Abstract

Objective

   Our previous study found that Achaete-scute complex homolog 1 (ASCL1)
   is involved in classifying BC subtypes with different prognostic
   and pathological characteristics. However, the biological role of ASCL1
   in BC still remains largely unexplored. This study aims to elucidate
   the function of ASCL1 in BC using bioinformatics analyses, as well as
   in vitro and in vivo experimental approaches.

Methods

   Data from the TCGA, GEO, and Human Protein Atlas databases were
   utilized to evaluate ASCL1 expression in BC and its association with
   patient prognosis. Genetic alterations in ASCL1 were assessed through
   the COSMIC and cBioPortal databases, while the TIMER2.0 database
   provided insights into the relationship between ASCL1 expression and
   key gene mutations in BC. The GDSC database was used to examine
   correlations between ASCL1 levels and sensitivity to standard
   chemotherapeutic agents. Associations between ASCL1 expression and
   cytokines, immunomodulatory factors, MHC molecules, and receptors were
   analyzed using Pearson and Spearman correlation methods. The TIP
   database was employed to investigate the connection between ASCL1
   expression and immunoreactivity scores, and six computational
   approaches were applied to evaluate immune cell infiltration.
   Functional assays were conducted on BC cell lines MCF-7 and MDA-MB-231,
   and nude mouse models were used for in vivo studies.

Results

   ASCL1 was found to be upregulated in BC and correlated with unfavorable
   prognosis and mutations in key oncogenes. Its expression was linked to
   immunomodulatory factors, immune cell infiltration, and
   immunoreactivity scores in the tumor microenvironment. Additionally,
   ASCL1 influenced tumor immune dynamics and chemosensitivity in BC.
   Overexpression of ASCL1 enhanced BC cell proliferation, migration and
   invasion, while its knockdown had the opposite effect. Notably,
   inhibition of ASCL1 increased BC cell sensitivity to paclitaxel both in
   vitro and in vivo. In addition, inhibition of ASCL1 activated
   ferroptosis in BC, including altered mitochondrial morphology,
   increased MDA and ROS levels, decreased GSH levels and reduced GSH/GSSG
   ratio. Mechanistically, inhibition of ASCL1 decreases the
   phosphorylation of CREB1, thus reducing the expression of GPX4. In
   summary, inhibition of ASCL1 increases paclitaxel sensitivity by
   activating ferroptosis via the CREB1/GPX4 axis.

Conclusions

   ASCL1 exerts oncogenic effects in BC and represents a potential
   therapeutic target for intervention.

   Keywords: breast cancer, ASCL1, ferroptosis, therapeutic target, drug
   sensitivity

1. Introduction

   Breast cancer is the most prevalent malignant tumor among women
   worldwide ([45]1), with 2,308,897 new cases in 2022, and its incidence
   continues to rise annually ([46]1, [47]2). Although a variety of
   therapeutic options are available—such as surgery, chemotherapy,
   endocrine therapy, radiotherapy, immunotherapy and anti-HER2 therapy—BC
   is still the leading cause of cancer-related deaths among women ([48]1,
   [49]3). This underscores the urgent need for advancements that can
   enhance patient survival.

   Molecular markers in cancer serve as measurable indicators at the
   molecular level, aiding in accurate diagnosis, risk assessment,
   treatment response prediction, and prognosis evaluation. Moreover, many
   of the new targeted therapies are effective only in patients harboring
   specific genetic mutations, making molecular biomarkers essential for
   identifying these patient subgroups ([50]4). Consequently, the
   discovery of novel BC tumor markers with high specificity and
   sensitivity is essential for improving patient outcomes.

   ASCL1, a member of the basic helix-loop-helix (bHLH) family of
   transcription factors ([51]5), was initially identified in the 1980s in
   Drosophila melanogaster as a proneural gene due to its ability to
   induce neural identity in naïve ectodermal cells ([52]6). As a key bHLH
   family member, ASCL1 regulates glial cell proliferation and specifies
   neuronal phenotype and subtype during forebrain development
   ([53]7–[54]10). This led to its early association with nervous system
   development. However, in 2014, ASCL1 was recognized as an oncogene and
   therapeutic target in high-grade neuroendocrine lung cancer ([55]11).
   Subsequent research has increasingly linked ASCL1 overexpression to
   oncogenic activity in various cancers, with significant associations
   with poor prognosis and therapeutic resistance. For example, Miyashita
   N et al. ([56]12, [57]13) reported that in lung adenocarcinoma,
   patients with ASCL1 high expression had an attenuated response to
   immunotherapy. Similarly, Wei J et al. ([58]14) found that among
   individuals with small cell lung cancer (SCLC), those who were
   ASCL1-positive had significantly poorer survival outcomes compared to
   ASCL1-negative patients. Furthermore, ASCL1 overexpression facilitates
   prostate cancer progression and metastasis, while its inhibition
   reduces tumor cell proliferation ([59]15). These findings consistently
   demonstrate ASCL1’s role in promoting tumor progression.

   In addition to promoting the progression of a wide range of tumors,
   ASCL1 has been recognized as a molecular marker for delineating SCLC
   subtypes. In 2019, Rudin et al. ([60]16) proposed to classify SCLC into
   four subtypes, A, N, Y, and P, based on four transcription factors
   (ASCL1, NeuroD1, YAP1, and POU2F3), advancing the development of
   precision therapy for SCLC. Currently, this classification criterion
   has been widely used in SCLC. In addition, a previous study by our team
   also demonstrated that ASCL1 is an important chromatin regulator that
   helps to classify BC subtypes with different prognostic and
   pathological features ([61]17). This finding suggests a potential role
   for ASCL1 in BC. However, the biological role of ASCL1 in BC still
   remains largely unexplored. Therefore, we conducted this study.

