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. 2025 Dec 29;4(3):365–378. doi: 10.1021/envhealth.5c00411

Cigarette Smoking-Regulated PRKCE Is Involved in Bladder Cancer Susceptibility through cGMP/PKG-Dependent Glycolysis

Yanping Xiao †,‡, Jinyue Zhang †,§, Yu Jia †, Hui Song †, Zhenguang Mao †, Fang Gao †,∥, Rui Zheng †,‡,*
PMCID: PMC13010304  PMID: 41883368

Abstract

The mechanism by which cigarette smoking affects bladder cancer susceptibility via glucose metabolism remains unclear. We identified bladder cancer-specific glucose metabolism-related genes (GMGs) using Molecular Signatures Database (MSigDB) and a case–control study (580 cases and 1,101 controls) through genetic association and interaction analyses with cigarette smoking. Among 811 GMGs, we observed that PRKCE rs4953292 G > A was significantly associated with increased bladder cancer risk [odds ratio (OR) = 1.19, 95% confidence interval (CI): 1.03–1.37, P adj = 1.87 × 10–2] and exhibited an interaction effect with cigarette smoking (P interaction < 0.05). Smokers with rs4953292 A allele had higher bladder cancer risk than nonsmokers with G allele (P trend < 9.09 × 10–3). We performed functional experiments using 4-aminobiphenyl (4-ABP)-treated bladder cancer cells and found that the rs4953292 A allele combined with 4-ABP decreased PRKCE expression levels in bladder cancer cells, which could upregulate PKG and phosphorylate VASP within the cGMP-PKG signaling pathway, enhance glucose uptake, lactate generation, and extracellular acidification rate (ECAR) to reprogram glycolysis, thus promoting bladder cancer susceptibility. This study elucidates that cigarette smoking-regulated PRKCE facilitates bladder cancer susceptibility by mediating glycolytic reprogramming through activating the cGMP-PKG signaling pathway. The findings provide valuable predictors for bladder cancer susceptibility, aiding in prevention strategies.

Keywords: cigarette smoking, glucose metabolism reprogramming, bladder cancer, PRKCE, cGMP-PKG signaling pathway

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1. Introduction

Bladder cancer poses a major health challenge worldwide, with an estimated 84,870 new cases and 17,420 deaths in 2025 in the United States. Although the age-standardized mortality rate of bladder cancer in China decreased by 32.1% from 1990 to 2021, it remains the primary burden of urological tumors in most provinces; its incidence is projected to rise in line with the overall trend of urological cancers from 2022 to 2040. The current mechanisms or prognostic factors for bladder cancer have been widely reported. For example, Gao et al. demonstrated that the roof plate-specific spondin (RSPO) gene family acted as risk factors for bladder cancer and promoted tumorigenesis; Liu et al. reported that targeting the newly identified ADAR2/circ_0001005/miR-200a-3p/PD-L1 pathway to impact antitumor immunity may suppress progression and boost immunotherapeutic efficacy in bladder cancer. More importantly, the tumorigenesis and progression of bladder cancer arise from an intricate interaction between genetic factors and environmental exposures. Cigarette smoking stands as the predominant risk factor for bladder cancer, with a substantial proportion of patients found to be active smokers at the time of diagnosis. , Notably, cigarette smoking status is closely associated with the development and progression of bladder cancer. , Nonetheless, the underlying causes and mechanisms governing cigarette smoking-related bladder cancer remain insufficiently elucidated. Therefore, we focused on the etiology and pathogenesis of bladder cancer, aiming to identify potentially specific early biomarkers with both theoretical and practical value.

Glucose metabolism disorders have been recognized as a core pathological mechanism driving bladder carcinogenesis, including altered aerobic glycolysis, the tricarboxylic acid (TCA) cycle, the pentose phosphate pathway (PPP), and glycogen metabolism. Studies have shown that the elevated glycolytic flux characteristic of bladder cancer depends on increased expression of enolase 1 (ENO1). ENO1 is a key rate-limiting enzyme in the glycolytic pathway and boosts glycolytic activity to facilitate bladder cancer progression. This underscores the critical role of glucose metabolism reprogramming, regulated by glucose metabolism-related genes (GMGs), in bladder cancer progression. Cigarette smoking has been shown to disrupt the TCA cycle and oxidative phosphorylation (OXPHOS), thereby promoting the progression of clear cell renal cell carcinoma. Nevertheless, whether cigarette smoking influences bladder cancer susceptibility by modulating GMG-mediated glucose metabolism reprogramming remains unclear. Recent genome-wide association studies (GWAS) have identified numerous genetic biomarkers for bladder cancer susceptibility using single-nucleotide polymorphisms (SNPs). ,, Notably, two variants (XbaI G > T and HaeIII T > C) within the gene encoding glucose transporter 1 (GLUT1) exerted a protective effect against bladder carcinogenesis.

