
==== Front
Discov Oncol
Discov Oncol
Discover Oncology
2730-6011
Springer US New York

39230832
1280
10.1007/s12672-024-01280-x
Analysis
Unraveling the role of bisphenol A in osteosarcoma biology: insights into prognosis and immune microenvironment modulation
Shiyao Liao 1
Yao Kang 23
Jun Lv 23
Yichen Lin 4
Tingxiao Zhao 23
Longtao Yao 256
Hong Zhou 1
Kai Zhou zhoukaizhoumiao@163.com

7
1 grid.506977.a 0000 0004 1757 7957 Center for Plastic & Reconstructive Surgery, Department of Orthopedics, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, Hangzhou, Zhejiang China
2 grid.506977.a 0000 0004 1757 7957 Cancer Center, Department of Orthopedics, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, Hangzhou, Zhejiang China
3 grid.506977.a 0000 0004 1757 7957 Department of Laboratory Medicine, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, Hangzhou, Zhejiang China
4 https://ror.org/04epb4p87 grid.268505.c 0000 0000 8744 8924 The Second Clinical Medical College of Zhejiang, Chinese Medical University, Hangzhou, Zhejiang China
5 grid.268099.c 0000 0001 0348 3990 Postgraduate Training Base Alliance of Wenzhou Medical University, Wenzhou, 325000 Zhejiang China
6 grid.506977.a 0000 0004 1757 7957 Department of Sports Medicine, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, Hangzhou, 310000 Zhejiang China
7 https://ror.org/03cyvdv85 grid.414906.e 0000 0004 1808 0918 Department of Orthopaedics, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325035 Zhejiang China
4 9 2024
4 9 2024
12 2024
15 40417 7 2024
26 8 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Background

Bisphenol A (BPA) is a common environmental pollutant, and its specific mechanisms in cancer development and its impact on the tumor immune microenvironment are not yet fully understood.

Methods

Transcriptome data from osteosarcoma (OS) patients were downloaded from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database. BPA-related genes were identified through the Comparative Toxicogenomics Database (CTD), yielding 177 genes. Differentially expressed genes were analyzed using the GSE162454 dataset from the Tumor Immune Single Cell Hub 2 (TISCH2). We constructed the prognostic model using univariate Cox regression and LASSO analysis. The model was validated using the GSE16091 dataset. GO, KEGG, and GSEA analyses were performed to investigate the mechanisms of BPA-related genes.

Results

A total of 15 BPA-related genes were identified as differentially expressed in OS. Univariate Cox regression and LASSO analysis identified four key prognostic genes (FOLR1, MYC, ESRRA, VEGFA). The prognostic model exhibited strong predictive performance with area under the curve (AUC) values of 0.89, 0.6, and 0.79 for predicting 1-, 2-, and 3-year survival, respectively. External validation using the GSE16091 dataset confirmed the model's high accuracy with AUC values exceeding 0.88. Our results indicated that the prognosis of the high-risk population is generally poorer, which may be associated with alterations in the tumor immune microenvironment. In the high-risk group, immune cells showed predominantly low expression levels, while immune checkpoint genes were significantly overexpressed, along with markedly elevated tumor purity. These findings revealed a correlation between upregulation of BPA-related genes and formation of an immunosuppressive microenvironment, leading to unfavorable patient outcomes.

Conclusion

Our study highlighted the significant association of BPA with OS biology, particularly in its potential role in modulating the tumor immune microenvironment. We offered a fresh insight into the influence of BPA on cancer development, thus providing valuable insights for future clinical interventions and treatment strategies.

Keywords

Bisphenol A
Osteosarcoma
Prognosis
Immune microenvironment
Single-cell sequencing
issue-copyright-statement© Springer Science+Business Media, LLC 2024
==== Body
pmcIntroduction

Osteosarcoma (OS), a primary malignancy of the bone marked by the development of immature bone or osteoid tissue, ranks among the most formidable types of bone cancer observed in clinical settings [1]. Its pathophysiology unfolds within the intricate microenvironment of bone tissue, where genetic aberrations drive the uncontrolled proliferation of osteoblasts, the cells responsible for bone formation [2]. This unchecked growth manifests as the development of osteoid or immature bone within the tumor mass, often leading to devastating consequences for affected individuals. Despite its relatively low incidence, approximately 4.4 cases per million individuals annually, OS predominantly afflicts adolescents and young adults, making it the second most common malignancy in this age group after leukemia [3]. Furthermore, its preference for the metaphyseal area of long bones, poses significant challenges in terms of surgical resection and functional preservation [4]. Although the integration of multimodal treatment approaches, such as neoadjuvant chemotherapy and limb-salvage surgery, has markedly enhanced survival rates over recent decades, the prognosis for OS remains guarded, especially in cases of metastatic disease where long-term survival rates plummet dramatically [5].

