Abstract

Objective

   Vehicle emissions (VEs) are considered important causes of air
   pollution in cities. This study aims to analyze the role of VEs in the
   progression of prostate cancer (PCa).

Methods

   We used the CTD database to obtain genes associated with VEs in
   prostate cancer to explore the associations between VEs and prostate
   cancer. LASSO regression analysis was subsequently used to construct a
   novel VEs-related progression model in the TCGA and GEO databases.
   Differences in the progression-free interval (PFI), clinical
   characteristics and immune characteristics were compared among patients
   with different VEs score. Finally, we confirmed that the changes of
   DNMT3B after VEs mediation and that DNMT3B promoted the progression of
   prostate cancer by preliminary experiments.

Results

   The analysis of VEs-enriched diseases revealed the most significant
   enrichment in the prostate cancer pathway. The GO enrichment analysis
   observed that VEs affected multiple signaling pathways and biological
   processes in PCa. On the basis of VEs-associated genes, we relied on
   the VEs scoring model to accurately evaluate the PFI (p < 0.05). In
   patients with high VEs scores, tumor mutation burden (TMB) (R = 0.29,
   p < 0.001) and the cytolytic activity (CYT) (R = 0.161, p < 0.001)
   scores were increased, as was the expression of immunosuppressive
   ligands. Functional experiments showed that knockdown of DNMT3B
   inhibited the proliferation, colony formation and migration of prostate
   cancer cells in vitro. In addition, we found that DNMT3B expression was
   significantly increased in tumor cells after VEs treatment.

Conclusions

   This study presents unique opinions into the impact of VEs on prostate
   cancer progression and highlights the need for more in-depth
   exploration of the mechanistic link between VEs and prostate cancer
   progression.

Supplementary Information

   The online version contains supplementary material available at
   10.1186/s12935-025-03906-8.

   Keywords: Vehicle emissions, Prostate cancer, Progression-free interval

Graphical Abstract

   [34]graphic file with name 12935_2025_3906_Figa_HTML.jpg

Supplementary Information

   The online version contains supplementary material available at
   10.1186/s12935-025-03906-8.

Introduction

   Vehicle emissions are important contributors to global air pollution,
   which has a serious adverse impact on human health [[35]1]. Most VEs
   originates from the combustion of liquid energy in vehicles, mainly
   including gasoline and diesel. VEs are composed of a variety of
   nontoxic and toxic components and particulates. Long-term excessive
   exposure to these toxic components can induce the development of a
   variety of serious diseases, such as cancer [[36]2, [37]3]. The study
   confirmed that VEs, rock sources and coal combustion accounted for more
   than 85% of the cancer risk [[38]4]. The main volatile organic
   compounds (VOCs) in VEs are benzene, toluene, ethylbenzene, and xylene
   [[39]5]. PM[2.5] in VEs can enter the body through breathing, leading
   to related diseases [[40]6]. Polycyclic aromatic hydrocarbons (PAHs)
   are easily absorbed into the blood circulation, and prolonged high
   exposure can lead to tumors at multiple sites, including the skin,
   lung, and bladder [[41]7]. Moreover, the results suggest that exposure
   to high levels of diesel engine emissions can increase the risk of
   bladder cancer [[42]8]. Ambient air pollution is a clear carcinogen of
   lung cancer, and diesel engine exhaust or emissions are classified as
   Group 1 carcinogens. There is also evidence of a positive association
   between the risk of death from lung cancer and the number of years of
   work with regular exposure to newly emitted vehicle exhaust [[43]9]. In
   addition, the chemistry of gasoline and diesel emissions has changed as
   engines and post-combustion control technologies have changed.

   Prostate cancer is a multifaceted health problem [[44]10]. In addition,
   environmental factors are one of the causes of prostate cancer
   [[45]11]. Studies have shown that organic pesticides, heavy metals,
   VEs, and polycyclic aromatic hydrocarbons are closely associated with
   prostate cancer [[46]12–[47]14]. Large amounts of ultrafine particles
   in exhaust fumes increase the risk of prostate cancer [[48]11]. Parent
   et al. reported that a positive association between elevated ambient
   NO[2] exposure and incident prostate cancer in Canada [[49]15]. Cohen
   et al. noted an increased risk of prostate cancer in Israeli men
   exposed to traffic-related air pollution [[50]16]. Furthermore,
   occupational exposure to diesel fuel or fumes is associated with a
   statistically significant increase in prostate cancer incidence
   [[51]17]. Similarly, exposure to aircraft emissions also significantly
   increased the risk of prostate cancer in male patients [[52]18].
   Therefore, the long-term exposure risk of VEs is closely related to the
   occurrence of prostate cancer.

