were performed using an unpaired, two-tailed Student’s t test. Depending on the relationship between the glycolysis score and bladder cancer patient survival, the “survminer” R package was used to determine the cutoff point for each subgroup of the dataset. A P value < 0.05 was considered statistically significant.

Results

Glycolysis level and M2 TAM infiltration in tumor tissue were related to the prognosis of bladder cancer

To explore the possible interaction between glycolysis and TAMs in the TME of bladder cancer, we first identified the relationship between glycolysis levels, M2 TAM infiltration, and the prognosis of bladder cancer patients by bioinformatics analysis. We identified 17 glycolysis genes that were differentially expressed in mRNA and protein levels by using transcriptional data from TCGA and GEO bladder cancer cohort and proteomic data from our bladder cancer samples (Fig. 1A, B). To investigate the biological of different functional groups of glycolysis genes, we classify bladder cancer patients with qualitatively different glycolysis subgroups based on the expression levels of glycolysis genes, and three distinct subgroups were eventually identified, namely, cluster A (n = 240), cluster B (n = 231), and cluster C (n = 100) (Supplementary 1A–C). Heatmap showed that glycolysis genes were significantly overexpressed in cluster A, which indicated cluster A has a higher glycolysis status than cluster B or cluster C (Fig. 1C). Survival analysis also showed that cluster A was associated with the poor overall survival of bladder cancer (Fig. 1D). Further analysis indicated that bladder cancer patients with distinct glycolysis status have different PD-1/PD-L1 expression levels and immune cells infiltration levels (Fig. 1E, F, Supplementary 1D). We found that patients with high glycolysis status have high M2 TAM infiltration levels in bladder cancer (Fig. 1G). In addition, multiple cytokine-related genes containing TGFB1 are highly expressed in bladder cancer patients with high glycolysis status (Supplementary 1E, F). Glycolysis-related marker (PKM) and M2 TAM marker (CD206) were selected to explore the correlation between glycolysis level and M2 TAM infiltration. The results showed that PKM2, CD206, TGF-β, and PD-L1 have a positive correlation in the TCGA bladder cancer cohort (Fig. 1H–M).

Fig. 1.

Fig. 1

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The glycolysis level and M2 TAM infiltration in tumor tissue were related to the prognosis of bladder cancer. A, B The mRNA and protein expression of differentially expressed glycolysis genes in bladder cancer samples and normal samples by using transcriptional data from TCGA and GEO bladder cancer cohorts and proteomic data from our bladder cancer samples. Tumor, red; normal, blue. *P < 0.05; **P < 0.01; ***P < 0.005. C The expression levels of glycolysis genes in different groups by using TCGA and GEO bladder cancer cohorts. Cluster A, blue; Cluster B, yellow; Cluster C, red. D Kaplan–Meier analyses for TCGA and GEO bladder cancer cohorts indicated a significant overall survival difference among the three groups. Cluster A, blue; Cluster B, yellow; Cluster C, red. E–G The expression of PD-L1, PD-1, and M2 TAM in different groups in TCGA and GEO bladder cancer cohorts. Cluster A, blue; Cluster B, yellow; Cluster C, red. H–M The correlation among PKM2, CD206, TGF-β, and PD-L1 expression in the TCGA bladder cancer cohort. Cluster A, blue; Cluster B, yellow; Cluster C, red

To further validate the above results, we examined the protein expression of PKM2, CD206, TGF-β, and PD-L1 by immunostaining tissue samples obtained from 70 patients with bladder cancer. We observed a significant relationship between PKM2, CD206, TGF-β, and PD-L1 expression in tumors (Fig. 2A–H). Furthermore, Kaplan–Meier survival analysis showed that PKM2, CD206, TGF-β, and PD-L1 expression was closely related to the poor prognosis of bladder cancer patients (Fig. 2I–L). Patients with CD206hi PKM2hiTGF-βhiPD-L1hi conferred significantly worse prognosis than those with CD206loPKM2loTGF-βloPD-L1lo (P < 0.001) (Fig. 2M). Taken together, these results indicated a close relationship and interaction mechanism between tumor cell glycolysis, TAM functional remodeling, and the expression of TGF-β and PD-L1 in the TME of bladder cancer.

Fig. 2.