This study aimed to identify and validate GMGs potentially involved in cigarette smoking-related bladder cancer by exploring SNPs within GMGs that interacted with cigarette smoking to influence bladder cancer susceptibility. Subsequently, we investigated the functions of the identified GMGs in cigarette smoking-related bladder cancer susceptibility with a focus on glucose metabolism reprogramming and its associated molecular mechanisms. The study offered a conceptual foundation for comprehending bladder cancer etiology and designing preventive and therapeutic interventions tailored to high-risk groups.

2. Materials and Methods

2.1. Study Subjects

The Nanjing Bladder Cancer (NJBC) cohort, comprising 580 cases and 1,101 controls, was utilized in this study. Cases were recruited from Nanjing, China, in the First Affiliated Hospital of Nanjing Medical University. Controls were randomly selected from individuals attending regular physical examinations and frequency-matched to cases by age (±5 years) and sex within the same regional area. Further details of the cohort are provided in our previous studies. ,, Individuals smoking at least one cigarette daily for over 1 year were categorized as smokers, while others were nonsmokers. Written informed consent was obtained from all participants in this study, which also received approval from the Institutional Review Committee at Nanjing Medical University (approval No. 2021–021).

2.2. Selection of GMGs

The Molecular Signatures Database (MSigDB) is a widely used gene set database containing tens of thousands of annotated gene sets, which can be searched by keywords to obtain gene sets related to specific biological processes. The process of glucose metabolism covers a variety of pathways, and to include comprehensive GMGs, we searched 125 gene sets in MSigDB (v2023.1.Hs) based on six keywords, namely “glucose”, “glycolysis”, “glycogen”, “pentose”, “pyruvate”, and “TCA”, of which 42 were GMG sets after quality control (updated as of June 2023). After the removal of duplicate genes and then the exclusion of those located on the sex chromosomes, a total of 811 genes comprised our final set of GMGs.

2.3. Treatment of Bladder Cancer Cells with 4-Aminobiphenyl (4-ABP), 2-Deoxy-d-Glucose (2-DG), and KT5823

The indicated cells at 70% confluency were treated with 0.2 mmol/L 4-ABP (Macklin, China) plus 0.2% rat liver S9 (Biopredic, France) for 12 h, and we regarded the above process (4-ABP plus S9) as 4-ABP treatment. Treatment with 20 mmol/L 2-DG (MCE, USA) for 12 h was performed to inhibit the glycolytic process in bladder cancer cells. KT5823 (Glpbio, USA), a selective inhibitor of cGMP-dependent protein kinase (PKG), was used at a concentration of 1 μmol/L for 12 h.

2.4. Measurement for Glucose Uptake, Lactate Production, and Extracellular Acidification Rate (ECAR)

The culture media of the indicated cells were collected to detect the glucose uptake or lactate production using colorimetric assay kits (Yeasen, China; Abbkine, USA), as indicated by the glucose or lactate concentration in the culture media. The ECAR was conducted by using a Seahorse XF96 Extracellular Flux analyzer (Agilent Technologies, USA). The 1.0 × 104/well indicated bladder cancer cells were seeded into a Seahorse XF96 Culture Microplate (Agilent Technologies, USA) and cultured overnight. Subsequently, glucose (10 mmol/L), oligomycin (1 μmol/L), and 2-DG (50 mmol/L) were added to the culture plate sequentially to detect ECAR (mPH/min).

2.5. Statistical Analysis

The Hardy–Weinberg equilibrium (HWE) was assessed with a Chi-square goodness-of-fit test. To evaluate the association between SNPs in GMGs and bladder cancer risk, logistic regression was used to determine odds ratios (ORs) and P-values. Additionally, the P-values were adjusted for age and sex, denoted as P adj. Furthermore, a joint analysis was conducted to evaluate the combined effects of the candidate SNPs and cigarette smoking status. The interaction between candidate SNPs and the cigarette smoking status was examined through multivariate logistic regression models. For survival analyses, a Kaplan–Meier curve was generated for the survival rates of patients with differential PRKCE expression performed with a log-rank test. Cox regression was used to calculate the overall survival (OS) hazard ratios (HRs) and 95% confidence intervals (CIs). A two-sided P < 0.05 was deemed statistically significant. Statistical analyses were conducted with R software (version 4.2.0) and PLINK (version 1.90).

3. Results

3.1. Identification of Cigarette Smoking-Specific GMGs in Bladder Cancer

Based on the MSigDB, we finally identified 42 GMG sets containing a total of 811 GMGs (Figure A). To obtain bladder cancer-related GMGs, a comparison of expression levels in 408 bladder cancer tissues and 19 bladder normal tissues from the Cancer Genome Atlas (TCGA) data sets was detected, revealing that 511 GMGs were differentially expressed (138 downregulated and 373 upregulated; P < 0.05) (Figure B). The flowchart of identifying cigarette smoking-specific GMGs in bladder cancer for this study is displayed in Figure S1. We derived a total of 73,163 SNPs in 511 GMGs from the 1000 Genomes Project, with 42,439 SNPs in 511 GMGs identified through quality control for the subsequent genetic association analysis. It was discovered that 442 independent SNPs in 167 GMGs (r 2 ≥ 0.80) were significantly associated with bladder cancer susceptibility based on 580 cases and 1,101 controls, adjusting for age and sex (P adj < 0.05) (Figure C). Stratified analysis of the above 442 SNPs by different cigarette smoking statuses was conducted, and we found that 271 SNPs in 113 GMGs exhibited different bladder cancer susceptibility patterns between smokers and nonsmokers in the Chinese population (P adj < 0.05) (Figure S2). A subsequent interaction analysis with cigarette smoking showed that 16 SNPs in 13 GMGs significantly interacted with cigarette smoking (P interaction < 0.05), thereby affecting bladder cancer risk (Table ).