   This study aims to systematically evaluate the multifaceted role of
   ASCL1 in BC, focusing on its influence on biological processes, tumor
   immunology, treatment response, and prognosis, using a combination of
   bioinformatics, in vitro, and in vivo methodologies. Additionally, this
   study elucidates the specific molecular mechanisms through which ASCL1
   regulates BC progression and assesses its potential as a novel
   therapeutic target.

2. Methods

2.1. Collection of data

   RNA-seq data of ASCL1 for 33 pan-cancers and normal breast tissues were
   obtained from the Cancer Genome Atlas (TCGA) and the Genotype-Tissue
   Expression Project (GTEx) database, respectively. Relationship between
   ASCL1 mRNA expression and clinical survival data were sourced from the
   Gene Expression Omnibus (GEO) database ([62]GSE11121, [63]GSE20711).
   Additionally, immunohistochemical staining data for ASCL1 in normal
   breast tissue and BC samples were collected from the Human Protein
   Atlas (HPA) database and the Fourth Hospital of Hebei Medical
   University, respectively. Further details on other databases utilized
   in this study are provided below.

2.2. Differential expression analysis

   To analyze ASCL1 mRNA expression, comparisons were made between normal
   and tumor tissues. Duplicate and missing RNA-seq entries from the GTEx
   and TCGA databases were excluded, and the remaining data were
   log-transformed to log[2](TPM + 1). Western blot analysis was used to
   evaluate the protein expression levels of ASCL1, comparing the human
   normal breast epithelial cell line (MCF-10A) to BC cell lines
   (MDA-MB-231 and MCF-7). Immunohistochemical staining further evaluated
   ASCL1 expression intensity in normal breast and BC tissues.

2.3. Survival analysis

   Based on the median ASCL1 mRNA level, patients were divided into a high
   ASCL1 expression group and a low ASCL1 expression group for subsequent
   analyses. Prognostic associations between ASCL1 expression and outcomes
   of patients with BC, including recurrence-free survival (RFS) and
   overall survival (OS), were investigated.

2.4. Genetic alteration analysis

   Mutation frequency and specific mutation sites of ASCL1 in BC were
   examined using the cBioPortal database ([64]18), while the Catalogue of
   Somatic Mutations in Cancer (COSMIC) database was employed to
   categorize mutation types in ASCL1 in BC ([65]19).

   Additionally, the relationship between the ASCL1 expression and
   mutations in key BC-related genes was also evaluated using a chi-square
   test. All mutation data of TCGA samples were derived from the Genomic
   Data Commons (GDC) database ([66]20).

2.5. Immunomodulatory analysis

   Correlation analyses between ASCL1 expression and immunomodulatory
   factors—including cytokines, immunoregulators, MHC molecules, and
   receptors—were performed across pan-cancer datasets using Pearson and
   Spearman methods. The relevant data were acquired from the TCGA
   database.

2.6. Immune activity score analysis

   The Tracking Tumor Immunophenotype (TIP) database was employed to
   assess the relationship between ASCL1 expression and immune activity
   scores. The TIP platform allowed systematic analysis and visualization
   of the proportions of tumor-infiltrating immune cells across the
   seven-step Cancer-Immunity Cycle ([67]21).

2.7. Immune cell infiltration analysis

   Data on immune cell infiltration were retrieved from the TIMER2.0
   database, and immune cell infiltration scores were calculated to
   analyze their correlation with ASCL1 expression using six methods:
   TIMER, EPIC, XCELL, CIBERSORT, QUANTISEQ, and MCP-COUNTER ([68]22).

2.8. Drug sensitivity analysis

   To explore the relationship between ASCL1 expression and sensitivity to
   common chemotherapeutic agents used in BC treatment. RNAseq data and
   corresponding clinical information for BC were obtained from the TCGA
   database. Subsequently, the chemotherapy response of each sample was
   predicted based on the Genomics of Drug Sensitivity in Cancer (GDSC)
   database. The prediction process was implemented using the pRRophetic
   package of R software, and the half-maximal inhibitory concentration
   (IC50) of the samples was estimated using ridge regression. All
   parameters were set as the default values. Using the batch effect of
   combat and tissue type of all tissues, the duplicate gene expression
   was summarized as a mean value.

2.9. Pathway enrichment analysis

   To investigate the pathways by which ASCL1 regulates BC progression,
   Gene Set Enrichment Analysis (GSEA) and Kyoto Encyclopedia of Genes and
   Genomes (KEGG) pathway analyses were employed to elucidate the
   differential gene enrichment between high ASCL1 and low ASCL1
   expression groups in BC.

2.10. Cell culture

   The three cell lines were obtained from the Cell Resource Centre of the
   Chinese Academy of Medical Sciences. All these cells were cultured
   using the DMEM medium containing 4.5 g glucose (Gibco, USA). The medium
   contained 10% fetal bovine serum (FBS) and 1% penicillin/streptomycin
   (Gibco, USA).

2.11. Lentivirus infection and cell transfection

   To generate stable ASCL1 knockdown or overexpression in BC cells,
   lentivirus-mediated infection was performed. Plasmids and lentiviruses
   were transfected into HEK-293T cells, followed by infection of BC cells
   with virus-packed plasmids. Stable cell lines were selected using
   puromycin.

   For cell transfection, The two BC cells were seeded in six-well plates.
   When the cells reached 70%-80% confluence, Lipofectamine 3000 and
   plasmids were used to transfect the cells. The efficiency of
   transfection was verified by qRT-PCR and Western blot analysis.

2.12. Cell viability assay

   In this study, the CCK-8 assay (Yeasen, China) was utilized to assess
   cell proliferation and identify the IC50 of paclitaxel. For
   proliferation analysis, cells (2 × 10^3) were seeded in the 96-well
   plates. CCK-8 reagent (10 μl) was added to each well on days 1, 2 and
   3, respectively, and incubated for 2 h. The absorbance was then
   measured at 450 nm and the value of optical density (OD) 450 was
   recorded.