   At present, the understanding of the effect of automobile exhaust on
   prostate cancer is still limited. In this study, we demonstrated that
   automobile exhaust promoted prostate cancer progression. Various
   databases were used to search for and analyze the effects of automobile
   exhaust on prostate cancer, and experiments were also conducted to
   verify the findings. In this study, we constructed a PFI prediction
   model based on VEs related genes. The purpose of this study is to
   explain the relationship between VEs, genes and PFI by bioinformatics
   methods. Patients with high expression of related genes are more likely
   to be affected by VEs in the future, and the disease progresses more
   rapidly. We expect that this study will make valuable contributions to
   the field of prostate cancer-related environmental pollution research.

Methods and materials

Cell culture

   The human prostate cancer cell lines PC-3, DU145, LNCaP and 22Rv1 were
   purchased from the Cell Bank of the Chinese Academy of Sciences
   (Shanghai, China). PC-3, LNCaP and 22Rv1 cells were cultured in
   RPMI-1640 (Gibco, USA), whereas DU145 cells were cultured in DMEM
   (Gibco, USA). The medium was supplemented with 10% fetal bovine serum
   (ExCell Bio, China) and 1% penicillin/streptomycin (Servicebio, China).
   The cells were cultured at 37 °C in a humidified 5% CO[2] environment.
   Cell-line validation was performed with the use of short tandem repeat
   (STR) analysis. Passage numbers in 5–15 cells were used in the article
   to reduce genetic drift.

RNA isolation and qRT‒PCR

   Total RNA was isolated and extracted using RNA-Quick purification kit
   (YiShan Biotech, China) according to the manufacturer’s instructions.
   Reverse transcription was then performed using Hifair^® III 1st Strand
   cDNA Synthesis SuperMix for qPCR (Yeasen, China). The expression of
   mRNAs in prostate cell lines was quantified via a CFX Connect Real-Time
   PCR Detection System (Bio-Rad, USA). The primer sequences for DNMT3B
   (forward primer: AGGGAAGACTCGATCCTCGTC, reverse primer:
   GTGTGTAGCTTAGCAGACTGG) and ACTIN (forward primer: TGGCACCCAGCACAATGAA,
   reverse primer: CTAAGTCATAGTCCGCCTAGAAGCA) were purchased from
   GenePharma (Shanghai, China).

Transient transfection

   DNMT3B siRNA oligonucleotides (si-1: 5’-CCACCUUCAAUAAGCUCGUTT-3’, si-2:
   5’-CGCCUCAAGACAAAUUGCUTT-3’) and control siRNA (si-NC:
   5’-UUCUCCGAACGUGUCACGUTT-3’) were obtained from GenePharma (Shanghai,
   China). The cells were seeded in 6-well plates at a density of 200 000
   cells per well. Transfection experiments were performed when the cell
   density had grown to 60-70%. Subsequently, 100 nM siRNA and 5 µl/ml
   Lipofectamine RNAiMAX (Invitrogen, USA) were mixed and allowed to stand
   for 30 min at room temperature. The mixture was added gently and slowly
   to the cell culture medium and incubated for 48 h for RNA extraction
   and cell function experiments.

Cell viability measurement

   Cell viability was assessed by a CCK-8 (K1018, APExBIO, USA) assay and
   a colony formation assay. The cells were seeded at 1000 cells per well
   in 96-well and 6-well plates after treatment under the corresponding
   conditions. The absorbance values were measured every 24 h via a CCK-8
   assay at a wavelength of 450 nm for 6 days. The culture medium was
   aspirated from a 96-well plate, and cell viability was measured after
   two hours of incubation with 100 µl of culture and 10 µl of CCK-8
   reagent. Cells were seeded in 6-well plates and fresh medium was
   changed every 4 days. Macroscopic clones were formed after 2 weeks, the
   medium was discarded, fixed with 4% paraformaldehyde (Servicebio,
   China), and stained with 0.1% crystal violet.

