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Elevated expression of Aurora-A/AURKA in breast cancer associates with younger age and aggressive features

Breast Cancer Research volume 26, Article number: 126 (2024) Cite this article

Abstract

Background and objective

Aurora kinase A (AURKA) is reported to be overexpressed in breast cancer. In addition to its role in regulating cell cycle and mitosis, studies have reported AURKA involvements in oncogenic signaling in suppressing BRCA1 and BRCA2. We aimed to characterize AURKA protein and mRNA expression in a breast cancer cohort of the young, investigating its relation to clinico-pathologic features and survival, and exploring age-related AURKA-associated biological processes.

Methods

Aurora kinase A immunohistochemical staining was performed on tissue microarrays of primary tumors from an in-house breast cancer cohort (n = 355) with information on clinico-pathologic data, molecular markers, and long and complete follow-up. A subset of the in-house cohort (n = 127) was studied by the NanoString Breast Cancer 360 expression panel for exploration of mRNA expression. METABRIC cohorts < 50 years at breast cancer diagnosis (n = 368) were investigated for differentially expressed genes and enriched gene sets in AURKA mRNA high tumors stratified by age. Differentially expressed genes and gene sets were investigated using network analyses and g:Profiler.

Results

High Aurora kinase A protein expression associated with aggressive clinico-pathologic features, a basal-like subtype, and high risk of recurrence score. These patterns were confirmed using mRNA data. High AURKA gene expression demonstrated independent prognostic value when adjusted for traditional clinico-pathologic features and molecular subtypes. Notably, high AURKA expression significantly associated with reduced disease-specific survival within patients below 50 years, also within the luminal A subtype. Tumors of high AURKA expression showed gene expression patterns reflecting increased DNA damage activation and higher BRCAness score.

Conclusions

Our findings indicate higher AURKA expression in young breast cancer, and associations between high Aurora-A/AURKA and aggressive tumor features, including higher tumor cell proliferation, and shorter survival, in the young. Our findings point to AURKA as a marker for increased DNA damage and DNA repair deficiency and suggest AURKA as a biomarker of clinical relevance in young breast cancer.

Introduction

The Aurora kinase family consists of the three highly conserved serine-threonine kinases A, B and C (gene names AURKA, AURKB and AURKC), being intracellular enzymes with essential roles during cell division, regulating cell proliferation and growth [1, 2]. Both AURKA and AURKB are frequently overexpressed in cancer [3, 4], and involved in tumor formation and progression [3, 5].

Aurora kinase A (Aurora-A) is considered as a key oncoprotein in breast cancer progression [6], where the overexpression has been associated with tumor growth, the basal-like phenotype, and poor prognosis [1, 6,7,8]. The overexpression of Aurora kinase A may be due to gene amplification, as reported in previous studies [1, 7, 9]. AURKA was originally named STK15/BTAK (Breast Tumor Amplified Kinase) due to amplification of chromosome 20q13 in breast cancer cell lines, the region where AURKA is located [10].

Studies have proposed that overexpression of AURKA leads to tumorigenic transformation and DNA instability [11,12,13], affecting response to cancer therapies [14,15,16]. AURKA is therefore suggested as a promising treatment target [2, 6, 17]. Moreover, in breast cancer, AURKA has been reported to outperform the proliferation marker Ki67 as a prognostic marker [18,19,20]. Until now, AURKA has not been investigated in an age-related context.

Here, we investigate the expression of Aurora-A protein/AURKA mRNA expression in relation to clinico-pathologic information and outcome, evaluating the prognostic impact in young breast cancer patients, and the relation to biological processes. We discovered that higher Aurora-A positive tumor cell counts (by IHC) and AURKA mRNA expression associated with young age at diagnosis, aggressive tumor characteristics, the basal-like phenotype, and high risk of recurrence. Moreover, we found high Aurora-A protein and AURKA mRNA expression to be associated with reduced survival, also with independent prognostic impact when adjusting for clinico-pathologic markers and molecular subtypes. Thus, we provide new insights regarding the prognostic significance of Aurora-A/AURKA within young breast cancer patients.

Materials and methods

Patient cohorts

The study included the Bergen in-house cohort of 355 breast cancer patients aged below 50 years at time of diagnosis, residing in Hordaland County, Norway, and diagnosed with primary invasive breast cancer during the period January 1996-December 2003 [21]. Although we had no information on ethnicity, this population was considered to be homogenous, with few cases of non-European origin in general at the time of inclusion. Prior to immunohistochemistry (IHC), 15 patients were excluded due to missing tissue blocks, and one patient was excluded as only fine needle cytology aspiration was available. This led to 339 cases available for IHC staining. A subset of the in-house cohort (n = 127) was analyzed by the NanoString Breast Cancer 360 panel to obtain mRNA gene expression profiles – see details below. Information on the clinical variables and breast cancer disease were obtained through local pathology registry (Dept. of Pathology, Haukeland University Hospital, Bergen, Norway) and the Cancer Registry of Norway [21]. The follow-up information, acquired from the Norwegian Cause of Death Registry, considered accurate and complete, included information on follow-up time, status at last follow-up, and cause of death. The last date of follow-up was June 30, 2017. Median follow-up time of survivors was 175 months (range 13–257 months).

