Single-cell transcriptomics uncover RNF130-mediated TNF-α pathway activation and worenine synergy with paclitaxel in breast cancer

Abstract

Background

Triple-negative breast cancer (TNBC) is distinguished by high invasiveness and a tendency for recurrence. Recent studies have suggested that E3 ubiquitin ligases play a crucial role in the initiation and progression of various tumors. However, there is still an absence of systematic understanding regarding the specific function and molecular mechanisms of its member gene RNF130 in TNBC.

Methods

This study conducted a comprehensive analysis of large-scale transcriptomic data from databases such as TCGA and GEO. Additionally, single-cell RNA sequencing data from multiple breast cancer samples and their liver metastases were analyzed to evaluate the expression pattern, prognostic significance, and potential regulatory role of RNF130 in the tumor microenvironment. The effects of RNF130 on breast cancer cell proliferation, apoptosis, and chemotherapy sensitivity were explored through in vitro cell experiments and in vivo mouse models. Furthermore, the study screened and evaluated the targeted inhibitory effect of the Traditional Chinese Medicine active component Worenine on RNF130, as well as its combined therapeutic effect with paclitaxel.

Results

The findings indicated that RNF130 was notably overexpressed in breast cancer tissues and associated with unfavorable patient survival outcomes. Single-cell transcriptomic analysis revealed that RNF130 was predominantly enriched in malignant epithelial cell populations and closely associated with tumor immune evasion phenotypes. RNF130 knockdown inhibited proliferation, induced apoptosis, reduced TNF-α pathway activation, and enhanced sensitivity to paclitaxel, whereas RNF130 overexpression exerted the opposite effects. Co-culture experiments further demonstrated that RNF130 depletion promoted M1 macrophage polarization while control cells induced M2-like phenotypes. Additionally, Worenine downregulated RNF130 expression and displayed a synergistic inhibitory effect with paclitaxel.

Conclusion

This study identifies RNF130 as a critical mediator of TNBC progression that regulates tumor growth, apoptosis, immune evasion, and metabolic reprogramming, partly through activation of the TNF-α signaling pathway. Furthermore, Worenine was found to reduce RNF130 expression and enhance the antitumor effect of paclitaxel, suggesting its potential utility in combination therapy for TNBC. These findings provide mechanistic insights into RNF130-driven malignancy and offer a foundation for developing future therapeutic strategies.

Graphical abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s13148-026-02057-5.

Introduction

Breast cancer (BC) ranks as one of the leading types of cancer affecting women worldwide, characterized by high heterogeneity and complex molecular pathological features [1–3]. With the rapid advancement of genomics and transcriptomics technologies, the understanding of molecular subtypes and pathogenic mechanisms of breast cancer has deepened [4, 5]. However, the high heterogeneity of breast cancer and its multiple resistance mechanisms continue to pose significant challenges in clinical treatment, highlighting the urgent need to identify new diagnostic biomarkers and therapeutic targets [6, 7].

Among the different breast cancer subtypes, triple-negative breast cancer (TNBC) is recognized as the most aggressive and has the poorest prognosis [8]. TNBC lacks human epidermal growth factor receptor 2 (HER2), progesterone receptors (PR), and estrogen receptors (ER), and thus does not have specific targeted therapies [9]. The tumor typically grows rapidly and is prone to metastasis [10]. Especially when TNBC metastasizes to the liver, patient prognosis significantly worsens, often resulting in a progression-free and overall survival [11]. Despite some progress in research on TNBC, many challenges remain due to its complex molecular features and metastatic mechanisms [12, 13]. Recent advances in precision oncology suggest that genomic alterations and pharmacogenomic features drive treatment response and resistance, supporting the need for individualized therapeutic strategies [14]. In addition, mechanistic studies such as the miR-584-5p/MSMO1 regulatory axis have demonstrated that metabolic reprogramming can activate PI3K–AKT signaling to promote breast cancer progression [15], underscoring the importance of integrating metabolic and signaling pathway crosstalk into TNBC biology. Therefore, a comprehensive analysis of the molecular mechanisms underlying TNBC, particularly its metastatic characteristics, is crucial for developing new therapeutic strategies and improving patient prognosis [16, 17].

Tumor development and progression are not only influenced by genetic or epigenetic alterations in tumor cells but are also closely related to interactions within the tumor microenvironment (TME), involving various cell subpopulations [18, 19]. Single-cell RNA sequencing (scRNA-seq) technology, due to its high resolution, can precisely analyze the gene expression and interactions between cells providing more detailed tumor heterogeneity information compared to traditional bulk sequencing [20, 21]. This technology offers new insights into the study of tumor evolution, resistance mechanisms, and cellular dynamics [22].

At the molecular level, E3 ubiquitination is an important mechanism that regulates protein homeostasis, cell signaling, and the cell cycle [23–26]. Studies have shown that E3 ubiquitin ligases are aberrantly expressed in various tumor types, where they are linked to tumor cell proliferation, metastasis, and the regulation of the immune microenvironment [27–29]. RNF130 (Ring Finger Protein 130), as a potential E3 ubiquitin ligase, has garnered increasing attention in certain cancers in recent years, yet its function and underlying mechanisms in breast cancer remain insufficiently explored [30].

Based on the above background, this study aims to explore the expression characteristics and potential biological functions of RNF130 in breast cancer. By analyzing bulk RNA-seq data and scRNA-seq data of BC from public databases such as TCGA and GEO, we comprehensively elucidated the differential expression of RNF130 in breast cancer, its prognostic value for patients, and its role in tumor microenvironment remodeling and immune evasion. Additionally, we validated the function of RNF130 in progress of breast cancer through experiments. To further explore intervention strategies targeting RNF130, we also screened and identified the targeted inhibitory effect of the Traditional Chinese Medicine active molecule Worenine on RNF130, and evaluated its synergistic effect with paclitaxel, proposing a novel approach for combination therapy in BC.

This study systematically revealed the key functions of RNF130 in breast cancer and its profound regulatory role in the tumor microenvironment through large-scale data mining and single-cell analysis. Our research not only provides new theoretical support for the role of E3 ubiquitination in tumorigenesis but also lays the foundation for developing precise therapeutic strategies targeting RNF130. As our understanding of the tumor microenvironment continues to deepen, future research on RNF130 and its associated signaling pathways may provide new directions for innovative treatment strategies in breast cancer.

Methods

Bioinformatics data processing

We selected three publicly available scRNA-seq datasets of BC from GSE176078 [31], GSE161529 [32], GSE249361 [33], detailed information on the selected samples is provided in Supplementary Table S1. To enhance the scope of the analysis, we integrated RNA-seq survival data from TCGA-BC [34]. Additionally, we selected the GSE20685 dataset [35], which contains abundant survival information, as the validation set. During data processing, the Seurat R package (version 4.3.0) was used for single-cell data analysis. Expression data for specific marker genes, such as erythrocyte genes and mitochondrial genes, were filtered. All data were normalized and dimension reduction was performed using PCA. To eliminate batch effects, we applied the Harmony package for data correction, the parameters used are detailed in Table S2. Next, the FindVariableFeatures function was used to select highly variable genes. Finally, based on the batch-corrected data, further dimension reduction and visualization were performed using UMAP and tSNE methods. The identification of marker genes was based on marker gene sets referenced in single-cell related literature. Additionally, the pseudo-temporal developmental trajectory of cells was constructed and visualized using the Monocle2 and Monocle3 tools.