In parallel, the pervasive presence of BPA in everyday consumer products has emerged as a pressing public health concern [6]. Originally formulated in the 1890s for application as a synthetic estrogen, BPA found its way into industrial processes, becoming a key component in the manufacture of polycarbonate plastics and epoxy resins [7]. Its widespread utilization in food and beverage containers, medical devices, and thermal paper receipts has resulted in ubiquitous exposure among human populations worldwide [8]. Despite its initial characterization as a weak estrogenic compound, mounting evidence suggests that BPA interferes with hormone signaling pathways and exerting adverse effects on various physiological processes [9]. Bisphenol A (BPA) and its analog bisphenol S (BPS) enhance ovarian cancer cell stemness by increasing CSC markers (OCT4, NANOG, SOX2) through a non-canonical PINK1/p53 mitophagic pathway, raising concerns about their potential health hazards [10]. Further, Recent research indicates a strong link between bisphenol A (BPA) exposure and prostate cancer, demonstrating that BPA enhances prostate cancer cell proliferation and invasion by binding effectively to the androgen receptor [11]. Of particular concern is the potential association between BPA and the development of OS [12]. While research into this association is ongoing, researchers have indicated the possible correlation between BPA and increased OS risk, particularly in animal models [13]. Furthermore, BPA’s ability to promote cellular proliferation and disrupt normal bone metabolism raises intriguing questions about its potential role in bone cancer pathogenesis [14]. This emphasizes the necessity for additional research into the mechanisms responsible for BPA-induced OS and the implementation of preventive strategies to mitigate exposure in at-risk populations. Concurrently, epidemiological studies have linked BPA exposure to various malignancies, including breast, prostate, and ovarian cancer, highlighting the multifaceted impact of this ubiquitous chemical on human health [15, 16].

In this study, we constructed and validated BPA-related predictive models aimed at forecasting outcomes in patients with OS. Additionally, we delved into the correlation between our model and factors such as immune invasion, response to immunotherapy, tumor microenvironment characteristics, and clinical variables. Furthermore, employing single-cell data, we employed thorough examination of the expression patterns of modeling genes across different cell types, revealing their involvement in the advancement of OS.

Methods

Data acquisition and processing

Download transcriptome data of 85 OS patients from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET). Identify BPA-related genes through comparison with the Comparative Toxicogenomics Database (CTD) [17]. Utilized the Gene Expression Omnibus (GEO) (GSE16091) [18] dataset as an external validation dataset, and obtain single-cell sequencing data from OS patients (GSE162454) [19, 20] from the Tumor Immune Single Cell Hub 2 (TISCH2) [21].

Single-cell sequencing analysis

The single-cell RNA sequencing data used in our study were sourced from the GSE162454 dataset available in the TISCH2 database. This dataset includes single-cell transcriptomic profiles from OS samples that were previously collected, processed, and sequenced by the original researchers. The data have been made publicly available for secondary analysis. TISCH2 includes annotated cell types based on known marker genes, which facilitated the identification and characterization of different cell populations in our analysis. The platform offers visualization tools such as t-SNE and UMAP plots to display the clustering of cells and the expression patterns of specific genes across different cell types. The platform’s integrated tools allowed us to effectively explore the differential expression of BPA-related genes and their potential roles in OS progression and the immunosuppressive microenvironment.

Prognostic model construction and functional enrichment analysis

Download genes that are differentially expressed in tumors from the GSE162454 dataset and identify their intersection with BPA-related genes. Constructing the BPA-related model using Univariate Cox and Least Absolute Shrinkage and Selection Operator (Lasso) regression analyses. The OS patients with survival data were evenly divided into two groups based on the median riskScore. Subsequently, randomly partition OS patients into training and testing sets at a 1:1 ratio and employ GSE16091 as an external validation set. Examine the accuracy and reliability of the model’s predictions using various algorithms. To further elucidate the biological functions and pathways associated with the identified genes, we conducted Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA). GO analysis categorized genes into biological processes, cellular components, and molecular functions, while KEGG analysis identified enriched pathways. GSEA assessed whether predefined gene sets exhibited significant differences between high and low-risk groups, providing insights into underlying biological mechanisms.