1.

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Identification of key glucose metabolism-related genes (GMGs) associated with cigarette smoking-related bladder cancer. (A) Identification of the GMG set and GMGs from the Molecular Signatures Database (MSigDB) (updated as of June 2023). (B) Volcano plot of differentially expressed cigarette smoking-related GMGs in bladder cancer from The Cancer Genome Atlas (TCGA) database. The blue and red plots are the dysregulated genes (P < 0.05). (C) Manhattan plot of the association analysis between SNPs in GMGs and bladder cancer risk in the Chinese population. (D) A heatmap of candidate SNPs’ functional annotations with scores from RegulomeDB, HaploReg v4.2, and 3DSNP v2.0.

1. Association Analysis of 16 Candidate SNPs on Bladder Cancer Risk in the Chinese Population .

          MAF
      
Chr SNP Position (GRCh37) Allele Located gene Cases Controls Adjusted OR (95% CI) P adj P interaction
1 rs12735818 65689670 C/T AK4 0.270 0.309 0.83 (0.70–0.97) 1.93 × 10–2 1.11 × 10–2
2 rs4953292 46191983 A/G PRKCE 0.492 0.450 1.19 (1.03–1.37) 1.87 × 10–2 3.42 × 10–2
2 rs2595202 46192017 G/T PRKCE 0.372 0.407 0.86 (0.74–1.00) 4.39 × 10–2 2.59 × 10–2
2 rs6706140 46193347 C/T PRKCE 0.409 0.361 1.23 (1.06–1.42) 6.77 × 10–3 7.13 × 10–3
2 rs77752295 46314064 A/C PRKCE 0.125 0.103 1.27 (1.01–1.59) 4.31 × 10–2 4.22 × 10–2
2 rs10496197 75120713 G/A HK2 0.372 0.407 0.86 (0.74–0.99) 4.19 × 10–2 4.09 × 10–2
4 rs73095183 23791725 C/A PPARGC1A 0.113 0.141 0.77 (0.62–0.97) 2.72 × 10–2 3.44 × 10–2
5 rs6880997 1113486 G/T SLC12A7 0.288 0.321 0.85 (0.73–1.00) 4.88 × 10–2 1.78 × 10–2
5 rs79502466 37297270 T/C NUP155 0.087 0.115 0.74 (0.58–0.95) 1.60 × 10–2 1.77 × 10–2
6 rs6906499 17665479 C/G NUP153 0.349 0.438 0.70 (0.60–0.81) 1.54 × 10–6 1.27 × 10–3
6 rs7745864 83969424 T/G ME1 0.075 0.096 0.75 (0.58–0.98) 3.71 × 10–2 3.51 × 10–2
11 rs75561897 66682609 C/G PC 0.299 0.256 1.25 (1.06–1.47) 8.32 × 10–3 7.90 × 10–3
11 rs10898833 70033210 A/G ANO1 0.205 0.170 1.26 (1.05–1.52) 1.31 × 10–2 1.35 × 10–2
15 rs4774371 61000663 A/G RORA 0.153 0.180 0.82 (0.68–1.00) 4.73 × 10–2 4.30 × 10–2
16 rs1657152 1875192 G/A HAGH 0.428 0.467 0.85 (0.73–0.98) 2.89 × 10–2 8.20 × 10–3
18 rs9961091 787847 C/G YES1 0.351 0.312 1.19 (1.02–1.39) 2.39 × 10–2 3.81 × 10–2
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a

Chr: chromosome;CI: confidence interval; MAF: minor allele frequency; OR: odds ratio; SNP: single-nucleotide polymorphism.

b

Minor allele/major allele.

c

Adjusted for age and sex in a logistic regression model.

To further filter the SNPs influencing the function of their hosting GMGs, we annotated the potential functions of the target SNPs and developed a scoring criterion to assess their function. Subsequently, 3 SNPs, specifically rs2595202 in PRKCE, rs4953292 in PRKCE, and rs75561897 in PC, were identified with high scores (Figure D), indicating their functional association with bladder cancer susceptibility. Subsequently, expression quantitative trait loci (eQTL) analysis was performed to further investigate the regulatory effect of 3 SNPs on their host GMG expression. Results revealed that the A allele of rs4953292 significantly reduced the expression of PRKCE in bladder cancer tissues (P < 0.05), while the other two had no significance (Figures A and S3). Moreover, PRKCE rs4953292 G > A was significantly associated with increased bladder cancer risk (OR = 1.19, 95% CI: 1.03–1.37, P adj = 1.87 × 10–2) (Table S1), and rs4953292 G > A in PRKCE resulted in an increased minimum free energy (MFE) of the predicted RNA secondary structure, indicating a regulatory effect on RNA molecule function (Figure S4). Altogether, these results suggest that PRKCE is the cigarette smoking-specific GMG in bladder cancer that warrants further investigation.

