Keratin 6A Overexpression in the Lymphovascular Invasion–Associated Tumor Subgroup Promotes Progression of Triple-Negative Breast Cancer
Article information
Abstract
Purpose
Lymphovascular invasion (LVI) is a strong predictor of poor prognosis in triple-negative breast cancer (TNBC), yet its molecular basis remains unclear. This study investigates epithelial regulators associated with LVI and their functional roles in TNBC progression.
Materials and Methods
We utilized single-cell sequencing data to further characterize epithelial cell populations in TNBC, identifying dominant epithelial clusters in LVI-positive TNBC tissues. The prognostic significance of dominant epithelial marker genes was explored through transcriptomic analysis and immunohistochemical staining of patient samples from our center. Additionally, the effects of the marker gene on TNBC cell invasion and metastasis were validated in vitro and in vivo.
Results
Single-cell data analysis revealed nine distinct epithelial cell clusters within TNBC tissues. Among these, cluster 4 was identified as the dominant epithelial subpopulation in LVI-positive TNBC, marked by the prognostic gene KRT6A. Multiple datasets confirmed KRT6A as a crucial prognostic marker in TNBC. Functional assays, including Cell Counting Kit-8, wound healing, transwell assays, and animal experiments, demonstrated that KRT6A knockdown significantly impaired the proliferation, invasion, and metastatic potential of TNBC cells. Mechanistically, KRT6A promoted epithelial-mesenchymal transition (EMT) and activated Wnt/β-catenin signaling by stabilizing β-catenin through glycogen synthase kinase-3β phosphorylation.
Conclusion
KRT6A promotes EMT and metastasis in TNBC via Wnt/β-catenin signaling, contributing to LVI and chemoresistance. It may serve as a prognostic biomarker and therapeutic target in TNBC.
Introduction
Breast cancer is the most common malignancy among women worldwide, with both its incidence and mortality rates continuing to rise. Despite significant advances in early detection and therapeutic strategies, the overall survival (OS) rate for breast cancer patients has improved only modestly. However, challenges such as treatment failure, recurrence, and drug resistance continue to present substantial obstacles in achieving better outcomes [1]. Breast cancer is highly molecularly heterogeneous, with distinct subtypes demonstrating considerable differences in clinical presentation, prognosis, and response to treatment [2]. In particular, triple-negative breast cancer (TNBC) presents a significant clinical challenge due to the lack of specific targeted therapies, resulting in poor treatment outcomes and an urgent need for novel molecular targets to improve clinical management [3]. Moreover, lymphovascular invasion (LVI) has been established as a crucial independent prognostic factor in primary breast cancer, closely associated with tumor invasion and metastasis [4,5]. One study found that LVI is an independent prognostic factor for disease-free survival and OS in breast cancer patients, and it is linked to lymph node metastasis [5]. LVI also holds prognostic significance in TNBC [6,7].
In this study, we analyzed single-cell RNA sequencing (scRNA-seq) data and identified keratin 6A (KRT6A), a member of the type II keratin family, as being highly expressed in LVI-positive TNBC. Located on chromosome 12q13.13, KRT6A is biologically associated with squamous metaplasia, a condition induced by KRT6A expression [8]. Previous studies have highlighted KRT6A’s key role in epithelial-mesenchymal transition (EMT) in lung adenocarcinoma [9], and it has been suggested as a potential diagnostic marker for nasopharyngeal carcinoma [10].
To investigate the biological functions of KRT6A in TNBC, we established an in vitro KRT6A knockout model. Our findings demonstrated that KRT6A knockdown significantly suppressed TNBC cell proliferation, invasion, and migration. Furthermore, KRT6A was found to play a critical role in regulating the EMT process in TNBC. This study provides novel insights into the molecular mechanisms underlying TNBC’s high metastatic potential. These results not only contribute to our understanding of TNBC but also offer a foundation for the development of new therapeutic strategies aimed at improving patient outcomes.
Materials and Methods
1. Data set source
The scRNA-seq data for 10 TNBC samples were obtained from the Gene Expression Omnibus (GEO) database (accession code GSE176078) [11]. Among these, four samples were diagnosed as LVI-positive TNBC. Additionally, bulk RNA-seq data from two cohorts were used: 115 TNBC samples from The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov) and 299 TNBC samples from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC). Furthermore, the GSE58812 microarray dataset, containing 107 TNBC samples, was also retrieved from GEO. Clinical survival data from these datasets were utilized for subsequent survival analyses.
2. Processing of scRNA-seq data
The scRNA-seq data were processed using the “Seurat” R package (v4.4.0) for quality control. Cells with gene counts ranging from 200 to 7,000 and unique molecular identifier counts below 60,000 were retained. Consistent with previous studies, cells exhibiting mitochondrial content greater than 20% were excluded to minimize the inclusion of dead or dying cells (S1 Fig.) [12]. For batch correction, we employed Harmony with default parameters (theta=2, lambda=1) after systematically testing multiple parameter combinations. Subsequently, the data were scaled and subjected to principal component analysis (PCA). We conducted a comprehensive evaluation of clustering resolutions from 0.1 to 1.0 (in 0.1 increments). The final resolution of 0.4 was selected (S2 Fig.). Dimensionality reduction and data visualization were performed using the Uniform Manifold Approximation and Projection (UMAP) method. Cell identities were annotated based on markers from the CellMarker database [13].
3. scRNA-seq data downstream analysis
A secondary dimensionality reduction was performed specifically on epithelial cells. Differentially expressed genes (DEGs) were identified using the “FindAllMarkers” function (parameters: min.pct=0.25, logfc.threshold=0.25, p < 0.05). To assess copy number variations (CNV), the “inferCNV” R package was employed, with endothelial and T/natural killer (NK) cells used as reference populations, assuming benign epithelial cells resemble these non-malignant cell types [14]. Epithelial cell differentiation trajectories were analyzed using the Monocle v2 package [15], with dimensionality reduction carried out by DDRTree. We utilized the following optimized parameters: max_components=2, and norm_method=”log”. The resulting differentiation trajectories were visualized using the “plot_cell_trajectory” function.