Transwell assay

   The cells were diluted in serum-free medium and seeded in Transwell
   chambers (#353097, Falcon, USA) at 50,000 cells per well. Chambers were
   placed in a culture plate containing complete medium to allow for the
   possible migration of cells across the membrane to the bottom chamber.
   After fixation with paraformaldehyde, the membranes were stained with
   1% crystal violet.

VEs collection

   VEs were collected using Tedlar air bag (W-3989, DuPont). VEs comes
   from a certain model of car, which has a total mileage of about
   50,000 km and uses 92# gasoline of Sinopec. The collected gas was mixed
   one-to-one with the medium for 24 h and then used for experiments.

Data acquisition and processing

   The genes of VEs mediated PCa were obtained from the Comparative
   Toxicogenomics Database. The transcriptome data, clinical data, and
   mutation data for PCa were obtained from The Cancer Genome Atlas (TCGA)
   database. The validation datasets [53]GSE116918 and [54]GSE94767 were
   downloaded from the Gene Expression Omnibus (GEO) database.

Gene set enrichment

   The “limma” package was utilized for computing the LogFoldChange and p
   value for each gene in different groups. All genes were imported for
   gene set enrichment analysis (GSEA) via the “Pi” package. Gene set
   variation analysis (GSVA) was performed on VEs score samples via the
   GSVA package.

Construction of the VE score

   First, the genes associated with VEs were analyzed by univariate Cox
   regression. Least absolute shrinkage and selection operator (LASSO) Cox
   regression analysis can perform variable selection and regularization,
   and by imposing a penalty term (λ × absolute slope) on the size of the
   model coefficients, overfitting can be avoided, which can further
   improve the accuracy and interpretability of the prediction model.
   Therefore, it is a sensible choice to build a prediction model based on
   VEs-associated genes. The LASSO regression algorithm was performed
   using the glmnet R package. In addition, 1000 replicates were performed
   for each cycle to prevent overfitting. According to the results of the
   operation, we find that the best λ value is between 1 and 6, that is,
   lambda.1se = 1, lambda.min = 6. Subsequently, cox regression analysis
   showed that DNMT3B, KDM2A, PAX9 and TSHZ1 were the genes used to
   construct the model. The VEs scoring formula was established as
   follows:
   graphic file with name d33e351.gif

Statistical analysis

   All experiments were carried out for three times. The data in this
   research were statistically analyzed and visualized via R language and
   GraphPad Prism. All comparisons between groups were performed via the
   Wilcoxon test. If not stated otherwise, p < 0.05 was considered
   statistically significant. ns, not significant, * p < 0.05, **
   p < 0.01, and *** p < 0.001.

Results

Expression landscape of VEs-associated genes in PCa

   We obtained 27 genes associated with VEs from the CTD database.
   (Supplement material 1 ). The levels of differences between tumor and
   normal tissues were further explored. The expression of B3GAT1, CHEK2,
   DNMT3B, MAP2K1, and RNF43 was markedly upregulated in tumor, whereas
   the expression of BCL2, CDKN1B, ITGB8, MBNL1, MECON, PAX9, PKNOX2,
   POU2F2, PPFIBP2, RNASEL, TCF4, TCF7L2, and TTC28 was markedly
   downregulated in tumor tissues (Fig. [55]1A). Furthermore, we
   investigated copy number changes (CNVs) in these genes and reported
   that CNVs were prevalent in 29 VEs-associated genes. Among them, KDM2A,
   PAX9, and MECOM presented extensive CNV gains, whereas RNLS, CHD3,
   CDKN1B, TSHZ1, BCL2, CDH1, TCF4, TTC28, PKNOX2, POU2F2, and CHEK2
   presented CNV loss (Fig. [56]1B). The gene networks describe a
   comprehensive picture of VEs-associated gene interactions and their
   impact on the progression-free interval (PFI) in PCa (Fig. [57]1C). We
   subsequently performed an enrichment analysis of the effects of VEs on
   prostate cancer via the CTD database. Among the VEs-enriched diseases,
   the main correlation was prostate-related diseases, especially prostate
   cancer (Fig. [58]1D). In Fig. [59]1D, it was mainly shown that VEs
   mainly affect biological processes in GO enrichment analysis. In
   addition, VEs affected pathways related to the PI3K/AKT signaling
   pathway, BH3 proteins/BCL-2 members, and EGFR tyrosine kinase inhibitor
   resistance, as shown by pathway enrichment analysis (Fig. [60]1D).

