SCNN1A expression in triple-negative breast cancer: clinical implications for prognosis and neoadjuvant therapy response

Abstract

Background

This study aimed to identify differential genes between pathological complete response (pCR) and non-pCR following neoadjuvant chemotherapy in triple-negative breast cancer (TNBC). Additionally, the expression and clinical significance of the differential gene SCNN1A in TNBC were explored.

Methods

Differential genes related to prognosis following neoadjuvant chemotherapy in TNBC were identified using the GEO database. Core genes were selected through the Cytoscape visualization and support vector machine (SVM) feature selection. The prognostic significance of these genes was assessed via online databases. SCNN1A expression and its correlation with clinicopathological data and neoadjuvant chemotherapy response were analyzed in 283 TNBC patients from the First Affiliated Hospital of Bengbu Medical University using immunohistochemistry.

Results

Eleven core genes, including SCNN1A, were identified from 912 differential genes. High SCNN1A expression was associated with poor prognosis in TNBC patients via online database analysis. Gene set difference analysis (GSVA) and Gene set enrichment analysis (GSEA) revealed that SCNN1A was involved in several metabolic pathways. The clinical data indicated that high SCNN1A expression was associated with advanced T (p = 0.037) and N stages (p = 0.011), but not with age, HER2 status, Ki-67 expression, or histological grade. High SCNN1A expression was significantly more frequent in non-pCR patients compared to pCR patients, and high SCNN1A expression was associated with significantly lower overall survival (OS) and disease-free survival (DFS).

Conclusion

SCNN1A overexpression is associated with poor prognosis and non-pCR status in TNBC patients undergoing neoadjuvant chemotherapy.

Introduction

Breast cancer is the most common malignant tumor in women, and its incidence is steadily increasing, making it the leading cause of cancer-related death among women worldwide. Approximately 15% of all new cancer cases in women are attributed to breast cancer [1]. Triple-negative breast cancer (TNBC) is defined by the absence of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) expression. TNBC accounts for about 15% to 20% of all breast cancer cases and is known for its high invasiveness, poor survival prognosis, and high incidence of recurrence and metastasis [2].

Advancements in gene chips and high-throughput next-generation sequencing (NGS) technologies have enabled researchers to exploit extensive gene databases within public repositories. In recent years, there has been a surge in the application of bioinformatics to mine microarray gene expression data. This has involved the analysis of differentially expressed genes that affect disease states and the identification of biomarkers for therapeutic intervention and prognostic assessment. Consequently, integrating gene expression data with bioinformatics methods offers a promising pathway to elucidate the pathogenesis of TNBC and identify potential therapeutic targets.

Material and methods

Gene expression profile data for 20 sample groups, comprising 10 samples each from the pathological complete response (pCR) group and the non-pathological complete response (non-pCR) group, were obtained by downloading the SeriesMatrixFile of dataset GSE25065. Differential expression between the pCR and non-pCR groups was analyzed by the limma package, with significant genes identified under a P-value < 0.05 and an absolute log fold change (|logFC|) > 1. Concurrently, raw mRNA expression data from the TCGA database to validate the findings. These differential genes were then introduced into the STRING online database to construct a protein–protein interaction (PPI) network. The top 100 genes were selected as key genes by Cytoscape for further analysis. The support vector machine (SVM) algorithm was utilized to select the tumor diagnostic markers, and the SVM-Recursive Feature Elimination (SVM-RFE) method, a machine learning technique that iteratively removes features to find the best variables, was applied to identify and evaluate characteristic genes in TNBC.

Using the SangerBox online analysis platform (http://vip.sangerbox.com/home.html), we analyzed the pan-cancer dataset to extract expression data for the SCNN1A gene across various samples, focusing specifically on breast cancer. Subsequently, the expression data of SCNN1A were analyzed to assess its prognostic significance in TNBC. This analysis was conducted using the KM Plotter tool (http://kmplot.com/analysis/) which processed mRNA-genechip data from the 203,453-at chip. Statistical significance was established at a p-value < 0.05.

The co-expression network of core genes was explored by correlation analysis based on the expression data from TNBC patients in the TCGA database. A filtering threshold for the correlation coefficient was set at 0.3, with a p-value < 0.05 considered statistically significant for establishing correlations. Visual representations of these correlations were generated using the "corrplot" and "circlize" packages, which facilitated the creation of correlation circle diagrams and heat maps, respectively.