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Cigarette smoking combined with rs4953292 G > A in PRKCE suppressed PRKCE expression in bladder cancer cells. (A) An expression quantitative trait loci (eQTL) analysis of rs4953292 G > A in PRKCE in bladder cancer tissues. Data were obtained from the TCGA database. (B) A forest plot of stratified analysis between rs4953292 in PRKCE and bladder cancer risk in the Chinese population (NMIBC: nonmuscle-invasive bladder cancer, MIBC: muscle-invasive bladder cancer). (C) Joint effect of rs4953292 in PRKCE and cigarette smoking status on bladder cancer susceptibility. (D) The combined effect of 4-aminobiphenyl (4-ABP) exposure and rs4953292 G or A allele in PRKCE on the expression level of PRKCE in bladder cancer cells was verified by using dual luciferase reporter gene assays. (E) The expression levels of PRKCE in bladder cancer cells transfected with vectors constructed with the coding sequence of PRKCE with rs4953292 G or A allele, namely, PRKCE [G] and PRKCE [A], and then exposed to DMSO/4-ABP, as detected by reverse transcription quantitative polymerase chain reaction (RT-qPCR). Each group was normalized to the PRKCE [G]+DMSO group. (F) The protein levels of PRKCE in bladder cancer cells transfected with PRKCE [G] and PRKCE [A] with DMSO/4-ABP treatment. *P < 0.05.

3.2. The Interaction Effect between Cigarette Smoking and PRKCE Variants Increased Bladder Cancer Susceptibility

Protein kinase C epsilon (PRKCE) belongs to the family of PKC proteins, encoding a kinase involved in a multitude of cellular functions, , which has been reported to regulate glycolytic flux. As shown in Figure B, we further performed stratified analyses of rs4953292 in PRKCE through demographic and clinical characteristics and found that the rs4953292 A allele in PRKCE exhibited an increased bladder cancer risk in smokers with bladder cancer (OR = 1.45, 95% CI: 1.14–1.85, P = 2.48 × 10–3), indicating that cigarette smoking enhanced the genetic susceptibility of PRKCE variants to bladder cancer. In addition, an elevated bladder cancer risk was observed in high-grade bladder cancer patients with the rs4953292 A allele in PRKCE (OR = 1.23, 95% CI: 1.01–1.49, P = 4.01 × 10–2) and muscle-invasive bladder cancer (MIBC) patients carrying the A allele (OR = 1.33, 95% CI: 1.04–1.71, P = 2.51 × 10–2). Considering that cigarette smoking is the most important environmental factor for bladder cancer, we performed joint analyses to further investigate PRKCE–cigarette smoking interactions and observed that smokers with the rs4953292 GA/AA genotype in PRKCE exhibited a 1.67-fold elevated bladder cancer risk relative to nonsmokers with the rs4953292 GG genotype (P trend < 9.09 × 10–3) (Figure C and Table S2). The above results further confirm that rs4953292 G > A in PRKCE is a risk factor for bladder cancer in smokers and provide insights into a potential genetic molecular mechanism underlying bladder cancer caused by cigarette smoking.

Next, we validated the role of cigarette smoking and rs4953292 G > A in the regulation of PRKCE expression in 4-ABP-treated bladder cancer cells. We first treated bladder cancer cells with different concentrations of 4-ABP (0, 0.05, 0.1, 0.2, 0.5 mmol/L) plus 0.2% rat liver S9 for 3, 6, and 12 h. , As shown in Figure S5A, cell viability assays revealed that the viability of both J82 and EJ cells remained stable when the 4-ABP concentration was 0.2 mmol/L or lower, whereas a marked decrease in viability was observed at 0.5 mmol/L. At the concentration of 0.2 mmol/L, we further detected PRKCE expression levels in cells treated for different durations (Figure S5B). The results revealed a time-dependent decrease in PRKCE expression in both J82 and EJ cells, with the lowest expression level observed at 12 h. Thus, 12 h was chosen as the optimal exposure duration to further investigate the mechanism of PRKCE-related bladder cancer. In summary, the final 4-ABP exposure condition for bladder cancer cells was determined to be 0.2 mmol/L plus 0.2% S9 for 12 h. We confirmed the eQTL effect of rs4953292 on PRKCE using a dual luciferase reporter gene assay and found a marked reduction in relative luciferase activity in bladder cancer cells expressing the A allele compared to those with the G allele, and cells expressing the A allele showed the lowest transcriptional activity of PRKCE after 4-ABP treatment (Figure D). As displayed in Figures E-F and S6, cells transfected with PRKCE [A] following 4-ABP treatment demonstrated the lowest PRKCE expression levels, which indicated that 4-ABP may combine with rs4953292 G > A to decrease the expression level of PRKCE in bladder cancer cells. The above results suggest that cigarette smoking may interact with rs4953292 G > A in PRKCE to significantly increase the bladder cancer risk.