Intercellular communication analysis

We used the CellChat package (version 1.6.1) to analyze the signaling interactions between different cell populations revealed by scRNA-seq data. First, a CellChat object was created using RNA expression data and cell annotation information, which was performed by the “createCellChat” function. Then, the “Secreted Signaling,” “ECM-Receptor,” and “cell–cell Contact” interaction databases were selected to construct and analyze the signaling network. The calculation of intercellular communication probabilities was achieved using the “computeCommunProb” function. In identifying global communication patterns, the “selectK” function was used to set the input and output patterns of information flow, with the nPatterns parameter set to 5 and the optimal number of communication patterns determined based on model stability and biological interpretability.

Survival analysis

A total of 2407 ubiquitin-associated genes were compiled, sourced from the integrated annotations of the Ubiquitin and Ubiquitin-like Conjugation Database (IUUCD) (http://iuucd.biocuckoo.org/). Additionally, several candidate genes were selected from the intersection of differentially expressed genes (DEGs) and E3 ubiquitin ligase genes. By analyzing the TCGA-BC and GSE20685 datasets. For each gene, the optimal cut-off value of expression was determined using the surv_cutpoint() function, which identifies the maximally selected rank statistics based on overall survival time (futime) and survival status (fustat). Samples were then categorized into high-expression and low-expression groups using surv_categorize(), where expression levels greater than the calculated cut-off were assigned to the high-expression group, and those below or equal to the cut-off to the low-expression group. Kaplan-Meier curves assessed survival, and the Log-Rank test compared them.

Enrichment analysis

In the enrichment analysis, the selection of DEGs was performed using the FindMarkers function (logFC > 0.25 and Padj.P < 0.05). GO and KEGG were analyzed with clusterProfiler, and GSEA and GSVA with their respective packages.

Immune cell subtype distribution evaluation

CibersortX and signature gene expression were used to assess immune cell subtype distribution in GSE20685. Spearman’s correlation analysis was used to uncover the relationships between key genes and different immune cell clusters.

Exploration of chemotherapy drug sensitivity

In this study, we analyzed the impact of key gene expression levels on chemotherapy resistance using the oncoPredict package. Spearman correlation analysis was conducted to explore the relationship between chemotherapy drug IC50 values and risk scores, aiming to identify drugs significantly associated with the risk score. For drugs with an absolute correlation value greater than 0.2, we further compared the IC50 value differences between the high-risk and low-risk groups.

Cell lines and cell culture

The human breast cancer cell lines MDA-MB-231 and MDA-MB-468, the human monocytic cell line THP-1, and the human embryonic kidney cell line HEK293T used in this study were purchased from Meisen Chinese Tissue Culture Collections (Meisen CTCC). MDA-MB-231, MDA-MB-468, and HEK293T cells were cultured in DMEM supplemented with 10% fetal bovine serum (FBS; Gibco) and 1% penicillin–streptomycin (Gibco). THP-1 cells were cultured in RPMI-1640 medium supplemented with 10% FBS and 1% penicillin–streptomycin. All cells were maintained in a humidified incubator at 37 °C with 5% CO₂.

Reverse transcription-quantitative polymerase chain reaction (RT-qPCR)

RNA was extracted with TRIzol and reverse transcribed using the Bio-Rad iScript cDNA Synthesis Kit. The reaction mixture was prepared following the SYBR-Green reagent manual and detected using the Bio-Rad CFX96 Real-Time PCR Detection System. Gene expression levels were normalized to GAPDH as the internal control, and relative expression was calculated using the 2^−ΔΔCt method. The sequences of all RT-qPCR primers employed in this study are listed in Table S3.

Western blotting (WB)

Cells were lysed using RIPA buffer. After separation by 10% SDS-PAGE, proteins were transferred to a membrane, blocked, and incubated with the primary antibody, followed by reaction with secondary antibody. The signal was detected using the UVP ChemStudio system. Antibody information is provided in Table S4.

SiRNA and ShRNA

In this study, three siRNA sequences and one shRNA sequence targeting the RNF130 gene were designed, and the shRNA was cloned into the pLKO.1 vector. The plasmids were co-transfected with psPAX2 and pMD2.G into HEK293T cells to produce lentivirus. After 48 h, the virus was collected, and breast cancer cells were infected and cultured for 24 h before collection and analysis. In the siRNA experiments, cells were transfected with Lipofectamine 3000 for 6 h, followed by continued culture for 24 h post-transfection. (sh-RNF130 and si-RNF130 sequences are provided in Table S5).

Establishment of stable RNF130-overexpressing cell lines

To establish RNF130-overexpressing cell lines, full-length human RNF130 cDNA was cloned into the pLVX-puro lentiviral expression vector under the control of the CMV promoter. The correctness of the construct was confirmed by restriction enzyme digestion and Sanger sequencing. The empty pLVX-puro vector was used as a negative control. pLVX-RNF130 (or empty vector), along with the packaging plasmids psPAX2 and pMD2.G (Addgene), were co-transfected into HEK293T cells at a ratio of 4:3:1 using Lipofectamine 3000 (Invitrogen). Viral supernatants were collected at 48 and 72 h post-transfection, centrifuged at 3000 rpm to remove debris, and filtered through 0.45 μm PVDF membranes to obtain lentiviral particles. MDA-MB-231 and MDA-MB-468 cells were seeded into 6-well plates and infected with the viral supernatant at approximately 40–50% confluency in the presence of 8 µg/mL polybrene (Sigma). After 24 h, the medium was replaced with fresh complete culture medium. Forty-eight hours post-infection, cells were subjected to 2 µg/mL puromycin selection for 5–7 days to generate stable RNF130-overexpressing (oe-RNF130) and empty vector control (Vector) cell lines. The overexpression efficiency was confirmed by RT-qPCR and Western blot analysis.

CCK-8

Cell viability was assessed using the CCK-8 assay kit (Beyotime). Cells were seeded in a plate and allowed to adhere before undergoing the corresponding treatment. CCK-8 reagent was added, and incubated. Finally, cell viability was measured at 450 nm.

Colony formation assay

Breast cancer cells were seeded at 500 cells/well, cultured for 14 days, fixed with paraformaldehyde, and stained with crystal violet. After staining, the cells were washed again, air-dried, and photographed for subsequent analysis.

Flow cytometry

Apoptosis Detection: Cells were cultured for an appropriate period before being collected. After digestion with trypsin and centrifugation, the cell concentration was adjusted. The cells were then stained with Annexin V-FITC and PI staining solution (Beyotime) and incubated in the dark. After incubation, apoptotic cells were analyzed using a flow cytometer.

Cell Cycle Detectio: Cells were cultured for an appropriate period before being collected. The samples were washed by centrifugation, and staining solution containing RNase A and PI (Beyotime) was added. The cells were incubated for 30 min at 37 °C in the dark. After staining, the distribution of the cell cycle phases was measured using a flow cytometer.