4. Identification of LVI-specific prognostic genes
DEGs in the LVI-specific epithelial cluster associated with OS in TNBC patients were identified from the scRNA-seq data. A univariate Cox regression analysis was first conducted to screen for DEGs significantly associated with OS in the TCGA dataset (p < 0.05). Following this, multivariate Cox regression was applied to identify independent prognostic genes. Genes with the most significant p-values were selected for further validation and analysis.
5. Functional enrichment analysis
Gene Set Enrichment Analysis (GSEA) was performed to identify biologically relevant pathways enriched in the data, focusing on Hallmark and Kyoto Encyclopedia of Genes and Genomes pathways. The “clusterProfiler” and “enrichplot” R packages were used for pathway analysis.
6. Drug sensitivity analysis
Drug sensitivity analysis was conducted using the “pRRophetic” R package to assess the sensitivity of TNBC samples in the TCGA dataset to common chemotherapy drugs [16]. The half-maximal inhibitory concentration (IC50) values for each drug were calculated using ridge regression.
7. Cell lines and cell culture
The normal breast cell line MCF10A, ER-positive cell line MCF7, human epidermal growth factor receptor 2 (HER2)–positive cell line T47D, TNBC cell lines (MDA-MB-231, CAL51), and HEK293T cell line were purchased from the American Type Culture Collection (ATCC). Cells were cultured in high-glucose Dulbecco’s modified Eagle’s medium (DMEM) or RPMI-1640 medium (Gibco) supplemented with 1% penicillin and streptomycin (HyClone) and 10% fetal bovine serum (Gibco). MCF10A cells were cultured in medium from Sunncell. All cultures were maintained in a humidified incubator at 37°C with 5% CO2.
8. Lentivirus production and generation of stable cell lines
Lentiviruses for knockdown (KRT6A short hairpin RNA [shRNA] 1, KRT6A shRNA2) were purchased from Shanghai Genesci Medical Technology Co., Ltd. Lentiviral vectors were co-transfected into HEK293T cells along with the packaging vectors psPAX2 (Addgene) and pMD2.G (Addgene) using Lipofectamine 2000 (Invitrogen), following the manufacturer’s instructions. Infectious lentiviral particles were collected 48 hours post-transfection, filtered through a 0.22 μm filter (Millipore), and used to transduce MDA-MB-231 and CAL51 cells. Stable cell lines were selected by adding 1 μg/mL puromycin to the culture medium. The sequences of the shRNAs used are provided in S3 Table.
9. Immunohistochemistry
Formalin-fixed, paraffin-embedded tumor tissue specimens were retrospectively collected from 120 non-metastatic TNBC patients who underwent surgical resection at Tianjin Medical University Cancer Institute and Hospital (TJMCH) between January 2011 and July 2015. In parallel, xenograft tumors from nude mice were harvested, fixed in 10% neutral-buffered formalin, embedded in paraffin, and sectioned at 6 μm for histopathological analysis. Immunohistochemical staining was performed on both clinical and murine samples following standard protocols. Briefly, sections were deparaffinized, rehydrated, and subjected to heat-induced epitope retrieval using citrate buffer (pH 6.0). Endogenous peroxidase activity was blocked with 3% hydrogen peroxide. Slides were incubated with the following primary antibodies: anti-KRT6A (10590-1-AP, Proteintech), anti-Ki67 (A20018, ABclonal), and anti-N-cadherin (A0433, ABclonal), followed by horseradish peroxidase (HRP)–conjugated secondary antibodies and DAB chromogen detection. Hematoxylin was used for nuclear counterstaining. For clinical samples, KRT6A protein expression was semi-quantitatively assessed using the H-score system (range 0–300), calculated as: H-score=(1×% weak staining)+(2×% moderate staining)+(3×% strong staining). Staining intensity was categorized as follows: 0, negative; 1+, weak (faint at 20×); 2+, moderate (distinct at 10×); 3+, strong (intense at 4×). Positivity was defined as ≥ 5% tumor cells exhibiting staining. Based on receiver operating characteristic curve analysis and prior cohort distribution, patients were stratified into low (< 150) and high (≥ 150) expression groups. For alternative validation, high expression was also defined as an H-score > 150 or strong staining in > 50% of tumor cells; low expression as < 100 or weak to negative staining in < 10% of tumor cells. KRT6A exhibited predominant cytoplasmic localization in TNBC tissues, with occasional membranous enrichment. Evaluation was focused on invasive tumor fronts and viable intratumoral regions, while necrotic, hemorrhagic, or artifact areas were excluded. Two independent pathologists, blinded to clinical and experimental information, assessed the staining, inter-observer agreement was quantitatively evaluated using Cohen’s Kappa coefficient, which was 0.85, indicating excellent consistency. For xenograft tumor sections, immunohistochemistry analysis of KRT6A, Ki-67, and N-cadherin was performed similarly. These markers were used to evaluate proliferative activity and EMT status in vivo. Quantification was based on positive cell percentages within five randomly selected high-power fields (400×) per tumor section.