3.3. Cigarette Smoking-Regulated PRKCE Is Involved in Bladder Cancer Susceptibility

Based on our in-house data, significantly lower PRKCE expression was assessed in bladder tumors compared to that in normal tissues (Figure A), as confirmed by findings from TCGA and the Gene Expression Omnibus (GEO) databases (Figure B-C). Then, an analysis of the Genotype-Tissue Expression (GTEx) database showed widespread PRKCE expression across various tissue types, with moderate levels observed in bladder tissues (Figure S7). TCGA database analyses also showed that PRKCE was broadly downregulated among multiple cancer types compared to normal tissues (Figure D). As shown in Figure S8, high PRKCE expression was significantly associated with better survival (HR = 0.23, 95% CI: 0.08–0.60, P = 0.003). The expression pattern of PRKCE was further evaluated in bladder normal and cancer cell lines, revealing reduced expression levels in bladder cancer cells (Figure E). Notably, PRKCE expression levels were notably reduced in bladder cancer cells after 4-ABP treatment (Figures F-G and S9). These findings collectively suggest a potential role for PRKCE in bladder cancer and indicate its regulation by cigarette smoking.

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Expression pattern of PRKCE in bladder cancer. (A) The expression levels of PRKCE as detected in bladder cancer tissues (n = 11) and paired normal tissues (n = 11) in in-house data. (B) The expression of PRKCE as quantified in bladder cancer tissues (n = 408) and normal tissues (n = 19) from the TCGA database. (C) The expression of PRKCE as quantified in bladder cancer tissues (n = 8) and normal tissues (n = 4) from the Gene Expression Omnibus (GEO) database (GSE65635). (D) The expression of PRKCE as measured in multiple tumor tissues compared with normal tissues from the TCGA database. (E) Differential expression of PRKCE between normal cells (SV-HUC-1) and bladder cancer cells (EJ, J82, and T24) as detected by RT-qPCR. (F) mRNA levels of PRKCE in J82 and EJ cells treated with NC/4-ABP. (G) The protein levels of PRKCE in J82 and EJ cells treated with NC/4-ABP. *P < 0.05.

To investigate the function of PRKCE in bladder cancer, we employed both overexpression and knockdown approaches using overexpression plasmids (Figure S10) and small interfering RNA (siRNA) (Figure S11), respectively, in bladder cancer cell lines for subsequent experiments. The siRNA with the highest knockdown efficacy among the three candidates was chosen for transfection, thereby effectively inhibiting the PRKCE expression in bladder cancer cells. Cell counting kit-8 (CCK-8) and 5-ethynyl-2’-deoxyuridine (EdU) assays showed that PRKCE knockdown enhanced the viability and proliferation of bladder cancer cells (Figure S12). The migration and invasion capabilities of bladder cancer cells were enhanced through PRKCE knockdown (Figures A and S13). The malignant phenotype experiments confirmed the oncogenic role of the reduced PRKCE levels in bladder cancer cells.

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Cigarette smoking-regulated PRKCE promotes bladder cancer progression by mediating glycolytic reprogramming. NC group: bladder cancer cells with NC-treated serve as the control group. si-PRKCE group: bladder cancer cells transfected with PRKCE small interfering RNA (siRNA). si-PRKCE+2-DG group: bladder cancer cells transfected with PRKCE siRNA and then treated with 2-Deoxy-d-glucose (2-DG) (a glycolytic inhibitor). 4-ABP group: bladder cancer cells treated with 4-ABP. 4-ABP+PRKCE-OE group: bladder cancer cells treated with 4-ABP after transfection with PRKCE overexpression plasmids. 4-ABP+PRKCE-OE+2-DG group: 4-ABP+PRKCE-OE group with additional 2-DG treatment. (A) Quantification of invaded and migrated bladder cancer cells. (B) Glucose uptake and (C) lactate production were measured in bladder cancer cells with 4-ABP or NC group. (D and E) PRKCE-OE group: EJ cells were transfected with PRKCE overexpression plasmids. Relative mRNA levels of glucose metabolism-related genes were measured in NC, 4-ABP, or PRKCE-OE groups of EJ cells. (F) The ECAR was measured in the NC, 4-ABP, PRKCE-OE, and 4-ABP+PRKCE-OE groups of EJ cells. ECAR curves of cells treated with glucose, oligomycin, and 2-DG. (G) Seahorse analysis showing the levels of glycolysis and glycolytic capacity in the NC, 4-ABP, PRKCE-OE, and 4-ABP+PRKCE-OE groups of EJ cells. *P < 0.05.