Fig. 1.

   [61]Fig. 1
   [62]Open in a new tab

   The VEs-associated gene landscape in prostate cancer. (A) Comparison of
   VEs-associated genes between the tumor and normal groups. (B)
   Frequencies of CNV gain and loss among VEs-associated genes. (C)
   Expression and interaction of VEs-associated genes in prostate cancer
   patients. The size of each cell represents the impact of each gene on
   the patient’s PFI. (D) Disease, GO, and pathway enrichment analyses of
   genes associated with VEs

Screening of genes for constructing the VEs score model

   Univariate Cox regression analysis was used to further investigate the
   associations between VEs-associated genes and the PFI. (Supplemental
   material 2 and Fig. [63]2A). Subsequently, LASSO analysis was used to
   construct the PFI model (Fig. [64]2B). Finally, DNMT3B, KDM2A, PAX9,
   and TSHZ1 were selected for PFI model construction (Supplemental
   material 3 and Fig. [65]2C). The sensitivity of the VEs score was
   assessed by comparing the concordance indices (C-index) values.
   Obviously, the C-index of the VEs scoring models were greater than
   those of the individual genes. (Fig. [66]2D). Kaplan-Meier analysis
   manifested that patients with high DNMT3B expression had relatively
   poor PFIs, whereas the opposite was observed for PAX9. However, the
   expression of KDM2A and TSHZ1 was not significantly associated with PFI
   ( Supplement material 4A ). GSEA indicated that high expression of
   DNMT3B was connected to E2F transcription factors, the G2/M checkpoint
   and mitotic spindle assembly (Fig. [67]2E). Moreover, high expression
   of PAX9 was connected to early response factors, the interferon gamma
   response and TNFα signaling (Fig. [68]2E). We subsequently performed
   protein‒protein interaction analysis for DNMT3B and PAX9 via GeneMANIA,
   aiming to explore the potential interactions between proteins related
   to these three proteins (Supplemental material and Fig. [69]2F). The
   crystal structure of the DNMT3B-DNMT3L complex was obtained from the
   Protein Data Bank.

Fig. 2.

   [70]Fig. 2
   [71]Open in a new tab

   Screening VEs score-related model genes. (A) Univariate Cox regression
   analysis of VEs-associated genes. (B) LASSO regression analysis of
   prognosis-related genes. (C) Multivariate Cox regression analysis of
   VEs-associated genes. (D) C-index curve of the PFI. (E) Kaplan‒Meier
   analysis of the PFI of prostate cancer patients with low and high
   expression of DNMT3B and PAX9 was performed with data from the TCGA
   database. (F) GSEA of DNMT3B and PAX9 in different expression groups.
   (G) Determination of genes that interact with DNMT3B and PAX9 in tumors
   via physical interactions. Crystal structure of the DNMT3B-DNMT3L
   complex