Immunohistochemistry analysis

Aurora-A staining was performed manually on 5 μm thin sections from formalin-fixed paraffin-embedded (FFPE) tissue microarray (TMA) blocks with three tumor cores per case [21]. The sections were deparaffinized in xylene, rehydrated through a series of graded alcohols and rinsed in distilled water. Microwave oven heating with Tris–EDTA (Dako/Agilent S1699), pH 6.0, 20 min using 6th sense technology for epitope retrieval. To reduce background staining, a peroxidase-blocking agent (Dako/Agilent S2023) was applied for 8 min before the primary antibody. The tissue sections were incubated at room temperature for 60 min using a monoclonal rabbit antibody against Aurora-A (#91,590) from Cell Signaling Technology, diluted 1:500 before secondary antibody. EnVision-HRP Rabbit (Dako/Agilent K4003) was added for 30 min. To add color at the site of the target antigen recognized by the primary antibody, 3DAB chromogen (Dako/Agilent K3468) was applied for 10 min. Finally, sections were rinsed in distilled water and counterstained with Hematoxylin (Dako/Agilent S3301).

Aurora-A scoring

All slides were examined and scored by a pathologist (EW), blinded to patient characteristics and outcome. The slides were evaluated using light microscopy (Nikon Eclipse E400) with an eye-piece graticule for counting at × 400 magnification. Care was taken to avoid areas of intense inflammation, fibrosis, necrosis, low cellularity, and poor fixation. The slides were scanned at low magnification (× 100) to identify and encircle the hot-spot defined as the area containing the highest density of Aurora-A positive tumor cells by visual impression. Of the three TMA cores from each tumor, 500 tumor cells were counted in one core. The number of tumor cells and Aurora-A positive tumor cells were recorded, calculating an Aurora-A positivity fraction. For tumors with small areas of invasive tumor (< 500 tumor cells), the total number of tumor cells present were recorded, counting also a 2nd and 3rd core, aiming for a higher tumor cell count (closer to 500). Any nuclear and/or cytoplasmic staining regardless of intensity was considered positive.

Gene expression resources

For the exploration of gene expression patterns related to AURKA in breast cancer, publicly available mRNA gene expression datasets from Molecular Taxonomy of Breast Cancer International Consortium (METABRIC), including information on clinico-pathologic and follow-up data and molecular subtypes, were analyzed (METABRIC discovery cohort, n = 939; METABRIC validation cohort, n = 845) [22]. Information on molecular subtyping based on PAM50 classification was available from original METABRIC study [22]. Expression data was log2-transformed and in cases of multiple probes per gene symbol in the gene expressions matrices, probes were collapsed according to the max probe expression per gene [23]. For valid comparison to our in-house cohort, we applied the same age cutoff (below 50 years) and excluded the normal-like subtype, resulting in two METABRIC age-adjusted cohorts of 204 and 164 patient samples (discovery and validation cohort). Among the 204 cases in the discovery cohort, 53 (26%) were aged below 40 years at diagnosis, while 151 (74%) were at age 40–49 years. In the validation cohort, 54 (33%) were aged below 40 years at diagnosis, while 110 (67%) were at age 40–49 years. In the in-house mRNA cohort (n = 127), 34 (27%) were aged below 40 years at diagnosis, while 93 (73%) were aged 40–49 among the 127 patients.

The online database, “Kaplan–Meier plotter” (www.kmplot.com) [24], was used to evaluate AURKA expression in relation to recurrence-free breast cancer survival in a merged dataset of Gene Expression Omnibus (GEO) cohorts, (n = 1660). Information of PAM50 molecular subtypes were available within the online tool [25]. Note that METABRIC data is part of the KM-plotter dataset (overlap n = 537/1660) and should therefore be regarded as a semi-independent cohort. For all datasets in this study, the normal-like subtype was excluded from analyses.

Gene expression data analyses

Differentially expressed genes (DEGs) between AURKA high and low tumors (cutoff: median) were identified based on Significance Analysis of Microarrays (SAM) [26]. Gene sets from the Molecular Signatures Database (MsigDB; www.broadinstitute.org/gsea/msigdb) significantly enriched in AURKA high tumors were explored by employing Gene Set Enrichment Analysis (GSEA; www.broadinstitute.org/gsea) [23]. The Compute overlaps tool was used to explore gene sets enriched in the identified list of DEGs in AURKA high patients. The software J-Express (www.molmine.com) was applied for SAM and GSEA analyses, including assessment of the gene set collections: Gene Ontology (GO)—the category biological function (C5/BP); Hallmark gene sets; Curated gene sets for the KEGG, Reactome, and WikiPathways categories of canonical pathways (C2/CP/KEGG/REACTOME/WIKIPATHWAYS). Based on enriched GO categories, functional characterization of the identified genes was done by use of the Cytoscape app BiNGO [27], showing overrepresented GO categories, adjusted for multiple testing by the Benjamini–Hochberg False Discovery Rate (FDR) correction method. P-values yielded by BiNGO indicates significance illustrated as a gradient from white to orange nodes (darker color represents higher statistical significance).