Animal experiment

In this study, six 4-week-old female Balb/c nude mice were randomly divided into two groups: the sh-RNF130 group and the sh-NC group, with three mice in each group. A total of 3 × 10⁶ MDA-MB-231 cells transfected with either sh-RNF130 or sh-NC were suspended in 200 µL of PBS and subcutaneously injected into the right flank of each mouse to establish the xenograft tumor model [36, 37]. Tumor volume was calculated using: Volume = length × width²/2. At the 4-week mark of the experiment, all mice were euthanized, and the tumors were quickly harvested for subsequent molecular biological analyses.

Differentiation of THP-1 cells into macrophages

THP-1 cells in the logarithmic growth phase were seeded into six-well plates and treated with phorbol 12-myristate 13-acetate (PMA) at a final concentration of 100 ng/mL. The cells were incubated at 37 °C in a 5% CO₂ atmosphere for 48 h. After incubation, the supernatant was carefully aspirated, and the cells were gently washed once with prewarmed PBS to remove residual PMA. The medium was then replaced with fresh RPMI 1640 complete medium without PMA, and the cells were cultured for an additional 24 h to allow for stabilization. Under an inverted microscope, the differentiated THP-1 cells exhibited adherent growth with a flattened or irregular morphology, characteristic of M0-type macrophages. The extent of differentiation was further validated by flow cytometric analysis of macrophage markers CD68, CD86, and CD206.

Transwell co-culture system of THP-1-derived macrophages and cancer cells

After differentiation of THP-1 cells into M0 macrophages using PMA, the cells were seeded into the lower chamber of a 6-well plate at a density of 2.0 × 10⁵ cells/well in 2 mL of RPMI-1640 complete medium containing 10% fetal bovine serum (FBS), and incubated overnight at 37 °C in a 5% CO₂ atmosphere to ensure proper adherence. The following day, Transwell inserts (polyester membrane, 0.4 μm pore size, Corning) were placed into each well. MDA-MB-231 cells stably expressing either si-RNF130 or si-NC were then seeded into the upper chamber at a density of 1.0 × 10⁵ cells/insert in 1.5 mL of medium. To meet the nutritional needs of both cell types, a 1:1 mixture of DMEM and RPMI-1640 complete medium supplemented with 10% FBS and 1% penicillin-streptomycin was used in both upper and lower chambers, with a final volume of 3.5 mL per well. The co-culture system was incubated for 48 h at 37 °C in 5% CO₂ to facilitate non-contact intercellular communication via diffusible factors [38, 39].

After 48 h of co-culture, macrophages from the lower chamber were harvested for subsequent Western blot analysis (CD86, CD206, iNOS; Proteintech) and flow cytometry (CD86 and CD206; Proteintech). Culture supernatants from each group were also collected and centrifuged at 1000×g for 10 min to remove cellular debris. Levels of macrophage polarization-related cytokines were measured using human ELISA kits (TNF-α, IL-6, IL-10, TGF-β1; Biyuntian Biotechnology) according to the manufacturers’ instructions. Absorbance was read at 450 nm using a microplate reader, and absolute concentrations of cytokines were calculated based on standard curves. All experiments were independently repeated five times (n = 5).

Results

scRNA-seq analysis reveals cellular characteristics of the BC microenvironment

This study comprehensively analyzed data from two primary breast cancer single-cell datasets and one breast cancer liver metastasis single-cell database (GSE176078, GSE161529, and GSE249361). First, cells with excessively high expression of mitochondrial and red blood cell genes were removed for data quality control. Next, high-variance gene selection and PCA were performed for initial cell classification (Figure S1A-B). We further evaluated the role of cell cycle genes in cell classification (Figure S1C) and analyzed the correlations between mitochondrial genes, feature gene counts (nFeature), red blood cell genes, and gene counts (nCount) (Figure S1D-F). Detailed information on nFeature, nCount, and other metrics for each sample is presented in Figure S1G-L. After completing quality control and batch effect correction, we integrated single-cell data from 158,524 cells across different samples. To reveal the cell classification structure, we used the Clustree tool for visualization at multiple resolutions (Fig. 1A). By adjusting the resolution, 10 major cell subtypes were identified using marker genes, including B-cells, CAFs, Cancer Epithelial, Endothelial, Hepatocytes, Myeloid, Normal Epithelial, Plasmablasts, PVL, and T-cells (Fig. 1B–D). The proportions of these cell subtypes in each sample are shown in Fig. 1E.

Fig. 1.

scRNA-seq Analysis. A Distribution of cell populations at different clustering resolutions. B–C. tSNE (B) and UMAP (C) visualizations of cell clusters and corresponding cell types. D Expression of marker genes across different cell subgroups. E Proportional distribution of cell clusters within individual samples

Heterogeneity analysis of breast cancer epithelial cells

Epithelial cells play a critical role in the initiation and progression of breast cancer. To explore the epithelial cells from different sources of breast cancer, we used tSNE and UMAP to visualize cells from ANT (adjacent normal tissue), LM (liver metastasis), and Primary (primary tumor tissue) sources (Fig. 2A). Based on this, five cancerous epithelial cell subtypes were identified and defined (Fig. 2B, C). These subtypes exhibited significant differences and characteristics in gene expression patterns and GO pathway enrichment analysis (Fig. 2D). Differential analysis further revealed transcriptomic differences between metastatic and primary BC epithelial cells (Fig. 2E). Additionally, the relative proportions of different cell subtypes in tumor samples were presented (Fig. 2F). These findings indicate that breast cancer epithelial cells exhibit considerable heterogeneity, and different subtypes may have distinct roles in gene expression and the tumor microenvironment.

Fig. 2.

Tumor cell heterogeneity. A Distribution of epithelial subgroups in various tissues. B UMAP and tSNE plots illustrating the spread of BC epithelial cells across subgroups. C Marker gene expression patterns for each BC epithelial subgroup. D Heatmap of gene expression profiles for epithelial subgroups, coupled with GO pathway analysis results. E Volcano plot highlighting differentially expressed genes between metastatic and primary tumors. F Proportional distribution of cell clusters within individual samples

Characteristics of myeloid cells, TILs subpopulations, and endothelial cells in breast cancer

To further investigate the function of myeloid cells, we performed clustering analysis at adjusted resolution (Fig. 3A) and visualized the distribution of myeloid cells using UMAP technology (Fig. 3B-E). Marker gene analysis identified four myeloid cell subpopulations: Cycling Myeloid, DCs, Macrophages, and Monocytes (Fig. 3E and G). GO enrichment analysis results revealed significant differences in the roles of these subpopulations in breast cancer (Fig. 3F). Moreover, gene expression in myeloid cells from metastatic breast cancer exhibited substantial heterogeneity compared to primary breast cancer (Fig. 3H). Finally, we presented the distribution patterns of these cell subpopulations across different breast cancer samples (Fig. 3I).

Fig. 3.