   For IC50 determination, cells (5 × 10^3) were seeded in 96-well plates
   and cultured for 24 hours. Afterwards, the cells were exposed to a
   paclitaxel-containing medium with varying concentrations. The tested
   concentrations for MCF-7 cells were 0, 0.01, 0.05, 0.1, 0.2, 0.5, and 1
   mg/L, while those for MDA-MB-231 cells were 0, 0.1, 0.5, 1, 2, 4, and 8
   mg/L. Cells were incubated for an additional 48 hours and then the
   OD450 of each well was measured.

   To evaluate the association between BC sensitivity to paclitaxel and
   ferroptosis, cells (5 × 10^3) were seeded in 96-well plates and
   cultured for 24 hours. Following this, drugs were added to the culture
   medium, with final concentrations of Erastin at 5 μM and ferrostatin-1
   at 2 μM. Paclitaxel concentrations used were 0.01 mg/L for MCF-7 and 1
   mg/L for MDA-MB-231. Cells were cultured for an additional 24 hours,
   and OD450 was measured.

2.13. Clone formation

   Cells (1000 cells/well) were seeded in 6-well plates. After 14 days of
   culture, cells were fixed with 4% paraformaldehyde for 25 minutes,
   stained with 1% violet crystal for 20 minutes and colonies were
   counted.

2.14. Wound healing assay

   For wound healing assays, cells were transfected and cultured using a
   6-well plate then a 1 ml pipette tip was used to create a scratch, and
   wound closure was observed and photographed at 0, 24, and 48 hours
   using the microscope at 4x magnification.

2.15. Transwell assay

   The Transwell assay was performed to assess the migration and invasive
   capacity of BC cells. For migration assays, 50,000 cells were
   inoculated in the upper chamber with DMEM medium without FBS. 650 μl
   DMEM medium containing FBS was added to the lower chamber. After 2
   days, the cells on the bottom of the upper chamber were fixed with 4%
   formaldehyde for 25 minutes, stained with 1% crystal violet for 20
   minutes, and then photographed at 10x magnification.

   For invasion assays, the matrix (Beyotime, China) was diluted at a 1:7
   ratio with FBS-free DMEM, and 60 µl was added to the upper chamber.
   After a 4-hour incubation to solidify the matrix gel, the remaining
   steps were identical to the migration assay.

2.16. Detection of ferroptosis indicators

   The malondialdehyde (MDA) kit (BOXBIO, China), the reactive oxygen
   species (ROS) detection kit (Report Biotech, China) and the glutathione
   (GSH) and oxidized glutathione (GSSG) assay kit (Beyotime, China) were
   used to detect MDA level, ROS level, GSH level and GSH/GSSG ratio in
   cells following the manufacturer’s protocol.

2.17. Quantitative reverse transcription PCR

   Total RNA was extracted from BC cells and then synthesized into cDNA
   and performed qRT-PCR. The quantification of target gene expression was
   calculated using the 2^−ΔΔCt method based on cycle threshold (Ct)
   values.

2.18. Western blot analysis

   Protein samples (40 µg each) extracted from the cells were first
   separated by electrophoresis on 10% SDS-PAGE. The proteins were then
   transferred to a PVDF membrane and blocked with 5% skimmed milk. The
   membrane was then incubated with primary antibody for 8-12 hours at 4°C
   and washed 3 times with PBST, then incubated with secondary antibody
   for 2 hours and washed 3 times with PBST.

2.19. Co-immunoprecipitation

   The rProtein A/G Magnetic IP/Co-IP Kit was used for Co-IP (ACE
   biotechnology, China). Cell lysates were first incubated with
   antibodies and magnetic beads at 4°C overnight. Then discard the
   supernatant and rinse the beads. Finally, the magnetic beads were
   eluted and subjected to western blotting.

2.20. In vivo study

   For in vivo experiments, female BALB/c nude mice (Vital River, China)
   were used. Stably knocked-down sh-ASCL1-MCF-7 cells and control
   sh-NC-MCF-7 cells (1 × 10^7 cells) were injected subcutaneously in the
   right axillary region of the mice. One week post-injection, paclitaxel
   was administered intraperitoneally at a dose of 1 mg/kg every three
   days. After three weeks, the mice were euthanized, and subcutaneous
   tumors were excised to measure their weight. In addition, tumor volume
   was measured every five days. The formula is tumor volume = π/6 ×
   length × width^2.

2.21. Statistical analysis

   GraphPad Prism 9.5 and R software were used for the statistical
   analyses. The t-test, Wilcoxon test, and two-way ANOVA were used to
   evaluate relationships between variables, and the log-rank test was
   applied to assess cancer prognosis. P < 0.05 was considered
   statistically significant.

3. Results

3.1. ASCL1 is highly expressed in BC

   The mRNA expression of ASCL1 was analyzed across multiple cancer types
   using combined data from the TCGA and GTEx databases, addressing the
   lack of normal individual data in TCGA. ASCL1 expression was
   significantly elevated in various cancer tissues, including BC ([69]
   Figures 1A, B ). Consistent results were obtained when analyzing TCGA
   data alone, showing increased ASCL1 expression in BC tissues compared
   to normal tissues, as confirmed by pairwise comparisons ([70]
   Figures 1C, D ).

Figure 1.

