The WT 1-AS/miR-206 axis regulates the proliferation and migration of breast cancer through the cuproptosis related gene BCL11A

Introduction

Breast cancer (BC) ranks among the most prevalent and harmful malignant tumors affecting women globally (1). It is characterized by multiple molecular subtypes, significant differences in clinical presentation, a high propensity for recurrence and metastasis, and poor prognosis (2). Although standard treatment protocols include surgery, radiotherapy, chemotherapy, and targeted therapies, tumor drug resistance and heterogeneity can lead to suboptimal treatment outcomes (3). Therefore, investigating the key molecular mechanisms underlying BC development and identifying more effective therapeutic targets are essential for enhancing the prognosis and quality of life of patients.

As our understanding of molecular biology advances, it is increasingly evident that many RNAs do not encode proteins but instead function as non-coding RNAs (ncRNAs) (4). High-throughput sequencing and transcriptome analysis have significantly deepened our understanding of ncRNAs, which are categorized by length and structure into different types, such as microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (5). miRNAs, typically 20–22 nucleotides long, regulate gene expression by binding to the 3' untranslated region (UTR) of target messenger RNAs (mRNAs), causing translational inhibition or mRNA degradation (6). These small RNAs play critical roles in various cancers (7). For instance, miR-21, miR-155, and miR-10b promote tumor growth by silencing tumor suppressor genes and enhancing tumor proliferation, invasion, and metastasis (8). miR-206 functions as a tumor suppressor, influencing cell cycle-related pathways, transcription factors, and growth factors (9). Its dysregulation has been linked to drug resistance and tumor invasiveness, which make it a promising biomarker for early diagnosis and targeted therapies (10). LncRNAs, which are generally longer than 200 nucleotides, exert diverse effects through interactions with DNA, RNA, and proteins (11). They regulate cellular processes via epigenetic modifications and post-transcriptional mechanisms (12). WT1-AS, an antisense lncRNA of the WT1 gene, modulates tumor gene expression and affects the interaction of WT1 with molecular targets (13). Additionally, lncRNAs can function as competitive endogenous RNAs (ceRNAs), binding miRNAs to reduce the inhibition of mRNA targets (14). This ceRNA mechanism represents a pivotal regulatory layer in tumor biology (15). In BC, the construction of ceRNA networks provides insights into complex gene regulatory systems and offers novel avenues for clinical intervention.

Regulated cell death (RCD) is a process in which cells undergo death in a controllable and programmable manner in response to external or internal signals; it is distinctly different from traditional passive necrosis (16). RCD includes not only typical apoptosis but also various recently identified subtypes, such as autophagic cell death, ferroptosis, pyroptosis, and cuproptosis, which have garnered significant attention in recent years (17). Cuproptosis, a recently identified subtype of RCD, has garnered considerable attention from the scientific community in recent years (18). The accumulation of excess copper ions in the cell and their abnormal binding to enzymes related to the tricarboxylic acid cycle led to protein aggregation, energy metabolism disorders, and reactive oxygen species (ROS) burst, thereby triggering cell death signals (19). Importantly, copper ions are not only participants but indispensable initiators of cuproptosis (20). Unlike other RCD pathways that rely on caspase activation, membrane rupture, or iron-dependent lipid peroxidation, cuproptosis requires a precise elevation of intracellular free copper to drive its unique biochemical cascade (21). Copper directly binds to lipoylated mitochondrial enzymes, causing their abnormal oligomerization while simultaneously depleting mitochondrial Fe-S cluster proteins (22). These dual events destabilize mitochondrial proteostasis and collapse oxidative phosphorylation, making copper availability the key limiting factor for whether cuproptosis can be successfully triggered. Experimental studies have consistently shown that increasing intracellular copper, through ionophores, transporters, or nanocarriers, substantially enhances cuproptosis sensitivity, whereas copper chelation effectively suppresses this pathway (20,23). Thus, cellular copper homeostasis determines the threshold for cuproptotic susceptibility and forms the mechanistic foundation for copper-dependent therapeutic strategies. Cuproptosis plays a role in the treatment of various tumors, including BC, through copper ion accumulation, mitochondrial dysfunction, and oxidative stress pathways (24). It not only directly induces BC cell death and inhibits tumor growth but may also enhance sensitivity to chemotherapy, induce immunogenic cell death, and suppress the invasive and metastatic potential of cancer cells (25). When it comes to clinical applications, the effectiveness of copper ion carriers, nanomaterials, and combination therapeutic strategies is under investigation. These may provide new treatment options for patients with BC and help develop effective strategies to overcome the high incidence and mortality rates of BC (26). As a novel form of cell death, cuproptosis shows promising application prospects in BC treatment and may constitute a new strategy to overcome the high incidence and mortality rates of BC (27).

BCL11A, a C2H2 zinc finger transcription factor, plays an essential role in the development and progression of various tumors (28). As an oncogene, BCL11A is involved in the regulation of cell proliferation, apoptosis, and transformation (29). BCL11A overexpression significantly promotes the proliferation and migration potential of cells (30). BCL11A enhances tumor cell proliferation, invasion, and metastasis by various mechanisms, including the upregulation of MDM2 expression, activation of the PI3K/AKT signaling pathway, and induction of epithelial-mesenchymal transition (31). In multiple tumors, including triple-negative BC and neuroblastoma, high BCL11A expression has been strongly linked to tumor progression and poor prognosis (32). However, its relationship with cuproptosis in BC has never been reported. A thorough investigation of BCL11A’s regulatory patterns in BC and its interactions with other key molecules is of great significance for developing novel therapeutic approaches.