Myeloid cells in the microenvironment of BC. A Cell population distribution evaluated under various clustering resolutions. B–D. UMAP representations showing distribution across distinct samples (B), clusters (C), clinical categories (D), and cell types (E). F Heatmap combined with GO analysis displaying differentially expressed genes in myeloid cells. G. Marker gene expression profiles across various cell subgroups. H. Volcano plot identifying differentially expressed genes in myeloid cells between metastatic and primary tumors. I. Proportional representation of cell clusters across individual samples

By clustering cell populations at different resolutions (Figure S2A), the diversity of endothelial cells in breast cancer was revealed. According to the expression profiles of marker genes, these cells were divided into multiple subpopulations, each presenting unique transcriptional characteristics (Figure S2B). Distribution analysis across different breast cancer samples showed significant differences in the presence of epithelial subpopulations in tumor tissues, indicating that different tumor types have varying effects on endothelial cells (Figure S2C). The distribution of normal epithelial cells across different subpopulations was visually displayed using UMAP and tSNE dimensionality reduction techniques (Figure S2D). Gene expression heatmaps and GO pathway analysis revealed the functional characteristics of epithelial cell subpopulations, pointing to their potential roles in the progression of BC (Figure S2E). Volcano plot analysis of DEGs between metastatic and primary tumors identified several significant key genes, which may be associated with tumor invasiveness and metastasis (Figure S2F). Furthermore, the proportional distribution of each subpopulation in different samples exhibited significant sample-specificity (Figure S2G), further emphasizing the heterogeneity of normal epithelial cells in breast cancer and their complexity in the TME.

By selecting an appropriate resolution (Figure S2A), we used tSNE and UMAP techniques to visualize the distribution characteristics of TILs and successfully identified 7 distinct TIL subpopulations through marker genes (Figure S3B-C and Figure S3E). These cell populations exhibited significant differences in gene expression and biological functions (Figure S3D). Additionally, we presented the distribution patterns of these cell subpopulations across different breast cancer samples (Figure S3F), further revealing the prominent heterogeneity of TILs between samples.

Exploration of cell–cell communication patterns in the TME

Through the analysis of communication frequency and intensity between cell subpopulations (Fig. 4A, B, Figure S4-S5), we revealed the interaction characteristics between different cell populations, illustrating the major communication patterns and key interactions. In the analysis of signal transmission and reception, we identified the main roles of different cell populations as signal sources and targets, and found that cancerous epithelial cells are both the primary signal source and the main signal receiver (Fig. 4C, Figure S6). Furthermore, an in-depth investigation of the primary trends in signal transmission and reception further highlighted the dominant signal transmitters and receivers in cell communication (Fig. 4D). These findings unveil the complex and organized communication network between cells in the breast cancer microenvironment, providing important clues for understanding the dynamic nature of the tumor microenvironment.

Fig. 4.

Intercellular communication analysis. A, B Overview of interaction frequency and intensity between different cell subpopulations, highlighting the strength of their communication. C Diagram showing the primary signal transmitters (sources) and their corresponding recipients (targets) among the cell types. D Analysis of key patterns in signaling roles, emphasizing the predominant transmitters and recipients in intercellular communication.

To further investigate the cell–cell interaction patterns, we employed the CellChat method in conjunction with pattern recognition techniques for comprehensive analysis. Using Silhouette scores, we effectively identified multiple cell communication patterns (Figure S7A and E) and categorized these patterns (Figure S7B and F). A Sankey diagram was used to visually present the communication network and related signaling pathways between cell subpopulations (Figure S7C and G). In addition, we explored modifications in major signaling pathways (Figure S7D and H). The results indicated that malignant tumor cells play a central role in both signal transmission and reception, highlighting their significance in the cell communication network.

Differential analysis of cell-to-cell communication in hepatic metastatic and primary BC

In the study of BC progression, the role of intercellular interactions is crucial. We compared the intercellular communication between primary BC tissues and hepatic metastatic BC tissues, and found that while both have complex communication structures (Fig. 5A), there are considerable differences between the two types of tissues (Fig. 5B, C). Additionally, we identified CD8 + T cells as major information transmitters in both tissues, but compared to primary breast cancer, malignant tumor cells and macrophages play more significant roles in hepatic metastatic breast cancer (Fig. 5D).

Fig. 5.

Cell-to-cell Communication in metastatic versus primary BC. A Network diagrams illustrating cellular interactions within metastatic and primary breast cancer samples. B–C: Heatmap (B) and bar graph (C) depicting the frequency and strength of cell interactions in metastatic compared to primary tumors. D Identification of key cellular signal providers and targets. E Comparison of signaling molecules between metastatic and primary tumors. F Distribution of incoming signals among various cell subtypes. G Distribution of outgoing signals across distinct cell subgroups. H Distribution of overall signals among various cell subtypes

We compared The prevalence and strength of intercellular signaling between metastatic and primary BC tissues. The findings revealed that signaling in metastatic breast cancer tissues was significantly enhanced. Through a systematic analysis of signaling pathways, we discovered that multiple signaling molecules were highly expressed in metastatic breast cancer, including GRN, GDF, NECTIN, HSPG, AGRN, CD22, TWEAK, CD96, TIGIT, CD23, OCLN, CX3C, EGF, LIGHT, CD70, SEMA7, FASLG, PVR, FLT3, CD39, SEMA3, CALCR, CDH1, and EPHB (Fig. 5E). These signaling molecules play a critical role in intercellular communication in metastatic BC, and their abnormal regulation in the TME may drive key phenomena such as tumor metastasis and immune evasion. Importantly, upon thorough review of previous literature, we found that these highly expressed signaling pathways and molecules are closely linked to E3 ubiquitination [40–50]. By comparing the overall signaling pathways, ligands, and receptor interactions, we further discovered important differences in the overall communication patterns, reception, and release of signals in breast cancer compared to normal tissues (Fig. 5F, H).

Functional study of RNF130 in E3 ubiquitination-related genes in breast cancer

Through the analysis of cell–cell communication, we identified that E3 ubiquitination modification has a pivotal role in metastatic BC. Based on this finding, we selected 2,142 genes associated with E3 ubiquitination and compared them with 1,537 genes that were importantly upregulated in metastatic BC compared to primary BC. By integrating survival data from the TCGA.BC and GSE20685 databases, we conducted a comprehensive analysis and identified RNF130 as an E3 ubiquitination-related gene. It is not only highly expressed in metastatic breast cancer tissues but also shows significant prognostic relevance in both the TCGA.BC and GSE20685 datasets (Fig. 6A–C, Figures S7-S9).

Fig. 6.

The role of RNF130 in BC progression. A Venn diagram illustrating the intersection of E3 ubiquitination-related genes, those highly expressed in metastatic versus primary tumors, and genes with prognostic significance in breast cancer. B–C. Validation of RNF130’s prognostic value using the TCGA.BC (B) and GSE20685 (C) datasets. D UMAP plots showing the variability of RNF130 expression in malignant breast cancer cells. E Volcano plot depicting the DEGs of RNF130 between high and low RNF130 expression groups. F–G. GO (F), GSVA (G), and KEGG (H) analyses of differentially expressed genes

Additionally, we isolated malignant cells from breast cancer tissues to further investigate the expression distribution of RNF130 in these cells (Fig. 6D). BC cells were grouped into high- and low-expression based on RNF130 median. Volcano plot visualization revealed significant gene expression differences between the high and low RNF130 expression groups, further validating the critical role of RNF130 in breast cancer cells (Fig. 6E).