3.4. Cigarette Smoking-Regulated PRKCE Promotes Bladder Cancer Progression by Mediating Glycolytic Reprogramming

To elucidate the reprogramming of glucose metabolism mediated by PRKCE in cigarette smoking-related bladder cancer, we first measured the glucose uptake and lactate production of bladder cancer cells with 4-ABP treatment. We found that 4-ABP increased glucose consumption (Figure B) and lactate production (Figure C) in bladder cancer cells. Then, the expression levels of key genes involved in glycolysis, the TCA cycle, the PPP, and glycogen metabolism were measured in bladder cancer cells treated with 4-ABP. In alignment with the recognized glycometabolic phenotype of bladder cancer, , 4-ABP was observed to significantly upregulate expression levels of glycolysis- and PPP-related genes, to downregulate expression levels of most TCA cycle-related genes, and to alter expression levels of genes associated with glycogen metabolism (Figures D and S14A), suggesting a potential role for cigarette smoking in bladder cancer progression through glycometabolic reprogramming.

PRKCE is essential for maintaining cellular glucose homeostasis through the regulation of intracellular glucose levels. To investigate the impact of PRKCE on glucose metabolism in bladder cancer cells, we measured the expression levels of key factors involved in glucose metabolism pathways in cells overexpressing PRKCE and found that the PRKCE overexpression significantly downregulated the expression of core rate-limiting enzyme genes of glycolysis, while no significant effects were observed in other glucose metabolism pathways (Figures E and S14B). Based on these findings, the glycolytic function of PRKCE within bladder cancer cells was further examined through Seahorse assays. It was discovered that PRKCE overexpression led to a reduction in ECAR in EJ and J82 cells, with 4-ABP having the opposite effect. Moreover, the promotion of glycolytic metabolism caused by 4-ABP was reversed upon PRKCE overexpression, suggesting that 4-ABP-regulated PRKCE mediates the glycolytic reprogramming of bladder cancer cells (Figures F-G and S14C-D). Additionally, as shown in Figures A and S12-S13, the glycolytic inhibitor 2-DG blocked the promotion of malignancy induced by PRKCE knockdown. Notably, highly expressed PRKCE attenuated the tumor-promoting effect of 4-ABP, and this attenuation was reversed by additional treatment with 2-DG. Collectively, these findings indicate that 4-ABP downregulates PRKCE and is involved in bladder cancer progression by mediating glycolytic reprogramming.

3.5. Cigarette Smoking-Regulated PRKCE Mediates Glycolytic Reprogramming in Bladder Cancer Cells through Activating cGMP-PKG Signaling Pathway

To elucidate the mechanism by which PRKCE mediates glycolytic reprogramming in bladder cancer, we first conducted a functional enrichment analysis of proteins that interacting with PRKCE using the STRING website (Version 12.0) (Figure S15A). The cGMP-PKG signaling pathway (gene ratio = 0.135, false discovery rate = 2.73 × 10–29) was identified as the most significant pathway in the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis (Figure A and Table S3). To further validate the association of the identified pathway with PRKCE in bladder cancer, a correlation analysis was conducted on the expression levels of PRKCE and the cGMP-PKG signaling pathway-related genes. We found a negative correlation between the expression of PRKCE and MAP2K2, ATP1B3, BAD, VASP, GNAI2, and PLCB3 within the cGMP-PKG signaling pathway (Figures B and S15B-F), which was subsequently verified by reverse transcription quantitative polymerase chain reaction (RT-qPCR) in EJ cells (Figure C). As shown in Figures D and S16, the cigarette smoking-regulated PRKCE effectively upregulated PKG and further phosphorylated vasodilator-stimulated phosphoprotein (VASP), a key downstream marker of the activated cGMP-PKG signaling pathway. To further validate the role of the cGMP-PKG signaling pathway, we measured ECAR levels of EJ and J82 cells treated with KT5823 (an inhibitor of the cGMP-PKG signaling pathway) or 4-ABP and found that KT5823 could reverse the promotion of glycolysis in bladder cancer cells resulting from PRKCE knockdown, while 4-ABP could enhance the effect (Figures E-F and S17). Taken together, these results provide evidence that cigarette smoking-regulated PRKCE may activate the cGMP-PKG signaling pathway to enhance glycolysis in bladder cancer cells.

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Cigarette smoking-regulated PRKCE mediates glycolytic reprogramming in bladder cancer cells through activating the cGMP-PKG signaling pathway. (A) Pathway enrichment analysis of proteins interacting with PRKCE based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. (B) A correlation analysis of the expression of PRKCE and MAP2K2 in bladder cancer tissues (n = 408), based on data from TCGA. (C) Expression levels of genes in the cGMP-PKG signaling pathway in EJ cells treated with NC, 4-ABP, or PRKCE siRNA. (D) The protein levels of PKG, phosphorylated VASP (p-VASP), and VASP were determined in EJ cells treated with NC, 4-ABP, or PRKCE siRNA. (E) EJ cells were transfected with PRKCE siRNA and then treated with KT5823 or 4-ABP, namely si-PRKCE+KT5823 and si-PRKCE+4-ABP, respectively. Detection of ECAR of the NC, si-PRKCE, si-PRKCE+KT5823, and si-PRKCE+4-ABP groups. (F) Seahorse analysis showing the levels of glycolysis and glycolytic capacity of the NC, si-PRKCE, si-PRKCE+KT5823, and si-PRKCE+4-ABP groups. *P < 0.05.