Here, we identified WT1-AS as a ceRNA for miR-206, which modulates BCL11A, a gene associated with cuproptosis. Findings from copper ion concentration and ROS assays confirmed that BCL11A suppression significantly enhances cuproptosis. Additionally, findings from functional enrichment and phenotypic analyses demonstrated that the WT1-AS/miR-206/BCL11A axis is essential for regulating BC cell proliferation and migration. Findings from luciferase reporter assays validated that WT1-AS regulates BCL11A expression by serving as a ceRNA for miR-206. We present this article in accordance with the MDAR reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1448/rc).

Methods

Data collection

We acquired four pairs of cancerous and adjacent non-cancerous tissue samples, along with clinical data from 122 patients with BC, from The Cancer Genome Atlas (TCGA; https://portal.gdc.cancer.gov/) database. To investigate the associations between gene expression and clinical outcomes, we performed univariate Cox regression and gene set enrichment analyses on the entire dataset. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Differential expression analysis

We conducted a differential expression analysis to identify miRNAs, mRNAs, and lncRNAs that exhibited significant changes in expression. We used the “limma” R package (version 3.52.4) to analyze and filter these molecules based on the log fold change (logFC) and associated P values. To present our findings clearly, we used the “ggplot2” R package (version 3.4.0) for preparing volcano plots and the “pheatmap” R package (version 1.0.12) for creating heat maps, both of which helped visually represent the differential expression data.

Consensus clustering

We performed consensus clustering using the expression data of selected genes with the “ConsensusClusterPlus” R package, exploring clusters from k =2 to 9 and iterating 1,000 times to ensure robust grouping. We evaluated the consensus matrix and cumulative distribution function (CDF) curves to select the optimal number of clusters. We analyzed survival differences across these clusters using the Kaplan-Meier method. We performed Chi-squared tests to assess the distribution of categorical characteristics among groups, providing insights into cuproptosis-related cluster distinctions.

Univariate Cox analysis

Univariate Cox analysis is a statistical method commonly used in survival research to assess the effect of individual variables—such as treatment options, disease stages, patient demographics, and molecular markers—on survival rates or recurrence. The use of the Cox proportional hazards model helps determine hazard ratios, P values, and confidence intervals, which aid the identification of key factors that are significantly correlated with survival outcomes.

Survival analysis

Using the TCGA biolinks package in R, we performed survival analysis to evaluate the prognostic relevance of the identified genes and differentially expressed lncRNAs. We categorized the patients into high- and low-expression groups based on the top and bottom third of expression levels. We prepared Kaplan-Meier curves to visualize survival differences, with a P value threshold of <0.05 indicating significant associations between gene expression and survival outcomes.

Gene set enrichment analysis (GSEA)

We performed GSEA to identify enriched gene sets in specific expression groups, in accordance with the protocol reported by the Broad Institute (http://www.broadinstitute.org/gsea). We restricted gene sets to those containing 15–500 genes. We assessed enrichment over 1,000 permutations. We selected enriched pathways based on a significance level of P<0.05 and false discovery rate <0.25, which helped us identify key biological processes associated with differences expression genes.

Construction of a ceRNA network

To construct the lncRNA-miRNA-mRNA interaction network, we initially used the miRcode database to identify potential lncRNA-miRNA binding interactions. Subsequently, to reliably identify miRNA-mRNA interactions, we predicted the miRNA-targeted mRNAs using multiple databases, including TargetScan, mirTarBase, and miRDB. With this approach, we attempted to explore the regulatory roles of lncRNAs and their associated miRNAs at the expression level, focusing on selecting suitable lncRNAs for constructing the ceRNA network. Finally, we integrated the significant co-expression relationships among lncRNAs, miRNAs, and mRNAs to build the lncRNA-miRNA-mRNA network, with network visualization conducted using Cytoscape version 3.7.2.

DAVID enrichment analysis

We analyzed differentially expressed genes for functional enrichment using the DAVID platform, focusing on Gene Ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways associated with BC. We applied a cutoff of P<0.05 to identify significant enrichment, with lower P values indicating stronger associations with specific functions or pathways. We prepared bubble plots to visualize the enrichment landscape, highlighting key biological processes involved in BC.

Cell culture

Human epithelial BC cell lines (BT474 and MCF7) and HEK293T cell line were purchased from the American Type Culture Collection (Manassas, VA, USA). We cultured BT474 and MCF7 in Dulbecco’s Modified Eagle Medium (Gibco, Grand Island, NY, USA) supplemented with 10% FBS and 1% penicillin-streptomycin (Gibco). We incubated the cells at 37 ℃ in 5% CO₂. We monitored and sub-cultured the cells regularly to sustain healthy growth.

Transfection

We used Lipofectamine 3000 (Invitrogen, L3000001, Carlsbad, CA, USA) to transfect mimics and inhibitors, which we obtained from Biomics (Nantong, China). To ensure efficient delivery and optimal transfection efficiency, we performed transfection using Lipofectamine 3000 according to the manufacturer’s instructions.

CCK-8 assay

We seeded BT474 and MCF7 cells in a 96-well plate at a density of 3,000 cells per well. At predefined time points (24 and 48 h), we added 10 µL of CCK-8 reagent (MCE, HY-K0301, New Jersey, USA) to each well. After a 2-hour incubation period at 37 ℃, we measured the absorbance at 450 nm using a microplate reader.