Immune microenvironment analysis

The single-sample gene set enrichment analysis (ssGSEA) algorithm employs a computational technique to infer the cellular composition of intricate tissues. By utilizing unique collections of genes expressed specifically in different cell types, it accurately assesses their relative abundance within a given sample. In our study, we utilized ssGSEA to calculate immune cell and functional enrichment scores, allowing us to uncover the detailed composition of immune cells present in the samples, thereby gaining deeper insights into the immunodynamics within the disease context under investigation. The ESTIMATE algorithm evaluates the abundance of immune cells in OS samples' tumor microenvironment (TME). The IMvigor210 database was used to validate differences in immune therapy among the model groups.

Statistical analysis

All analyses were conducted using R 4.2.2. Two-tailed statistical tests were utilized, with a significance level set at p < 0.05. Chi-square tests were employed for comparisons of categorical variables, while comparisons of continuous variables utilized either the Wilcoxon rank-sum test or the t-test. The optimal cutoff value was determined using the “survminer” package. Cox regression and Kaplan–Meier (KM) analysis were performed using the “survival” package.

Results

Screening of differentially expressed BPA-related genes

This article investigates the process as shown in Fig. 1. Utilizing the TISCH2 database, this study performed an analysis on the GSE162454 data, which facilitated the coclustering of eight diverse cell types (Fig. 2A). The distribution of these cell types across various samples was depicted in Fig. 2B, with monocytes, malignant cells, and fibroblasts emerging as the most prevalent cell types (Fig. 2C). Moreover, Fig. 2D illustrated the expression profiles of cell-specific marker genes across different cell types. In our investigation of OS, we identified 177 BPA-related genes sourced from the CTD. Subsequently, leveraging a stringent criterion (Adjusted p-value < 0.001), we isolated 6070 genes exhibiting significant differential expression using TISCH2. Finally, through the intersection of BPA-related genes with the pool of differentially expressed genes, we pinpointed 15 genes of interest (Fig. 2E).Fig. 1 Flow chart of our study

Fig. 2 Selection of Differentially Expressed BPA-related Genes. A Analysis of single-cell distribution from GSE162454; B assessment of distinct cell composition in each osteosarcoma (OS) sample; C quantification of distinct cells across all OS patients; D expression patterns of typical marker genes in various cells; E intersection of BPA-related genes and differentially expressed genes

Construction and prognostic analysis of the BPA-related prognostic model

We conducted single-factor Cox regression analysis of the 15 intersecting genes and identified four significant genes at a threshold of p < 0.05 (Fig. 3A). Subsequently, employing LASSO analysis with optimized parameter λ, we finalized a set of four prognostic feature genes to establish the BPA-related model for OS patients within the TARGET-OS dataset (Fig. 3B and C). KM curve demonstrated that high-risk group exhibited poorer prognosis (Fig. 3D). Furthermore, we observed an increase in the number of deceased patients with rising riskScore (Fig. 3E). Figure 3F illustrated the expression patterns of the four modeling genes across various risk groups. Our model exhibited high accuracy in predicting prognosis of OS patients at 1, 2, and 3 years, with area under the curve (AUC) values exceeding 0.79 (Fig. 3G). Univariate and multivariate independent prognostic analyses further validated the model’s capability as an independent prognostic factor, accurately predicting patient outcomes (Fig. 3H and I). In an external validation cohort, high-risk OS patients demonstrated inferior clinical outcomes with AUC values surpassing 0.88 (Fig. 3J and K).Fig. 3 Establishment and Validation of the BPA-Related Prognostic Model. A Results from Univariate Cox regression analysis of intersecting genes; B, C application of Lasso analysis to identify prognostic BPA-related genes with the minimum lambda value; D KM survival curves comparing high and low-risk groups; E scatter plot reordering the risk curves of each sample according to riskScore and sample survival status; F expression of modeling genes among different groups; G AUC prediction values for the first, second, and third years of the model; H, I outcomes of univariate and multivariate independent prognosis analysis for the model; J KM survival curve comparing high and low-risk groups in GSE16091; K AUC prediction values for the first, second, and third years of the model in GSE16091