The VEs score model as a predictor of the PFI

   Patients with high VEs scores had significantly worse PFIs in the TCGA
   database (Fig. [72]3A). In the two validation cohorts, we reached the
   same conclusion (Fig. [73]3B). The evaluation of clinical information
   for TCGA prostate cancer patients revealed that the VEs score was
   significantly different from the Gleason score, T stage, and N stage
   (Fig. [74]3C and D). We observed an increase in the VEs score with
   increasing Gleason score (Fig. [75]3C). GSVA revealed that in addition
   to E2F-related pathways and the G2/M checkpoint pathway, other
   signaling pathways were observably activated in the low VEs score group
   (Fig. [76]3E). However, according to the results of our analysis,
   patients with high VEs scores had poor PFIs. Moreover, the cell cycle
   signaling pathway was activated in the high VEs score group (Supplement
   material 4B). Androgen receptor (AR) pathway is the core factor in the
   pathogenesis of PCa. Nevertheless, androgen response was worse in the
   high VEs score group (Fig. [77]3E and Supplement material 4B).
   Subsequently, we explored the associations of AR signaling
   pathway-related genes with VEs scores, which generally showed a
   negative trend with AR (Fig. [78]3F). These results indicate that the
   high VEs score group has greater malignant potential. Furthermore,
   there was no difference in AR mutation among the different VEs score
   groups (Fig. [79]3I). We subsequently observed a highly positive
   correlation between TMB and VEs scores (Fig. [80]3G). The top 10
   mutation genes in the low VEs score group included SPOP, TTN, FOXA1,
   KMT2D, MUC16, TP53, SYNE1, ATM, RP1, and RYR2. The top 10 mutation
   genes in the high VEs score group included TP53, TTN, SPOP, KMT2D,
   MUC16, FOXA1, KMT2C, LRP1B, PTEN, and SPTA1 (Fig. [81]3H).

Fig. 3.

   [82]Fig. 3
   [83]Open in a new tab

   VEs score model and associations with survival and clinical features.
   (A-B) Kaplan‒Meier analysis of the VEs score low and high groups in the
   TCGA, [84]GSE116918 and [85]GSE94767 cohorts. (C) VEs scores of
   different Gleason score groups in TCGA. (D) Differences in clinical
   characteristics between the low- and high-VEs score groups. (E) GSVA
   revealed pathways enriched with different VEs scores. (F) Pearson’s
   correlation analysis between the VEs score and the expression of AR
   signaling pathway-related genes. (G) Relationship between VEs score and
   TMB. (H) Top 10 mutated genes in the two VEs score groups. (I) Mutation
   comparison of the AR gene in different VEs score groups

Predictive potential of the VEs score in the response to cancer immunotherapy

   To reveal the predictive effect of the VEs score on cancer
   immunotherapy efficacy, we downloaded and evaluated the activity score
   of the cancer immune cycle from the Tracking Tumor Immunophenotype
   database. The VEs score was positively correlated with the release of
   cancer cell antigens, priming and activation, T-cell recruitment,
   eosinophil recruitment, basophil cell recruitment, B-cell recruitment,
   Th2 cell recruitment, and Treg cell recruitment. However, Th17 cell
   recruitment, MDSC recruitment and recognition of cancer cells by T
   cells were inversely associated with the VEs score (Fig. [86]4A and
   Supplementary material 6 ). In addition to immune cells, immune-related
   regulatory factors also play a key role in immunotherapy [[87]19].
   Except for CTLA4, there was no difference in the expression of other
   genes between the two groups. (Fig. [88]4B). Meanwhile, we investigated
   the association between the VEs score and major histocompatibility
   complex (MHC) and found that TNFSF14, TNFSF18, TNFSF25, and CD28 were
   increased in the high VEs score group. (Fig. [89]4C). In terms of the
   expression of CTLA4, the high VEs score group was more sensitive to
   immunotherapy (Fig. [90]4D). Moreover, the cytolytic activity (CYT)
   presented a positive correlation with VEs score (Fig. [91]4E). GSEA
   demonstrated that immune-associated pathways were upregulated in the
   high VEs score groups (Fig. [92]4F). Therefore, we believe that
   patients with high VEs scores have a more active immune status and are
   more suitable for combination immunotherapy.

Fig. 4.

   [93]Fig. 4
   [94]Open in a new tab

   Estimation of anticancer immune activity via the VEs score. (A) Heatmap
   of the seven-step cancer immunity cycle and VEs score. (B-C) The
   expression of immune checkpoint-related genes and the MHC gene set in
   the two VEs score groups. (D) IPS between the two VEs score groups. (E)
   Relationships between VEs scores and CYT scores. (F) GSEA for the high
   VEs score group

Effect of the VEs-associated gene DNMTB3 on prostate cancer progression

   To explore the function of DNMT3B in PCa, two small interfering RNAs
   (siRNAs) targeting DNMT3B were transfected into PCa cells. The mRNA
   level of DNMT3B was markedly knocked down (Fig. [95]5A). Subsequently,
   CCK8 and colony formation assays were used to verify the interference
   of DNMT3B knockdown on proliferation. The proliferation ability of
   tumor cells was significantly inhibited when DNMT3B was knocked down
   (Fig. [96]5B and C). A cell migration assay revealed that decreased
   DNMT3B expression resulted in decreased migration (Fig. [97]5D).
   Therefore, the above results suggest that DNMT3B has a critical role in
   prostate cancer progression. Moreover, we observed that the expression
   level of DNMT3B was increased after VEs treatment ( Supplement material
   7).