Transwell assay

Log-phase cells were seeded in a 24-well plate. Upon adherence, the culture medium was replaced with fresh medium containing 10 µg/mL mitomycin C, followed by incubation for 1–2 hours at 37 ℃ to inhibit cell proliferation. After treatment, the medium was aspirated and the cells were washed twice with phosphate buffered saline (PBS). The cells were then trypsinized, resuspended in serum-free medium, and adjusted to the desired density. For the migration assay, transwell chambers (Corning, 3470, Corning, NY, USA) were placed into a new 24-well plate. The 200 µL aliquot of the cell suspension was added to the upper chamber, while the lower chamber was loaded with 500–600 µL of complete medium supplemented with 10% fetal bovine serum (FBS) as a chemoattractant. After a 24-hour incubation, the cells that had migrated to the lower surface of the membrane were fixed and stained with 0.1% crystal violet (MCE, HY-B0324A) for 15 minutes. We imaged the cells and counted the number of cells in five different fields.

qPCR

We isolated total RNA from BC cell lines using TRIzol reagent (Invitrogen, 15596026CN). We conducted complementary DNA (cDNA) synthesis using the PrimeScript RT Master Mix (Takara, RR036A, Kusatsu, Shiga, Japan) with random primers in accordance with the manufacturer’s instructions. We amplified the cDNA using TB Green Premix Ex Taq II (Takara, RR820A) on an ABI Prism 7500 real-time PCR system (Applied Biosystems, Foster City, CA, USA). We used glyceraldehyde 3-phosphate dehydrogenase as the internal control for normalization. We analyzed each sample in triplicate and quantified the relative expression levels using the 2^−ΔΔCT method.

Lipid peroxidation measurement

Cells were plated in 12-well plates and treated with the specified reagents. After treatment, cells were harvested, washed with PBS, and resuspended in PBS containing 5 µM DCFH-DA (D399, Thermo Fisher Scientific, Waltham, MA, USA) in the dark at 37 ℃ for 20 min. After incubation, the cells were washed twice with PBS to remove excess dye. The fluorescent signal was measured using flow cytometry with an excitation wavelength of 488 nm and emission wavelength of 525 nm. A minimum of 10,000 cells per sample were analyzed. The relative mean fluorescence intensity (MFI) was calculated to evaluate ROS production, and results were analyzed using FlowJo software.

Luciferase reporter gene assay

We constructed plasmids containing wild-type and mutant 3' UTR sequences of the lncRNA and mRNA and inserted them into the GP-miRGLO luciferase reporter vector (Genepharma, Shanghai, China). We co-transfected the constructs (WT1-AS-WT and WT1-AS-MUT) with miR-206 mimics or inhibitors into BC cell lines. After transfection, we quantified luciferase activity using the Dual-Luciferase Reporter Assay System (Promega, E1910, Madison, WI, USA) in accordance with the manufacturer’s instructions.

Statistical analysis

We performed statistical analysis using GraphPad Prism 10.1.0, presenting data as mean ± SD to summarize central tendency and variability. We conducted pairwise comparisons using the independent Student’s t-test, assuming normality and equal variances. For comparisons involving three or more groups, we used one-way analysis of variance (ANOVA) to evaluate the significance of the differences among group means. A P value less than 0.05 was considered statistically significant, with all tests being two-tailed to account for the directionality of the effects. We performed all experiments in triplicate.

Results

Identification of differentially expressed lncRNAs, miRNAs, and mRNAs in BC

We conducted a differential analysis of the gene expression profiles of BC tissues and normal tissues using data from the TCGA-BRCA database. As shown in the volcano plot and heat map (Figure 1A,1B), we identified 1,012 upregulated lncRNAs, 12 upregulated miRNAs, 1,831 upregulated mRNAs, 1,604 downregulated lncRNAs, 11 downregulated miRNAs, and 22,68 downregulated mRNAs (Figure S1).

Figure 1 Identification of differentially expressed lncRNAs, miRNAs, and mRNAs in breast cancer. (A) The volcano plot shows the expression profiles in breast cancer and normal tissue, including lncRNAs, miRNAs, and mRNAs. Red indicates upregulated expression and blue indicates downregulated expression. (B) Heat maps shows the expression profiles in breast cancer and normal tissue, including lncRNAs, miRNAs, and mRNAs. lncRNA, long non-coding RNA; mRNA, messenger RNA; miRNA, microRNA.

Consensus clustering analysis of cuproptosis

We performed separate cluster analyses of BC samples based on genes associated with ferroptosis and cuproptosis. First, the consensus matrix analysis for both approaches indicated an optimal cluster number of k =2 (Figure 2A and Figure S2A,S2B). Subsequently, Kaplan-Meier analysis revealed no significant survival difference between the subgroups defined by ferroptosis genes. In contrast, the cuproptosis-based subgroups exhibited a significant survival difference, with Group 1 showing a markedly longer survival time than Group 2 (P=0.044, Figure 2B). Furthermore, we conducted a differential expression analysis between the two cuproptosis-based subgroups. The volcano plot and heatmap (Figure 2C,2D and Figure S2C) identified 45 upregulated and 40 downregulated genes.

Figure 2 Consensus clustering analysis of cuproptosis. (A) Cuproptosis (left) and ferroptosis (right) patients clustering heatmap, consensus matrix k =2. (B) The survival analysis between two cuproptosis clusters (left) and ferroptosis clusters (right). (C) The volcano plot shows the upregulated (red) and downregulated (blue) genes in Group 1 vs. Group 2 (P value <0.05 and |Log₂FC| ≥1.0). (D) Heatmap shows the different genes of the two groups. Red indicates upregulated expression and blue indicates downregulated expression. Group 1: cluster group 1 from the cuproptosis-based analysis. Group 2: cluster group 2 from the cuproptosis-based analysis. FC, fold change.

ceRNA network analysis in BC

Increasing evidence suggests that the ceRNA network plays a critical role in BC. This prompted us to analyze the ceRNA network. To establish the ceRNA network, we used differentially expressed lncRNAs and miRNAs along with differentially expressed mRNAs associated with cuproptosis clustering analysis. As shown in Figure 3A and Figure S3, we identified 238 interaction axes, comprising 14 lncRNAs, 1 miRNA, and 17 mRNAs. Our data analysis revealed that among these 17 mRNAs, only BCL11A and MT1H were differentially expressed between BC and normal tissues. Given that BCL11A is a transcription factor regulating cell division and apoptosis, with established relevance to BC progression, we focused our subsequent investigation on the ceRNA axes associated with BCL11A in BC (Figure 3B).