We further conducted enrichment analysis on the DEGs. According to the results of the GO enrichment analysis, RNF130 appears to play multiple critical roles in BC. It is involved in regulating apoptosis-related signaling pathways, suggesting that RNF130 may influence tumor cell survival and chemotherapy resistance. At the same time, RNF130 might play a key role in responding to chemical stress and regulating peptidase activity, further reflecting its potential function in the process of breast cancer cells adapting to environmental stress. Moreover, the association of RNF130 with the extracellular matrix and cell adhesion structures implies that it may promote tumor metastasis by regulating cell migration and invasion capabilities. Its core function in E3 ubiquitination modification and its ability to bind with cadherins further suggest that RNF130 could influence the development and progression of breast cancer through regulating signal transduction, protein degradation, and intercellular connections. Therefore, RNF130 is a promising therapeutic target (Fig. 6F).

According to the results of the GSVA analysis, RNF130 may play a role in BC through multiple key signaling pathways. Significantly enriched pathways include Cell Adhesion Molecules and the TNF signaling pathway, suggesting that RNF130 may influence tumor cell invasion and migration by regulating intercellular connections and signal transduction. Additionally, the enrichment of the JAK-STAT signaling pathway indicates that RNF130 may be involved in regulating the inflammatory response and immune evasion in BC. Other significant pathways, such as Ubiquitin-Mediated Proteolysis, further support the important role of RNF130 as an E3 ubiquitin ligase in BC (Fig. 6G).

According to the results of the KEGG enrichment analysis, RNF130 may exert its effects in breast cancer through multiple signaling pathways, especially the TNF signaling pathway, where it may influence the tumor microenvironment and progression by regulating inflammation and immune responses. Additionally, the enrichment of RNF130 in the estrogen signaling pathway and TGF-β signaling pathway suggests its potential involvement in hormone-dependent breast cancer and tumor metastasis. Moreover, RNF130 is also linked to several cancer-related and viral infection-related pathways (Fig. 6H).

Exploration of the role of RNF130 in the TME and drug sensitivity of breast cancer

To investigate the mechanism of RNF130 in the immune microenvironment of breast cancer, we employed CIBERSORTx technology combined with scRNA-seq data to analyze its impact on the TME of BC. Analysis of the GEO database revealed significant heterogeneity in immune cell infiltration in breast cancer (Figure S10A). Further investigation showed that malignant breast cancer epithelial cells are the major component of this microenvironment (Figure S10B). In-depth analysis demonstrated that RNF130 is significantly correlated with most infiltrating immune cells in the TME, suggesting that RNF130 may promote BC progression by regulating the immune microenvironment (Figure S10C). Additionally, it was found that patients with high macrophage expression had a worse prognosis (Figure S10D).

We further analyzed the potential impact of RNF130 on chemotherapy resistance in breast cancer. Using the OncoPredict tool, we conducted an in-depth study of data from the GSE20685 (Figure S11A) and TCGA.BC (Figure S11B-D) databases to understand how RNF130 modulates the response of BC to chemotherapy drugs. Through data transformation and processing, we evaluated the IC50 of various chemotherapy drugs and examined the Spearman correlation between these values and RNF130 expression levels. Molecular docking analysis revealed that commonly used first-line chemotherapy drugs, such as paclitaxel, methotrexate, and doxorubicin, exhibited high binding affinity with RNF130 (Figure S11E). Combining these findings, we conclude that high RNF130 expression enhances breast cancer’s resistance to multiple chemotherapy drugs.

RNF130 May promote macrophage polarization

Given the crucial role of macrophages in the TME of BC, we further explored the potential role of RNF130 in macrophage polarization. First, we isolated macrophages and classified them based on marker genes (Fig. 7E), selecting an appropriate resolution to categorize them into seven major subtypes (Fig. 7A–D). To better understand the spatiotemporal changes in macrophages, we applied the Monocle 2 algorithm to conduct pseudotime analysis of their differentiation process. Through cell ordering and trajectory plotting, we clearly depicted the macrophage population, subtype distribution, pseudotime progression, and the expression pattern of RNF130 in a two-dimensional plot (Fig. 7F–I). These analyses revealed the differentiation paths of cells under different gene expression patterns, showing the dynamic changes of RNF130 during macrophage polarization.

Fig. 7.

RNF130 induces spatiotemporal evolution of macrophage cells. A. Distribution of cell populations evaluated under various clustering resolutions. B–D. tSNE and UMAP representations showing distribution across different samples (B), clusters (C), and cell types (D). E. Marker gene expression profiles across various cell subgroups. F–I. Monocle 2 trajectory analysis depicting: macrophage cell clustering patterns (F), expression levels of RNF130 (G), pseudotime progression (H), and identification of cell types (I). J Heatmap illustrating hierarchical clustering based on pseudotime progression. K Heatmap showing marker genes associated with specific cell clusters. L Monocle 3 trajectory analysis visualizing macrophage cell subgroup differentiation. M Pseudotime progression of macrophage cells as shown in Monocle 3 analysis. N 3D view of cell type dynamics from Monocle 3. O Three-dimensional depiction of RNF130 expression levels. P Flow cytometric analysis of macrophage markers following induced differentiation. Q. Schematic illustration showing the co-culture of RNF130-knockout MDA-MB-231 cells with M0 macrophages. R. After 48 h of co-culture, flow cytometric analysis of M1 and M2 macrophage marker genes. S. Western blot analysis of M1 and M2 macrophage marker genes. T. ELISA analysis of TNF-α, IL-6, IL-10, and TGF-β1 in supernatants from macrophages co-cultured with si-NC or si-RNF130 MDA-MB-231 cells

Further analysis revealed that the expression of RNF130 gradually increased during the transformation of macrophages from M0 to M1 and then to M2 type. Notably, a significant increase in RNF130 expression coincided with the stage of high concentration of M2 macrophages (Fig. 7J–K). In addition, spatiotemporal analysis conducted with Monocle 3 further validated this process, clearly demonstrating the evolutionary transition from M0_TAM to M1_TAM, and then to M2_TAM. Throughout this process, the expression of RNF130 continuously increased (Fig. 7L–O). These results suggest that RNF130 may promote macrophage polarization, particularly toward the M2 type, thereby facilitating immune evasion and tumor progression in breast cancer. To further validate the above bioinformatics findings, THP-1 cells were differentiated into macrophages, and flow cytometry was performed to assess the expression of surface markers. The results showed that the differentiated cells predominantly exhibited an M0 macrophage phenotype, characterized by high expression of CD68 and low expression of CD86 and CD206 (Fig. 7P). Based on this, RNF130-knockout MDA-MB-231 cells were co-cultured with M0 macrophages to simulate the potential influence of RNF130 on macrophage function within the tumor immune microenvironment (Fig. 7Q). After 48 h of co-culture, macrophages were collected from the lower chamber and subjected to flow cytometry and Western blot analysis to evaluate their phenotypic changes. Interestingly, compared with the control group, RNF130-deficient MDA-MB-231 cells significantly promoted the polarization of M0 macrophages toward the M1 phenotype, whereas control cells tended to induce M2 polarization (Fig. 7R–S). Furthermore, ELISA analysis of cytokines in the co-culture supernatants revealed that macrophages co-cultured with si-RNF130 MDA-MB-231 cells exhibited significantly increased levels of TNF-α and IL-6, while the concentrations of IL-10 and TGF-β1 were markedly reduced. These results further support that RNF130 deficiency in MDA-MB-231 cells promotes the pro-inflammatory M1 phenotype and suppresses the M2 polarization of macrophages (Fig. 7T). Given that M2 macrophages are typically associated with tumor-promoting tumor-associated macrophages (TAMs), these findings suggest that RNF130 may contribute to tumor progression by modulating macrophage polarization within the tumor immune microenvironment.