Fig. 2

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The expression of PKM2, CD206, TGF-β, and PD-L1 in bladder cancer by IHC. A–H A significant relationship between PKM2, CD206, TGF-β, and PD-L1 expression in tumors (P < 0.05, Spearman analysis). A, negative; B, positive. I–L Kaplan–Meier survival analyses showed that PKM2, CD206, TGF-β, and PD-L1 expression were closely related to the prognosis of bladder cancer patients (P < 0.05, log-rank test). M Patients with CD206hi PKM2hiTGF-βhiPD-L1hi conferred a significantly worse prognosis than those with CD206loPKM2loTGF-βloPD-L1lo (P < 0.001, log-rank test)

Bladder cancer cells re-educated M2 TAMs through lactate and promoted TGF-β secretion in the TME

The glycolysis mechanism of tumor cells has been demonstrated to play an important role in the functional remodeling of immune cells in the tumor microenvironment. To investigate the effect of glycolysis in bladder cancer cells on M2 TAMs, we treated M2 TAMs with lactate or bladder cancer cell supernatant and found that TGF-β secretion was increased significantly in the supernatant of M2 TAMs. Moreover, the LDH inhibitor galloflavin obviously reduced the concentration of lactate in bladder cancer cells and then decreased TGF-β secretion in M2 TAMs (Fig. 3A–C). We further cocultured bladder cancer cells and M2 TAMs and validated that TGF-β secretion was significantly increased in the coculture system, but downregulated by the addition of galloflavin (Fig. 3D–F).

Fig. 3.

Fig. 3

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Bladder cancer cells re-educated M2 TAMs through lactate and promoted TGF-β secretion. A The level of TGF-β secreted by M2 TAMs after treatment with lactate or bladder cancer cell supernatant for 48 h. B, C The LDH inhibitor galloflavin obviously reduced the concentration of lactate in bladder cancer cells and then decreased TGF-β secretion in M2 TAMs. D–F Bladder cancer cells and M2 TAMs were cocultured and the role of lactate in TGF-β secretion in the coculture system was validated. G, H HIF-1α levels and I, J TGF-β levels were measured in M2 TAMs cultured in lactate or a HIF-1 inhibitor (YC-1) for 48 h. Gene expression was analyzed by RT–qPCR and Western blotting. N = 3 biological replicates. A, B, D, E, I, J) TGF-β levels were measured using a TGF-β ELISA kit. C, F Lactate levels were measured using a lactate colorimetric kit. A, D N = 3 biological replicates; statistical significance was determined using one-way ANOVA followed by Dunnett’s multiple comparisons test. B, C, E, F, G, I, J) N = 3 biological replicates; statistical significance was determined using an unpaired t test (two-tailed). *P < 0.05; **P < 0.01; ***P < 0.005

We next explored the mechanism of lactate-induced upregulation of TGF-β in M2 TAMs. As shown in Fig. 3G, H, the expression of HIF-1α mRNA and protein was significantly increased in lactate-treated M2 TAMs. Furthermore, TGF-β secretion in lactate-treated M2 TAMs and the coculture system was significantly decreased by the HIF-1α inhibitor YC-1(Fig. 3I–J). These data suggest an important role of HIF-1α in lactate-induced upregulation of TGF-β in M2 TAMs.

M2 TAMs promoted glycolysis in bladder cancer cells via TGF-β constituting a feed-forward loop in the TME

To investigate the effect of M2 TAM functional remodeling on glycometabolism in bladder cancer cells, we first detected the expression of key glycolysis-associated genes (GLUT1, HK2, PKM2, and LDHA) in bladder cancer cells after coculture with M2 TAMs. Intriguingly, compared with culture alone, the mRNA and protein expression levels of glycolysis-associated genes in bladder cancer cells were significantly increased, as was the concentration of lactate in the coculture system (Fig. 4A–F). However, the TGF-β inhibitor or TGF-β knockdown via siRNA significantly decreased the expression of glycolysis-associated genes and the concentration of lactate in the coculture system (Fig. 4G–K). We further explored the mechanism by which TGF-β promotes glycolysis and lactate production in bladder cancer cells. We pretreatment with a specific inhibitor of Smad3 or Smad3 knockdown significantly decreased the expression of glycolysis-associated genes and lactate production in bladder cancer cells (Fig. 4M–P). Our results indicated that the Smad2/3 signaling pathways may be involved in the process by which TGF-β promotes glycolysis in bladder cancer cells.

Fig. 4.