Figure 3 The ceRNA network analysis in breast cancer. (A) Sankey diagram showing the ceRNA network. (B) The lncRNA-miRNA-mRNA pattern diagram. ceRNA, competitive endogenous RNA; lncRNA, long non-coding RNA; mRNA, messenger RNA; miRNA, microRNA.

Inhibition of BCL11A can enhance the sensitivity of BC cells to cuproptosis inducers

To investigate the role of BCL11A in cuproptosis, BT474 and MCF7 cells were treated with varying concentrations of elesclomol (a copper-inducing agent), yielding IC50 values of 21.96 and 24.54 nM, respectively (Figure 4A). Cells were then treated with 0, 10, and 20 nM of elesclomol and CuCl₂. Under uninduced conditions (without elesclomol), BCL11A knockdown resulted in slightly elevated ROS levels compared to the control group, while no significant difference in copper concentration was observed. In control cells, a low dose of elesclomol alone did not significantly alter ROS levels. In contrast, elesclomol induction in BCL11A-knockdown cells led to a marked reduction in ROS, suggesting that BCL11A inhibition alleviates oxidative stress (Figure 4B). Furthermore, upon elesclomol induction, BCL11A knockdown increased copper concentration (Figure 4C). This indicates that BCL11A inhibition may enhance the sensitivity of BC cells to cuproptosis inducers.

Figure 4 Inhibition of BCL11A can enhance the sensitivity of breast cancer cells to cuproptosis inducers. (A) CCK8 assay was used to measure cell viability 24 hours after exposure to increasing doses of elesclomol (0, 1, 5, 50, 200 nM). (B) BCL11A knockdown cells and control groups cells were treated with increasing doses of elesclomol (0, 10, 20 nM) for 24 hours, and the levels of ROS were measured using flow cytometry. (C) BCL11A knockdown cells and control groups cells were treated with increasing doses of elesclomol (0, 10, 20 nM) for 24 hours, and the concentration of copper (Cu²⁺) was measured. All experiments were repeated at least three times. ns, none was statistically significant; *, P<0.05; **, P<0.01; ****, P<0.0001. CCK8, Cell Counting Kit-8; IC50, half maximal inhibitory concentration; NC, normal control; ROS, reactive oxygen species.

WT1-AS may regulate the nucleic acid and protein levels of BCL11A

To further explore the regulatory ceRNA axes in BC, we screened for lncRNAs that regulate BCL11A expression. We conducted gene expression analysis on the top five lncRNAs with the highest expression levels. The box plot in Figure 5A shows that HOTAIR, LINC00243, MIAT, SRGAP3-AS2, and WT1-AS are all expressed at high levels in BC. To assess the prognostic value of these lncRNAs in BC, we divided them into high- and low-expression groups based on the median value and performed survival analysis. The Renyi survival analysis results indicated that only WT1-AS expression showed a statistically significant difference between the two groups (Figure 5B). According to the ceRNA hypothesis, lncRNA expression is positively correlated with mRNA expression. To determine whether these lncRNAs can regulate BCL11A, we used antisense oligonucleotides (ASOs) to silence the expression of lncRNAs and measured the expression of lncRNAs and BCL11A using qPCR. Interestingly, only the depletion of WT1AS led to a significant decrease in BCL11A expression (Figure 5C). This finding confirmed that WT1-AS regulates the nucleic acid and protein levels of BCL11A.

Figure 5 WT1-AS may regulate the nucleic acid and protein levels of BCL11A. (A) Differential expression analysis of HOTAIR, LINC00243, MIAT, SRGAP3-AS2 and WT1-AS between breast cancer samples (red) and normal samples (blue) in TCGA. (B) Kaplan-Meier survival curve of high and low expression of 5 lncRNAs correlated with overall survival of breast cancer patients. The horizontal coordinate unit is days. (C) The expression of lncRNA and BCL11A was verified by qPCR, when ASO was used to silence the expression of HOTAIR, LINC00243, MIAT, SRGAP3-AS2 and WT1-AS, respectively. All experiments were repeated at least three times. ns, none was statistically significant; ***, P<0.001; ****, P<0.0001. ASO, antisense oligonucleotide; lncRNA, long non-coding RNA; N, normal; NC, normal control; qPCR, Quantitative Polymerase Chain Reaction; T, tumor; TCGA, The Cancer Genome Atlas.

WT1-AS, miR-206, and BCL11A proteins regulate the malignant progression of BC

Based on previous experimental results, we confirm that WT1-AS/miR-206/BCL11A may constitute a regulatory ceRNA axis in BC. To further elucidate the regulatory roles of these genes in BC, we categorized WT1-AS, miR-206, and BCL11A into high- and low-expression groups and conducted differential expression analysis and DAVID enrichment analysis (Figure S4A-S4C). These were significantly expressed during cell proliferation and migration (Figure 6A). Additionally, we performed loss-of-function experiments to determine whether these three genes affect the proliferation and migration potential of BC cells. The CCK-8 assay (Figure 6B) demonstrated that the knockdown of WT1-AS, miR-206, and BCL11A significantly inhibited cell viability. Conversely, the transwell assay showed that suppressing these three genes reduced the migratory potential of BC cells compared to that in the control group (Figure 6C). These bioinformatics data and experimental results confirmed that WT1-AS, miR-206, and BCL11A genes can regulate the malignant progression of BC.

