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. 2025 Aug 29;25(1):179. doi: 10.1007/s10142-025-01676-0

MZB1-Driven Endoplasmic reticulum stress model as a predictor of breast cancer progression and survival

Purong Zhang 1, Rui Wang 2, Yuying Wang 1, Ning Zhang 3,✉, Ke Luo 4,✉
PMCID: PMC12397153  PMID: 40879787

Abstract

Endoplasmic reticulum (ER) stress and its associated unfolded protein response (UPR) have been demonstrated to play a crucial role in cancer’s progression, but their prognostic significance in breast cancer (BC) remains unclear. In this study, a reliable ER-related gene signature was developed for the purpose of predicting BC prognosis and investigating the associated immune landscape. By utilizing public datasets and analytical methods, we developed a 16 ER-related gene risk signature and verified its efficacy in predicting prognosis in independent patient groups. Patients in the high-risk group exhibited significantly poorer survival rates. Single-cell analysis revealed that the low-risk group exhibited stronger immune interactions. Conversely, the high-risk group exhibiting elevated immune checkpoints may signify an immunosuppressive microenvironment or heightened sensitivity to immune checkpoint inhibitor therapy. In vitro and vivo experiments confirmed that knocking down the expression of Marginal Zone B And B1 Cell Specific Protein (MZB1) significantly inhibited the proliferation, invasion, and tumorigenesis of breast cancer. The 16 ER-related gene signature is capable of effectively categorizing breast cancer patients into different risk levels, thereby providing a basis for personalized therapy. MZB1 has been identified as a significant regulatory factor, suggesting its potential as a target for the treatment of breast cancer.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10142-025-01676-0.

Keywords: Breast cancer, Endoplasmic reticulum stress, Machine learning, Tumor microenvironment, MZB1

Introduction

Breast cancer (BC) remains the most common cancer in women globally, with 2.3 million new cases and 685,000 deaths annually as of 2020. Metastasis drives its high mortality, accounting for over 90% of BC-related deaths. While early detection and targeted therapies have improved survival in high-income regions, disparities persist in low-resource settings (Torre et al. 2015; Valastyan and Weinberg 2011). BC’s molecular heterogeneity—exemplified by subtypes like ER+, HER2+, and triple-negative breast cancer—complicates standardized treatment. Identifying high-risk populations, prognostic biomarkers (e.g., BRCA mutations, Ki-67), and novel therapeutic targets is critical for personalized management (Perou et al. 2000; Waks and Winer 2019). Advances in high-throughput sequencing, next-generation sequencing (NGS), and multi-omics have refined molecular subtyping, enabling tools like Oncotype DX and MammaPrint to guide prognosis and therapy. While molecular profiling increasingly complements histopathology (e.g., tumor grade/stage), integration of both approaches maximizes diagnostic and predictive accuracy (Peppercorn et al. 2008).

The endoplasmic reticulum (ER) is a critical cellular organelle responsible for calcium homeostasis, lipid metabolism, protein synthesis, post-translational modification, and intracellular transport (Tajiri et al. 2004). In cancers such as BC, tumor cells exposed to unfavorable conditions, including nutrient deprivation, hypoxia, elevated metabolic demands, and oxidative stress. These conditions can disrupt ER function, thereby triggering ER stress (Chen and Cubillos-Ruiz 2021; Cubillos-Ruiz et al. 2017; Xu et al. 2022). This stress response subsequently triggers the unfolded protein response (UPR), a complex series of events that disrupts protein homeostasis (Patra et al. 2023). Sustained or severe ER stress, in conjunction with subsequent activation of the UPR, enables breast cancer cells to adapt to adverse conditions, thereby promoting their survival and progression (.Xu, Liu, Liang, Fei, Zhang, Wu and Tang 2022). Therefore, the ER stress pathway plays a significant role in the oncogenesis and progression of breast cancer. Recently, Fan et al. developed a prognostic model based on ER stress-related genes (272 genes) that demonstrated strong predictive performance for BC patients, suggesting that the development of prognostic models for breast cancer based on ER-related genes is a promising area of research (Fan et al. 2023).

MZB1 (Marginal Zone B and B1 Cell Specific Protein) is a protein found in the ER that plays a pivotal role in regulating the immune system (Suzuki et al. 2019). Dysregulation of this protein has been associated with various diseases, including cancer (Rosenbaum et al. 2014; Tang et al. 2023; Zhang et al. 2021). In cancer research, MZB1 has demonstrated oncogenic properties, influencing tumor cell proliferation, invasion, migration, and apoptosis. For instance, epigenetic suppression of MZB1 is associated with the malignant phenotype of gastric cancer, while its knockdown enhances cell proliferation and metastasis (Kanda et al. 2016). MZB1 has also been identified as a hub gene affecting rectal adenocarcinoma cell behavior and a potential prognostic marker for poor outcomes in chronic lymphocytic leukemia, follicular lymphoma, and diffuse large B-cell lymphoma(.Herold et al. 2013). Notably, MZB1 expression has been linked to poor prognosis in estrogen receptor-positive BC (Watanabe et al. 2020). However, its precise role and prognostic significance in BC remain unclear.