10. Western blotting
Cells or harvested tissues were lysed using RIPA buffer (R0020, Solarbio), and protein concentration was measured with a BCA protein assay kit (PC0020, Solarbio). The samples were then mixed with protein sample loading buffer (LT 101, Epizyme) and boiled for sodium dodecyl sulfate polyacrylamide gel electrophoresis preparation. Proteins were separated on the gel and transferred to a polyvinylidene fluoride (PVDF) membrane using a standard Bio-Rad wet transfer system (WJ002, Epizyme). The PVDF membrane was then blocked with 5% skim milk (D8304, Solarbio) and incubated with the primary antibody overnight at 4°C. On the following day, the membrane was incubated with the secondary antibody, and chemiluminescence detection was performed using the Omni-ECLFemto photochemiluminescence kit (SQ201, Solarbio). The primary antibodies used were as follows: anti-KRT6A (1:10,000, 10590-1-AP, Proteintech), anti–glyceraldehyde 3-phosphate dehydrogenase (GAPDH; 1:5,000, 10494-1-AP, Proteintech), anti–N-cadherin (1:1,000, A0433, ABclonal), anti–E-cadherin (1:20,000, 20874-1-AP, Proteintech), anti–β-catenin (1:5,000, 51067-2-AP, Proteintech), anti-vimentin (1:20,000, A19607, ABclonal), anti-Slug (1:10,000, 12129-1-AP, Proteintech), anti-Snail (1:1,000, A11794, ABclonal), anti–C-myc (1:2,000, 10828-1-AP, Proteintech), anti–cyclinD1 (1:5,000, 60186-1-Ig, Proteintech), anti–glycogen synthase kinase-3β (GSK3β; 1:5,000, 82061-1-RR, Proteintech), anti–p-GSK3β-Tyr216 (1:1,000, 291-25-1-AP, Proteintech), anti–p-GSK3β-Ser9 (1:2,000, 67558-1-Ig, Proteintech), and anti–p-β-catenin (Ser33/37/Thr41) (1:1,000, # 9561, CST). The secondary antibodies used were as follows: HRP-conjugated goat anti-rabbit IgG (1:5,000, A0208, Beyotime) and goat anti-mouse IgG (1:5,000, A0216, Beyotime).
11. RNA extraction and real-time quantitative polymerase chain reaction
Total RNA was extracted from breast cancer cells using RNA extraction solution (G3013, Servicebio). The mRNA was then reverse-transcribed into cDNA using the HiScript II First Strand cDNA Synthesis Kit (R212-01, Vazyme). Real-time quantitative polymerase chain reaction (RT-qPCR) was performed following the manufacturer’s instructions, using HiScript II Q RT SuperMix for quantitative polymerase chain reaction (qPCR) (R223-01, Vazyme) and specific primers. Relative RNA expression was calculated using the 2−ΔΔCt method, with GAPDH as an internal control. The primer sequences used are provided in S4 Table.
12. Animal studies
All animal experiments were approved by the Ethics Committee of the Tianjin Medical University Cancer Institute and Hospital (approved No. NSFC-AE-2021199), and conducted in accordance with institutional and national guidelines for animal care and use. Female BALB/c nude mice (6 weeks old) were purchased from SPF Biotechnology and housed under specific pathogen-free conditions. To establish a tumor xenograft model, 1×106 MDA-MB-231 cells (either KRT6A wild-type or KRT6A-knockdown) were injected subcutaneously into the right flank of each mouse. Age- and weight-matched mice were randomly assigned to two groups (n=5 per group). Tumor growth was monitored every 2-3 days using caliper measurements once the average tumor size reached 50 mm3. Tumor volume was calculated using the formula: tumor volume=(length×width2)/2. All tumor measurements were conducted by investigators blinded to group allocation. To establish the MDA-MB-231 lung metastasis tumor model, 5×105 MDA-MB-231 cells suspended in 100 μL phosphate buffered saline were injected into the tail vein of BALB/C nude mice. For in vivo imaging, mice were intraperitoneally injected with D-luciferin (150 mg/kg, PerkinElmer). Mice were anesthetized using an inhalation anesthesia system (VetEquip, Inc.) with a mixture of 1.5% isoflurane and air. Ten minutes after D-luciferin injection, in vivo imaging was performed using the IVIS Spectrum system (PerkinElmer), while anesthesia was maintained with 1% isoflurane and air. Concurrently, micro computed tomography (CT) scans of the lung tissue were conducted. Mice were anesthetized with 2% isoflurane gas and secured in the Minerve animal chamber with tape. Vital signs were monitored using a respiratory sensor during scanning. Lung CT images were analyzed using OsiriX MD 12.0, and metastatic burden was quantified independently by two radiologists using Amira software (Thermo Fisher) with semi-automated lesion segmentation (threshold: ≥ 3 voxels at 50 Hounsfield unit). Humane endpoints were strictly observed: mice were euthanized if tumors exceeded 1,500 mm3, if body weight loss exceeded 20%, or if signs of distress or impaired mobility were observed, in accordance with National Institutes of Health guidelines. All animal procedures complied with the ARRIVE 2.0 reporting standards [17].
13. Cell Counting Kit-8 assay and colony formation assay
A total of 2,000 MDA-MB-231 and CAL51 cells infected with lentiviral shRNA were seeded in 96-well plates. After 2 hours of incubation with Cell Counting Kit-8 (CCK-8), optical density values were measured at 450 nm using a spectrophotometer. For the colony formation assay, 1,000 MDA-MB-231 and CAL51 cells infected with the corresponding lentivirus were plated in 10 cm culture dishes, and 10 mL of culture medium was added. The cells were cultured for 2 weeks. Colonies were then fixed and stained with 0.1% crystal violet (in methanol) for 30 minutes. The number of colonies was counted, and images were captured. For each experiment, three independent samples were analyzed for each group, and all experiments were performed in triplicate.
14. Wound healing and cell migration assays
To evaluate cell migration ability, a wound healing assay was performed. MDA-MB-231 and CAL51 cells (1×106 cells/mL) were seeded in 6-well plates and transduced with lentivirus-mediated shRNA. When the cells reached 90%-100% confluence, a wound was created in each well using a sterile 10 μL pipette tip, and images were captured at 0 hours. The cells were then cultured in serum-free DMEM medium, and images were taken at 24 hours to assess wound closure.
For further validation of migration, a Transwell assay was performed. In this experiment, no Matrigel was added to the Transwell chamber. All other procedures followed those of the invasion assay. For the invasion assay, Transwell filters (Becton Dickinson) were coated with 30 μL of Matrigel. After lentivirus-mediated shRNA treatment, 1×105 MDA-MB-231 and CAL51 cells were seeded in the upper chamber with 200 μL of serum-free DMEM medium. The lower chamber contained 600 μL of DMEM medium supplemented with 10% fetal bovine serum. After 24 hours, cells were fixed with 4% paraformaldehyde, stained with 0.1% crystal violet for 30 minutes, and then observed and counted under a microscope. Images were captured for analysis. Each experiment was performed in triplicate with three independent samples to ensure result reliability.