In vitro investigation of the role of RNF130 in BC

We assessed the mRNA expression of RNF130 in various BC cell lines, including MCF-7, T47D, ZR-75-1, SK-BR-3, BT-474, MDA-MB-231, and MDA-MB-468, as well as the normal breast epithelial cell line 184A1. Quantitative analysis was performed using qRT-PCR, and the results showed that the mRNA levels of RNF130 were generally elevated in all BC cell lines compared to the GES-1 cells, with particularly significant expression observed in MDA-MB-231 and MDA-MB-468 cells (Fig. 8A). WB analysis was performed to validate these findings at the protein level (Fig. 8B–C). To explore the role of RNF130 in breast cancer cells, we designed two siRNAs targeting RNF130: si-RNF130#1 and si-RNF130#2. The experiments demonstrated that all two siRNAs effectively reduced RNF130 expression, with si-RNF130#1 showing a more pronounced knockdown effect (Fig. 8D and Figure S12A-B). Therefore, si-RNF130#1 was used in subsequent experiments.

Fig. 8.

In vitro experiments assessing the role of RNF130 in BC. A Quantitative analysis of RNF130 mRNA expression in BC cell lines and normal human breast epithelial cell (184A1). B–C. Western blot analysis of RNF130 expression in MDA-MB-231, MDA-MB-468, and 184A1. D. Efficiency of RNF130 knockdown in BC cells using two siRNA constructs (si-RNF130#1 and si-RNF130#2). E. CCK-8 assay indicated a considerable decrease in BC cell proliferation upon RNF130 knockdown. F–H. EDU (F–G) and colony formation assay (H) indicating decreased proliferative capacity in MDA-MB-231 and MDA-MB-468 cells after RNF130 knockdown. I–K. Cell cycle analysis demonstrating specific cell cycle arrest in MDA-MB-231 and MDA-MB-468 cells following RNF130 knockdown, underscoring the role of RNF130 in cell proliferation. L–M. Flow cytometry analysis revealing increased apoptosis in BC cells due to RNF130 knockdown. N. Western blot analysis further confirming elevated apoptosis levels in RNF130-silenced cells. O. WB analysis of EMT-related genes expression. P. WB analysis of TNF-alpha signaling pathway-related genes expression

We found that silencing RNF130 significantly decreased the proliferation rate of breast cancer cells, as assessed by the CCK8 assay (Fig. 8E). Further confirmation of this phenomenon was obtained through EDU assays (Fig. 8F-G) and colony formation assays (Fig. 8H), which demonstrated that RNF130 knockdown effectively inhibited the proliferative capacity of MDA-MB-231 and MDA-MB-468 cells, as evidenced by a reduction in EDU-positive cells and a significant decrease in colony numbers. Cell cycle analysis revealed significant cell cycle arrest at specific phases in MDA-MB-231 and MDA-MB-468 cells treated with si-RNF130, further supporting the important role of RNF130 in cell proliferation (Fig. 8I–K). Flow cytometry and WB results indicated that silencing RNF130 significantly increased cell apoptosis (Fig. 8L–N), suggesting that RNF130 may inhibit cell growth by promoting apoptosis. After knocking down RNF130, we observed a marked attenuation of epithelial-mesenchymal transition (EMT), as shown by an upregulation of E-cadherin and downregulation of N-cadherin, SNAI1, and Vimentin at the protein level. This suggests that the loss of RNF130 may, to some extent, suppress the EMT process, thereby potentially reducing the invasion and migration capacity of breast cancer cells (Fig. 8O).

Based on prior bioinformatics analysis of single-cell transcriptomics, we hypothesized that the TNF-alpha signaling pathway might be closely associated with the function of RNF130. To further validate this hypothesis, we examined the expression and phosphorylation levels of key molecules in the TNF-alpha signaling pathway. The experimental results showed that, after RNF130 knockdown, the expression level of TNF-alpha was significantly reduced, accompanied by a decrease in the phosphorylation levels of P65 (p-P65), IκBα, IKKα, and IKKβ (p-IκBα, p-IKKα, p-IKKβ) (Fig. 8P). These results suggest that RNF130 plays an important role in maintaining the activity of the TNF-alpha signaling pathway, and its knockdown may further impact the malignant phenotype of BC cells by inhibiting this pathway.

To further investigate the biological function of RNF130, stable RNF130-overexpressing cell lines were established in MDA-MB-231 and MDA-MB-468 cells. RT-qPCR analysis revealed a significant increase in RNF130 mRNA levels compared to the control group (Figure S12C), which was further confirmed by elevated protein expression detected via Western blot (Figure S12D–E). EdU assays demonstrated that RNF130 overexpression markedly increased the proportion of EdU-positive cells in both TNBC cell lines, indicating enhanced proliferative capacity (Figure S12F). In addition, Annexin V-FITC/PI flow cytometry analysis showed that, under treatment with 0.5 µM paclitaxel, both early and late apoptosis rates were reduced in the RNF130-overexpressing group (Figure S12G), suggesting that RNF130 suppresses paclitaxel-induced apoptosis and may contribute to chemoresistance.

In addition, to further investigate the role of RNF130 in breast cancer progression, we conducted further validation in animal models. We established a mouse model of breast cancer and assessed the impact of RNF130 silencing on tumor growth. To ensure that the shRNA constructs exhibited reliable knockdown efficiency prior to use in animal experiments, we performed additional validation in stable cell lines using both RT-qPCR and Western blot assays (Figure S12H–J). The results showed that sh-RNF130 significantly reduced RNF130 protein expression, confirming its effective knockdown capability at the cellular level. Experimental data showed that tumors in mice treated with sh-RNF130 were significantly smaller in volume compared to the sh-NC control group (Fig. 9A), with a notable reduction in both the overall tumor volume and weight (Fig. 9B–C). A series of additional experiments further confirmed the effects of RNF130 knockdown. Immunofluorescence analysis (Fig. 9D) demonstrated a significant decrease in fluorescence signals in the sh-RNF130 group, indicating that RNF130 expression was markedly reduced and the knockdown efficiency was high. This result provided a reliable foundation for subsequent functional analyses. Additionally, H&E staining (Fig. 9E) was used to compare the structure of ex vivo tumor tissues. The results showed that tumors in sh-NC had a dense structure, while the RNF130 knockdown tumors displayed a noticeably looser structure. This phenomenon suggests that RNF130 may play a crucial role in maintaining the structural integrity of tumor tissues. Through TUNEL staining (Fig. 9F), we assessed apoptosis levels in the tumor tissues. After RNF130 knockdown, there was a significant increase in TUNEL-positive cells, indicating a marked elevation in the apoptosis rate. This further supports the key role of RNF130 in inhibiting tumor cell apoptosis. These in vivo results strongly suggest that RNF130 knockdown effectively inhibits tumor growth and imply that RNF130 may exert its suppressive effects on tumor development by promoting tumor cell apoptosis and preventing cell cycle.