In this study, we aimed to construct a novel risk model incorporating ER-related genes (a total of 664 genes) to predict survival in BC patients. We investigated the association between the risk score and cell proportions, as well as cancer cell communication, using single-cell RNA sequencing (scRNA-seq) data. Additionally, we conducted in vitro experiments to evaluate the effects of MZB1 expression changes on BC cell behavior and in vivo studies to assess its impact on tumor size. Our findings may provide insights into the role of MZB1 as a potential prognostic biomarker and therapeutic target in breast cancer.

Materials and methods

Collection of Endoplasmic reticulum (ER)-related genes

ER-related genes were initially collected from the GeneCards database (https://www.genecards.org/) using a score threshold of > 1 as the selection criterion, resulting in 664 ER-related genes. The identification of differentially expressed genes (DEGs) in the TCGA-BRCA cohort was accomplished through the implementation of Cox regression analysis, with the statistical significance of these genes being assessed through false discovery rate (FDR) correction. A total of 27 differentially expressed genes with differential significance were identified. To further refine these genes, a Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis was performed, introducing a penalty factor (λ) to avoid overfitting. The model achieved optimal accuracy with 16 genes, which were selected for subsequent analysis. All analyses were FDR-corrected according to the Benjamini-Hochberg method.

Data acquisition and processing

RNA sequencing data for 1,207 breast cancer (BC) samples and 114 normal control samples were obtained from The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/). Corresponding clinical data, including age, gender, clinical stage, TNM stage, survival time, and survival status, were also retrieved.

To validate the prognostic model, two independent cohorts (GSE20685 and GSE21653) were obtained from the Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo). Additionally, single-cell RNA sequencing (scRNA-seq) data (GSE195861) were collected from GEO to assess the expression of model genes across different cell subtypes.

The GEO datasets were pre-processed using the Robust Multi-Array Average (RMA) method for background correction, normalization, and summarization. Batch effects were corrected using the ComBat function of the sva package, and gene expression values were log2-transformed for analysis. All analyses were FDR-corrected according to the Benjamini-Hochberg method.

Construction and validation of the risk score model

A risk score model was developed using the ER-related DEGs. Univariate Cox regression analysis was initially performed to identify genes significantly associated with prognosis (P < 0.05). These genes were then subjected to LASSO regression (glmnet R package) with 10-fold cross-validation to determine the optimal penalty coefficient (λ) for dimensionality reduction. Genes with non-zero coefficients in the LASSO model were selected, and multivariate Cox regression analysis was used to identify independent prognostic genes. Risk scores for each patient were calculated as: Risk Score=∑ (Expression of Gene × Coefficient).

The TCGA cohort was used as the training set, while GSE20685 and GSE21653 served as external validation sets. Kaplan-Meier survival curves were generated using the survfit function, and differences between high- and low-risk groups were evaluated using the log-rank test. Prognostic performance was assessed using time-dependent receiver operating characteristic (ROC) curves generated with the time ROC package, with the area under the curve (AUC) calculated at multiple time points. All analyses were FDR-corrected according to the Benjamini-Hochberg method.

Development and evaluation of a prognostic nomogram

Univariate and multivariate Cox regression analyses were performed to identify independent prognostic factors, including risk scores and clinical variables. A nomogram was constructed to predict 1-, 3-, and 5-year overall survival probabilities for BC patients. Calibration curves and time-dependent ROC curves were used to evaluate the accuracy and consistency of the nomogram. Decision curve analysis (DCA) was further applied to assess the net clinical benefit of the combined nomogram model. All analyses were FDR-corrected according to the Benjamini-Hochberg method.

Functional enrichment analysis

Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed on the model-related genes using the ClusterProfiler package in R. The analyses identified the biological processes, molecular functions, and pathways enriched in the selected genes, providing insights into their potential roles in BC progression. All analyses were FDR-corrected according to the Benjamini-Hochberg method.

Validation of model genes using scRNA-seq analysis

The GSE195861 scRNA-seq dataset was used to explore the expression of model genes across different cell subtypes in BC. The Seurat R package was employed for data processing, including: converting matrices to Seurat objects with stringent quality control; normalizing the data and performing principal component analysis (PCA) on highly variable genes; annotating cell subtypes using CellMarker 2.0. This analysis enabled visualization of the expression distribution and interrelationships of model genes within distinct cell populations. All analyses were FDR-corrected according to the Benjamini-Hochberg method.