Internal validation of the BPA-related prognostic model

Across all test cohorts, OS patients in the high-risk group exhibited poorer clinical outcomes (Fig. 4A and G). We observed an increase in the number of deceased patients with rising riskScores (Fig. 4B and H). Heatmaps illustrated the expression profiles of modeling genes in all sets (Fig. 4C and I). Additionally, the model demonstrated high accuracy in predicting the prognosis of OS patients at 1, 2, and 3 years, with consistently high AUC values in all sets (Fig. 4D and J). Furthermore, through univariate and multivariate independent prognostic analyses, we found significant differences in riskScores across all test cohorts, further confirming the high accuracy of the prognostic model in predicting the prognosis of OS patients (Fig. 4E, F, K, L).Fig. 4 Internal Validation of BPA-related Prognostic Model. Survival differences between different groups in the training set (A) and validation set (G); B, H scatter plot reordering the risk curves of each sample according to riskScore and sample survival status in all sets; C, I expression of modeling genes among different groups in all sets; D, J AUC prediction values for the first, second, and third years of the model in all sets; E, K outcomes of univariate independent prognosis analysis for the model in all sets; F, L outcomes of multivariate independent prognosis analysis for the model in all sets

Nomogram construction and prognostic analysis

Then, we integrated additional clinical data to construct a nomogram (Fig. 5A). Prognostic calibration curves demonstrated that the predictive value of BPA-related model at 1, 3, and 5 years showed no significant deviation from the ideal curve (Fig. 5B). Decision curve analysis (DCA) was employed to depict the change in net benefit values with varying risk probability thresholds, based on the model’s predicted values. We utilized the glm function to build binary logistic models and employed the rmda package to calculate corresponding net benefit rates and visualize them. The model curve was predominantly positioned above the “All” and “None” curves, indicating its high predictive value (Fig. 5C). Precision-Recall (PR) curves reflected the relationship between precision and recall, indicating that compared to other clinical variables, the model exhibited the highest prediction accuracy (Fig. 5D). Diagnostic calibration plots were utilized to depict the difference between the predicted and actual probabilities, evaluating the model’s goodness-of-fit. We utilized the glm function to construct binary logistic models and employed the rms package to conduct calibration analysis and visualization, with a sampling frequency of 200. Our findings revealed no significant differences between the predicted and observed values, indicating good model fit (Fig. 5E).Fig. 5 Prognostic Features of BPA-related Prognostic Model. A Development of a nomogram integrating clinical characteristics and riskScore; B predictive calibration curves of the model at 1, 3 and 5 years; C DCA curves for clinical characteristics and riskScore; D PR curves for clinical characteristics and riskScore; E diagnostic calibration curves for the model

Clinical features of BPA-related prognostic model

We analyzed the expression differences of riskScore across various clinical pathological variables. It was evident that there were no significant differences in riskScore between males and females, as well as between age groups (> 15 and ≤ 15) (Fig. 6A and B). However, compared to patients without metastasis, those with metastasis showed significantly higher expression of riskScore (Fig. 6C). Patients with primary tumors located in the arm/hand region exhibited significantly higher riskScore (Fig. 6D). Subsequently, we revealed that high-risk group generally exhibited a poorer prognosis across most clinical pathological variables (Fig. 6E–L). We performed comprehensive functional enrichment analyses to elucidate the molecular mechanisms influenced by BPA-related genes in osteosarcoma (OS) progression and immune responses. Differential expression analysis highlighted significant enrichment in biological processes related to oxidative stress response, cytosolic ribosome, and structural constituent of ribosome, suggesting the involvement of these pathways in OS progression (Fig. 7A and B). KEGG pathway analysis identified significant participation of BPA-related genes in pathways such as the HIF-1 and AMPK signaling pathways, known for their roles in tumor growth and immune evasion (Fig. 7C and D). Gene Set Enrichment Analysis revealed an immunosuppressive microenvironment characterized by alterations in phenylalanine, tyrosine, and fructose and mannose metabolism (Fig. 7E and F).Fig. 6 Clinical Pathological Features of BPA-related Prognostic Model. A–D Differential expression of risk scores among different clinical-pathological variables (A gender, B age, C metastasis, D primary tumor location); E–L survival disparity between high and low-risk groups across various clinicopathological variables

Fig. 7 Functional enrichment Analysis. A, B GO and KEGG analysis with the patients in low-risk group. C, D GO and KEGG analysis with the patients in high-risk group. E, F GSEA analysis between the high-risk and low-risk groups