15. Protein stability assay using cycloheximide
To assess the stability of β-catenin protein, MDA-MB-231and CAL51 cells were treated with cycloheximide (CHX; 50 μg/mL, C7698, Sigma-Aldrich) to inhibit de novo protein synthesis. Cells were harvested at 0, 10, 20, and 30 minutes after CHX treatment. Total protein was extracted using RIPA lysis buffer and analyzed by Western blotting. β-catenin protein levels at each time point were quantified and normalized to GAPDH to evaluate the degradation rate.
16. Chemotherapy drug sensitivity assay
To assess chemotherapy drug sensitivity, MDA-MB-231 and CAL51 cells were seeded into 96-well plates at a density of 1×104 cells per well and incubated overnight. Cells were subsequently treated with a range of concentrations of four chemotherapeutic agents for 48 hours. The concentration gradients were as follows: doxorubicin: 0-10 μM (MDA-MB-231), 0-100 nM (CAL51); docetaxel: 0-100 nM (MDA-MB-231), 0-20 nM (CAL51); cisplatin: 0-100 μM (both cell lines); vinorelbine: 0-100 nM (both cell lines). All drugs were purchased from Selleck Chemicals and dissolved in dimethyl sulfoxide, then diluted in culture medium to the indicated working concentrations. Cell viability was assessed using the CCK-8 (Beyotime) following the manufacturer’s instructions. After drug treatment, 10 μL of CCK-8 solution was added to each well and incubated for 2 hours at 37°C. Absorbance was measured at 450 nm using a spectrophotometer. The IC50 values were determined by nonlinear regression curve fitting using GraphPad Prism 9.0 (GraphPad Software).
17. Statistical analysis
The chi-square test was used to compare proportions between groups. Differences between two groups were assessed using the Wilcoxon rank-sum test. Survival analysis was conducted using Kaplan-Meier curves to estimate patient survival. The multivariate Cox regression was applied to identify independent prognostic factors. Spearman’s rank correlation coefficient was used to evaluate correlations between variables. All statistical analyses were performed in R ver. 4.3.1 (R Foundation for Statistical Computing), with p-values < 0.05 considered statistically significant.
Results
1. scRNA-seq annotation of TNBC
After applying strict quality control filters, a total of 42,300 cells from 10 TNBC scRNA-seq samples were retained for further analysis. To account for technical variability, the raw data were normalized and batch effects were corrected. Clustering analysis was then performed on the batch-corrected data, revealing 21 distinct cell clusters identified through PCA (S5 Fig.). Dimensionality reduction was applied using a graph-based method to visualize the major clusters (Fig. 1A). Cell types were annotated based on marker genes from the GSE176078 dataset. A total of seven different cell types were identified, including epithelial cells, endothelial cells, mesenchymal cells, plasma cells, B cells, T/NK cells, and myeloid cells (Fig. 1B). As shown in Fig. 1C, distinct marker gene expression patterns were observed between the annotated cell types. To further investigate differences between tumor samples, we divided the cells from LVI-positive and LVI-negative tissues into separate subgroups. These subgroups were then visualized using UMAP clustering, which provided a clear overview of the proportion of each cell type in the respective groups (Fig. 1D and E).
Integration and clustering of single-cell RNA sequencing data to explore the heterogeneity of triple-negative breast cancer (TNBC). (A) Uniform Manifold Approximation and Projection (UMAP) visualization of the 10 TNBC samples. (B) The seven cell types were characterized based on specific marker genes. (C) A dot plot depicting the expression of marker genes across the seven cell types. (D) The UMAP plot of subgroups illustrates the distinction between lymphovascular invasion (LVI)–positive and LVI-negative groups. (E) The bar plot illustrates the differences in cell proportions between the LVI-positive and LVI-negative groups.
2. The heterogeneity of epithelial cell clusters revealed by scRNA‑seq
Following the implementation of dimensionality reduction clustering, we successfully discerned the presence of nine distinct subgroups within epithelial cell (Fig. 2A). Among these clusters, cluster 4 and cluster 8 are highly enriched in LVI-positive epithelial cell (Fig. 2B). To distinguish between normal and epithelial cells based on CNVs, we applied the inferCNV algorithm to explore single-cell data. Based on the inferCNV results, we categorized cells with CNV levels similar to T/NK cells and endothelial cells as normal epithelial cell. As shown in Fig. 2C, cluster 8 has the lowest CNV and is close to T cells and endothelial cells, so we define it as normal epithelial cell, while other clusters have significantly higher CNVs and are therefore defined as tumor epithelial cells. To further investigate the origin of epithelial cells, we performed trajectory analysis using the Monocle algorithm on the epithelial cell subpopulations. The trajectory analysis revealed that cluster 4 predominates during the middle and late stages of differentiation (Fig. 2D-F). Given that cluster 4 epithelial cell is the most enriched in malignant cells in LVI-positive TNBC, we will focus on this cluster for further analysis.
Cell subgroup analysis of epithelial cell. (A) Uniform Manifold Approximation and Projection (UMAP) of the nine epithelial clusters. (B) The proportion of lymphovascular invasion (LVI)–positive and LVI-negative epithelial cells across the nine epithelial clusters. (C) The copy number variations scores of epithelial and non-epithelial cell populations. (D) Epithelial clusters trajectory analysis. (E) Differentiation trajectories of epithelial clusters. (F) Clusters 4 epithelial cell trajectory analysis.