Gene set difference analysis (GSVA) is a non-parametric, unsupervised method used to evaluate the enrichment of gene sets, translating gene-level changes into pathway-level changes. The gene sets were downloaded from the Molecular Signatures Database (MSigDB, version 7.0). Each gene set was comprehensively scored to evaluate the biological function changes in different samples. According to the expression of core genes, patients were categorized into high and low expression groups. The differences in signaling pathways between these groups were further analyzed by Gene set enrichment analysis (GSEA). The reference gene sets for this analysis were also obtained from the MSigDB. Pathway differential expressions between subtypes were analyzed, and significantly enriched gene sets were prioritized based on their consistency scores.

A total of 283 cases of TNBC were reviewed from the First Affiliated Hospital of Bengbu Medical University between January 2016 and June 2022. Among the patients, 48 underwent neoadjuvant chemotherapy, with 23 achieving pCR and 25 not achieving pCR. This study was approved by the Ethics Committee of Bengbu Medical University (2023YJS024). The relevant paraffin-embedded specimens and clinical data were collected from our hospital. All diagnoses of TNBC were confirmed by re-examination of the tissue blocks by at least two associate professors from the Department of Pathology. The medical records of the TNBC patients were reviewed, and telephone follow-ups were conducted to obtain information on the general condition of the patients, survival status, and other relevant data.

Immunohistochemistry

For immunohistochemical analysis, the SCNN1A antibody was sourced from Affinity Company. Additional reagents, including PBS buffer and DAB chromogenic solution, were acquired from Xiamen Haibiao Technology Co., Ltd (China). All specimens were fixed in 10% neutral-buffered formalin, embedded in paraffin, and sectioned into 4 μm slices. The expression of SCNN1A was detected using the immunohistochemical Elivision method, following the manufacturer's instructions.

Standard for interpretation of immunohistochemical results

For immunohistochemical analysis, SCNN1A expression was assessed by observing its localization and staining intensity in the cytoplasm and on the cell membrane/serous. The cytoplasmic staining was characterized by brown particles indicating a positive reaction. High power microscopic observation was conducted in five randomly selected fields, with each field containing at least 200 cells.

The results were evaluated based on the proportion of positive stained cells and the intensity of staining. Staining intensity was graded as follows: 3 = strong brown expression, 2 = moderate brown expression, 1 = light yellow for weak expression, and 0 = no shading for non-expression. The extent of staining was categorized by the percentage of positive cells: < 5% = 0, 5%−25% = 1, 26%−50% = 2, 51%−75% = 3, and > 75% = 4. The final score was determined by multiplying the intensity grade by the extent category, with scores of 10–12 indicating strong positivity (+ + +), 6–9 moderate positivity (+ +), and 3–5 weak positivity ( +). A score of ≥ 3 was considered positive, while a score < 3 was negative. Additionally, the overall positive rate was calculated.

Statistical methods

Clinical data were analyzed using SPSS software. The chi-square test and T-test were employed for univariate analyses to examine differences within categorical and continuous variables, respectively. The log-rank test was utilized to compare groups, specifically focusing on survival outcomes. Survival analysis was conducted using the Kaplan–Meier method.

Result

Differential gene analysis and core gene screening

The SeriesMatrixFile data file from the GSE25065 dataset was retrieved from the GEO public platform, identifying 912 differential genes, including 440 up-regulated and 472 down-regulated genes. A volcano plot was generated to visually represent these results (Fig. 1). Subsequently, the PPI network for these differential genes was accessed via the STRING online database (Fig. 2). Using Cytoscape software, the top 100 genes based on BETWEENNESS centrality were initially selected as key genes. To refine this selection, the SVM feature selection algorithm was employed to identify characteristic genes specific to TNBC, further evaluated using the SVM-RFE algorithm. This process resulted in the identification of 11 core genes—MAPK3, OCA2, H2AFZ, CYP19A1, VPS39, STK11, SCNN1A, SMNDC1, UBE2D1, BSG, and HIVEP3—through the intersection of the top 100 characteristic genes and the top 100 BETWEENNESS genes (Fig. 3).