   [71]Figure 1
   [72]Open in a new tab

   ASCL1 is highly expressed in BC and correlates with poor prognosis. (A)
   Comparative analysis of ASCL1 mRNA expression across pan-cancers,
   including BC, and normal tissues based on data from the TCGA and GTEx
   databases. (B, C) ASCL1 mRNA expression comparison between BC and
   normal tissues using data from (B) the combined TCGA and GTEx
   databases, and (C) the TCGA database alone. (D) ASCL1 mRNA expression
   in paired BC and adjacent normal tissues from the TCGA database. (E)
   Immunohistochemical staining of ASCL1 in normal breast tissue from the
   HPA database. (F) Immunohistochemical staining of ASCL1 in BC tissues.
   (G) Protein expression levels of ASCL1 in normal breast epithelial
   cells and BC cell lines. (H, I) Prognostic impact of ASCL1 expression
   on (H) RFS, (I) OS in patients with BC. (*P < 0.05; **P < 0.01; ***P <
   0.001).

   Further investigation into ASCL1 protein levels revealed a similar
   trend. IHC data from the HPA database showed that ASCL1 was typically
   undetectable in normal breast tissue ([73] Figure 1E ), while strong
   positivity was observed in BC samples ([74] Figure 1F ). Western blot
   analysis further supported these results, indicating significantly
   higher ASCL1 protein levels in BC cells than in the normal breast
   epithelial cells ([75] Figure 1G ). These results consistently suggest
   that ASCL1 is overexpressed in BC.

3.2. ASCL1 is associated with poor prognosis in BC

   The relationship between ASCL1 expression and prognosis in BC was also
   evaluated. Patients with elevated ASCL1 expression had significantly
   poorer RFS and OS compared to those with lower expression levels ([76]
   Figures 1H, I ). All these results imply that ASCL1 may play a
   cancer-promoting role in BC.

3.3. Genetic alterations of ASCL1 in BC

   The frequency and types of ASCL1 mutations in primary breast cancer
   were examined using a dataset of 2051 samples with complete DNA
   sequencing data from the cBioPortal database ([77]23, [78]24).
   Additionally, two metastatic BC datasets, including 481 patients from
   The Metastatic Breast Cancer Project and 216 patients from Lefebvre C
   et al. ([79]25), totaling 697 patients, were analyzed for ASCL1
   alteration types and frequencies in metastatic cases. In primary BC,
   the ASCL1 alteration frequency was found to be 0.4% (9/2051) ([80]
   Figure 2A ). In contrast, the frequency in metastatic BC was
   significantly higher at 5% (35/697), consisting of 14 cases of
   amplification, 19 cases of deep deletion, and 2 cases of missense
   mutations, with a statistically significant difference between primary
   and metastatic groups (P < 0.001) ([81] Figure 2B ). Mutation mapping
   further identified specific sites of missense mutations in ASCL1 in
   metastatic cases ([82] Figure 2C ). In addition, we further analyzed
   the mutation frequency and type of ASCL1 in BC using the COSMIC
   database. Among 2779 BC samples that were tested, 7 exhibited ASCL1
   mutations. These included 2 synonymous substitutions, 1 in-frame
   insertion, 1 in-frame deletion, and 3 other mutation types ([83]
   Figure 2D ). Both synonymous substitutions involved C>T transitions
   ([84] Figure 2E ).

Figure 2.

   [85]Figure 2
   [86]Open in a new tab

   Genetic alterations of ASCL1 in BC and their relationship to mutations
   in other key genes. (A, B) Oncoprint representation of ASCL1 genetic
   alterations in (A) primary BC and (B) metastatic BC, based on data from
   the cBioPortal database. (C) Diagram showing ASCL1 mutations across
   protein domains in metastatic BC, derived from cBioPortal data. (D, E)
   Types of (D) ASCL1 mutations and (E) specific substitution mutation of
   ASCL1 in BC from the COSMIC database. (F) Mutation landscape of key
   genes stratified by high and low ASCL1 expression in BC.

3.4. ASCL1 correlates with mutations in major key genes in BC

   The gene mutation landscape was compared between the high- and
   low-ASCL1 expression groups in BC ([87] Figure 2F ). Significant
   differences were observed in the mutation rates of several key genes,
   including TP53, GATA3, PI3KR1, SMG1, and UTP20. Notably, the mutation
   rates of TP53 and PIK3R1—genes strongly linked to poor prognosis in
   BC—were significantly higher in the ASCL1-high group, suggesting a
   potential association between elevated ASCL1 expression and BC
   progression.

3.5. ASCL1 is associated with immunomodulatory factors, immune cell
infiltration, and immune activity score in BC

   To investigate the impact of elevated ASCL1 expression on BC, the
   relationship between ASCL1 and tumor immunity was examined. Given the
   critical role of the tumor microenvironment in modulating immune
   responses ([88]26), ASCL1’s association with various immunomodulatory
   factors—including cytokines, immunomodulators, MHC molecules, and
   receptors—was assessed across multiple cancers, including BC. Both
   Pearson and Spearman correlation analyses demonstrated a strong
   association between ASCL1 and immunomodulatory factors in pan-cancer
   contexts ([89] Figures 3A, B ). In BC specifically, ASCL1 expression
   was negatively correlated with these factors.

Figure 3.

   [90]Figure 3
   [91]Open in a new tab

   Correlation between ASCL1 expression and immunomodulatory factors in
   pan-cancer analysis. (A) Pearson and (B) Spearman correlation analyses
   between ASCL1 and immunomodulatory factors from the TCGA database.