3. KRT6A is a crucial malignant biomarker in LVI-positive TNBC
First, 111 marker genes from cluster 4 epithelial cells in the single-cell data were intersected with the TNBC expression matrix from the TCGA dataset. To identify independent prognostic genes, univariate and multivariate Cox regression analyses were performed. Univariate Cox analysis identified DEFB1, KRT6A, MAST4, PCSK1, PRSS23, PSMA2, SKP1, ZBTB16, and ZFP36 as significant prognostic predictors (Fig. 3A). Subsequently, multivariate Cox regression analysis revealed KRT6A, PSMA2, and ZBTB16 as independent risk factors (Fig. 3B). Among these, KRT6A exhibited the smallest p-value, underscoring its strong prognostic significance. The expression profile of KRT6A across different breast cancer subtypes in the TCGA database revealed that its expression in TNBC was significantly higher than in normal breast tissue, as well as in the luminal and HER2 subtypes (Fig. 3C). FeaturePlot analysis from the scRNA-seq dataset further confirmed that KRT6A expression was enriched in epithelial cells (Fig. 3D), particularly in LVI-positive epithelial cells (Fig. 3E and F). GSEA highlighted that high KRT6A expression in TNBC is associated with enrichment in basal cell carcinoma (Fig. 3G) and cancer-related pathways (Fig. 3H). These findings suggest that KRT6A is a critical prognostic biomarker in TNBC.
KRT6A as a key marker in epithelial cells of lymphovascular invasion (LVI)–positive triple-negative breast cancer (TNBC). (A) Univariate Cox regression analysis of genes highly expressed in epithelial Cluster 4 cells from the TCGA (The Cancer Genome Atlas)–TNBC dataset. (B) Multivariate Cox regression analysis of genes highly expressed in epithelial Cluster 4 cells from the TCGA-TNBC dataset. (C) Expression of KRT6A across different breast cancer (BRCA) subtypes in the TCGA database. (D) Feature plots illustrating KRT6A expression across nine distinct cell types in single-cell RNA sequencing data. (E) Feature plots showing KRT6A expression across nine cell types in single-cell RNA sequencing data, comparing LVI-positive and LVI-negative subgroups. (F) Violin plot depicting KRT6A expression levels in LVI-positive versus LVI-negative epithelial cells. (G) Gene Set Enrichment Analysis (GSEA) indicating that high KRT6A expression in TNBC tissues is associated with basal cell carcinoma–related pathways in the TCGA-TNBC dataset. NES, normalized enrichment score. (H) GSEA showing that high KRT6A expression in TNBC tissues is associated with cancer-related pathways in the TCGA-TNBC dataset.
4. High expression of KRT6A is associated with poor prognosis in TNBC patients
To validate the prognostic significance of KRT6A in TNBC, we analyzed data from the TCGA, GEO, METABRIC, and a patient cohort from TJMCH institution. Kaplan-Meier survival analysis of the TCGA cohort revealed that patients with high KRT6A expression had significantly shorter OS compared to those with low KRT6A expression (Fig. 4A). This finding was further confirmed in the METABRIC and GSE58812 datasets (Fig. 4B and C). Multivariable Cox regression models incorporating established prognostic factors revealed KRT6A’s independent predictive value. In the TCGA cohort analysis (adjusting for T category [T1-T4], N category [N0-N3], M category, AJCC stage, age, and KRT6A expression), high KRT6A expression remained significantly associated with worse prognosis (hazard ratio [HR], 1.184; 95% confidence interval [CI], 1.052 to 1.380; p=0.015) (Fig. 4D). This independence was further confirmed in our TJMCH validation cohort (HR, 2.12; 95% CI, 1.34 to 3.35; p=0.001) (Fig. 4E) after adjusting for clinicopathological parameters including detailed staging. Additionally, KRT6A immunohistochemical staining performed on retrospective tissue samples from 120 TNBC patients at our center (Fig. 4F) further confirmed that high KRT6A expression was associated with shorter OS compared to low expression (Fig. 4G). Clinical relevance analysis revealed that high KRT6A expression was linked to a higher rate of LVI positivity (Fig. 4H) and lymph node metastasis (Fig. 4I). Collectively, these results suggest that high KRT6A expression is associated with poor prognosis in TNBC.
KRT6A is associated with poor prognosis in triple-negative breast cancer (TNBC). (A) Kaplan-Meier survival curves for overall survival (OS) in TCGA (The Cancer Genome Atlas)–TNBC dataset. (B) Kaplan-Meier survival curves for OS in METABRIC-TNBC dataset. (C) Kaplan-Meier survival curves in GSE58812 dataset. (D) Multivariable Cox regression analysis of prognostic factors in TCGA-TNBC cohort. (E) Multivariable Cox regression analysis of prognostic factors in Tianjin Medical University Cancer Institute and Hospital (TJMCH) cohort. (F) Representative immunohistochemical images showing KRT6A expression. (G) Kaplan-Meier survival curves for OS in the TJMCH dataset. (H) Proportion of lymphovascular invasion (LVI)–positive and LVI-negative tissues in patients with high and low KRT6A expression. (I) Proportion of lymph node metastatic tissues in patients with high and low KRT6A expression.
5. Knockdown of KRT6A results in decreased tumor growth in vitro
To explore and validate whether KRT6A serves as a potential oncogene in TNBC, we conducted a series of functional experiments, including proliferation, migration, and invasion assays. As an initial step, we analyzed the expression levels of KRT6A in TNBC cells using qPCR and Western blot techniques. The results demonstrated that, compared to other breast cancer subtypes, KRT6A expression was significantly elevated in the TNBC cell lines MDA-MB-231 and CAL51 (Fig. 5A and B), suggesting its potential association with the unique biological characteristics of TNBC. To further investigate the functional role of KRT6A, we employed specific shRNA (shKRT6A) to knock down KRT6A expression in MDA-MB-231 and CAL51 cells. RT-qPCR analysis confirmed a significant reduction in KRT6A expression following shRNA transfection (Fig. 5C). Clonogenic assays revealed that the clonogenic capacity of MDA-MB-231 and CAL51 cells was markedly reduced in shKRT6A-transfected cells compared to the negative control group, with a statistically significant difference (Fig. 5D). Moreover, CCK-8 proliferation assays further showed that KRT6A silencing significantly inhibited the proliferation of both TNBC cell lines (Fig. 5E), consistent with the results from the clonogenic assays. In summary, these findings suggest that the knockdown of KRT6A substantially impairs the proliferation capacity of TNBC cells. This discovery supports the hypothesis that KRT6A acts as a potential oncogene in TNBC and provides critical functional evidence for further investigation.