Fig. 5.

   [98]Fig. 5
   [99]Open in a new tab

   DNMT3B promotes the proliferation of prostate cancer cells in vitro.
   (A) qRT‒PCR analysis of DNMT3B expression levels in DNMT3B-knockdown
   and control cells. (B) Cell viability was assessed in DNMT3B-knockdown
   prostate cancer cells. (C) Colony formation assays were performed in
   DNMT3B‐knockdown prostate cancer cells. (D) Representative images of
   prostate cancer cells migration assays showing cell migration after
   downregulation of DNMT3B

Discussion

   VEs are important contributors to air pollution and adversely affect
   air quality and human health [[100]1]. Outdoor air pollution is
   classified as a Group 1 carcinogen [[101]20]. In particular,
   traffic-related air pollutants contain a variety of carcinogenic
   compounds [[102]21]. In addition to increasing cancer risk,
   epidemiological studies have shown that these pollutants may trigger
   several other diseases, such as cardiovascular disease, stroke,
   respiratory infections, and adverse neurological effects [[103]22].
   Some studies have revealed an association between prostate cancer and
   VEs or occupations exposed to VEs [[104]17]. The morbidity of PCa is
   high in Western populations. Lifestyle and environmental factors
   contribute to the pathogenesis of PCa [[105]23].

   The role of environmental factors in tumorigenesis is an important area
   of research. Our study provides a new perspective on the association
   between VEs and prostate cancer. Using univariate Cox analysis and
   LASSO analysis, we created a score associated with VEs that divided the
   PCa cohort into two groups. The patients with high VEs scores had worse
   PFI. This finding was subsequently confirmed in an external validation
   dataset. Moreover, we observed that the androgen response pathway was
   inhibited in the high VEs group, which had a worse PFI. This may be
   caused by relevant mechanisms that are not AR-dependent. The research
   has shown that SIX2 promotes the progression of prostate cancer cells
   through Wnt/β-catenin signaling pathway [[106]24]. The non-genomic
   functions of AR have an important impact on the progression of prostate
   cancer [[107]25]. Similarly, epithelial-to-mesenchymal transition
   [[108]26] neuroendocrine transformation [[109]27] and glucocorticoid
   receptors [[110]28] are also critical for prostate cancer progression.
   Our results suggest that the VEs score has some value in evaluating the
   progression of PCa. These findings not only further support that VEs
   can significantly influence cancer prognosis but also highlight the
   potential benefit of our predictive scoring model in prostate cancer
   management.

   At present, immune checkpoint blockade has achieved clinical success in
   the treatment of a variety of malignant tumors, but it is still at a
   relatively basic stage in the treatment of prostate cancer.
   Characteristics such as heterogeneity, cold TME and low number of
   neoantigens lead to the inefficiency of prostate cancer immunotherapy
   [[111]29]. Notably, the VEs score was significantly associated with
   immune-related characteristics. Diesel exhaust particles can induce
   oxidative stress and pro-inflammatory response to mediate macrophage
   activation and dysfunction in the lung [[112]30]. Patients with higher
   VEs scores had more active immune statuses. Among them, T cells,
   Eosinophil cells, Basophils cells, B cells, Th2 cells and Treg cells
   were more active in the high VEs score group. T cells suppress
   anti-tumor immunity, which is a great challenge in cancer immunotherapy
   [[113]31, [114]32]. Shimon et al. revealed that Treg cells inhibit the
   CD8^+ T cells, drive the induction of thymocyte selection-related high
   mobility group protein (TOX), and mediate genuine CD8^+ T-cell
   exhaustion [[115]33]. Tumor-infiltrating B cells and plasma cells are
   emerging anti-tumor targets as participants in the anti-tumor response
   [[116]34]. Similarly, eosinophils infiltrate multiple solid tumor types
   and interact directly with tumor cells through non-mutually exclusive
   mechanisms [[117]35]. These hyperactive immune cells could serve as
   effective therapeutic targets in the future. In the clinic, the
   development of CD8^+ T cell-related targets, such as PD-1/PD-L1 and
   CTLA4, is more well developed [[118]36, [119]37]. However, the
   expression of CTLA4 was increased in the VEs high score group, which
   made it highly possible to benefit from ICI treatment. Meanwhile, we
   found that a high VEs score was connected with Treg cell recruitment.
   Therefore, the development of drugs targeting Treg cells for these
   patients can be more effective.