Figure 6 WT1-AS, miR-206, and BCL11A proteins regulate the malignant progression of breast cancer. (A) The bubble diagrams of GO enrichment analysis in low expression group and high expression group among WT1-AS, miR-206 and BCL11A by DAVID database. (B) Proliferation ability of BT474 and MCF7 cells was significantly suppressed after WT1-AS/miR-206/BCL11A silencing by CCK8 assay. (C) Migration ability of BT474 and MCF7 cells was significantly suppressed after WT1-AS/miR-206/BCL11A silencing by transwell assay. Crystal violet staining was performed in the transwell assay, and images were captured at 50× magnification. All experiments were repeated at least three times. ***, P<0.001; ****, P<0.0001. ASO, antisense oligonucleotide; CCK8, Cell Counting Kit-8; DAVID, The Database for Annotation, Visualization and Integrated Discovery; GO, Gene Ontology; NC, normal control; OD, optical density.

WT1-AS and miR-206 regulate the protein levels of BCL11A

To further investigate the relationship among WT1-AS, miR-206, and BCL11A, we conducted qPCR experiments to measure the expression levels of miR-206 and BCL11A after WT1-AS knockdown. miR-206 expression increased but BCL11A expression decreased with the downregulation of WT1-AS (Figure 7A). Additionally, we transfected BT474 and MCF7 cells with miR-206 Mimics and inhibitors. When miR-206 expression increased, the levels of WT1-AS and BCL11A decreased (Figure 7B). Conversely, when miR-206 expression was inhibited, the expression of WT1-AS and BCL11A increased (Figure 7C). This demonstrated that WT1-AS, BCL11A, and miR-206 are negatively correlated, which is consistent with the trends outlined in the ceRNA hypothesis.

Figure 7 WT1-AS and miR-206 regulate the protein levels of BCL11A. (A) The qPCR experiment measured the expression level of WT1-AS, miR-206 and BCL11A after WT1-AS expression was inhibited in BT474 and MCF7 cells. (B) The qPCR experiment measured the expression level of WT1-AS, miR-206 and BCL11A in BT474 and MCF7 cells following the addition of a miR-206 mimic. (C) The qPCR experiment measured the expression level of WT1-AS, miR-206 and BCL11A in BT474 and MCF7 cells following the addition of a miR-206 inhibitor. All experiments were repeated at least three times. ****, P<0.0001. ASO, antisense oligonucleotide; NC, normal control; qPCR, Quantitative Polymerase Chain Reaction.

WT1-AS acts as a ceRNA that promotes the malignant progression of BC through the miR-206/BCL11A axis

To determine whether WT1-AS acts as a ceRNA for miR-206, mediating malignant progression in BC, we used biological prediction to identify the potential binding sites between WT1-AS and miR-206. We then introduced complementary mutations on the predicted binding sites (Figure 8A). Dual-luciferase reporter assays revealed that the co-transfection of miR-206 Mimic and wild-type WT1-AS reduced the luciferase activity. However, when mutated WT1-AS was co-transfected with miR-206 Mimic, no change was observed in the luciferase activity (Figure 8B). Next, we conducted a co-expression analysis for the high WT1-AS expression and high BCL11A expression groups as well as the low WT1-AS expression and low BCL11A expression groups (Figure 8C). DAVID analysis of the co-expressed genes revealed that they were expressed at high levels in the cell migration pathway (Figure 8D). CCK8 assay results demonstrated that inhibition of WT1-AS expression reduced cellular growth viability in the control group, whereas it did not significantly alter the growth capacity of cells transfected with sh-BCL11A (Figure 8E). To validate this finding, we performed transwell assays. The results indicated that in the control group, inhibiting WT1-AS expression reduced the migratory potential of the cells. However, WT1-AS inhibition did not significantly alter the migratory ability of cells transfected with sh-BCL11A (Figure 8F). These results suggest that WT1-AS, as a ceRNA for miR-206, regulates BCL11A protein expression and influences the malignant progression of BC.

Figure 8 WT1-AS acts as a ceRNA that promotes the malignant progression of breast cancer through the miR-206/BCL11A axis. (A) Binding sites between WT1-AS and hsa-miR-206 predicted by the ENCORI database. (B) Luciferase assays were performed to test the effect of miR-206 on wild-type or mutant WT1-AS. (C) Venn diagram showed the overlapping genes of WT1-AS groups and BCL11A groups. (D) DAVID enrichment results of genes co-regulated by WT1-AS and BCL11A. (E) The CCK8 assay results of BT474 cells in which BCL11A was knocked down and which were treated with a WT1-AS inhibitor. (F) The transwell results of BT474 cells with knockdown BCL11A treated with WT1-AS inhibitor group. Crystal violet staining was performed in the transwell assay, and images were captured at 50× magnification. All experiments were repeated at least three times. ns, none was statistically significant; ***, P<0.001; ****, P <0.0001. ASO, antisense oligonucleotide; CCK8, Cell Counting Kit-8; ceRNA, competitive endogenous RNA; DAVID, The Database for Annotation, Visualization and Integrated Discovery; ENCORI, The Encyclopedia of RNA Interactomes; MUT, mutant; NC, normal control; OD, optical density; WT, wild type.