Knockdown of KRT6A reduces the proliferative capacity of triple-negative breast cancer (TNBC) cells. (A) Real-time quantitative polymerase chain reaction (RT-qPCR) was used to assess KRT6A mRNA expression levels in various TNBC cell lines. (B) Western blot analysis was performed to detect KRT6A protein expression in TNBC cell lines. (C) RT-qPCR was conducted to measure KRT6A mRNA expression in MDA-MB-231 and CAL51 cells transfected with KRT6A shRNA1 and KRT6A shRNA2. (D) Colony formation assays were performed to evaluate the proliferative capacity of MDA-MB-231 and CAL51 cells transfected with KRT6A PCDH, KRT6A shRNA1, or KRT6A shRNA2, after 2 weeks of culture in serum-containing Dulbecco’s modified Eagle’s medium. (E) Cell viability in MDA-MB-231 cells was assessed using the Cell Counting Kit-8 (CCK-8) assay 48 hours post-transfection with KRT6A PCDH, KRT6A shRNA1, or KRT6A shRNA2. (F) Cell viability in CAL51 cells was evaluated using the CCK-8 assay 48 hours after transfection with KRT6A PCDH, KRT6A shRNA1, or KRT6A shRNA2. NC, negative control; shRNA, short hairpin RNA. Data represents mean±standard error of mean, ns (not significant), *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.
6. Knockdown of KRT6A results in decreased tumor metastasis in vitro
To further explore the role of KRT6A in promoting the invasive and migratory capacities of cancer cells in TNBC, we performed a series of functional assays. Transwell assays were utilized to assess cell migration and invasion, with Matrigel added to the upper chamber for invasion assays and omitted for migration assays. The results demonstrated that KRT6A knockdown significantly impaired the migration and invasion abilities of both MDA-MB-231 and CAL51 cells (Fig. 6A and B). Additionally, wound healing assays were conducted to validate the effect of KRT6A on cell migration. Consistent with our hypothesis, silencing KRT6A notably reduced the migratory ability of MDA-MB-231 and CAL51 cells (Fig. 6C and D). Together with the findings from the proliferation assays (Fig. 5), these results provide compelling evidence that KRT6A functions as an oncogene in TNBC, playing a pivotal role in promoting tumor growth, invasion, and metastasis.
Knockdown of KRT6A reduces the invasive and migratory abilities of triple-negative breast cancer (TNBC) cells. (A, B) MDA-MB-231 and CAL51 cells were transfected with KRT6A PCDH, KRT6A shRNA1, or KRT6A shRNA2. Transwell assays were performed 24 hours post-transfection to assess the invasive and migratory capacities of TNBC cells. (C, D) Wound healing assays were conducted to evaluate the migratory abilities of MDA-MB-231 and CAL51 cells after transfection with KRT6A PCDH, KRT6A shRNA1, or KRT6A shRNA2. Representative images were captured at 0 and 24 hours. NC, negative control; shRNA, short hairpin RNA. Data represents mean± standard error of mean, *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.
7. Knockdown of KRT6A results in decreased tumor growth and metastasis in vivo
To investigate the role of KRT6A in vivo, both xenograft tumor and lung metastasis models were established (Fig. 7A). In the xenograft tumor model, MDA-MB-231 cells stably transfected with either KRT6A negative control (NC) or KRT6A shRNA were subcutaneously injected into nude mice. Subsequent analysis of tumor weight and growth curves revealed that KRT6A knockdown significantly inhibited tumor growth in vivo (Fig. 7B). Immunohistochemistry analysis showed significantly reduced expression of Ki67 and CDH2 in the KRT6A knockdown group compared to the control group (Fig. 7C). Additionally, for the lung metastasis model, MDA-MB-231 cells stably transfected with KRT6A NC or KRT6A shRNA were injected into the tail vein of nude mice. Fluorescence imaging of the lungs, CT scans, and the quantification of metastatic nodules indicated that KRT6A knockdown markedly suppressed metastasis (Fig. 7D-F). These results suggest that downregulation of KRT6A expression inhibits TNBC cell proliferation and metastasis in vivo, in line with the in vitro findings.
Knocking down KRT6A reduces the proliferation and metastasis ability of triple-negative breast cancer cells in vivo. (A) Schematic illustration of the xenograft and lung metastasis models used in mice. (B) Representative images of subcutaneous xenograft tumors (n=5 per group). Tumor volume was measured and tumor weight was analyzed every 3 days. (C) Immunohistochemistry staining of xenograft tumors showing the expression of KRT6A, Ki67, and CDH2. (D) Representative images of luminescence intensity in the lung metastasis models (n=5 per group). (E) Computed tomography imaging of mouse lungs. The two images on the left represent the control group, and the two images on the right correspond to the KRT6A knockdown group. (F) Representative images of metastatic lung tumors (arrows) in mice. Lung weight changes were also analyzed. NC, negative control; shRNA, short hairpin RNA. All data are expressed as mean±standard deviation. **p < 0.01, ****p < 0.0001.
8. Mechanisms by which KRT6A enhances the progression of TNBC
To investigate the mechanisms through which KRT6A promotes the progression of TNBC, we analyzed its expression in relation to key signaling pathways. The comparison of high- and low-expression KRT6A groups in the TCGA dataset revealed significant enrichment of the EMT and Wnt/β-catenin signaling pathways in the high-expression group (Fig. 8A and B). Correlation analysis between KRT6A and EMT-related genes in TNBC showed a strong positive association with CDH2, SNAIL, and vimentin (Fig. 8C-E). To further elucidate whether KRT6A influences EMT and cell migration through the Wnt/β-catenin pathway, we conducted RT-qPCR and Western blot assays in MDA-MB-231 and CAL51 cells. The results demonstrated that KRT6A knockdown significantly reduced the expression of EMT-associated genes and upregulated the expression of CDH1 (Fig. 8F-I). Taken together, collectively, these findings suggest that KRT6A enhances TNBC progression by regulating EMT and the Wnt/β-catenin signaling pathway. These insights contribute to a deeper understanding of the molecular mechanisms through which KRT6A drives TNBC progression.