   The relationship between ASCL1 expression and immune cell infiltration
   was further explored using six different analytical methods,
   categorizing patients into ASCL1-high and ASCL1-low expression groups.
   ASCL1 expression showed associations with a wide range of cell types,
   including B cells, Treg cells, NK cells, CD4^+ and CD8^+ T cells, M1
   and M2 macrophages, myeloid dendritic cells, neutrophils, monocytes,
   endothelial cells, and mast cells ([92] Figures 4A-F ). Notably, the
   ASCL1-high group exhibited increased M2 macrophage infiltration and
   decreased M1 macrophage infiltration. Given that M2 macrophages are
   known to promote tumor progression and suppress immune function in
   malignancies ([93]27–[94]29) ([95] Figure 4A ), these results indicate
   a potential role for ASCL1 in immune evasion. However, we also found
   that the effects of ASCL1 on the immune response were not entirely
   consistent, for example, patients in the high ASCL1 group had less
   neutrophil infiltration and more infiltration of Treg cells, CD4^+ and
   CD8^+ T cells. Normally neutrophils and Treg cells are considered to be
   pro-tumor progression cells, while CD4^+ and CD8^+ T cells are cells
   that inhibit tumor progression. These results seem to demonstrate that
   there are conflicting roles for the ASCL1 tumor immune response, but
   what is certain is that ASCL1 is associated with the infiltration of
   immune cells in BC. We also assessed the relationship between ASCL1
   expression and immunoreactivity scores, revealing that ASCL1 expression
   was also associated with immune response scores in BC ([96] Figure 4G
   ).

Figure 4.

   [97]Figure 4
   [98]Open in a new tab

   Association of ASCL1 expression with tumor immunity and chemotherapy
   response in BC. (A-F) Correlations between ASCL1 expression and immune
   cell infiltration, using various methods: (A) QUANTISEQ, (B) TIMER, (C)
   EPIC, (D) MCP-COUNTER, (E) CIBERSORT, and (F) XCELL. (G) Correlation
   between ASCL1 expression and immune activity score in BC. (H-K)
   Comparison of IC50 values for common chemotherapeutic drugs between
   high and low ASCL1 expression groups in BC: (H) docetaxel, (I)
   paclitaxel, (J) cisplatin, and (K) doxorubicin. (*P < 0.05; **P < 0.01;
   ***P < 0.001; ****P < 0.0001), ns, not significant.

3.6. ASCL1 correlates with drug sensitivity in BC

   Building on these results, the relationship between ASCL1 expression
   and sensitivity to chemotherapeutic drugs was evaluated using the GDSC
   database. Differences in the IC50 values of commonly used
   chemotherapeutics, including docetaxel, paclitaxel, cisplatin, and
   doxorubicin, were compared between the high- and low-ASCL1 expression
   groups. The IC50 values for all four drugs were significantly higher in
   the ASCL1-high group ([99] Figures 4H-K ), indicating reduced
   sensitivity to these treatments. This suggests that elevated ASCL1
   expression negatively impacts chemotherapy efficacy in BC.

3.7. Inhibition of ASCL1 inhibits BC progression in vitro

   The bioinformatics findings suggested a critical role for ASCL1 in BC
   progression, prompting further experimental validation. To investigate
   this, BC cell lines stably overexpressing ASCL1 (OE-ASCL1-MCF-7,
   OE-ASCL1-MDA-MB-231) were established. Both qRT-PCR and Western blot
   analyses confirmed that ASCL1 mRNA and protein levels were
   significantly increased in overexpressing cells ([100] Figures 5A, B ).
   In terms of functional tests, CCK-8 and colony formation tests showed
   that ASCL1 overexpression significantly increased the proliferation of
   BC cells ([101] Figures 5C, D ). The wound healing assay demonstrated a
   marked increase in cell migration ([102] Figures 5E-H ), which was
   further corroborated by Transwell assays showing that ASCL1
   significantly promoted both migration and invasion ([103] Figures 5I-L
   ). These results collectively demonstrate that ASCL1 drives malignant
   behaviors in BC cells in vitro, promoting proliferation, migration and
   invasion.

Figure 5.

   [104]Figure 5
   [105]Open in a new tab

   ASCL1 promotes BC cell progression. (A, B) Assessment of ASCL1
   overexpression efficiency through (A) qRT-PCR and (B) Western blot
   analysis. (C, D) (C) Colony formation assay and (D) CCK-8 assay showing
   that ASCL1 overexpression enhances the proliferation of MCF-7 and
   MDA-MB-231 cells. (E, F) Wound healing assay demonstrating enhanced
   migration of (E) MCF-7 and (F) MDA-MB-231 cells with ASCL1
   overexpression (scale bar = 500μm). (G, H) Histogram of percentage of
   wound migration area for (G) MCF-7 and (H) MDA-MB-231 cells. (I, J)
   Transwell assay revealing that ASCL1 overexpression promotes migration
   and invasion of (I) MCF-7 and (J) MDA-MB-231 cells (scale bar = 200μm).
   (K, L) Histogram of the results of transwell assay on (K) MCF-7 and (L)
   MDA-MB-231 cells. (*P < 0.05; **P < 0.01; ***P < 0.001; ****P <
   0.0001).

   To investigate the potential of targeting ASCL1 to inhibit BC
   progression in vitro, ASCL1 expression was knocked down in BC cells
   using a transfected plasmid. Both qRT-PCR and Western blot analyses
   confirmed a significant reduction in ASCL1 levels ([106] Figures 6A, B
   ). ASCL1 inhibition led to a marked decrease in cell proliferation
   ([107] Figure 6C ). The wound healing and Transwell assays demonstrated
   that ASCL1 suppression significantly reduced cell migration and
   invasion ([108] Figures 6D-K ). Given the role of EMT in promoting cell
   migration ([109]30), the relationship between ASCL1 and EMT was
   examined. Western blot analysis confirmed that ASCL1 inhibition
   suppressed the EMT process, thereby reducing migration and invasion
   ([110] Figure 6L ). More importantly, ASCL1 inhibition led to an
   enhanced sensitivity to paclitaxel, indicating reduced drug resistance
   ([111] Figures 6M, N ). Further analysis showed that ASCL1 knockdown
   resulted in the downregulation of the ATP-binding cassette transporter
   (ABCG2), a protein linked to drug resistance in BC, also known as the
   breast cancer resistance protein ([112]31) ([113] Figure 6O ). The
   reduction in ABCG2 expression further supports the increased
   sensitivity of BC cells to paclitaxel following ASCL1 inhibition. These
   results collectively indicate that targeting ASCL1 effectively impairs
   BC progression in vitro.