Cell culture, transfection, RNA extraction, and quantitative Real-Time PCR (qRT-PCR)

Human breast cancer cell lines (MDA-MB-231, MDA-MB-468) were obtained from the American Type Culture Collection (ATCC). Cells were cultured in Dulbecco’s Modified Eagle’s Medium (DMEM, Hyclone, USA) containing 10% fetal bovine serum (FBS, Gibco, USA) and 1% penicillin-streptomycin at 37 °C with 5% CO₂. MZB1 knockdown was achieved using MZB1-targeted shRNA (Genomeditech, Shanghai, China) transfected with Lipofectamine 2000 (Invitrogen, Carlsbad, CA, USA) according to the manufacturer’s instructions. Total RNA was extracted using TRIzol reagent (Invitrogen) and quantified with NanoDrop 2000 (Thermo Fisher Scientific). Reverse transcription was performed with the PrimeScript™ RT kit (TaKaRa Bio Inc.), and qRT-PCR was conducted using an ABI7500 PCR system (Thermo Fisher Scientific), with GAPDH as the internal reference. Primer sequences are shown: MZB1 forward, 5’-CTCACAGGCCCAGGACTTAG-3’, reverse, 5’-TGTGGCTGACACCTTCTCTG-3’; GAPDH forward, 5’-GGAGCGAGATCCCTCCAAAAT-3’, reverse, 5’-GGCTGTTGTCATACTTCTCATGG-3’.

Cell proliferation assay

Cell proliferation was measured using the Cell Counting Kit-8 (CCK-8, Beyotime, China). Cells in 96-well plates were assessed daily for three consecutive days, and absorbance at 450 nm was measured using a microplate reader.

Colony formation assay

Approximately 500 cells were seeded per well in 6-well plates and incubated for two weeks. Colonies were fixed with 4% paraformaldehyde and stained with crystal violet. Colony numbers were analyzed using ImageJ software.

Apoptosis assay

BC cells were inoculated in 6-well plates. Twenty-four hours after transfection, cells were collected and stained with Annexin V-FITC and propidium iodide (PI) solution (BD Biosciences, USA). Flow cytometry data were subsequently processed using BD FACSDiva software V6.1.3 (BD Biosciences).

Wound healing assay

BC cells in exponential growth phase were digested with trypsin and inoculated in six-well plates at a density of 5 × 105 cells per well. After approximately 12 h, once the cells had adhered, a linear scratch was formed in the center of each well using a sterile 1 ml pipette tip. The dislodged cells were gently rinsed away using phosphate buffered saline. The wells were then replenished with fresh medium supplemented with 2% FBS and incubated at 37 °C. The cells were then removed from the wells using an inverted microscope. Cell migration into the scratches at 0 and 24 h was observed using phase contrast on an inverted microscope.

Transwell assay

The matrix gel (Beyotime, Guangzhou) was diluted with FBS-free medium at a ratio of 1:5, and 50 µl was aspirated into the upper chamber and allowed to stand for 3–4 h. 100 µl of cell suspension was added to the upper chamber of the transwell, and 500 µl of serum-containing medium was added to the lower chamber. The excess cells were aspirated with a sterile cotton swab, fixed with 4% formaldehyde, stained with 0.1% crystal violet, and the number of cell migration was recorded.

Invivo experiment

Male BALB/c nude mice (6–8 weeks old) were purchased from Beijing Viton Lihua Laboratory Animal Technology Co. Following MZB1 knockdown, 2 × 10⁶ MDA-MB-231 cells were injected subcutaneously into the right flank of the mice. Tumor volume was measured every 4 days, and mice were euthanized 24 days after injection. Tumors were excised, weighed, and analyzed histologically. All animal experiments were performed in accordance with the National Institutes of Health Guide for the Care and Use of Laboratory Animals (NIH Publication No. 85 − 23, revised 1985) and approved by the Ethics Committee of The Affiliated Hospital of Guizhou Medical University (YJ-20240919-001).

Statistical analysis

The in vitro results of this study were based on at least three independent biological replicates. All values are represented as mean ± SEM, and the data were analyzed using GraphPad Prism 8.0. Spearman or Pearson correlation coefficients were calculated as appropriate. The chi-square and Wilcoxon tests were used for group comparisons. A p-value < 0.05 was considered statistically significant. All statistical analyses were performed using R software (version 4.0.3).