Fig. 1

Volcanic of differential genes

Fig. 2

PPI network protein

Fig. 3

screen the core genes

Co-expression and pathway enrichment analysis of SCNN1A

The co-expression network of the SCNN1A gene was analyzed, identifying 456 motifs that exhibited a significant correlation with SCNN1A expression. The positively correlated genes included MLPH, BCL2L1, FOXA1, PRR15, TSPAN1, DUSP5, SMIM14, PKIB, AC018629.1, and AGR3, while negatively correlated genes included TRIM65, E2F5, PAPSS1, POLR3G, VIM-AS1, MRPL15, LINC01956, SNHG16, SCARB1, and EN1 (Fig. 4). The heat map of top 10 positively and negatively correlated genes were generated (Fig. 5), and relationships were also visualized in a co-expression circle map (Fig. 6).

Fig. 4

Correlation positive correlation gene

Fig. 5

Heat map of the correlated TOP10 genes with positive/negative correlation coefficients

Fig. 6

Correlation coefficient positive/negative correlation TOP10 gene correlation circle

Pathway enrichment analyses using GSVA and GSEA revealed distinct signaling pathways associated with SCNN1A expression levels. GSVA indicated that high SCNN1A expression was predominantly associated with pathways like ESTROGEN RESPONSE EARLY, KRAS SIGNALING DN, and XENOBIOTIC METABOLISM. Conversely, lower SCNN1A expression correlated with enrichment in MYC TARGETS V2, MYC TARGETS V1, and UNFOLDED PROTEIN RESPONSE pathways (Fig. 7). In addition, GSEA results showed enrichment of SCNN1A in metabolic pathways, including Butanoate metabolism, Taurine and hypotaurine metabolism, and Tyrosine metabolism (Fig. 8).

Fig. 7

Enrichment results of GSVA pathway of SCNN1A

Fig. 8

GSEA enrichment results of SCNN1A

Gene expression and prognostic analysis in TNBC

SCNN1A expression was analyzed across various cancer types using online databases, revealing a statistically significant higher expression in breast cancer tissues compared to normal tissue (Fig. 9). Focusing on TNBC, Kaplan–Meier survival analysis demonstrated that higher SCNN1A expression correlates with poorer prognosis, including overall survival (OS), recurrence-free survival (RFS), and distant metastasis-free survival (DMFS) (Fig. 10).

Fig. 9

Expression of SCNN1A in breast cancer tissues

Fig. 10

SCNN1A survival prognosis in triple-negative breast cancer, (a) is OS, (b) is RFS, and (c) is DMFS

Clinicopathological data and correlation of SCNN1A expression

In this study, we analyzed paraffin-embedded tissue samples from 283 TNBC patients, among which 48 patients underwent neoadjuvant therapy (pCR, n = 23; non-pCR, n = 25). Patient demographics indicate that 155 patients were under 60 years old, while 128 were older than 60. Detailed clinicopathological characteristics are presented in Table 1.

Table 1 Clinical data parameter table of triple-negative breast cancer

Immunohistochemical staining revealed that SCNN1A was predominantly expressed in the cytoplasm, with strong staining observed in 151 cases (53.36%), indicating high expression, and weaker staining in 132 cases (46.64%), indicating low expression (Figs. 11 and 12). A significant association was found between high SCNN1A expression and advanced T stage (p = 0.037) and N stage (p = 0.011). However, no significant correlations were observed with age, HER2 status, Ki-67 index, or histological grade (Table 2).

Fig. 11

Low expression of SCNN1A, × 400

Fig. 12

High expression of SCNN1A, × 400

Table 2 Clinical data correlation between different SCNN1A expression in triple-negative breast cancer

Prognostic impact of SCNN1A expression in TNBC

Survival analysis was performed on 235 TNBC patients, excluding the 48 who completed standard neoadjuvant therapy due to the short follow-up duration ranging from 4 to 22 months. The analysis revealed that patients with high expression of SCNN1A had a 5-year disease-free survival (DFS) rate of 58.33%, compared to 63.89% for those with low expression (Fig. 13). Similarly, the 5-year OS rates significantly differed, being 77.42% in patients with high expression versus 80.32% in those with low expression (Fig. 14).