Figure 6.

   [114]Figure 6
   [115]Open in a new tab

   Knockdown of ASCL1 inhibits BC progression in vitro. (A, B) Evaluation
   of ASCL1 knockdown efficiency following plasmid transfection using (A)
   qRT-PCR and (B) Western blot analysis. (C) CCK-8 assay indicates that
   ASCL1 knockdown suppresses BC cell proliferation in vitro. (D, E) Wound
   healing assay showing that ASCL1 knockdown inhibits migration in (D)
   MCF-7 and (E) MDA-MB-231 cells (scale bar = 500μm). (F, G) Histogram of
   percentage of wound migration area for (F) MCF-7 and (G) MDA-MB-231
   cells. (H, I) Transwell assay demonstrating that ASCL1 knockdown
   suppresses migration and invasion in (H) MCF-7 and (I) MDA-MB-231 cells
   (scale bar = 200μm). (J, K) Histogram of the results of transwell assay
   on (J) MCF-7 and (K) MDA-MB-231 cells. (L) ASCL1 knockdown inhibits the
   EMT process in BC. (M, N) ASCL1 knockdown enhances paclitaxel
   sensitivity in (M) MCF-7 and (N) MDA-MB-231 cells. (O) Reduction in
   ABCG2 expression following ASCL1 knockdown in BC cells. (*P < 0.05;
   **P < 0.01; ***P < 0.001; ****P < 0.0001).

3.8. Inhibition of ASCL1 improves BC paclitaxel sensitivity through
activation of ferroptosis

   To further investigate the impact of ASCL1 inhibition on BC cells, we
   obtained differentially expressed genes (DEGs) from the TCGA-BRCA
   cohort between the ASCL1 high- and low-expression groups and then
   performed GSEA analysis. The analysis showed that the DEGs were
   enriched in the ferroptosis pathway, indicating a potential link
   between ASCL1 and ferroptosis ([116] Figure 7A ). To validate this
   association, intracellular levels of MDA, ROS, GSH, and GSSG were
   measured, as these are key markers of ferroptosis ([117]32, [118]33).
   We found that ASCL1 inhibition resulted in increased levels of MDA and
   ROS, along with decreased GSH levels and a reduced GSH/GSSG ratio in
   both sh-ASCL1-MCF-7 and sh-ASCL1-MDA-MB-231 cells ([119] Figures 7B-F
   ). Transmission electron microscopy provided further evidence,
   revealing characteristic features of ferroptosis in the sh-ASCL1 group
   compared to the sh-NC group, including shrunken mitochondria, reduced
   size, loss of cristae, and increased membrane density ([120]
   Figures 7G, H ) ([121]34). These results collectively confirm that
   ASCL1 inhibition induces ferroptosis in BC cells.

Figure 7.

   [122]Figure 7
   [123]Open in a new tab

   ASCL1 knockdown activates ferroptosis in BC cells. (A) GSEA indicates
   an association between ASCL1 and ferroptosis in BC. (B) Increased MDA
   levels due to ASCL1 knockdown. (C) Decreased GSH levels in BC cells
   with ASCL1 knockdown. (D) Reduction in the GSH/GSSG ratio following
   ASCL1 knockdown. (E, F) Elevated ROS levels following ASCL1 knockdown
   in BC cells (scale bar = 500μm). (G, H) TEM showing changes in
   mitochondrial morphology characteristic of ferroptosis after ASCL1
   downregulation in (G) MCF-7 and (H) MDA-MB-231(scale bar = 2μm and 500
   nm). (I, J) ASCL1 knockdown enhances paclitaxel sensitivity associated
   with ferroptosis in (I) MCF-7 and (J) MDA-MB-231 cells (Erastin: 5 μM,
   ferrostatin-1: 2 μM, paclitaxel (MCF-7): 0.01 mg/L, paclitaxel
   (MDA-MB-231): 1 mg/L). (*P < 0.05; **P < 0.01; ***P < 0.001; ****P <
   0.0001), ns, not significant.

   Given the earlier observation that ASCL1 inhibition enhances paclitaxel
   sensitivity in BC cells, the potential involvement of ferroptosis was
   further explored. CCK-8 assays confirmed that sh-ASCL1 BC cells
   exhibited lower viability compared to sh-NC cells when treated with
   paclitaxel alone. However, co-treatment with the ferroptosis inhibitor
   ferrostatin-1 and paclitaxel eliminated the cell viability differences
   between the two groups. This suggests that ASCL1’s effect on paclitaxel
   sensitivity is linked to ferroptosis. Additionally, co-treatment with
   the ferroptosis activator erastin and paclitaxel significantly reduced
   cell viability, with a more pronounced reduction observed in the
   sh-ASCL1 group compared to the sh-NC group ([124] Figures 7I, J ).
   These results indicate that ASCL1 inhibition may enhance the
   sensitivity of BC cells to paclitaxel through the activation of
   ferroptosis.

3.9. Inhibition of ASCL1 activates ferroptosis via the CREB1/GPX4 axis in BC

   To elucidate the underlying mechanisms, 474 DEGs, comprising 248
   upregulated and 226 downregulated genes, were identified between high-
   and low-ASCL1 expression groups from the TCGA-BRCA cohort using a
   cutoff of |log2FC| > 1 and adjusted P < 0.05 for KEGG pathway analysis.
   The results indicated an association between ASCL1 and the cAMP
   signaling pathway ([125] Figure 8A ). Through bioinformatics analysis,
   we finally identified cAMP-responsive element-binding protein 1 (CREB1)
   as the target gene of ASCL1 for regulating ferroptosis in BC ([126]
   Supplementary Figure 1 ). CREB1 is a phosphorylation-dependent
   transcription factor. It regulates the transcription of GPX4, a key
   inhibitor of ferroptosis ([127] Supplementary Figure 1 ). Thus, the
   mechanism by which ASCL1 regulates ferroptosis in BC may be related to
   the CREB1/GPX4 axis.