Results

Development and validation of an ER-Related gene prognostic model

Initially, a total of 664 genes related to the ER were retrieved from the GeneCards database.27 genes that were differentially expressed and statistically significant in the TCGA-BRCA cohort were identified by Cox regression analysis and FDR correction. Subsequently, a total of 16 genes that exhibited substantial disparities were subjected to LASSO regression analysis for the purpose of further investigation (Fig. 1A-B). Risk scores were calculated based on the expression levels of these 16 genes, and patients in the TCGA-BRCA cohort were categorized into high- and low-risk groups based on the median score. Principal Component Analysis (PCA) demonstrated distinct distribution patterns between high- and low-risk patients (Fig. 1C). Kaplan-Meier (KM) survival analysis showed that high-risk patients had significantly poorer survival outcomes (P < 0.05, Fig. 1D).

Fig. 1.

Fig. 1

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Screening of ER-related genes (ERGs) and construction of prognostic models. (A) Forest plot of one-way Cox regression analysis of ER receptor-related genes (ERGs). Shows the risk ratios (HRs) and their 95% confidence intervals (95% CIs) for each gene. Green color indicates protective factors (HR < 1) and blue color indicates risk factors (HR > 1). P < 0.05 indicates a significant association between these genes and patient prognosis. (B) Partial likelihood deviance curve from LASSO regression analysis. The variation of the curve reflects how gene selection at different λ values affects the model bias, and the determined optimal λ helps to screen key genes for constructing prognostic models. (C) Principal component analysis (PCA) scatter plot. Based on the expression profiles of screened ERGs, samples were grouped by risk (high risk: red; low risk: blue) and plotted on the PC1 - PC2 dimensions. (D-F) Kaplan–Meier curves for survival analysis. Using TCGA (D), GSE20685 (E), and GSE21653 (F) datasets, these curves illustrate overall survival differences between high-risk and low-risk groups, with statistically significant (P < 0.05). (G-I) Time-dependent receiver operating characteristic (ROC) curves. For TCGA (G), GSE20685 (H), and GSE21653 (I) datasets, these curves show the relationship between sensitivity and 1-specificity for 1-year, 3-year, and 5-year survival predictions, along with corresponding area under the curve (AUC) values

The model’s predictive accuracy was validated using two independent external cohorts (GSE20685 and GSE21653), where high-risk patients consistently showed worse survival outcomes (P < 0.05; Fig. 1E-F). Time-dependent ROC analysis further confirmed the model’s predictive performance, with area under the curve (AUC) values exceeding 0.7 at 1, 3, and 5 years in the TCGA cohort (Fig. 1G), greater than 0.65 at 3 and 5 years in the GSE20685 cohort (Fig. 1H), and above 0.6 in all periods for the GSE21653 cohort (Fig. 1I). These results indicate the robust prognostic capability of the ER-related gene model.

Risk score correlation with clinicopathological characteristics

We assessed the prognostic value of the risk score alongside clinical parameters using Cox regression analysis. Univariate analysis identified significant associations between overall survival (OS) and factors such as age, pathological stage, T stage, N stage, and risk score (P < 0.05; Fig. 2A). Multivariate analysis further demonstrated that age, pathological stage, and risk score were independent prognostic factors for OS (P < 0.05; Fig. 2B).

Fig. 2.

Fig. 2

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Correlation analysis of risk models with clinicopathologic indicators. (A-B) Forest plot. Hazard ratios and P-values for age, gender, tumor stage (T, N), and risk grade. P < 0.05 indicates a significant relationship between these factors and prognosis, thus identifying the main factors affecting prognosis. (C, J) ROC curves show the AUC of risk score (Risk), age, gender and tumor stage (T, N).The higher the AUC, the better the prediction of prognostic indicators. (D-F) Box plots show the relationship between risk score and the distribution of age (D), distant metastasis (E) and tumor stage (F), respectively. Differences between groups reflect the correlation between risk scores and clinical characteristics. (G) Nominal histograms. Includes factors such as age, gender, and tumor stage. Expected probabilities were calculated from the scores. (H, I) Calibration curve testing the accuracy of the nominal histogram in predicting prognosis. A closer fit to the diagonal line indicates a more reliable prediction

Risk scores exhibited superior prognostic accuracy compared to other clinical characteristics (Fig. 2C). Stratification analysis revealed that older age, advanced pathological stage, and metastasis were associated with higher risk scores (Fig. 2D-F).

To enhance clinical application, we developed a nomogram integrating the risk score and significant clinical variables for predicting 1-, 3-, and 5-year survival probabilities (Fig. 2G). Calibration curves confirmed the model’s prediction accuracy (Fig. 2H), and decision curve analysis (DCA) demonstrated that the nomogram, which incorporated the risk score, provided greater net clinical benefit (Fig. 2I-J).