Fig. 4

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M2 TAMs promoted glycolysis in bladder cancer cells via the TGF-β/Samd2/3 pathway in the TME. A–D Detection of the expression of glycolysis-associated genes (GLUT1, HK2, PKM2, and LDHA) in bladder cancer cells after coculture with M2 TAMs and the concentration of lactate in the coculture system. E–L Glycolysis-associated gene (GLUT1, HK2, PKM2, and LDHA) expression in bladder cancer cells and the concentration of lactate in the coculture system treated with the neutralizing antibody AF-246 or siTGF-β were further analyzed. M–P The protein expression of glycolysis-associated genes in bladder cancer cells and lactate production in the coculture system after pretreatment with specific inhibitors of Smad3 (SIS3) or siSmad3. Q–S MB49 cells were subcutaneously injected into the mice with or without SB431542 treated. In two weeks, tumor sizes (Q), volumes (R), and weight (S) were measured. T Representative images of IHC staining for HK2, PKM2, GLUT1, and LDHA protein in mice tumor tissues with or without TGF‑β receptor kinase inhibitor SB431542. Scale bars: 100 µm. U The protein expression levels of the indicated proteins were displayed. Gene expression was analyzed by Western blot. N = 3 biological replicates. Gene expression was analyzed by RT–qPCR and Western blot, N = 3 biological replicates; statistical significance was determined using an unpaired t test (two-tailed). B, D, F, H, J, L, N, P The lactate levels were measured using a lactate colorimetric kit. N = 3 biological replicates; statistical significance was determined using an unpaired t test (two-tailed). *P < 0.05; **P < 0.01; ***P < 0.005

To validate the role of M2 TAMs-secreted TGF-β in promoting bladder cancer glycolysis in vivo, we subcutaneously injected MB49 cells into mice that were further treated with TGF‑βR inhibitor SB431542. The experimental scheme along with the treatment protocol is shown in Supplementary 1G. The results showed that SB431542 inhibits tumor growth, tumor volume, and tumor weight (Fig. 4Q–S). IHC staining found that SB431542 inhibited HK2, PKM2, GLUT1, and LDHA expression in mice (Fig. 4T, U). These results implied that M2 TAMs could promote glycolysis in bladder cancer via TGF-β in the TME.

M2 TAMs promoted CSCs and EMT in bladder cancer via TGF-β in the TME

CSCs and EMT have been proven to be vital processes for cancer cells to obtain a higher ability of proliferation, invasion, and metastasis. To explore the effects of M2 TAMs on the CSC activities and EMT process of bladder cancer cells, we cocultured bladder cancer cells and M2 TAMs for 48 h with or without a TGF-β inhibitor. Colony formation assays showed that tumor sphere formation was significantly increased in bladder cancer cells and that the expression of the CSC marker CD44 was also upregulated after coculture with M2 TAMs (Fig. 5A, B). However, the TGF-β inhibitor significantly decreased the formation of tumor spheres and CD44 expression in bladder cancer cells, which indicated that M2 TAMs could promote the acquisition of CSC-like properties of bladder cancer cells by TGF-β (Fig. 5C). Moreover, we found that the epithelial marker E-cadherin was downregulated, while the mesenchymal marker Vimentin was upregulated in bladder cancer cells (Fig. 5D). Transwell assays showed that M2 TAMs promoted the invasion and migration of bladder cancer cells (Fig. 5E). However, the TGF-β inhibitor significantly inhibited the above process of EMT in bladder cancer cells (Fig. 5F, G). These findings indicated that M2 TAMs could promote tumor EMT by TGF-β in the TME, thereby enhancing the migratory and invasive behaviors of bladder cancer cells.

Fig. 5.

Fig. 5

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M2 TAMs promoted CSCs and EMT in bladder cancer by TGF-β in the TME. A A colony formation assay was used to quantify the number of T24 and 5637 cells after coculture with M2 TAMs or treatment with the TGF-β neutralizing antibody AF-246 in the coculture system. B, C The mRNA and protein expression levels of CD44 in bladder cancer cells after coculture with M2 TAMs or treatment with AF-246 in the coculture system. D, F The mRNA and protein expression levels of E-cadherin and Vimentin in bladder cancer cells after coculture with M2 TAMs or treatment with AF-246 in the coculture system. E, G Invasion and migration of bladder cancer cells after coculture with M2 TAMs or treatment with AF-246 in the coculture system were measured by Transwell assays. Gene expression was analyzed by RT–qPCR and Western blot, N = 3 biological replicates; statistical significance was determined using an unpaired t test (two-tailed). *P < 0.05; **P < 0.01; ***P < 0.005