   Current extensive research on cancer biomarkers has accelerated the
   diagnosis and treatment of cancer. We used four potential biomarkers
   (DNMT3B, KDM2A, PAX9, and TSHZ1) to construct a novel VEs score-related
   prognostic model. DNMT3B is a member of the DNA methyltransferase
   family whose function is de novo methylation, and it mediates the
   occurrence of a variety of tumors [[120]38–[121]41]. Further
   experiments confirmed that DNMT3B could promote PCa progression. In
   addition, we proved that VEs could promote DNMT3B mRNA expression in
   vitro. Multiple components in VEs have been verified to promote DNMT3B
   expression. PM2.5 exposure enhanced DNMT3B expression through ROS/AKT
   [[122]42]. This implies that oxidative stress is involved in the
   regulation of DNMT3B. FAS-AS1 and DNMT3B formed a mutual inhibitory
   loop, and DNMT3B increased in benzene exposure to inhibit FAS-AS1
   expression [[123]43]. Moreover, continuous PAHs exposure, especially
   Benzo[a]pyrene, significantly increased the expression of DNMT3A and
   DNMT3B [[124]44].

   The limitation of this research is that although our results revealed a
   clear link between VEs exposure and gene expression patterns in
   prostate cancer. Other unmeasured environmental exposures may
   simultaneously correlate with vehicular exhaust exposure and prostate
   cancer progression. Furthermore, models of VEs exposure, such as in
   vitro treatment, may not fully replicate real-world environmental
   exposures. We preliminarily verified that DNMT3B promoted prostate
   cancer progression. However, DNMT3B may not be the sole epigenetic
   regulator influenced by vehicular exhaust. PAHs in VEs are known to
   promote tumorigenesis via activation of the aryl hydrocarbon receptor
   (AhR) pathway, an independent mechanism that could act as an
   uncontrolled confounder. Studies supported that the abnormal changes in
   single nucleotide polymorphisms of DNMT3B lead to DNA hypomethylation
   or hypermethylation [[125]45]. Hypermethylation of DNMT3B and a novel
   epigenomic subtype associated with somatic mutations were found in 22%
   of prostate cancer patients [[126]46]. In addition, coexisting
   pollutants such as polyphenols [[127]47] and organic pollutant
   [[128]48] can interfere with DNMT3B expression. Therefore, further
   studies on the regulatory mechanism of VEs and DNMT3B are needed in the
   future.

Electronic supplementary material

   Below is the link to the electronic supplementary material.
   [129]Supplementary Material 1^ (8.7KB, xlsx)
   [130]Supplementary Material 2^ (9.9KB, xlsx)
   [131]Supplementary Material 3^ (8.7KB, xlsx)
   [132]Supplementary Material 4^ (462.6KB, docx)
   [133]Supplementary Material 5^ (12.3KB, xlsx)
   [134]Supplementary Material 6^ (792.4KB, docx)
   [135]Supplementary Material 7^ (3.3MB, docx)

Author contributions

   Bingzheng An: Writing– original draft, Formal analysis. Shuo Chen:
   Supervision, Project administration. Chen Zhang: Validation, Software.
   Junyan Wang: Visualization, Formal analysis. Zhaoxin Guo: Supervision,
   Project administration. Ze Gao: Writing– review & editing, Resources,
   Project administration.

Funding

   This work was supported by grants from Shandong Provincial Natural
   Science Foundation (No. ZR2023QH192).

Data availability

   No datasets were generated or analysed during the current study.

Declarations

Ethics, consent to participate, and consent to publish

   Not applicable.

Competing interests

   The authors declare no competing interests.

Footnotes

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References