Expression patterns, genetic alterations, and functional enrichment of model genes

Expression analysis revealed that nine genes (DERL1, HSP90AA1, TAP1, G6PD, AIFM1, MZB1, VHL, STT3A, EMC2) were significantly upregulated, while four genes (FKBP5, Sect. 63, MAP2K6, JAK2) were significantly downregulated in BC tissues compared to normal tissues (P < 0.05; Fig. 3A).

Fig. 3.

Fig. 3

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Genetic and transcriptional alterations of ERGs in breast cancer. (A) Box-plots of the expression distribution of various ERGs in breast cancer tissues (red) and normal tissues (blue). (B) Frequency of copy number variation (CNV) statistics of ERGs in breast cancer, distinguishing between gain (GAIN, pink) and loss (LOSS, green). (C) Heatmap showing the correlation between ERG methylation and mRNA expression. Different colors indicate the direction and intensity of the correlation between methylation and expression. (D) Scatter-plot showing differences in ERG methylation between breast cancer and normal tissues. (E) Correlation heatmap showing the correlation between ERG expression and clinical features. (F) GO analysis of ERG enrichment using pie charts. Different colored rings correspond to biological process, molecular function and cellular component classification, respectively. (G) Bubble plot showing ERG enrichment for KEGG pathways. The size of the bubbles represents the number of genes and the color represents the P value

Genomic analysis identified that MAP2K6, DERL1, and EMC2 exhibited the highest copy number gains, while STT3A showed the most significant copy number loss (Fig. 3B). Despite their low mutation frequencies (Fig. 3C), methylation analysis demonstrated that VHL, AIFM1, and HSP90AA1 had significantly reduced DNA methylation levels, possibly explaining their upregulated expression (Fig. 3D).

Correlation analysis showed a strong positive association between AIFM1 and G6PD (R = 0.54), while negative correlations were generally weak (Fig. 3E). Functional enrichment analysis indicated that these genes were primarily involved in the regulation of nitric oxide synthase biosynthesis, cellular response to chemical stress, necroptosis, and protein processing in the ER (Fig. 3F-G).

Single-Cell RNA sequencing (scRNA-seq) analysis of risk scores in cellular subtypes

To investigate the cellular distribution of model genes, we conducted scRNA-seq analysis. The t-SNE analysis classified the cells into six subtypes: cancer cells, myeloid cells, CD8+ T cells, CD4+ T cells, B cells, and monocytes (Fig. 4A). Differential expression analysis revealed distinct patterns of model gene expression across cell types (Fig. 4B). Risk scores were calculated for each cell, and cells were categorized into high- and low-score groups using the median value (Fig. 4C). High-score cells were significantly enriched in cancer cells but reduced in monocytes (Fig. 4D).

Fig. 4.

Fig. 4

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Risk score-based profiling of the tumor microenvironment. (A) Single-cell level t-SNE (t-distributed stochastic neighborhood embedding) downscaling clustering plot with different colors representing various cell types in the tumor microenvironment (cancer cells, myeloid cells, CD8+ T cells, CD4+ T cells, B cells, monocytes). (B) Heatmap of specific gene expression in different cell clusters. Color gradients are used to show differences in gene expression. (C) t-SNE plots grouped by risk scores (SCORE_UP and SCORE_DOWN) demonstrating the differences in single-cell distribution between different risk groups, indicating the association between risk scores and cell population distribution. (D) Stacked bar graphs of the proportion of various cell types in different risk subgroups (differentiated by color). Differences in the proportions of various cell types between high- and low-risk groups were quantified. (E-F) Communication plots of different cells in the high-risk and low-risk groups. Nodes represent cell types and lines indicate the association between cells. (G-H) Bubble plots of cell-cell communication analysis for the high-risk and low-risk groups, respectively, demonstrating the significance of the communication pathways between different cell types by dimensions such as color and size

Cell communication analysis indicated that CD4+ T cells exhibited significantly reduced communication in high-risk cells, while B cell interactions, especially with CD8+ T cells, were significantly enhanced (Fig. 4E-F). Notably, In the high-risk group, MIF signaling (CD74+CD44 and CD74+CXCR4) was significantly upregulated in CD4+ T cells’ interactions with other immune cells (Fig. 4G-H).

Pathway enrichment analysis of highly expressed genes in high-risk cells revealed that endopeptidase regulator activity was primarily enriched in tumor cells, B cell activation was enhanced in B cells, and DNA replication preinitiation was active in T cells (Figure S1A-C). Furthermore, high-risk cells exhibited increased expression of immune checkpoint molecules (PD-1, PD-L1, CTLA-4, TNF-α), suggesting a potential for immune evasion within the tumor microenvironment (Figure S1D).