Identifying immune features of the prognostic model

Firstly, using the ssGSEA algorithm, we calculated the degree of immune cell infiltration in each OS sample. We found that most of the most immune cells were underexpressed in high-risk group, especially Activated B cell, Natural killer T cell, and T helper cell (Fig. 8A). Next, we found that riskScore was significantly negatively correlated with the majority of immune cells (Fig. 8B). In TME, stromal score, immune score, and estimate score were significantly higher in low-risk group, whereas tumor purity was significantly higher in high-risk group (Fig. 8C). Moreover, most immunosuppressive checkpoint genes were significantly upregulated in the low-risk group (Fig. 8D). Regarding immune functional scores, parainflammation, T cell inhibition, and checkpoint immune scores were significantly higher in the low-risk group (Fig. 8E). To further guide clinical decision-making, we evaluated immune therapy differences in the model using the IMvigor210 database. Demonstrated a poorer prognosis in the high-risk group, with a significantly higher proportion of patients experiencing stable disease (SD) or disease progression (PD) in this group (Fig. 8F and G).Fig. 8 Immunological Features of the BPA-Related Prognostic Model. A Comparison of the expression levels of 23 immune cells in BPA-related prognostic model; B correlation analysis between riskScore and 23 immune cells; C variation in tumor microenvironment scores in BPA-related prognostic model; D contrast in expression of immunosuppressive checkpoints in BPA-related prognostic model; E difference in expression of immune function scores in BPA-related prognostic model; F survival disparities between high- and low-risk groups in the IMvigor210 dataset; G variation in the proportion of immune therapy response in the IMvigor210 database

Prognostic and immunological characteristics of modeling genes

We further analyzed the expression patterns of four modeling genes (FOLR1, MYC, ESRRA, VEGFA) across different cell types. The results indicate that ESRRA was significantly upregulated in osteoblasts (Fig. 9A and B). FOLR1 showed significantly higher expression in tumor cells (Fig. 9C and D). MYC exhibited significantly elevated expression in tumor cells and fibroblasts (Fig. 9E and F). VEGFA displayed significantly increased expression in monocytes (Fig. 9G and H). The KM survival curves demonstrated significantly poorer prognosis in patients with high expression of these four modeling genes compared to those with low expression (Fig. 10A–D). Furthermore, except for a positive correlation between ESRRA and natural killer cells, the remaining three genes were significantly negatively correlated with the majority of immune cells (Fig. 10E–H).Fig. 9 Single-cell Sequencing Analysis. Distribution of expression of 4 modeling genes in different cells (A ESRRA, C FOLR1, E MYC, G VEGFA); Expression levels of 4 modeling genes in different cells (B ESRRA, D FOLR1, F MYC, H VEGFA)

Fig. 10 Prognostic and Immunological Features of Modeling Genes. KM survival curves for 4 modeling genes (A MYC, B FOLR1, C ESRRA, D VEGFA); Correlation analysis between 4 modeling genes and 23 immune cells (E MYC, F FOLR1, G ESRRA, H VEGFA)

Discussion

Epidemiologically, OS exhibits a bimodal age distribution, with a peak incidence occurring during the adolescent growth spurt and another smaller peak among older adults [22]. The exact etiology of OS remains elusive; however, several factors have been implicated in its pathogenesis. Environmental factors such as exposure to ionizing radiation and certain chemicals, including BPA, have been suggested as potential risk factors [23, 24]. Additionally, genetic predisposition, as evidenced by familial clustering and hereditary cancer syndromes like Li-Fraumeni syndrome and hereditary retinoblastoma, underscores the multifactorial nature of OS development [25, 26].

The intricate interplay between genetic alterations and environmental exposures likely contributes to OS tumorigenesis. Dysregulation of key signaling pathways, such as the p53, RB, and Wnt pathways, has been implicated in OS pathogenesis [27, 28]. Moreover, emerging evidence suggests that dysregulated immune responses and alterations in the tumor microenvironment play pivotal roles in OS progression [29]. Despite the progress made in multimodal therapeutic strategies that integrate surgery, chemotherapy, and radiation therapy, the outlook for OS patients with metastatic or recurrent disease remains bleak. As a result, there is an imperative for innovative therapeutic interventions aimed at addressing the fundamental molecular mechanisms that propel OS tumorigenesis and progression. Immunotherapy, targeted therapies directed against specific molecular alterations, and innovative approaches harnessing the tumor immune microenvironment hold promise for improving outcomes in OS patients [30, 31]. Nevertheless, additional research and clinical trials are necessary to confirm the effectiveness and safety of these emerging treatment modalities.