Figure 8.

   [128]Figure 8
   [129]Open in a new tab

   Inhibition of ASCL1 activates ferroptosis via the CREB1/GPX4 axis. (A)
   KEGG analysis indicates an association between ASCL1 and the cAMP
   pathway. (B) Protein docking map and binding site of ASCL1 to CREB1.
   (C) Immunofluorescence assay illustrates co-localization of ASCL1 with
   CREB1. (D) Co-IP assay indicates that ASCL1 binds to CREB1 in BC. (E)
   ASCL1 knockdown inhibits the CREB1/GPX4 pathway in BC cells. (*P <
   0.05; **P < 0.01; ***P < 0.001).

   To verify our hypothesis, we first performed protein docking on the
   binding of ASCL1 to CREB1. [130]Figure 8B displays the structures and
   binding sites of the two proteins. The green one is ASCL1, the pink one
   is CREB1, and the blue part is the binding site of the two proteins. It
   was found that there was a very tight hydrogen bond connection between
   the two proteins, suggesting that there may be a potential association
   between ASCL1 and CREB1. We then implemented immunofluorescence
   experiments and found that ASCL1 and CREB1 co-localized in BC cells and
   that inhibition of ASCL1 significantly suppressed the co-localization
   ([131] Figure 8C ). The Co-IP experiments also revealed that ASCL1 was
   bound to CREB1 in BC and that inhibition of ASCL1 promotes complex
   dissociation ([132] Figure 8D ). These results suggest a possible
   regulatory association between ASCL1 and CREB1. Western blot analysis
   further confirmed that inhibition of ASCL1 in BC cells reduced the
   phosphorylation of CREB1 at Ser133 ([133] Figure 8E ). CREB1 is a
   phosphorylation-dependent transcription factor whose activity is
   significantly boosted upon phosphorylation ([134]35). Reduced
   phosphorylation of CREB1 resulted in decreased expression of GPX4,
   leading to activation of ferroptosis ([135] Figure 8G ). In summary,
   this study confirmed that ASCL1 regulates ferroptosis through the
   CREB1/GPX4 axis in BC.

3.10. Inhibition of ASCL1 increases BC sensitivity to paclitaxel in vivo

   The cytological experiments demonstrated that ASCL1 inhibition enhances
   paclitaxel sensitization in vitro. To confirm these effects in vivo,
   experiments were conducted using female BALB/c nude mice, following the
   protocol outlined in [136]Figure 9A . The results showed significantly
   lower tumor volume and weight in the sh-ASCL1 group compared to the
   sh-NC group ([137] Figures 9B-D ). Notably, western blot analysis
   further demonstrated that knockdown of ASCL1 inhibited the CREB1/GPX4
   pathway in vivo ([138] Figure 9E ). These results were consistent with
   those of in vitro experiments, jointly demonstrating that targeting
   ASCL1 could inhibit BC progression and increase BC sensitivity to
   paclitaxel by inducing ferroptosis.

Figure 9.

   [139]Figure 9
   [140]Open in a new tab

   ASCL1 knockdown enhances BC sensitivity to paclitaxel in vivo. (A)
   Schematic representation of the in vivo experimental design. (B)
   Photograph of tumors collected at the end of the experiment. (C) Tumor
   volume progression over time in both groups during the experiment. (D)
   Comparison of tumor weights between the two groups at the conclusion of
   the experiment. (E) ASCL1 knockdown inhibits the CREB1/GPX4 pathway in
   vivo. (***P < 0.001; ****P < 0.0001).

4. Discussion

   Previous studies have implicated ASCL1 in the initiation
   and progression of various cancers, where it promotes tumor cell
   proliferation ([141]11–[142]13), supports cell survival and
   differentiation ([143]36), and influences drug sensitivity ([144]37).
   However, its role in BC remains unclear. In this study, we elucidated
   the function and regulatory mechanism of ASCL1 in BC through
   comprehensive bioinformatics analysis, cytological experiments and
   animal experiments.

   Our bioinformatics analysis revealed that ASCL1 expression is
   significantly elevated in BC and associated with poor prognosis.
   Additionally, ASCL1 mutation frequency was notably higher in metastatic
   BC compared to primary tumors, and its expression correlated with
   mutation rates in key BC driver genes, such as TP53 and PIK3R1. The
   study further explored the relationship between ASCL1 expression and
   various immunomodulatory factors, including cytokines,
   immunoregulators, MHC molecules, and receptors. Given the established
   role of immunomodulatory factors in shaping the tumor microenvironment
   and influencing immune responses ([145]26, [146]38, [147]39), our
   findings of a negative correlation between ASCL1 levels and these
   factors suggest an immunosuppressive effect in BC. Notably, ASCL1
   expression was inversely associated with CXCL10 and CCL20, consistent
   with previous research ([148]13), which showed that ASCL1 downregulates
   these chemokines, impairing immune cell migration and contributing to
   an “immune desert” phenotype, thus compromising immune responses in
   cancers such as lung adenocarcinoma ([149]13). The analysis also showed
   a link between ASCL1 expression and immune cell infiltration as well as
   immunoreactivity scores, which also demonstrates a possible association
   of ASCL1 with the immune response to BC.

   In vitro experiments corroborated the bioinformatics findings,
   demonstrating that ASCL1 overexpression accelerates BC progression by
   enhancing cell proliferation, migration, and invasion, whereas ASCL1
   inhibition yields the opposite effects. Importantly, the inhibition of
   ASCL1 significantly increased paclitaxel sensitivity both in vitro and
   in vivo, aligning with the bioinformatics data and suggesting that
   targeting ASCL1 could improve therapeutic outcomes in BC.