Fig. 13

5-year DFS in different SCNN1A expression populations

Fig. 14

5-year OS in different SCNN1A expression populations

Discussion

The three principal modalities for the treatment of malignant breast tumors are surgery, radiotherapy, and systemic chemotherapy, each of which presents unique advantages and challenges. It is well-recognized that curing breast cancer often requires more than a single treatment modality, leading to the common clinical approach of combination therapy to optimize therapeutic outcomes [3]. For patients with TNBC, chemotherapy and surgery remain the backbone of standard treatment. Some studies have demonstrated that higher expressions of TILs in patients with TNBC were associated with improved signifcantly DFS, OS, and pCR outcomes [4]. Despite the poor prognosis associated with TNBC, attributed to its aggressive biological behavior, these tumors frequently exhibit a relatively good response to chemotherapy, distinguishing them from other breast cancer subtypes. There has been a growing focus on targeted therapies for TNBC in recent years, driven by the urgent need to improve prognostic outcomes. Although TNBC lacks well-defined molecular targets, extensive research has identified signal pathway mutations post-neoadjuvant chemotherapy in many patients. These mutations present new opportunities for targeted interventions, which are currently under clinical investigation.

In this study, we utilized the GEO database to screen 912 differential genes from dataset GSE25065, which included 440 up-regulated and 472 down-regulated genes. Using Cytoscape, the top 100 key genes were identified, and the SVM feature selection algorithm was applied to determine the top 100 characteristic genes. The intersection of these groups resulted in the identification of 11 core genes: MAPK3, OCA2, H2AFZ, CYP19A1, VPS39, STK11, SCNN1A, SMNDC1, UBE2D1, BSG, and HIVEP3. SCNN1A encodes a subunit of the epithelial sodium channel (ENaC), which is primarily expressed in epithelial tissue and functions as a sodium osmotic channel. Previous studies have highlighted the diverse roles of SCNN1A in various malignant tumors. For instance, both SCNN1A mRNA and protein levels of SCNN1A have been documented in ovarian cancer, where high expression of SCNN1A correlates with reduced OS and disease-specific survival (DSS), underscoring its association with poor prognosis in ovarian cancer [5]. Moreover, inhibition of SCNN1A has been shown to effectively curb proliferation, invasion, distant migration, and epithelial-mesenchymal transition (EMT) in pancreatic cancer cells [6]. Given these findings, our study aimed to explore the relationship between the expression of SCNN1A and prognosis in TNBC.

The co-expression analysis of the SCNN1A gene revealed 456 significantly expressed genes, with the PKIB gene in the TOP10 gene and positively correlated with SCNN1A. This suggests that PKIB may be involved in the pathogenesis of TNBC. PKIB, or CAMP-dependent protein kinase inhibitor β, is considered a regulator of CAMP-dependent protein kinase A signaling pathway. Triple-negative breast cancer is significantly related to the expression of PKIB. It is hypothesized that PKIB may modulate the Akt pathway by activating or phosphorylating Akt, thus promoting breast cancer progression via the Akt signal pathway [7]. Akt, a serine/threonine kinase, facilitates angiogenesis, cell growth, and proliferation and provides resistance against pro-apoptotic stimuli when activated or phosphorylated [8]. In various cancers, the Akt pathway is recognized as a crucial conduit for promoting invasive metastasis and has emerged as a promising target for cancer therapy [9]. Elevated phosphorylation levels of Akt are associated with poor prognosis in cancers [10]. Furthermore, studies have indicated that 17 β-estradiol can upregulate the expression and surface abundance of α-ENaC by activating the Akt signaling pathway and increasing phosphorylated Akt levels [11]. Given that SCNN1A encodes a subunit of the ENaC, and positively correlated with PKIB, it is plausible that SCNN1A may be implicated in the phosphorylation of Akt. Consequently, the positive correlation between SCNN1A and PKIB suggests a potential mechanism underlying the onset and progression of TNBC, indicating both as potential targets for therapeutic intervention [12].

SCNN1A is located on chromosome 12p13.31 and forms part of a multi-subunit complex that comprises three subunits (α, β, and γ). SCNN1A specifically encodes α-subunit of ENaC, which is predominantly situated in the distal regions of kidney, distal colon, lung, and the epithelial tips of exocrine gland ducts. Each subunit of ENaC, including SCNN1A, SCNN1B, and SCNN1G, plays a critical role in sodium ion transport across the epithelium. Some studies have shown that sodium ions (Na⁺) influx through ENaC promotes mitotic signaling and initiates the cell cycle. Inhibition of this sodium transport has been observed to reduce DNA synthesis necessary for cell proliferation. ENaC-mediated Na⁺ influx enhances cancer cell proliferation, migration, and invasion [13]. Conversely, blocking Na⁺ influx can trigger cell cycle arrest and induce apoptosis in cancer cells. This has been demonstrated in both in vitro and in vivo studies, where SCNN1A overexpression was shown to drive osteosarcoma tumor growth [14] and was implicated in the aggressive behaviors of ovarian cancer cells [15].