4. Discussion

To identify cigarette smoking-specific GMGs on bladder cancer susceptibility, we employed gene-environment interaction and joint strategies to identify an SNP rs4953292 G > A in PRKCE, which downregulated the expression of PRKCE and increased bladder cancer risk combined with cigarette smoking. Mechanistically, cigarette smoking decreased PRKCE expression levels and thus led to glycolytic reprogramming of bladder cancer cells via the activation of the cGMP-PKG signaling pathway to promote bladder cancer progression (Figure ).

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Graphical abstract of this study. In the proposed model, 4-ABP downregulated PRKCE expression, promoting bladder cancer susceptibility via increasing glycolysis levels with the activation of the cGMP-PKG signaling pathway.

Similar to other tumors, bladder cancer undergoes reprogramming of energy metabolism to sustain uncontrolled cell proliferation. An abnormal metabolism of glucose, serving as the primary source of nutrition for bladder cancer cells, is one of the hallmarks of this disease. Aerobic glycolysis, best known for the reprogramming of energy metabolism proposed by Otto Warburg, , signifies the phenomenon where cancer cells primarily derive energy through glycolysis, even when an adequate oxygen supply is present. Furthermore, bladder cancer exhibits alterations in the PPP, TCA cycle, glycogen metabolism, and OXPHOS. Cigarette smoking stands as one of the most substantial risk factors for bladder cancer, − with cigarette smoking being attributed to approximately 50% of bladder cancer cases. Cigarette smoking can lead to glucose metabolism reprogramming, which may subsequently influence tumor progression. Consistent with previous findings, we validated the impact of cigarette smoking on the reprogramming of glucose metabolism in bladder cancer cell lines. It was measured that the levels of glucose uptake and lactate production were elevated in bladder cancer cells treated with 4-ABP, a critical cigarette carcinogen in human bladder cancer. Furthermore, the expression levels of key factors in the various pathways of glucose metabolism were altered. However, the changes in the expression of key factors in each pathway were inconsistent. For instance, isocitrate dehydrogenase (IDH) of the TCA cycle, known for promoting aerobic glycolysis, , demonstrated a tendency for different molecular isoforms to be expressed in a complementary manner in the two bladder cancer cell lines.

GWAS have revealed numerous genetic variants associated with complex human diseases, including over a dozen loci associated with bladder cancer risk in European and East Asian populations. Studies have demonstrated that GWAS-identified genetic variants may interact with cigarette smoking in bladder cancer susceptibility. ,− Accordingly, we assessed genetic variants in GMGs associated with bladder cancer risk in a Chinese population and examined their interaction with cigarette smoking status. In this study, we discovered a significant association between rs4953292 G > A in PRKCE and increased susceptibility to bladder cancer, along with its interaction with cigarette smoking status. Stratification analysis showed that the risk effect of the rs4953292 A allele was present in smokers. A subsequent joint analysis indicated a 67% higher risk of bladder cancer in smokers carrying the rs4953292 GA/AA genotype when compared to nonsmokers with the rs4953292 GG genotype. Additionally, we treated cells transfected with PRKCE [G] or PRKCE [A] with 4-ABP exposure and found that compared with PRKCE [G], PRKCE [A] could significantly downregulate PRKCE expression levels after 4-ABP treatment. These results suggest that the rs4953292 A allele could be a promising target for increased bladder cancer susceptibility in smokers.

The SNP rs4953292, located in the intron of PRKCE, was found to significantly decrease PRKCE expression based on eQTL analysis. We confirmed the regulatory effect of rs4953292 on PRKCE expression in bladder cancer cell lines through dual luciferase reporter gene assays. Moreover, our findings revealed that rs4953292 in PRKCE can alter the RNA secondary structure and free energy, suggesting its impact on the mRNA stability and post-transcriptional regulation of PRKCE in bladder cancer. The PRKCE gene has previously been recognized to be involved in glucose homeostasis and energy metabolism, both of which are closely associated with tumorigenesis. PRKCE has been recognized as a possible prognostic indicator for kidney renal clear cell carcinoma, but its association with bladder cancer remains uncertain. Our research revealed a decreased PRKCE expression in bladder cancer tissues according to our in-house samples, bladder cancer cell lines’ experiments, and databases of TCGA and GEO. Functionally, PRKCE knockdown significantly promotes bladder cancer cell proliferation, migration, and invasion ability in vitro, indicating low expressed PRKCE’s role in bladder tumor progression. PRKCE in neuronal cells is associated with enhanced glycolysis, while elevated protein levels are linked to the inhibition of gluconeogenesis in diabetic rats. To explore the role of PRKCE in glucose metabolism in bladder cancer, we measured the expression of key factors involved in various pathways of glucose metabolism in bladder cancer cell lines overexpressing PRKCE. The results showed that PRKCE regulated glucose metabolism with a strong targeted specificity. It specifically downregulated the core rate-limiting enzyme genes (GLUT1, PKM1, LDHA) controlling glycolytic flux − to efficiently inhibit overall glycolytic activity, without affecting other glycolytic genes or molecules in other glucose metabolic pathways. This precise, key node-targeting pattern supports the antitumor effect of PRKCE via reprogramming glycolysis. Additionally, we confirmed the inhibitory effect of PRKCE overexpression on glycolysis in bladder cancer cells through ECAR assays. Moreover, we discovered that treatment with the glycolysis inhibitor 2-DG in PRKCE-knockdown bladder cancer cells reversed its previously observed promotional effect on malignancy.