M2 TAMs promoted PD-L1 mRNA methylation in bladder cancer via TGF-β/Smad2/3 in the TME

Previous studies have demonstrated that M2 TAM infiltration and TGF-β secretion in the tumor microenvironment are closely related to PD-L1/PD-1 inhibitor resistance. To validate it, an immunotherapeutic cohort was included in our study: advanced urothelial cancer with the intervention of atezolizumab, an anti-PD-L1 antibody (IMvigor210 cohort) (Zhao et al. 2015). The cohort confirmed the therapeutic advantage and clinical response to anti-PD-L1 in patients with low CD206 and TGF-β expression to those with high CD206 and TGF-β expression (Fig. 6A, B). In addition, upregulation of PD-L1 expression was also shown in bladder cancer cells after coculture with M2 TAMs (Fig. 6C). Moreover, the TGF-β inhibitor significantly inhibited PD-L1 expression in the coculture system (Fig. 6D).

Fig. 6.

Fig. 6

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M2 TAMs promoted PD-L1 expression in bladder cancer by TGF-β in the TME. A, B Therapeutic advantage and clinical response to anti-PD-L1 in patients with low CD206 and TGF-β expression to those with high CD206 and TGF-β expression. C, D The expression of PD-L1 in bladder cancer cells after coculture with M2 TAMs or treatment with AF-246 in the coculture system. E, F The level of PD-L1 mRNA m6A methylation in bladder cancer cells after coculture with M2 TAMs or treatment with AF-246 in the coculture system. G, H The level of PD-L1 mRNA m6A methylation in bladder cancer cells after treatment with TGF-β or SIS3. I, J The expression of METTL3 in bladder cancer cells after coculture with M2 TAMs or treatment with AF-246 in the coculture system. K, L The level of METTL3 in bladder cancer cells after treatment with TGF-β. M, N Immunoprecipitation was performed with anti-METTL3 to examine the interaction between METTL3 and Samd2/3. O, P The expression of METTL3 in bladder cancer cells after treatment with TGF-β or SIS3. Q Representative images of IHC staining for m6A, PD-L1, and METTL3 protein in mice tumor tissues with or without TGF‑β receptor kinase inhibitor SB431542. Scale bars: 50 µm. R The protein expression levels of the indicated proteins were displayed. Gene expression was analyzed by RT–qPCR and Western blot, N = 3 biological replicates; statistical significance was determined using an unpaired t test (two-tailed). *P < 0.05; **P < 0.01; ***P < 0.005

A recent study indicated that Methyltransferase-like 3 (METTL3) has been proven to act as an m6A methyltransferase, which regulated PD-L1 mRNA m6A methylation is important for the expression of PD-L1 and immune escape (Ni et al. 2022). However, the role of M2 TAM-secreted TGF-β in the expression of PD-L1 mRNA m6A level has not been well studied. By coculturing bladder cancer cells and M2 TAM, we found that the m6A levels of PD-L1 mRNA isolated from T24 and 5637 cells were significantly elevated than that of their corresponding control cells (Fig. 6E, F). However, the TGF-β inhibitor significantly decreased the m6A levels of PD-L1 mRNA in bladder cancer cells (Fig. 6E, F), which indicated that M2 TAMs could promote the PD-L1 mRNA m6A levels of bladder cancer cells by TGF-β. To further validated the above results, bladder cancer cells were treated with 10 ng/ml TGF-β for three days. Similarly, m6A RNA-immunoprecipitation (RIP) qPCR showed that the m6A levels of PD-L1 mRNA were significantly increased than in control cells (Fig. 6G, H). In addition, Bertero et al. proposed that Smad2/3 promotes the binding of the m6A methyltransferase complex to a subset of transcripts involved in early cell fate decisions (Bertero et al. 2018). Pretreatment with specific inhibitors of Smad2/3 significantly decreased the m6A levels of PD-L1 mRNA in T24 and 5637 cells (Fig. 6G, H). To clarify the role of M2 TAM in the PD-L1 mRNA m6A level, we investigate the protein expression of METTL3 in bladder cancer cells. We observed that the METTL3 expression was significantly increased in T24 or 5637 cells after coculturing with M2 TAM (Fig. 6I, J). In contrast, the TGF-β inhibitor decreased the expression of METTL3 in the coculture system (Fig. 6I, J). Our results also found that TGF-β could promote METTL3 expression in a dose-dependent manner (Fig. 6K, L). Co-IP showed that METLL3 can interact with Smad2/3 in T24 and 5637 cells (Fig. 6M, N). TGF-β-induced METTL3 expression also was reduced in bladder cancer cells, which were treated with the Smad3 inhibitor (Fig. 6O, P). In vivo, IHC staining showed that SB431542 inhibited m6A level, PDL-1, and METTL3 protein expression in mice (Fig. 6Q, R). Therefore, the above results indicated that M2 TAMs could enhance PD-L1 mRNA m6A levels to modulate immune escape in bladder cancer cells through the TGF-β/Smad2/3 pathway.