Functional characterization of MZB1 in BC cell lines

ER stress plays a role in promoting tumor growth in breast cancer(Xu et al.,2022). The investigation reveals that increased expression of DERL1, HSP90AA1, FKBP5, MAP2K6, STT3A, and Sect. 63 has been demonstrated to promote ER stress (Cherepanova et al. 2016; Linxweiler et al. 2017; Lu et al. 2024; Pan et al. 2023; Yi et al. 2024; Zhang et al. 2023). Reduced or absent expression of JAK2, VHL, TAP1, and G6PD has been shown to exacerbate ER stress (Kuo et al. 2017; Mele et al. 2019; Paldino and Fierabracci 2023; Yang et al. 2024a, b). The study also examines the role of MZB1 in maintaining ER stress homeostasis (Rosenbaum et al. 2014). Based on previous analyses of the expression profiles of these genes in breast cancer (Fig. 3A), we investigated several key genes, namely DERL1, HSP90AA1, STT3A, JAK2 and MZB1, whose expression trends in breast cancer were consistent with their ER stress-promoting functions. MZB1 has been shown to regulate ER stress and to play an important role in immunomodulation. For this reason, it was selected as the object of this study. Under physiological conditions, MZB1 is a molecule located in the ER that directly facilitates the correct folding and assembly of immunoglobulin chains, thereby playing an essential role in regulating immunoglobulin secretion (Rosenbaum et al. 2014). Therefore, we hypothesize that MZB1 may be a key nodal molecule linking ER stress and immune response in breast cancer. To explore the functional role of MZB1 in BC, we utilized shRNA-mediated knockdown in MDA-MB-231 and MDA-MB-468 cells. Effective knockdown was achieved with MZB1 shRNA-1 and shRNA-2 (Fig. 5A). CCK-8 assays demonstrated a significant reduction in cell viability following MZB1 knockdown (Fig. 5B). Flow cytometry analysis revealed a significant increase in apoptosis rates in MZB1-knockdown cells compared to controls (P < 0.05; Fig. 5C), indicating that MZB1 may suppress apoptosis in BC cells.

Fig. 5.

Fig. 5

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MZB1 knockdown efficiency and apoptosis rate in BC cell lines. (A) measures the relative mRNA expression levels of the MZB1 gene in two BC cell lines, MDA-MB-231 and MDA-MB-468, under conditions of MZB1 knockdown (shMZB1-1, shMZB1-2) and without knockdown (WT, NC). (B) The CCK-8 assay to detect the proliferation ability (reflected by OD450 values) of the two BC cell lines at 1–4 days under different treatments (NC, shMZB1-1, shMZB1-2). (C) flow cytometry to detect the apoptosis of the two BC cell lines under different treatments, and the right-hand bar charts quantify the apoptosis rates. The data were expressed as mean ± SD (n = 3). *Indicates a significant difference at the 0.05 level; *P < 0.05, ** P < 0.01, *** P < 0.001

MZB1 promotes BC cell proliferation, migration, and invasion

We further investigated the effects of MZB1 on BC cell behaviors. Scratch assays showed a significant reduction in wound closure following MZB1 knockdown, indicating impaired migration (Fig. 6A). Colony formation assays demonstrated reduced colony numbers in MZB1-knockdown cells (Fig. 6B), and Transwell assays revealed decreased invasion capabilities (Fig. 6C).

Fig. 6.

Fig. 6

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Migration, clone formation, invasiveness, cell viability and in vivo tumorigenicity of BC cell lines. (A) The wound-healing assay was employed to examine the migration ability of MDA-MB-231 and MDA-MB-468 BC cells. It presents images of wound closure in the NC, shMZB1-1, and shMZB1-2 groups at 0 h and 24 h, with bar graphs quantifying the migration rates (n = 3). (B) The clone formation assay was utilized to evaluate the clonogenic potential of BC cells. It shows images of clones in the NC, shMZB1-1, and shMZB1-2 groups, with bar graphs counting the number of clones (n = 3). (C) The Transwell assay was used to detect the invasive ability of BC cells. It presents stained images of invaded cells in each group, with bar graphs quantifying the invasion rates (n = 3). Scale bar: 50 μm. (D) In vivo tumorigenicity was analyzed. It shows images of tumors in the NC, shMZB1-1, and shMZB1-2 groups in animal models, plots the tumor volume growth curves over time, and compares the final tumor weights (n = 6). The data were expressed as mean ± SD. *Indicates a significant difference at the 0.05 level; *P < 0.05, ** P < 0.01, ***P < 0.001

In vivo, MZB1-knockdown MDA-MB-231 cells were subcutaneously injected into BALB/c nude mice. Tumor growth was significantly inhibited in the MZB1-knockdown group, as indicated by reduced tumor volume and weight (Fig. 6D). These findings suggest that MZB1 promotes BC cell proliferation, migration, and invasion both in vitro and in vivo.