Fig. 9.

Effects of RNF130 knockdown on tumor structure and apoptosis. A Representative images of excised tumors. B–C Tumor volume growth curve (B) and differences in tumor weight (C) between the sh-NC and sh-RNF130 groups. D Immunofluorescence confirms efficient RNF130 knockdown in the sh-RNF130 group (scale bar = 50 μm). E HE staining (scale bar = 50 μm). F Tunel staining (scale bar = 50 μm)

In conclusion, our findings suggest that silencing RNF130 may limit the growth of breast cancer cells by Suppressing cell proliferation, promoting cell apoptosis, and blocking the cell cycle. These results indicate that RNF130 could be a potential therapeutic target, offering new strategies for the treatment of breast cancer.

Worenine May inhibit breast cancer cell progression by targeting RNF130

To identify traditional Chinese medicine (TCM) active ingredients associated with RNF130 that exhibit good absorption, efficacy, and clinical application potential, we screened 2,583 active components from the TCMSP database based on standard screening criteria (oral bioavailability (OB) ≥ 30% and drug-likeness (DL) ≥ 0.18) [51–56]. These 2,583 compounds were subsequently docked with RNF130, and their binding energies were calculated. To ensure the reliability of the data, three independent simulations were performed. Based on the calculation results, we visually displayed the top 200 compounds with the lowest binding energies using a violin plot (Figure S12A). Ultimately, three candidate monomers with the lowest binding energies were selected: Worenine, Carboxyatractyloside, and Glycyrrhizin (Fig. 10A).

Fig. 10.

Worenine inhibits breast cancer progression by targeting RNF130 and synergizes with paclitaxel. A Molecular docking identifies Worenine, carboxyatractyloside, and Glycyrin. B–C CCK8 assay shows that Worenine, carboxyatractyloside, and Glycyrin inhibit the proliferation in a dose-dependent manner, with Worenine showing the strongest effect. D WB reveals that Worenine significantly reduces RNF130 protein levels. E–F Worenine induces concentration-dependent apoptosis. G Combined treatment with Worenine and paclitaxel further reduces MDA-MB-231 cell proliferation compared to single treatments. H–K SynergyFinder analysis confirms significant synergy between Worenine and paclitaxel with positive scores across ZIP, Bliss, HSA, and Loewe models. L The combination of 10 µM Worenine and 0.5 µM paclitaxel achieves over 90% inhibition of cell proliferation. M–N. Combined treatment enhances apoptosis more effectively than monotherapies

Subsequently, we treated MDA-MB-231 and MDA-MB-468 cell lines with three different monomers at varying concentrations for 48 h and assessed their inhibitory effects on cell proliferation using the CCK8 assay. The findings suggested that all three monomers exhibited significant inhibitory effects, which increased with concentration, with Worenine displaying the strongest activity in both cell lines (Fig. 10B-C). Based on these results, we further conducted experiments using the IC50 concentration of each monomer to explore the specific inhibitory mechanisms on breast cancer cell activity and investigate their potential as therapeutic targets. WB analysis revealed that Worenine significantly inhibited the expression of RNF130 protein in breast cancer cell lines, whereas the other monomers showed weaker regulatory effects on this protein (Fig. 10D). Further studies indicated that Worenine significantly induced apoptosis in breast cancer cells in a dose-dependent manner (Fig. 10E-F), suggesting that it may exert its inhibitory effect on tumor cells by downregulating RNF130 expression and promoting cell apoptosis. This provides new evidence and directions for further research on Worenine as a potential candidate drug for breast cancer treatment.

Paclitaxel is frequently utilized in chemotherapy for BC, primarily inhibiting tumor growth by interfering with the cell division process. To investigate the synergistic effect of Worenine combined with paclitaxel, we conducted a combination treatment experiment on MDA-MB-231 cells and assessed its inhibitory effect on cell proliferation using the CCK8 assay (Fig. 10G). The findings revealed that the combination treatment significantly enhanced the inhibition of cell proliferation compared to monotherapy. To more accurately quantify the synergistic effect of the two drugs, we used the SynergyFinder Plus software to analyze the synergy across various concentration combinations. The synergistic scores calculated using the ZIP, Bliss, HSA, and Loewe models were 9.68, 9.87, 22.49, and 14.71, respectively (Fig. 10H–K). Although there were some differences in the scores between the models, the results consistently indicated that Worenine and paclitaxel exhibited significant synergistic effects in combination treatment.

To further explore the synergistic effect of Worenine and paclitaxel in vivo, we systematically analyzed the inhibitory effects of various concentration combinations on cell proliferation and evaluated the results using the four scoring models in SynergyFinder (Figure S12B). The experimental results showed that the actual cell inhibition rates were significantly higher than the predicted values from the HSA, Loewe, Bliss, and ZIP models, further confirming the significant synergistic effect between the two drugs. This suggests that the combination of Worenine and paclitaxel produces an anti-tumor effect superior to the theoretical predictions. Notably, under the condition of 10 µM Worenine combined with 0.5 µM paclitaxel, the cell inhibition rate still exceeded 90%, even at lower drug concentrations (Fig. 10L). This result highlights the unique advantage of combination therapy in enhancing treatment efficacy, offering the potential to reduce drug dosages and minimize side effects. Further experiments showed that, at this concentration combination, the joint treatment significantly enhanced apoptosis levels in breast cancer cells, with a stronger induction effect compared to monotherapy (Fig. 10M–N). This indicates that Worenine can synergize with paclitaxel by promoting tumor cell apoptosis, significantly inhibiting the growth and progression of BC.

Taken together, these findings clearly demonstrate the synergistic effect of Worenine and paclitaxel, showing excellent anti-cancer efficacy not only in in vitro models but also providing strong support for its potential application in combination therapy for breast cancer. This combination therapy strategy could offer a more efficient and less toxic new approach for clinical treatment of breast cancer, while also laying a solid foundation for future drug development and mechanistic research.

Discussion

TNBC is a subtype of BC that lacks expression of ER, PR, and HER2, which significantly limits clinical treatment options [57]. Since TNBC does not express traditional target proteins, chemotherapy and immunotherapy are the primary treatment modalities [58]. Recent studies suggest that the onset and progression of TNBC may be closely related to the immune microenvironment [59]. Single-cell sequencing technology can be used to analyze the distribution and function of immune cell subsets of TNBC patients [60, 61]. This research helps to further understand the composition and functional state of TNBC cells, and deepens the understanding of the TNBC immune microenvironment. These findings provide a theoretical foundation for the clinical management and treatment strategies of TNBC. In this study, we comprehensively analyzed TNBC single-cell datasets from the GEO database and classified 158,524 breast cancer-related cells using the Seurat package in R. By comparing the TME of chemotherapy-responsive TNBC patients with that of non-responsive TNBC patients, we found significant changes in the immune cell composition within breast tissue, further revealing the intrinsic heterogeneity of TNBC cells.