Discussion

Energy metabolism reprogramming and immune evasion are two emerging hallmarks of cancers. The interplay between metabolism and immunity in the TME plays paramount roles in the initiation, progression, prognosis, and immunotherapy resistance of bladder cancer. In the present study, we confirmed a close correlation between tumor glycolysis, M2 TAM infiltration, and the prognosis of bladder cancer patients and highlighted a feed-forward loop based on aerobic glycolysis and TGF-β between TAMs and bladder cancer cells in the TME.

The main mechanism of glycometabolism reprogramming in tumor cells is the enhancement of aerobic glycolysis, also referred to as the Warburg effect. Aerobic glycolysis plays an important role in the proliferation, growth, invasion, and treatment of cancer. Lactate, the major metabolite of aerobic glycolysis, is an energy-rich signaling molecule that shuttles between cells under both physiological and pathological conditions. Elevated lactate levels in TME can impair the functions of CTLs by inhibiting T-cell receptor-trigged activation of the p38 and JNK/c-JUN pathways, which are important for the production of IFN-γ (Wang et al. 2021). Furthermore, lactate can regulate CD4+T-cell polarization and reduce the percentage of the antitumoral T-helper 1 subset by inducing SIRT1-mediated deacetylation/degradation of the T-bet transcription factor (Comito et al. 2019). In addition, growing evidence highlights that glycolysis and tumor-derived lactate could skew TAMs toward an immunosuppressive phenotype (Zhang et al. 2022). Our previous study found that lactate could not only inhibit the function of T cells but also play important roles in the reprogramming and recruitment of TAMs. In the present study, we showed that TGF-β secretion in M2 TAMs was increased after treatment with lactate, bladder cancer cell supernatant, or bladder cancer cells but obviously decreased by inhibiting lactate production. These findings indicate that bladder cancer cells can re-educate M2 TAMs through lactate and promote TGF-β secretion in the TME. Previous studies have shown that the HIF-1α signaling pathway may be involved in the polarization of TAMs and TGF-β secretion. We found that HIF-1α expression was significantly increased in lactate-treated M2 TAMs, while a HIF-1α inhibitor significantly reduced TGF-β secretion in lactate-treated M2 TAMs and the coculture system. These data suggest that the HIF-1α signaling pathway may play an important role in the lactate-induced upregulation of TGF-β in M2 TAMs.

TAMs can also affect the glycometabolism of tumor cells in the interaction between tumor metabolism and immunity. TAMs that exhibit promoting phenotype have been reported to cooperate with tumor cells in the induction of tumor development, and even have been considered an important cause of resistance against anti-immune therapies. Jeong et al. demonstrated that TAMs could enhance tumor hypoxia and aerobic glycolysis in mouse subcutaneous tumors and patients with non-small-cell lung cancer (Chen et al. 2019). Chen et al. also described that TAMs enhanced the aerobic glycolysis and apoptotic resistance of breast cancer cells via extracellular vesicle-packaged HIF-1α-stabilizing lncRNA (Chen et al. 2021). In this study, we confirmed that the expression of glycolysis-associated genes and lactate production in bladder cancer cells was significantly increased after coculture with M2 TAMs and attenuated by treatment with a TGF-β inhibitor. Mechanistically, we identified that the Smad2/3 signaling pathways may be involved in the process by which TGF-β enhances glycolysis in bladder cancer cells. These findings indicate that M2 TAMs can promote glycolysis in bladder cancer cells via TGF-β, constituting a feed-forward loop between TAMs and bladder cancer cells in the TME.