Molecular mechanism of KRT6A in promoting triple-negative breast cancer (TNBC) progression. (A, B) Gene Set Enrichment Analysis revealed that high KRT6A expression in TNBC tissues were significantly enriched in the epithelial-mesenchymal transition (EMT) pathway and the Wnt/β-catenin signaling pathway in the TCGA (The Cancer Genome Atlas)–TNBC dataset. NES, normalized enrichment score. (C-E) Spearman correlation analysis based on the TCGA database showed a significant positive correlation between KRT6A expression and EMT-related genes (CDH2, SNAIL, and vimentin). (F, G) Real-time quantitative polymerase chain reaction analysis demonstrated altered mRNA expression levels of EMT-related genes (e.g., CDH2, SNAIL, and vimentin) in CAL51 and MDA-MB-231 cells transfected with KRT6A shRNA. (H, I) Western blot analysis revealed changes in protein expression levels of Wnt/β-catenin signaling components and EMT-related markers (e.g., β-catenin, E-cadherin, N-cadherin, and vimentin) in CAL51 and MDA-MB-231 cells transfected with KRT6A shRNA.
9. KRT6A enhances β-catenin stability by modulating GSK3β activity in TNBC cells
To investigate the molecular mechanism by which KRT6A regulates β-catenin expression, we knocked down KRT6A in CAL51 and MDA-MB-231 TNBC cells and analyzed the key components of the GSK3β/β-catenin signaling pathway. As shown in Fig. 9A and B, knockdown of KRT6A did not alter the total protein level of GSK3β or its phosphorylation at Tyr216. However, phosphorylation at Ser9 was markedly increased, indicating potential inhibition of GSK3β kinase activity. Notably, phosphorylation of β-catenin was elevated upon KRT6A knockdown, while total β-catenin levels were significantly reduced, suggesting a possible decrease in β-catenin stability. To further confirm whether KRT6A regulates β-catenin stability, CHX chase assays were performed. Cells were treated with CHX (50 μg/mL) and collected at the indicated time points. As shown in Fig. 9C and D, β-catenin degraded more rapidly in KRT6A-depleted cells compared to controls, indicating reduced protein stability. Taken together, these results suggest that KRT6A may promote β-catenin stability by enhancing Ser9 phosphorylation of GSK3β, thereby inhibiting GSK3β-mediated phosphorylation of β-catenin and reducing its degradation. This mechanism may underlie the positive regulation of β-catenin signaling by KRT6A in TNBC cells.
KRT6A modulates the glycogen synthase kinase-3β (GSK3β)/β-catenin pathway and promotes β-catenin stabilization in triple-negative breast cancer cells. (A, B) Western blot analysis of key components in the GSK3β/β-catenin signaling axis in CAL51 and MDA-MB-231 cells following stable knockdown of KRT6A using two independent short hairpin RNA (shRNAs). (C, D) Cycloheximide (CHX) chase assays were conducted to assess β-catenin protein stability in CAL51 and MDA-MB-231 cells after KRT6A knockdown. Cells were treated with CHX (50 μg/mL) and harvested at the indicated time points (0, 10, 20, and 30 minutes).
10. Chemotherapy drug sensitivity analysis
To explore the association between KRT6A expression and chemotherapy resistance in TNBC, we first analyzed drug sensitivity data from the TCGA database. As shown in Fig. 10A, D, G, and J, patients with high KRT6A expression exhibited significantly higher predicted IC50 values for multiple chemotherapy agents commonly used in TNBC treatment, including doxorubicin, docetaxel, cisplatin, and vinorelbine, indicating a potential link between elevated KRT6A levels and reduced chemosensitivity. To validate these findings, we performed in vitro drug response assays in two TNBC cell lines (MDA-MB-231 and CAL51) with KRT6A knockdown. Consistent with the bioinformatic analysis, CCK-8 assays showed that silencing KRT6A significantly decreased the IC50 values of all four drugs in both cell lines (Fig. 10B, C, E, F, H, I, K, and L). For example, in MDA-MB-231 cells, KRT6A knockdown reduced the IC50 of doxorubicin from 1.58 μM to 0.84 μM, and docetaxel from 7.34 nM to 4.37 nM. Similar reductions were observed in CAL51 cells (e.g., vinorelbine from 9.07 nM to 4.19 nM). These results suggest that KRT6A contributes to chemotherapy resistance in TNBC.
KRT6A promotes chemotherapy resistance in triple-negative breast cancer (TNBC) through modulation of drug sensitivity. (A, D, G, J) Box plots showing the predicted IC50 values for doxorubicin, docetaxel, cisplatin, and vinorelbine in TNBC samples from The Cancer Genome Atlas (TCGA) database, stratified by high and low KRT6A expression. Patients with high KRT6A expression exhibited significantly higher IC50 values, indicating reduced sensitivity to these chemotherapeutic agents. (B, E, H, K) Dose-response curves in MDA-MB-231 cells following treatment with the indicated drugs. (C, F, I, L) Corresponding assays in CAL51 cells. IC50 values were determined using Cell Counting Kit-8 assays after 48 hours of drug treatment. Knockdown of KRT6A significantly reduced IC50 values for all four agents, indicating enhanced drug sensitivity. Data are presented as mean±standard deviation. Statistical significance was assessed using unpaired t tests; p < 0.05 was considered statistically significant. NC, negative control; shRNA, short hairpin RNA.
Discussion
TNBC is a highly aggressive subtype of breast cancer characterized by early metastasis, high recurrence rates, and poor prognosis [18,19]. LVI has been identified as a key prognostic factor in TNBC, serving as an independent predictor of adverse outcomes and chemotherapeutic response [4]. Previous studies have suggested a strong association between EMT and LVI. For instance, Snail has been shown to promote LVI formation via EMT in urothelial carcinoma [20], and a similar correlation between EMT and LVI has been reported in endometrial cancer [21]. However, the molecular mechanisms underlying LVI in TNBC remain poorly understood. This study focuses on the potential regulatory role of the structural protein KRT6A in EMT and LVI.