The development of breast cancer is typically caused by genetic mutations and protein dysfunction, with these alterations playing significant roles in tumor initiation, progression, and therapeutic response [62]. Protein ubiquitination modification, particularly the role of E3 ubiquitin ligases, is crucial in regulating multiple essential cellular processes [63]. Recent pan-cancer analyses indicate that ubiquitination-related genes such as AIMP2 exhibit broad oncogenic roles across multiple tumor types and correlate with immune infiltration and clinical prognosis [64], supporting the translational potential of RNF130 as a therapeutic target. In our study, we found that the E3 ligase RNF130 is significantly overexpressed in TNBC and is closely associated with clinical prognosis. Through bioinformatics tools including various enrichment analyses, we revealed the potential role of RNF130 in promoting BC progression, providing a new target for future cancer therapies.

By conducting both in vitro and in vivo analyses, we further established the role of RNF130 as a promising therapeutic target for BC. The in vitro results showed that Silencing RNF130 inhibited BC cell proliferation and induced apoptosis, highlighting its key role in tumor development. Our study further established an RNF130 overexpression system, revealing that upregulation of RNF130 significantly promotes TNBC cell proliferation while attenuating paclitaxel-induced apoptosis. These findings suggest that RNF130 may function dually as a growth-promoting factor and a regulator of chemoresistance in breast cancer.

In our previous scRNA-seq analysis, we found that the TNF-alpha signaling pathway is likely closely associated with RNF130 function. We observed that, after RNF130 knockdown, the expression level of TNF-alpha was significantly reduced, accompanied by a decrease in the phosphorylation levels of P65 (p-P65), IκBα, IKKα, and IKKβ (p-IκBα, p-IKKα, p-IKKβ). These results indicate that RNF130 plays a crucial role in maintaining the activity of the TNF-alpha signaling pathway. The activation of key molecules in the TNF-alpha signaling pathway, such as NF-κB, is regulated by RNF130, further highlighting its importance in modulating immune responses and the malignant phenotype of tumor cells. In vivo mouse models further validated the inhibitory effects of RNF130 on tumor volume and growth rate, providing strong support for the development of molecular targeted therapies against RNF130. Beyond the validated TNF-α/NF-κB activation, GSVA also indicated a modest positive association between RNF130 expression and JAK–STAT signaling. Given that TNF-α can induce STAT3 activation through IL-6–mediated crosstalk [PMID: 25174402], RNF130-driven inflammatory signaling may indirectly potentiate JAK–STAT pathway activity. Although further experimental confirmation is required, these findings suggest that RNF130 may participate in a broader NF-κB–STAT inflammatory regulatory network.

Traditional Chinese medicine (TCM) have emerged as important sources of anti-tumor agents, with many plant-derived small molecules demonstrating regulatory effects on cell death, metabolism, epigenetics, and immune pathways in cancer therapy [65–67]. Recent studies have shown that monomer of TCM can modulate tumor progression through multi-target molecular mechanisms rather than single-pathway inhibition. For example, epicatechin has been reported to remodel oncogenic and tumor-suppressive microRNA networks in breast and colorectal cancer cells, thereby exerting anti-proliferative and cytostatic effects [68]. These findings underscore the therapeutic potential of natural compounds as modulators of cancer-related signaling pathways. In line with these observations, we found that the herbal alkaloid Worenine reduced RNF130 expression and consequently increased apoptosis in breast cancer cells. When combined with paclitaxel, Worenine exhibited a synergistic inhibitory effect on cell proliferation, even at relatively low concentrations. Although the precise mode of action and direct interaction between Worenine and RNF130 require further biochemical validation, these findings indicate that modulation of the RNF130-associated signaling axis may enhance the anti-tumor efficacy of paclitaxel and support the potential value of Worenine in combination therapy strategies.

Research has shown that the TME plays a crucial role in the tumor progression and immune evasion of TNBC. In particular, macrophages, as an essential component of the tumor immune microenvironment, not only play a key role in immune evasion but also directly influence tumor growth, metastasis, and chemoresistance. Our study suggests that RNF130 is significantly associated with various immune cells and may affect tumor immune evasion mechanisms by regulating the distribution and function of these cells. The dynamic changes in RNF130 expression within immune cells, as well as its spatiotemporal progression in different macrophage subtypes, imply that RNF130 has a diverse role in macrophage differentiation and functional regulation. It may affect macrophage polarization and function by ubiquitinating key immune molecules within macrophages, thereby promoting tumor immune evasion.

Despite these findings, several limitations should be acknowledged. First, this study employed a Balb/c nude mouse xenograft model, which lacks mature T cells and therefore cannot fully recapitulate the complexity of the human tumor immune microenvironment. As a result, our in vivo findings primarily reflect the direct effects of RNF130 on tumor proliferation and apoptosis, while its immunomodulatory functions require further investigation in immunocompetent or humanized immune system mouse models. Second, the synergistic interaction between Worenine and paclitaxel was inferred from SynergyFinder-based modeling. Although these results suggest potential therapeutic synergy, they still require in vivo validation—particularly in NSG or humanized mouse models that more closely mimic the clinical immune context—to determine the true efficacy and mechanism of combined treatment. Finally, although molecular docking and preliminary pull-down assays indicate a possible interaction between Worenine and RNF130, direct biochemical evidence for a physical binding relationship is still lacking. Future work will involve Co-IP, SPR, ITC, and domain-truncation analyses to further elucidate the precise nature of their interaction and to determine whether Worenine directly targets RNF130 or modulates its expression through indirect mechanisms.

Conclusion

In this study, we comprehensively characterized the expression pattern and functional role of the E3 ubiquitin ligase RNF130 in triple-negative breast cancer (TNBC). By integrating single-cell transcriptomic analyses with in vitro and in vivo experimental validation, we demonstrated that RNF130 is markedly upregulated in TNBC and metastatic lesions, and contributes to enhanced tumor proliferation, reduced apoptosis, and remodeling of inflammatory signaling, primarily through activation of the TNF-α/NF-κB axis.

Furthermore, we identified Worenine, as a potential modulator of RNF130-associated signaling. Worenine reduced RNF130 expression and exhibited synergistic anti-tumor effects with paclitaxel in vitro, indicating its promise as a candidate for combination therapy. Although the exact mode of interaction between Worenine and RNF130 requires further biochemical validation, these findings highlight RNF130 as a promising therapeutic target and provide a foundation for future development of RNF130-based interventions.

Overall, this study enriches the understanding of ubiquitination-related regulatory mechanisms in TNBC and suggests that targeting RNF130 may offer new opportunities for precise therapeutic strategies in breast cancer.

Supplementary Information

Below is the link to the electronic supplementary material.

Author contributions

Author CRediT StatementMi Hu carried out data curation, formal analysis, validation, and visualization. Liangbin Huang wrote the original draft, conducted investigation, and developed methodology. Hongzhuan Deng provided resources. Zhifeng Chen developed the software. Guanghui Cheng acquired funding and managed the project. Xinchun Liu conceptualized the study, supervised the research, and reviewed and edited the manuscript. All authors read and approved the final manuscript.

Funding

Not applicable.

Data availability

No datasets were generated or analysed during the current study.

Declarations

Ethics approval and consent to participate

Animal studies were conducted with the approval of the Institutional Animal Care and Ethics Committee of the Affiliated Changsha Central Hospital, University of South China (No. 2023-ky-131), following both NIH and institutional animal care guidelines.

Consent for publication

Not Applicable.

Competing interests

The authors declare no competing interests.