BPA’s implication in carcinogenesis through multiple mechanisms highlights its potential role in cancer biology [32]. Firstly, BPA exerts its carcinogenic effects by acting as an endocrine disruptor, interfering with hormonal pathways, and disrupting normal physiological processes [33]. Numerous studies have demonstrated BPA’s ability to mimic estrogen, thereby promoting the proliferation of hormone-sensitive cancer cells such as those found in breast cancer [34]. Additionally, BPA has been demonstrated to activate multiple signaling pathways implicated in cell growth and survival contributing to tumorigenesis [35]. Moreover, exposure to BPA has been correlated with epigenetic changes, such as DNA methylation and histone modifications, which disrupt gene expression patterns and contribute to cancer development [36].

The association between BPA and TME, particularly the immune microenvironment, has garnered significant attention in recent years. BPA has been shown to modulate immune responses by altering the function of immune cells and promoting an immunosuppressive milieu conducive to tumor growth and progression [37]. For instance, exposure to BPA has been connected with the suppression of T cell function and the facilitation of regulatory T cell differentiation, consequently attenuating anti-tumor immune responses [38]. Furthermore, Inflammation induced by BPA has been associated with the recruitment of immune-suppressive cells fostering an immunosuppressive TME that aids tumor evasion from immune surveillance [39, 40]. Our research indicated that the high-risk group with OS generally exhibited poorer prognosis. We speculated that this may be associated with alterations in TME. Besides, in high-risk group, immune cells were predominantly underexpressed, while a majority of immunosuppressive checkpoints were significantly overexpressed. Additionally, tumor purity was also markedly elevated in the high-risk group. These findings suggested that upregulation of BPA-related genes was associated with establishment of an immunosuppressive microenvironment, contributing to unfavorable patient outcomes.

Studies investigating the role of BPA in other types of cancer have also provided valuable insights into its carcinogenic potential beyond its well-documented effects in hormone-related cancers. For instance, researches have linked BPA exposure to the growth and metastasis of prostate cancer, indicating its role in stimulating prostate cancer cell proliferation and invasion through estrogen receptor-mediated signaling pathways [41]. Moreover, emerging evidence has highlighted link between BPA and heightened susceptibility to ovarian cancer, as studies have shown BPA's capacity to enhance ovarian cancer cell development and progression via estrogen receptor-dependent mechanisms [42].

The findings presented in this study enhance our comprehension of BPA’s involvement in cancer biology and its potential ramifications for cancer onset and advancement. Nevertheless, it was crucial to recognize several limitations inherent in our study. Firstly, our study primarily focused on elucidating the mechanisms through which BPA contributes to carcinogenesis and its association with the tumor microenvironment. While our findings provide valuable insights into these aspects, they are based on experimental and observational data, which may not fully capture the complexity of BPA's effects in vivo. Further studies, including in vivo animal models and epidemiological investigations, are warranted to validate our findings and elucidate the long-term consequences of BPA exposure on cancer risk. Additionally, although we explored the link between BPA exposure and immune dysregulation in the tumor microenvironment, our study did not extensively investigate the mechanistic foundations of these interactions. Further investigation into the specific immune pathways modulated by BPA and their impact on tumor progression is warranted to provide a more comprehensive understanding of BPA's role in cancer immunology.

Conclusion

Our research underscores the intricate relationship between BPA exposure and cancer biology. BPA-related prognostic models demonstrate relatively accurate prediction of OS patient outcomes. Furthermore, our research emphasized the significance of these models in shaping the immune landscape of the tumor microenvironment, facilitating an immunosuppressive environment conducive to tumor progression. By considering environmental factors like BPA in cancer risk assessment and intervention strategies, our study lays the groundwork for targeted improvements in cancer prevention and treatment outcomes.

Acknowledgements

Not applicable.

Author contributions

ZK designed this work. LSY and KY wrote the manuscript. LSY and LJ were responsible for collecting data. LYC and ZTX performed the bioinformatics analysis. YLT and ZH performed data analysis and error correction. All authors have read and approved the manuscript.

Funding

This study was supported by grants from the Zhejiang Provincial Health Bureau Science Foundation of China,No. 2022ky034 and Zhejiang Provincial Natural Science Foundation LTGY24H060009.

Data availability

All data supporting the conclusions of the study are provided in the manuscript. Sequence data of OS patient that support the findings of this study have been deposited in TARGET (https://ocg.cancer.gov/programs/target) and GEO (https://www.ncbi.nlm.nih.gov/geo/) datasets.

Declarations

Competing interests

The authors declare no competing interests.

Publisher's Note

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

Liao Shiyao and Kang Yao contributed equally to this work.
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