Through transcriptomic database analyses and validation in clinical tissue samples, we found that KRT6A is highly expressed in LVI-positive TNBC and displays the highest expression levels among all breast cancer subtypes. Functional assays further demonstrated that KRT6A significantly enhances the proliferation, migration, and invasion of TNBC cells, accompanied by EMT activation—as evidenced by upregulation of N-cadherin and suppression of E-cadherin. Clinical data also revealed a strong association between high KRT6A expression and poor patient survival outcomes. While our findings provide initial evidence of the oncogenic role of KRT6A, we acknowledge that further mechanistic studies are needed to distinguish causality from correlation.
Mechanistically, we observed that KRT6A promotes phosphorylation of GSK3β at Ser9, thereby enhancing the stability of β-catenin and activating downstream signaling through the canonical Wnt/β-catenin pathway. A similar mechanism has been reported in nasopharyngeal carcinoma, where KRT6A upregulates β-catenin and drives metastasis [10], suggesting a conserved role for KRT6A as a modulator of Wnt signaling across cancer types. Although we confirmed KRT6A’s effect on EMT and β-catenin stabilization, whether it directly regulates β-catenin transcriptional activity remains to be elucidated.
KRT6A has been implicated in tumor progression across various malignancies [22]. In lung adenocarcinoma, KRT6A overexpression has been associated with enhanced stemness features and altered EMT marker expression, suggesting it may promote EMT partially through regulating stem-like properties [9]. While in colorectal cancer, its expression correlates with tumor stage and invasiveness [23]. These findings are largely consistent with our results in TNBC but also highlight potential tissue-specific differences in its mechanism of action. In our TNBC model, KRT6A appears to drive EMT predominantly via activation of the Wnt/β-catenin pathway, suggesting functional reprogramming distinct from traditional EMT transcription factor–driven mechanisms.
Under physiological conditions, KRT6A is a typical epithelial cytoskeletal protein involved in stress-induced proliferation and squamous differentiation [24]. However, accumulating evidence indicates that certain keratins—such as KRT17 and KRT19—can acquire non-structural functions in cancer, participating in signal transduction, stemness maintenance, and immune regulation [25-27]. Our study suggests that KRT6A may undergo similar functional reprogramming in TNBC, shifting from a structural role to an active regulator of EMT, Wnt signaling, and cell fate. This transformation may be driven by tumor-specific epigenetic and microenvironmental cues and represents an important adaptive mechanism during tumor progression [28].
Furthermore, the Wnt/β-catenin pathway has been extensively implicated in chemotherapy resistance in various cancers, mainly by promoting EMT and the maintenance of cancer stem cell–like traits [29-32]. In our study, high KRT6A expression was negatively correlated with the sensitivity of TNBC cells to several chemotherapeutic agents, including paclitaxel, docetaxel, cisplatin, and vinorelbine. IC50 assays confirmed that KRT6A enhances chemoresistance, possibly through activation of the EMT-Wnt axis and reinforcement of cellular survival mechanisms.
In summary, our study reveals that KRT6A, beyond its traditional structural role, promotes TNBC invasion, metastasis, and chemoresistance by regulating EMT and stabilizing β-catenin to activate the Wnt/β-catenin pathway. These findings support the potential of KRT6A as both a prognostic biomarker and a therapeutic target in TNBC. Future investigations should explore whether KRT6A directly regulates β-catenin target gene transcription or interacts with co-regulators to drive malignant phenotypes. It is also worth investigating whether KRT6A contributes to drug resistance through non-canonical Wnt signaling or other parallel pathways.
This study identifies KRT6A as a key regulator of TNBC progression. KRT6A promotes EMT, enhances cellular migration and invasion, and contributes to chemoresistance, potentially through activation of the canonical Wnt/β-catenin signaling pathway via GSK3β phosphorylation and β-catenin stabilization. These findings suggest that KRT6A undergoes functional reprogramming in TNBC, transitioning from a structural cytoskeletal protein to a signaling modulator involved in tumor aggressiveness.
Clinically, high KRT6A expression correlates with poor prognosis and reduced chemotherapy sensitivity, indicating its value as a potential prognostic biomarker and therapeutic target in TNBC. Future studies are warranted to clarify whether KRT6A directly regulates β-catenin transcriptional activity or interacts with other signaling components, including non-canonical Wnt pathways, to promote malignant phenotypes and drug resistance.
Electronic Supplementary Material
Supplementary materials are available at Cancer Research and Treatment website (https://www.e-crt.org).
Notes
Ethical Statement
The Medical Ethics Committee of Tianjin Cancer Hospital approved the relevant retrospective clinical study protocol (approval number: bc2020105). The experiments were conducted with the fully informed consent of the subjects. The study was conducted in accordance with the principles of the Declaration of Helsinki. All animal experiments were approved by the Ethics Committee of the Tianjin Medical University Cancer Institute and Hospital (approval number: NSFC-AE-2021199).
Author Contributions
Conceived and designed the analysis: Luo W, Zou Y, Jiang Y, Ruan X, Yu Y.
Collected the data: Luo W, Zou Y, Jiang Y, Ma X, Hai L, Jia W, Liu W, Meng R, Ruan X, Yu Y.
Contributed data or analysis tools: Luo W, Zou Y, Jiang Y, Guo S, Wang Y, Cao X, Ruan X, Yu Y.
Performed the analysis: Luo W, Zou Y, Jiang Y, Ma X, Liu Y, Cao X, Ruan X, Yu Y.
Wrote the paper: Luo W, Zou Y, Jiang Y, Ruan X, Yu Y.
Conflict of Interest
Conflict of interest relevant to this article was not reported.
Funding
This work was supported by grants from the National Natural Science Foundation of China (82103386,82172835).