It is well known that the CSCs, EMT, and PD-L1/PD-1 induced immune escape play important roles in the mechanisms of tumorigenesis, growth, metastasis, recurrence, and drug resistance. Recent studies have demonstrated that TAMs are closely related to PD-L1/PD-1 inhibitor resistance in the TME (Gordon et al. 2017; Peranzoni et al. 2018). Meanwhile, TGF-β signaling blockade has shown great potential in boosting the sensitivity of PD-L1/PD-1 inhibitors (Mariathasan et al. 2018; Tauriello et al. 2018). Here, we found that M2 TAMs could promote CSCs, and EMT in bladder cancer by TGF-β in the TME. More importantly, we confirmed a positive correlation between M2 TAM infiltration and PD-L1 expression in bladder cancer tissues. Recent studies indicated that m6A methylation is important for the expression of PD-L1 in TME, and m6A-induced PD-L1 expression is mainly regulated by METTL3 (Ni et al. 2022; Wan et al. 2022). However, whether M2 TAMs could regulate the m6A levels of PD-L1 by the TGF-β in bladder cancer is unknown. Our study first validated that M2 TAMs could enhance PD-L1 mRNA m6A levels to modulate immune escape in bladder cancer cells through the TGF-β/Smad2/3 pathway. These results suggest that the TGF-β feed-forward loop between TAMs and bladder cancer cells may be an important mechanism of malignant tumor progression and PD-L1/PD-1 inhibitor resistance.

Conclusions

To conclude, we demonstrate that bladder cancer cells can re-educate M2 TAMs through lactate and promote TGF-β secretion via the HIF-1α signaling pathway. Reciprocally, M2 TAMs can promote glycolysis in bladder cancer cells by TGF-β via the Smad2/3 signaling pathway. Thus, a feed-forward loop is constituted between TAMs and bladder cancer cells, which acts as a link of metabolic and immune reprogramming in the TME and is closely related to the regulation of CSCs, EMT, and the PD-L1/PD-1 pathway in bladder cancer. Further studies on this feed-forward loop and other important metabolic/immune regulatory factors of the PD-L1/PD-1 pathway are suggested and warranted to improve PD-1/PD-L1 inhibitor therapy and develop new treatment strategies for bladder cancer.

Supplementary Information

Below is the link to the electronic supplementary material.

432_2023_5164_MOESM1_ESM.tif (17.4MB, tif)

Supplementary 1. (A) The TCGA and GEO bladder cancer cohorts were divided into three clusters when k = 3. (B) Consensus clustering cumulative distribution function (CDK) fork = 2 to 9. (C) Relative change in area under the CDF curve for k = 2 to 9. (D) The difference in the immune cell infiltration among three glycolysis clusters by using the CIBERSORT dataset. (E, F) The difference in the cytokine-related gene expression among three glycolysis clusters by using TCGA and GEO bladder cancer cohorts. (G) The experimental scheme along with the treatment protocol (TIF 17857 KB)

Author contributions

CS wrote the main manuscript text. JiL and WJ validated data. XZ and XZ was analyzed data by software. XY and YW reviewed and edited. All authors reviewed the manuscript.

Funding

This work was supported by the National Natural Science Foundation of China (No. 81972378, 81101932).

Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Declarations

Conflict of interest

The authors have no relevant financial or non-financial interests to disclose.

Ethics approval

This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Affiliated Hospital of Qingdao University.

Consent to participate

Informed consent was obtained from all individual participants included in the study.

Consent for publication

Not applicable.

Footnotes

Publisher's Note

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

Chengquan Shen and Jing Liu have contributed equally to this work.

Contributor Information

Xuecheng Yang, Email: yangxuecheng@qdu.edu.cn.

Yonghua Wang, Email: wangyonghua@qdu.edu.cn.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

432_2023_5164_MOESM1_ESM.tif (17.4MB, tif)

Supplementary 1. (A) The TCGA and GEO bladder cancer cohorts were divided into three clusters when k = 3. (B) Consensus clustering cumulative distribution function (CDK) fork = 2 to 9. (C) Relative change in area under the CDF curve for k = 2 to 9. (D) The difference in the immune cell infiltration among three glycolysis clusters by using the CIBERSORT dataset. (E, F) The difference in the cytokine-related gene expression among three glycolysis clusters by using TCGA and GEO bladder cancer cohorts. (G) The experimental scheme along with the treatment protocol (TIF 17857 KB)

Data Availability Statement

The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.

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