   Enrichment analysis revealed a link between ASCL1 and ferroptosis, a
   distinct form of cell death characterized by iron-dependent lipid
   peroxidation, differing from apoptosis and necrosis ([150]40). Emerging
   evidence suggests that ASCL1 can protect cells from oxidative stress,
   thereby promoting differentiation ([151]41). Since oxidative stress and
   lipid peroxidation are key precursors to ferroptosis ([152]42), this
   study further investigated the role of ASCL1 in this pathway. Knockdown
   of ASCL1 increased intracellular levels of ROS and MDA, specific
   markers of lipid peroxidation associated with ferroptosis ([153]33).
   Additionally, transmission electron microscopy showed mitochondrial
   changes characteristic of ferroptosis following ASCL1 inhibition,
   indicating that ASCL1 may be involved in the key steps of the
   ferroptosis process in BC. Inhibition of ASCL1 significantly activated
   ferroptosis in BC cells.

   Previous research has demonstrated the therapeutic potential of
   inducing ferroptosis in cancer treatment, including overcoming drug
   resistance ([154]43). For instance, in hepatocellular carcinoma, Nrf2
   pathway activation confers resistance to sorafenib by inhibiting
   ferroptosis ([155]44). Similarly, in cisplatin-resistant head and neck
   cancers, inducing ferroptosis reverses resistance ([156]45). Consistent
   with these results, this study showed that ASCL1 inhibition enhances BC
   sensitivity to paclitaxel, while the use of a ferroptosis inhibitor
   abolished this effect. Conversely, applying ferroptosis inducers
   further amplified the paclitaxel sensitivity, suggesting that ASCL1
   inhibition augments paclitaxel efficacy through ferroptosis activation
   in BC.

   Mechanistic studies were conducted to elucidate the underlying
   processes. GSH, catalyzed by GPX4, scavenges intracellular ROS,
   providing a defense against oxidative stress-induced cell death.
   The GSH/GPX4 pathway serves as a key mechanism for inhibiting
   ferroptosis. Findings from this study revealed that there is an
   interaction between ASCL1 and CREB1 in BC and that GPX4 is a downstream
   target of the transcription factor CREB1. Inhibition of ASCL1 reduces
   the phosphorylation of CREB1, which in turn decreases the expression of
   GPX4 and consequently activates ferroptosis.

   Our study initially revealed that targeting ASCL1 activates ferroptosis
   in BC via the CREB1/GPX4 axis thereby increasing BC sensitivity to
   paclitaxel and inhibiting BC progression. These results are expected to
   provide new insights into the treatment of BC. However, the cytological
   studies in this study only investigated the effects of ASCL1 on
   proliferation, migration, invasion, and paclitaxel sensitivity in BC,
   and further studies are still needed to clarify the other roles of
   ASCL1 in BC and its potential as a therapeutic target. In addition,
   this study only preliminarily revealed the effect of ASCL1 on
   ferroptosis, and it remains to be investigated the mechanism in more
   depth in the future. These are the limitations of this study.

5. Conclusions

   This study demonstrated that ASCL1 is upregulated in BC and correlates
   with poor prognosis. ASCL1 plays multiple oncogenic roles in BC,
   influencing tumor immunity, drug sensitivity, and cellular behaviors
   such as proliferation, migration, invasion, and EMT. While ASCL1
   overexpression promotes these malignant processes, its knockdown
   produces the opposite effects. Notably, ASCL1 inhibition enhances BC
   sensitivity to paclitaxel both in vitro and in vivo by activating
   ferroptosis via the CREB1/GPX4 axis. These findings suggest that ASCL1
   could serve as a promising prognostic biomarker and therapeutic target
   for patients with BC.

Funding Statement

   The author(s) declare financial support was received for the research,
   authorship, and/or publication of this article. This research was
   supported by grants from the Innovation Team Support Program of the
   Fourth Hospital of Hebei Medical University (2023B01).

Data availability statement

   The original contributions presented in the study are included in the
   article/[157] Supplementary Material . Further inquiries can be
   directed to the corresponding author/s.

Ethics statement

   Ethical approval was not required for the studies on humans in
   accordance with the local legislation and institutional requirements
   because only commercially available established cell lines were used.
   The animal study was approved by Ethics Committee of the Fourth
   Hospital of Hebei Medical University. The study was conducted in
   accordance with the local legislation and institutional requirements.

Author contributions

   XY: Formal analysis, Methodology, Visualization, Writing – original
   draft. YiL: Methodology, Writing – review & editing. YP: Validation,
   Writing – review & editing. YC: Methodology, Writing – review &
   editing. BH: Formal analysis, Writing – review & editing. TZ:
   Validation, Writing – review & editing. SZ: Methodology, Writing –
   review & editing. CG: Project administration, Writing – review &
   editing. YuL: Project administration, Writing – review & editing. XL:
   Methodology, Writing – review & editing. JH: Conceptualization, Writing
   – review & editing. LM: Conceptualization, Writing – review & editing.

Conflict of interest

   The authors declare that the research was conducted in the absence of
   any commercial or financial relationships that could be construed as a
   potential conflict of interest.

Generative AI statement

   The authors declare that no Generative AI was used in the creation of
   this manuscript.

Publisher’s note

   All claims expressed in this article are solely those of the authors
   and do not necessarily represent those of their affiliated
   organizations, or those of the publisher, the editors and the
   reviewers. Any product that may be evaluated in this article, or claim
   that may be made by its manufacturer, is not guaranteed or endorsed by
   the publisher.

Supplementary material

   The Supplementary Material for this article can be found online at:
   [158]https://www.frontiersin.org/articles/10.3389/fimmu.2025.1546794/fu
   ll#supplementary-material
   [159]DataSheet1.docx^ (919.6KB, docx)

References