Discussion

A prognostic model was constructed on the basis of 16 ER-related genes. The model demonstrated efficacy in risk stratification of breast cancer, exhibiting high predictive accuracy across multiple breast cancer cohorts. Furthermore, an analysis of changes in the ratio of immune cells to cellular communication across risk stratification was conducted, and the function of the key gene MZB1 was validated. These results offer further insight into the impact of ER-related genes on patient prognosis, immune system interactions, and tumor progression.

In comparison to existing breast cancer prediction models that are based on more than 200 ER-related genes (Yang et al. 2024a, b; Yi et al. 2024), a more comprehensive genetic screen was created (16 key genes out of 664 ER genes). This model had a 5-year area under the curve (AUC) of greater than 0.7 in the TCGA cohort. Furthermore, analysis of scRNA-seq data revealed that, in high-risk patients, CD4+ T cell interactions were impaired, while interactions between B cells and CD8+ T cells were enhanced. This finding suggests a correlation between ER stress and immunosuppression, a mechanism that has not been previously observed in other models. Most importantly, we have experimentally identified and elucidated the malignant pro-carcinogenic function of MZB1. In summary, we have expanded the scope of gene coverage, integrated single-cell technology, and genetic validation to provide a more accurate predictive model of ER stress with translational potential.

The tumor microenvironment (TME) is critical in cancer progression, encompassing immune cells, stromal cells, and extracellular matrix components(.Zheng et al. 2024). Our study revealed that the TME exhibited distinct immune landscapes between high- and low-risk patients. scRNA-seq analysis showed enhanced communication between CD4+ T cells and other immune cells, including CD8+ T cells and monocytes, in low-risk tumors. This active immune crosstalk may contribute to improved anti-tumor immunity, aligning with the favorable prognosis in low-risk patients. Conversely, in high-risk tumors, immune cell interactions are reduced, and there is a significant increase in key immune checkpoint molecules (PD-1, PD-L1, CTLA-4, and TNF-α). The PD-1/PD-L1 and CTLA-4 pathways are important mediators of T cell exhaustion, dysfunction, and immune evasion (Lin et al. 2024). Thus, high-risk patients may be characterized by an immunosuppressive tumor microenvironment. Concurrently, heightened immune checkpoint expression has been shown to predict enhanced tumor sensitivity to immune checkpoint inhibitor (ICI) therapy (He et al. 2021). This suggests that high-risk breast cancer patients identified by ER gene modeling may benefit from ICI therapy. Furthermore, the high-risk group exhibited significantly elevated levels of macrophage migration inhibitory factor (MIF) in the context of CD4+ T cell interactions with other immune cells. MIF plays a pivotal role in the regulation of inflammation, immunity, and tumorigenesis (Sumaiya et al. 2022). However, the relationship between MIF and ER stress remains to be fully elucidated. The findings of this study indicate that high-risk individuals, as classified according to the 16 ER-related gene model, may mediate immunoregulatory processes through MIF. Subsequent studies of this mechanism are anticipated to facilitate the elucidation of the interaction between ER pathways and immune pathways in breast cancer.

MZB1, an ER-resident protein with a critical role in immune protein synthesis and secretion, emerged as a key factor in our model (.Belkaya et al. 2013). Our functional experiments demonstrated that MZB1 knockdown in BC cells significantly reduced their proliferation and invasion while increasing apoptosis. These effects were further validated in vivo, where MZB1 knockdown led to reduced tumor growth. Mechanistically, MZB1 is known to alleviate ER stress by enhancing protein folding capacity, particularly in immune cells such as B cells and plasma cells (Kapoor et al. 2020). This protective role in maintaining ER homeostasis may provide a survival advantage to cancer cells under stressful conditions. Furthermore, as a target of microRNA-185, MZB1 influences T cell development, further linking it to immune regulation within the TME (Li et al. 2022). Our findings highlight MZB1 as a potential therapeutic target, particularly in high-risk patients where its overexpression may contribute to tumor progression and immune evasion. Moreover, our findings on MZB1 suggest its potential as a dual biomarker for prognosis and a therapeutic target. Inhibiting MZB1 may restore ER homeostasis and enhance tumor cell sensitivity to apoptosis, offering a novel therapeutic strategy for high-risk BC patients.