Additionally, ENaC-mediated Na⁺ influx influences intracellular signaling pathways, such as the stabilization of GILZ1 via SGK1. GILZ1 inhibits the ubiquitination and subsequent proteasome-mediated degradation of SGK1, thus extending its half-life and augmenting its steady-state expression. Moreover, the increase of Na⁺ influx through ENaC accelerates Na⁺-H⁺ exchange, raising H⁺ concentration in the tumor microenvironment and promoting its acidification. An acidic tumor microenvironment has been associated with enhanced invasive capabilities of tumor cells. Furthermore, it has been suggested that the sodium channel cENaC mediates the inflammatory stress response triggered by IL-17, which may foster tumor proliferation and metastasis.

Some studies have demonstrated that silencing SCNN1A inhibits the proliferation, migration, and EMT in pancreatic cancer cells [16, 17]. Furthermore, in vitro studies indicated that both HOXD9 and SCNN1A exhibited high expression in pancreatic cancer tissues and cells. The downregulation of these genes was associated with a reduction in cell proliferation, migration, and EMT [18]. According to our database analysis, HOXD9 is predicted to be a transcription factor of SCNN1A, and the silencing of HOXD9 correspondingly reduced SCNN1A expression, suggesting a positive transcriptional correlation between HOXD9 and SCNN1A [19]. Moreover, it has been found that SCNN1A can form a positive feedback loop with HOXD9, thereby continuously enhancing its oncogenic effects and aggravating cancer progression. HOXD9, a homeobox gene, is implicated in the pathogenesis of several malignancies, including ovarian and cervical cancers [20]. It promotes the growth, invasion, and metastasis of gastric cancer cells through transcriptionally activating the RUN and FYVE domain-containing protein 2. Furthermore, in TNBC cells and tissues, the low expression of miR-205 alleviated the inhibition of the HOXD9-Snail1 axis on cell proliferation and chemotherapy resistance. High levels of miR-205 expression and Snail1 knockdown significantly inhibited cell proliferation, indicating a negative correlation between the miRNA levels of miR-205 and the expression of HOXD9 protein in BT-549 cells [21]. Additionally, the HOXD9 gene can directly transcribe and activate the Snail1 gene. Consequently, it can be hypothesized that miR-205 may inhibit the inhibitory effect of HOXD9-Snail1 on the proliferation and chemotherapy resistance of TNBC cells and tissues. MicroRNAs (miRNAs) are endogenous, small, non-coding single-stranded RNAs that play crucial roles in various biological processes, including tumorigenesis, proliferation, metastasis, EMT, tumor stem cell maintenance, and drug resistance by targeting mRNA for degradation or translational repression [22]. Specifically, miR-205, located on chromosome 1q32.2, is expressed in breast, thymus, and prostate and regulates their development. Among these, miR-205-5p has been shown to be critical in several cancers, particularly exhibiting the lowest expression in TNBC, and this suppressed miR-205-5p expression is associated with high frequency mutation of TP53 [23]. This study found that a cooperative interaction between miR-200a, miR-200b, and miR-205 significantly inhibits the expression of ZEB1 and SIP1, key transcription factors involved in EMT [24]. With the decrease of ZEB1 and SIP1, we observed a corresponding increase in E-cadherinm RNA levels, suggesting that the suppression of these microRNAs is sufficient to induce EMT. EMT is recognized as a critical driver of cancer metastasis and invasion, characterized by enhanced migration ability, invasiveness, resistance to apoptosis, and increased production of extracellular matrix components [25, 26]. These changes reflect the transition from polarized epithelial cells to cells with a more mesenchymal-like phenotype and morphology [27]. Further experiments demonstrated a positive correlation between HOXD9 and SCNN1A transcription. It is postulated that SCNN1A could modulate the expression of miR-205 via the HOXD9-Snail1 axis. This modulation likely occurs through a positive feedback loop between SCNN1A and HOXD9, which may enhance the EMT to promote proliferation and increase chemotherapy resistance in TNBC cells.