Discussion

BC is characterized by high levels of malignancy, a high propensity for metastasis and recurrence, and the unavailability of effective treatment options, which often lead to poor clinical prognosis (33). Identifying new diagnostic and therapeutic strategies for BC is an urgent need. In recent decades, various ncRNAs, especially lncRNAs and miRNAs, have been shown to contribute to the occurrence and development of BC through the ceRNA mechanism (34). The core of the ceRNA network lies in the competition between different RNA molecules for binding to the same miRNA sites, which affects the translation and expression of key tumor-related genes (14). This plays an important regulatory role in cell proliferation, drug resistance, and immune evasion (35). lncRNAs can work as “sponges” for miRNAs, influencing the activity of tumor suppressors or oncogenes (36). For example, lncRNA H19 competes with miRNA let-7 to form the H19/let-7/LIN28 reciprocal feedback loop (37). This mechanism reduces the inhibition of target genes by let-7, thus promoting LIN28 expression, maintaining BC stem cell characteristics, and enhancing tumor cell clone formation, migration, and mammosphere formation potential. This regulatory model of lncRNAs acting as miRNA sponges reveals new mechanisms underlying BC development and provides potential targets for therapeutic strategies. Here, we developed a ceRNA regulatory network in BC and confirmed that WT1-AS acts as a ceRNA for miR-206 in regulating malignant progression in BC. By elucidating the molecular connections in this lncRNA-miRNA-target gene axis, we aimed to provide feasible ideas for more precise BC treatment and diagnostic strategies.

In investigations on the proliferative advantages and survival mechanisms of BC cells, a novel form of RCD known as “cuproptosis” has garnered attention from researchers (38). Its primary characteristic is that when copper ions accumulate in large quantities within cells and bind to proteins, such as tricarboxylic acid cycle-related enzymes, they tend to induce energy metabolism-related disorders and excessive oxidative stress, thus causing irreversible cell death (39). Since BC is often accompanied by metabolic reprogramming and microenvironmental changes, cuproptosis may be crucial for inhibiting tumor growth and metastasis (40). Beyond its mechanistic implications, cuproptosis has also begun to show translational potential in anticancer therapy. Several copper-modulating agents have entered preclinical or early clinical evaluation, providing preliminary evidence for leveraging copper-dependent cell death in solid tumors. For instance, elesclomol, a small-molecule copper ionophore, was previously assessed in phase I/II clinical trials and demonstrated the capacity to elevate intracellular copper levels and trigger mitochondrial metabolic stress (41). Although its efficacy varied across tumor types, subsequent studies suggest that tumors with heightened oxidative phosphorylation dependency, including subsets of BC, may be more sensitive to copper-dependent cytotoxicity. In parallel, novel copper chelators such as tetrathiomolybdate (TM) are being investigated to disrupt copper-dependent oncogenic signaling; TM has reached clinical testing in metastatic BC and showed potential in suppressing tumor angiogenesis and metastasis by systemically lowering bioavailable copper (20). These findings highlight two complementary therapeutic strategies: increasing intracellular copper to induce cuproptosis, or restricting copper availability to disrupt tumor-supportive pathways. As cuproptosis-related metabolic vulnerabilities become clearer, combining copper-modulating therapy with existing endocrine therapy, chemotherapy, or immunotherapy may further enhance antitumor efficacy. Such evidence underscores the translational value of cuproptosis and positions it as an emerging therapeutic avenue in BC management. However, systematic research on how cuproptosis affects BC is yet to be conducted, with relatively limited literature available. Additional experimental and clinical evidence is needed to elucidate the molecular pathways. Of note, other types of RCD, such as ferroptosis and pyroptosis, are also closely related to tumor drug resistance and changes in the immune microenvironment. Thus, RCD plays an indispensable role in BC biology. In our study, we performed consensus clustering using BC samples with 13 cuproptosis-related genes, classifying patients into two groups with distinct cuproptosis levels. We then performed differential analysis to identify cuproptosis-related genes in BC. Their significance in tumor prognosis and potential therapeutic sensitivity cannot be ignored. The targeted regulation of these genes or pathways may lead us to new possibilities for personalized BC treatment. Combining the two major fields of ceRNA and cuproptosis research may help elucidate the complexity of tumor cell survival and death regulatory networks, providing relevant evidence for subsequent drug development and clinical translation.

BCL11A, which we identified as a cuproptosis-related gene, encodes a transcriptional regulator closely associated with cell proliferation, drug resistance, and metastasis in various malignant tumors. High BCL11A expression is often linked to enhanced tumor invasiveness and poor patient prognosis, indicating its significant role in tumor biology (42). As our understanding of the ceRNA theory deepens, BCL11A may be considered a potential core target gene in this network that forms mutually regulatory positive and negative feedback loops with specific lncRNAs or miRNAs (43). For instance, BCL11A participates in an important ceRNA network in BC, namely the SNHG16-has-miR-190b-BCL11A axis. In this network, the lncRNA SNHG16 acts as a molecular sponge for miR-190b, competitively binding to miR-190b, thereby reducing the inhibitory effect miR-190b on BCL11A (44). This leads to the upregulation of BCL11A expression, which promotes BC cell proliferation, invasion, and metastasis. However, the specific mechanism of action of BCL11A as a cuproptosis-related gene in BC remains insufficiently investigated, making it difficult to systematically identify its molecular regulatory network. Here, we established a co-regulatory network of BCL11A and ncRNAs in BC, conducting a comprehensive analysis of how BCL11A integrates into the ceRNA network. This further elucidates the underlying mechanisms of BC development and progression, offering feasible entry points for designing clinical diagnosis and treatment strategies.