In addition to the MZB1 gene, further investigation is necessary to elucidate the functions of other genes in the16 ER-related gene model. HSP90AA1 and G6PD have been identified as promoting factors of breast cancer progression by regulating ER function (Mele et al. 2019, Zagouri et al. 2010). DERL1, STT3A, EMC2, and Sect. 63 play important roles in regulating protein homeostasis(Hu et al. 2023; Pleiner et al. 2021; Shi et al. 2022; Zhang et al. 2023). Up-regulation of DERL1/STT3A has been demonstrated to increase ER-associated degradation (ERAD), a process that facilitates the removal of misfolded proteins (Shchedrina et al. 2011). In contrast, down-regulation of Sect. 6 has been shown to impede protein transport (Mades et al. 2012), while the deletion of EMC2 has been observed to disrupt membrane protein homeostasis (Pleiner et al. 2021). In the context of breast cancer, a functional imbalance among these four genes has been shown to exert a synergistic effect, resulting in impaired ER function. Furthermore, AIFM1 has been demonstrated to trigger programmed cell death in breast cancer cells (Shan et al. 2022), and the over-expression of the VHL gene has been shown to impede breast cancer growth and metastasis (Wang et al. 2024). However, the precise mechanisms through which these genes contribute to breast cancer progression by regulating ER function remain to be elucidated. Aberrant expression of the immunomodulatory genes TAP1, FKBP5, and JAK2 may be associated with an underlying immunosuppressive microenvironment in high-risk patients (Chen et al. 2022; Ling et al. 2017; Xia et al. 2023). It is noteworthy that while abnormal copy number and reduced expression of MAP2K6 were identified, the direct correlation with ER stress remains to be elucidated. In summary, the genes previously mentioned can be used as markers of ER stress and immune networks, which play effective roles in breast cancer progression. However, the precise mechanisms underlying these effects require further elucidation through experimental investigation.

Despite the model’s demonstrated efficacy in predicting outcomes, it is important to note its inherent limitations. Given the heterogeneity of breast cancer and the known differences in ER stress responses between subtypes (Chang et al. 2020; Yi et al. 2024), there is a need to validate the prognostic ability of the 16 ER-related gene model in patients with breast cancer subtypes. This validation will assess the general applicability of the model. Secondly, although the fundamental function of the MZB1 gene has been demonstrated, the specific roles and mechanisms of MZB1 in ER stress and immunomodulation require further exploration. Furthermore, the synergistic mechanisms by which model genes (such as MZB1, DERL1, HSP90AA1) regulate ER stress pathways, especially the “folding protein response” or immunomodulatory activation, remain to be elucidated. Additionally, although single-cell RNA sequencing analysis revealed altered immune system interactions in high-risk patients, the role of ER stress in modifying the tumor microenvironment has yet to be demonstrated. Consequently, subsequent studies should prioritize the validation of subtype-stratified cohorts to thoroughly investigate the regulatory mechanisms of gene networks. These efforts should be complemented by experiments, such as in vitro and in vivo immunoassays, to augment the clinical translational potential of this model.

Conclusions

In this study, a prognostic signature was constructed and validated based on 16 ER-related genes, which effectively stratifies breast cancer risk. Patients with high-risk status exhibit reduced survival rates and an immunosuppressive microenvironment, potentially rendering them more susceptible to immunotherapy. In contrast, low-risk patients demonstrate enhanced immune activity. Key functional experiments confirm MZB1 as a central driver of breast cancer progression. The model provides a foundation for personalized treatment decisions, including the identification of potential beneficiaries of immunotherapy. In the future, further exploration of the model and the mechanism of MZB1 is warranted, along with a comprehensive evaluation of its therapeutic targeting potential to facilitate clinical translation.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary Material 1 (153.7KB, docx)

Author contributions

PZ and KL wrote the main manuscript text. PZ, RW, YW and NZ prepared Figs. 1, 2, 3, 4, 5 and 6. All authors reviewed the manuscript.

Funding

This study was supported by Sichuan Science and Technology Program (2023YFS0103) and Beijing Medical Award Foundation (YXJL-2021-0092-0333).

Data availability

The dataset used and/or analyzed in this study is available from the corresponding author on reasonable request.

Declarations

Ethics approval and consent participate

The experimental protocol was approved by the Ethics Committee of The Affiliated Hospital of Guizhou Medical University (YJ-20240919-001). No patient was involved in this study.

Animal ethics

All animal experiments were performed in accordance with the National Institutes of Health Guide for the Care and Use of Laboratory Animals (NIH Publication No. 85 − 23, revised 1985) and approved by the Ethics Committee of The Affiliated Hospital of Guizhou Medical University and Shanghai Yaojian Biotechnology Co., LTD. (YJ-20240919-001).

Conflict of interest

The authors declare no competing interests.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note

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

Contributor Information

Ning Zhang, Email: zhangning_uh@hust.edu.cn.

Ke Luo, Email: 18984385211@163.com.

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Supplementary Materials

Supplementary Material 1 (153.7KB, docx)

Data Availability Statement

The dataset used and/or analyzed in this study is available from the corresponding author on reasonable request.

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