GSVA results showed that patients with high expression of SCNN1A exhibited enrichment in several key signaling pathways, including ESTROGENRESPONSEEARLY, KRASSIGNALINGDN, XENOBIOTICMETABOLISM. Further, GSEA identified significant involvement of SCNN1A in the metabolism of tyrosine, taurine and sub-taurine, and butyric acid. These findings are particularly relevant given the increased rates of glycolysis, lipogenesis, and production of volatile organic metabolites observed in the serum of breast cancer patients compared to healthy individuals [28]. Most cancers, including TNBC, demonstrate metabolic plasticity, adapting to highly glycolytic pathways even in the presence of oxygen, a phenomenon known as the Warburg effect [29]. This metabolic reprogramming supports rapid cell growth and proliferation by efficiently converting glucose into lactic acid, which accumulates in the tumor microenvironment, leading to acidification. Activation of tyrosine kinase receptors, such as the fibroblast growth factor receptor (FGFR) and epidermal growth factor receptor (EGFR), have been recognized as a driver of malignancy in numerous cancers. These receptors enhance glycolysis and oxidative phosphorylation, in which FGFR activation facilitates the recovery of lactic acid and the provision of energy for oxidative phosphorylation [30]. Moreover, treatment with epidermal growth factor has been shown to increase the invasive potential of TNBC cells through the tyrosine phosphorylation of β-catenin [31]. The acidic tumor microenvironment resulting from elevated lactic acid levels not only facilitates cancer invasion and metastasis but also contributes to immune escape mechanisms. SCNN1A's involvement in tyrosine metabolism suggests a potential regulatory role in these processes by influencing glycolysis and subsequently the acidity of the tumor environment. However, the full implications of SCNN1A's role in these pathways and its potential impact on tumor progression and immune escape in TNBC require further investigation and validation through in vitro studies.

The analysis of online databases revealed that SCNN1A expression was elevated in breast cancer tissues compared to normal tissues. Importantly, the high expression of SCNN1A was associated with poor prognosis in TNBC, significantly affecting OS, RFS, and DMFS. Further investigations confirmed that elevated SCNN1A expression correlated with advanced T stage (p = 0.037) and N stage (p = 0.011), although no significant relationships were observed with age, HER2 status, Ki-67 expression, or histological grade. These findings underscore the importance of SCNN1A as a marker for tumor progression, suggesting that its heightened expression may contribute to the advanced staging of TNBC.

Survival analyses reinforced these observations, demonstrating a notable decline in both OS and DFS in patients exhibiting high SCNN1A expression. The consistency of these results across bioinformatic and clinical datasets highlights the robustness of SCNN1A as a prognostic indicator. Thus, the data strongly suggest that high SCNN1A expression is linked to a decreased OS rate and poorer outcomes in TNBC. Based on these outcomes, we speculated that there is a significant association between the high expression of SCNN1A and adverse prognosis in TNBC. This evidence supports further exploring SCNN1A as a potential therapeutic target for TNBC management.

In this study, we observed that patients with non-pCR to neoadjuvant chemotherapy in TNBC exhibited significantly higher levels of SCNN1A expression compared to those achieving a pCR (P = 0.041), suggesting that high SCNN1A expression might predict non-pCR outcomes. These findings could guide the development of strategies to increase pCR rates through targeted interventions in SCNN1A pathways. However, given the limited number of TNBC cases undergoing neoadjuvant chemotherapy in our dataset, further validation with a larger cohort is essential to confirm these conclusions.

Neoadjuvant chemotherapy has emerged as a cornerstone in the treatment of triple-negative breast cancer (TNBC), particularly for its ability to reduce tumor burden and improve surgical outcomes. This study focuses on the clinically challenging population of patients who fail to achieve a pathological complete response (non-pCR), utilizing bioinformatics analyses to identify SCNN1A as a potential therapeutic target. Further validation using clinical and pathological data demonstrated that differential expression of SCNN1A significantly influences patient prognosis and the efficacy of neoadjuvant therapy. These findings suggest that SCNN1A may serve as a promising target for the development of novel TNBC therapies.

However, this study has certain limitations. The relatively small sample size of patients receiving neoadjuvant treatment limits the generalizability of the findings, underscoring the need for larger-scale studies to confirm the results. Furthermore, as this represents the initial exploration of SCNN1A by our research group, subsequent investigations are required to validate its role and elucidate the underlying mechanisms through in vitro and in vivo experiments. These efforts will be critical in establishing a robust foundation for translational research and clinical application. Ongoing related experiments are expected to further advance our understanding, and updates in this area are highly anticipated.