In conclusion, our research findings demonstrate that WT1-AS can act as a competitive binding molecule for miR-206, thereby upregulating BCL11A expression and influencing the establishment of the malignant phenotype of BC cells. Integrating ncRNAs and BCL11A into new diagnostic or therapeutic strategies could serve as a feasible approach for precision interventions in BC. We also speculate that the WT1-AS/miR-206/ceRNA axis may be associated with RCDs, particularly cuproptosis. Our findings offer insights into the optimization of tumor therapy at the RCD level. Our experimental and analytical findings further confirm the critical position of this axis in BC progression, laying the foundation for subsequent scientific research and clinical applications.

Conclusions

Overall, this study indicated that WT1-AS/miR-206/BCL11A axis as a key regulator in BC progression. We demonstrate that WT1-AS sequesters miR-206 to upregulate BCL11A, thereby enhancing tumor proliferation and migration. Importantly, BCL11A suppression increases intracellular copper and ROS levels, promoting cuproptosis—a link that mechanistically connects ceRNA activity with RCD. These findings not only elucidate a novel oncogenic pathway but also propose targeting cuproptosis as a promising strategy for BC therapy.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the MDAR reporting checklist. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1448/rc

Data Sharing Statement: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1448/dss

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Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1448/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

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References

1. De Miglio MR, Mello-Thoms C. Editorial: Reviews in breast cancer. Front Oncol 2023;13:1161583. [Crossref] [PubMed]
2. Fan JH, Zhang S, Yang H, et al. Molecular subtypes predict the preferential site of distant metastasis in advanced breast cancer: a nationwide retrospective study. Front Oncol 2023;13:978985. [Crossref] [PubMed]
3. Ye F, Dewanjee S, Li Y, et al. Advancements in clinical aspects of targeted therapy and immunotherapy in breast cancer. Mol Cancer 2023;22:105. [Crossref] [PubMed]
4. Poliseno L, Lanza M, Pandolfi PP. Coding, or non-coding, that is the question. Cell Res 2024;34:609-29. [Crossref] [PubMed]
5. Wang Z, Cui Q, Su C, et al. Unveiling the secrets of non-coding RNA-encoded peptides in plants: A comprehensive review of mining methods and research progress. Int J Biol Macromol 2023;242:124952. [Crossref] [PubMed]
6. Yadav P, Tamilselvan R, Mani H, et al. MicroRNA-mediated regulation of nonsense-mediated mRNA decay factors: Insights into microRNA prediction tools and profiling techniques. Biochim Biophys Acta Gene Regul Mech 2024;1867:195022. [Crossref] [PubMed]
7. Li Y, Jin H, Li Q, et al. The role of RNA methylation in tumor immunity and its potential in immunotherapy. Mol Cancer 2024;23:130. [Crossref] [PubMed]
8. Singh R, Ha SE, Yu TY, et al. Dual Roles of miR-10a-5p and miR-10b-5p as Tumor Suppressors and Oncogenes in Diverse Cancers. Int J Mol Sci 2025;26:415. [Crossref] [PubMed]
9. Bahari Khasraghi L, Nouri M, Vazirzadeh M, et al. MicroRNA-206 in human cancer: Mechanistic and clinical perspectives. Cell Signal 2023;101:110525. [Crossref] [PubMed]
10. Ramoni D, Montecucco F. MicroRNA-206 as a promising epigenetic approach to modulate tumor-associated macrophages in hepatocellular carcinoma. World J Gastroenterol 2024;30:4503-8. [Crossref] [PubMed]
11. Tani H. Biomolecules Interacting with Long Noncoding RNAs. Biology (Basel) 2025;14:442. [Crossref] [PubMed]
12. Han Y, Li S, Oyang L, et al. Novel insights into lncRNAs as key regulators of post-translational modifications in cancer: mechanisms and therapeutic potential. Cell Oncol (Dordr) 2025;48:1219-44. [Crossref] [PubMed]
13. Zhang X, Jin M, Liu S, et al. The roles and molecular mechanisms of long non-coding RNA WT1-AS in the maintenance and development of gastric cancer stem cells. Heliyon 2023;9:e14655. [Crossref] [PubMed]
14. Wu J, Zhang C, Li H, et al. Competing endogenous RNAs network dysregulation in oral cancer: a multifaceted perspective on crosstalk and competition. Cancer Cell Int 2024;24:431. [Crossref] [PubMed]
15. Yilmaz A, Ari Yuka S. The role of ceRNAs in breast cancer microenvironmental regulation and therapeutic implications. J Mol Med (Berl) 2025;103:33-49. [Crossref] [PubMed]
16. Chen Y, Li X, Yang M, et al. Research progress on morphology and mechanism of programmed cell death. Cell Death Dis 2024;15:327. [Crossref] [PubMed]
17. Luo M, Luan X, Yang C, et al. Revisiting the potential of regulated cell death in glioma treatment: a focus on autophagy-dependent cell death, anoikis, ferroptosis, cuproptosis, pyroptosis, immunogenic cell death, and the crosstalk between them. Front Oncol 2024;14:1397863. [Crossref] [PubMed]