Building on these findings, we proposed that 4-ABP promotes the malignant progression of bladder cancer by downregulating PRKCE expression, thereby upregulating aerobic glycolysis. It is worth noting that the role of PRKCE in promoting cell proliferation by elevating aerobic glycolysis has been demonstrated in prostate cancer. To confirm our hypothesis, we overexpressed PRKCE in 4-ABP-treated bladder cancer cells and observed that the overexpression of PRKCE reverted the role of 4-ABP in upregulating the glycolytic pathway and promoting the malignant phenotype of bladder cancer cells. Furthermore, additional treatment with 2-DG resulted in a lower degree of malignancy. We conducted preliminary research into the mechanism by which cigarette smoking-induced downregulation of PRKCE mediates glycolytic reprogramming in bladder cancer. Both cigarette smoking and downregulated PRKCE were found to activate the cGMP-PKG signaling pathway, and the use of the pathway inhibitor KT5823 blocked their effects on the glycolytic pathway. Moreover, the regulatory effects of this pathway on glycolysis have been demonstrated in human endothelial progenitor cells (EPCs). In conclusion, the downregulation of PRKCE by cigarette smoking is implicated in the progression of bladder cancer through the mediation of glycolytic reprogramming via activation of the cGMP-PKG signaling pathway.

However, our epidemiological survey only included basic smoking information, and we will collect comprehensive smoking information, such as smoking duration and daily smoking amount, in future studies to address this gap. Although our cohort has been reported in multiple previous studies, ,, validation of rs4953292 in PRKCE in a multicenter Chinese population cohort remains necessary, and we are currently collecting relevant samples. Multiple testing corrections were not applied in the association analysis in our exploratory study, which may increase the risk of potential false-positive associations. The bladder cancer cells (J82 and EJ) used in our study both belonged to MIBC cell lines; , this likely explained the consistent responsiveness of them to 4-ABP treatment.

In summary, our study highlighted the impact of glucose metabolism-related genes on the risk of cigarette smoking-related bladder cancer. Mechanistically, 4-ABP decreased the expression levels of PRKCE, resulting in increased aerobic glycolysis driven by the activation of the cGMP-PKG signaling pathway, thereby significantly promoting bladder cancer progression. Our study provided preliminary insights into the role of glycolytic reprogramming in cigarette smoking-related bladder cancer. These findings offer theoretical guidance for studying the etiology of bladder cancer and for the prevention and treatment of susceptible populations.

Supplementary Material

eh5c00411_si_001.pdf (2.5MB, pdf)

Acknowledgments

We are grateful to Professor Meilin Wang and Professor Mulong Du for their expert guidance and support. We also thank all faculty and students of the Environmental Genomics Laboratory for their support and assistance. This study was supported in part by the Nanjing Medical University Science and Technology Development Foundation (NMUB20240264) and the National Natural Science Foundation of China (82130096) as well as the Priority Academic Program Development of Jiangsu Higher Education Institutions (Public Health and Preventive Medicine).

The data that support the findings of this study are available from the corresponding author upon reasonable request.

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/envhealth.5c00411.

  • Materials and methods, association analysis (Table S1), joint effect analysis (Table S2), enriched pathway of PRKCE-interacting proteins (Table S3), sequences of primers and siRNAs (Table S4), flowchart of identifying GMGs (Figure S1), stratification analysis (Figure S2), eQTL analysis (Figure S3), PRKCE mRNA folding structure prediction (Figure S4), 4-ABP exposure concentration and time selection (Figure S5), the band density of PRKCE (Figure S6), the expression of PRKCE in pan-tissues (Figure S7), Kaplan–Meier survival analysis (Figure S8), the band density of PRKCE (Figure S9), validation of PRKCE overexpression efficiency (Figure S10), validation of PRKCE-knockdown efficiency (Figure S11), detection of proliferation ability of cells (Figure S12), detection of migration and invasion ability of cells (Figure S13), effects of cigarette smoking or PRKCE on glucose metabolism (Figure S14), the biological function of PRKCE (Figure S15), the band density of PKG and p-VASP/VASP (Figure S16), and detection of glycolysis of cells (Figure S17) (PDF)

#.

Y.X., J.Z., Y.J., and H.S. contributed equally to this work. Y.X.: Writingoriginal draft, Conceptualization, Methodology, Data curation, Visualization, Software. J.Z.: Resources, Methodology, Data curation, Funding acquisition. Y.J.: Data curation, Formal analysis, Methodology. H.S.: Data curation, Methodology, Software. Z.M.: Resources, Methodology. F.G.: Resources, Methodology. R.Z.: Writingreview and editing, Conceptualization, Funding acquisition, Supervision.

The authors declare no competing financial interest.

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Associated Data

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

Supplementary Materials

eh5c00411_si_001.pdf (2.5MB, pdf)

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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