18. Pan C, Ji Z, Wang Q, Zhang Z, et al. Cuproptosis: Mechanisms, biological significance, and advances in disease treatment-A systematic review. CNS Neurosci Ther 2024;30:e70039. [Crossref] [PubMed]
19. Xie J, Yang Y, Gao Y, et al. Cuproptosis: mechanisms and links with cancers. Mol Cancer 2023;22:46. [Crossref] [PubMed]
20. Mehmud S. Wu, S. Zhang. The Role of Copper Ions in Mediating the Anti-Cancer Effects Using Nanomaterials. Adv Therap 2025;8:2400426.
21. Gu J, Guo C, Ruan J, et al. From ferroptosis to cuproptosis, and calcicoptosis, to find more novel metals-mediated distinct form of regulated cell death. Apoptosis 2024;29:586-604. [Crossref] [PubMed]
22. Lin CH, Chin Y, Zhou M, et al. Protein lipoylation: mitochondria, cuproptosis, and beyond. Trends Biochem Sci 2024;49:729-44. [Crossref] [PubMed]
23. Zhu Z, Song M, Ren J, et al. Copper homeostasis and cuproptosis in central nervous system diseases. Cell Death Dis 2024;15:850. [Crossref] [PubMed]
24. Liu X, Luo B, Wu X, et al. Cuproptosis and cuproptosis-related genes: Emerging potential therapeutic targets in breast cancer. Biochim Biophys Acta Rev Cancer 2023;1878:189013. [Crossref] [PubMed]
25. Shen Z, Qiu Y, Ding H, et al. Cuproptosis and Cuproptosis-Based Synergistic Therapy for Cancer Treatment. ChemMedChem 2024;19:e202400216. [Crossref] [PubMed]
26. Noh D, Lee H, Lee S, et al. Copper-Based Nanomedicines for Cuproptosis-Mediated Effective Cancer Treatment. Biomater Res 2024;28:0094.
27. Qian J, Zhao L, Xu L, et al. Cell Death: Mechanisms and Potential Targets in Breast Cancer Therapy. Int J Mol Sci 2024;25:9703. [Crossref] [PubMed]
28. MumtazKHussainIIftikharFPrioritization of The Zinc finger domain within BCL11A by the Amelioration capability of hemoglobinopathies using CRISPR-Cas9 technology.bioRxiv 2023. Available online: https://www.biorxiv.org/content/10.1101/2023.11.28.568974v2.full.pdf
29. Kątnik E, Gomułkiewicz A, Piotrowska A, et al. BCL11A Expression in Breast Cancer. Curr Issues Mol Biol 2023;45:2681-98. [Crossref] [PubMed]
30. Xu S, Huang L, Liu X, et al. The transcription factor Bcl11a is essential for B-1a cell maintenance during aging. Proc Natl Acad Sci U S A 2025;122:e2501974122. [Crossref] [PubMed]
31. Yin J, Xie X, Quan Y, et al. RNA-seq analysis reveals candidate genes associated with proliferation, invasion, and migration in BCL11A knockdown B-NHL cell lines. Ann Hematol 2023;102:1845-56. [Crossref] [PubMed]
32. He ZY, Zhuo RG, Yang SP, et al. CircNCOR1 regulates breast cancer radiotherapy efficacy by regulating CDK2 via hsa-miR-638 binding. Cell Signal 2023;109:110787. [Crossref] [PubMed]
33. Xiong X, Zheng LW, Ding Y, et al. Breast cancer: pathogenesis and treatments. Signal Transduct Target Ther 2025;10:49. [Crossref] [PubMed]
34. Yang S, Wang X, Zhou X, et al. ncRNA-mediated ceRNA regulatory network: Transcriptomic insights into breast cancer progression and treatment strategies. Biomed Pharmacother 2023;162:114698. [Crossref] [PubMed]
35. To KKW, Zhang H, Cho WC. Competing endogenous RNAs (ceRNAs) and drug resistance to cancer therapy. Cancer Drug Resist 2024;7:37. [Crossref] [PubMed]
36. Rajakumar S, Jamespaulraj S, Shah Y, et al. Long non-coding RNAs: an overview on miRNA sponging and its co-regulation in lung cancer. Mol Biol Rep 2023;50:1727-41. [Crossref] [PubMed]
37. Ghasemian M, Zehtabi M, Dari MAG, et al. The emerging roles of long non-coding RNA (lncRNA) H19 in gynecologic cancers. BMC Cancer 2024;24:4. [Crossref] [PubMed]
38. Zhu Z, Zhu K, Zhang J, et al. Elucidating the evolving role of cuproptosis in breast cancer progression. Int J Biol Sci 2024;20:4872-87. [Crossref] [PubMed]
39. Zhang L, Deng R, Liu L, et al. Novel insights into cuproptosis inducers and inhibitors. Front Mol Biosci 2024;11:1477971. [Crossref] [PubMed]
40. Li Z, Zhang H, Wang X, et al. Identification of cuproptosis-related subtypes, characterization of tumor microenvironment infiltration, and development of a prognosis model in breast cancer. Front Immunol 2022;13:996836. [Crossref] [PubMed]
41. Tang D, Kroemer G, Kang R. Targeting cuproplasia and cuproptosis in cancer. Nat Rev Clin Oncol 2024;21:370-88. [Crossref] [PubMed]
42. Angius A, Pira G, Cossu-Rocca P, et al. Deciphering clinical significance of BCL11A isoforms and protein expression roles in triple-negative breast cancer subtype. J Cancer Res Clin Oncol 2023;149:3951-63. [Crossref] [PubMed]
43. Bure IV, Nemtsova MV. Mutual Regulation of ncRNAs and Chromatin Remodeling Complexes in Normal and Pathological Conditions. Int J Mol Sci 2023;24:7848. [Crossref] [PubMed]
44. Gong PJ, Shao YC, Huang SR, et al. Hypoxia-Associated Prognostic Markers and Competing Endogenous RNA Co-Expression Networks in Breast Cancer. Front Oncol 2020;10:579868. [Crossref] [PubMed]