Functional enrichment analysis based on Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG), and Reactome [28,29,30] was performed using g:Profiler (http://biit.cs.ut.ee/gprofiler/gost) [31].

In-house gene expression profiling by NanoString

The mRNA gene expression of the 776 genes in the NanoString human BC360 panel was measured in total RNA isolated from formalin-fixed paraffin-embedded (FFPE) breast cancer tissues, using the NanoString nCounter® platform (NanoString Technologies Inc., Seattle, WA, USA). After considering purity and quality requirements (optical density A260/280: 1.75–2.2, A260/230: 1.45–2.2, DV200 > 50%), we obtained RNA profiling for 127 cases (detailed in [32]). For each sample, 300 ng of RNA was hybridized to the BC 360 gene expression panel [33] and signal reads processed by the NanoString nCounter® platform according to manufacturer’s protocol. Pre-processing, quality control, and normalization was performed using nSolver (version 4.0, supported by dependent software R; version 3.3.2; https://cran.r-project.org).

Data were analyzed by the software ROSALIND® (Version 3.35.13.0; https://rosalind.onramp.bio/; San Diego, CA, United States) and nSolver. Fold changes and significance scores were calculated as described in the nCounter® Advanced Analysis 2.0 NanoString User Manual [34]. Significant P-values (P < 0.05) were adjusted for multiple genetic comparisons using the Benjamini–Hochberg method of estimating false discovery rates [35]. Differentially expressed genes (DEGs) between BC in patients aged < 40 and 40–49 years at the time of diagnosis were identified. Volcano plots of differential expression data were plotted using the − log10 (P-value) and log2 fold change.

PAM50 molecular subtypes and Risk of Recurrence (ROR) score

The 127-sample in-house gene expression dataset was analyzed using the Research Use Only (RUO) version of the NanoString PAM50 algorithm to classify each subject into an intrinsic molecular subtype: luminal A, luminal B, basal-like, and HER2-enriched BCs. This RUO PAM50-based subtype classifier model for the NanoString nCounter Dx Analysis System is consistent with the published qRT-PCR-based PAM50 assay (curated list of 50 genes to distinguish breast tumors into the molecular breast cancer subtypes) [36, 37].

The Prosigna Risk of Recurrence (ROR) score (range 0–100) was calculated by using weighted coefficients to the four molecular subtypes (luminal A, luminal B, HER2-enriched, basal-like), the tumor size (measures dichotomized into ≤ 2.0 cm vs.  > 2.0 cm), and a proliferation score [36,37,38].

Statistical methods

Data were analyzed using SPSS (version 25.0, IBM corp., Armonk, NY, USA). Spearman’s rank correlation test was applied when comparing bivariate continuous variables, and Spearman’s correlation coefficients (ρ) were reported. When analyzing differences in distribution of continuous variables between two or more categories, Person’s chi-square, Mann–Whitney U or Kruskal–Wallis tests were applied. For univariate survival analyses, applying recurrence or death from breast cancer endpoints, the Kaplan–Meier product-limit method (log-rank test) was applied. Multivariate breast cancer-specific survival analysis was performed by Cox’ proportional hazards regression model. Variables were included in the Cox survival analyses after evaluating their log-minus-log plot. For multivariate analyses, only patients with information on all variables were included. The calculations were done according to the backward stepwise likelihood ratio test. All statistical tests were two-sided, and statistical significance was assessed at 5% level.

Results

High Aurora-A expression associates with aggressive tumor characteristics and young age

When performing Aurora-A immunohistochemistry staining on our in-house FFPE tissue microarray cohort from primary breast cancer, Aurora-A showed cytoplasmic and/or nuclear staining with varying intensity and proportion of positive tumor cells (Fig. 1A–C). The proportion of Aurora-A positive tumor cells ranged from 0 to 93% (median 10%). In the in-house cohort, we demonstrated a significant association between AURKA gene expression and Aurora-A protein (IHC) positive tumor cell fraction (P = 0.004, ρ = 0.70, data not shown).

Fig. 1
figure 1

Immunohistochemical staining of Aurora-A, and AURKA mRNA/IHC expression across clinico-pathologic characteristics. A–C: High (A), medium (B), and low (C) proportion of Aurora kinase A (Aurora-A) positive tumor cells. All images magnification × 400. D–H Aurora-A expression across histological grade (D), ER status (E), PR status (F), HER2 status (G), and Ki67 status (H). I–M AURKA mRNA expression across histological grade (I), ER status (J), PR status (K), HER2 status (L), and Ki67 staus (M). Data shown with error-bars representing 95% confidence interval of the mean, and P-values by Mann–Whitney U-test. Expression values are displayed as IHC staining index and Log2 transformed mRNA expression values. All data from the in-house cohort

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When investigating how the Aurora-A protein expression was related to clinico-pathologic variables, we found associations between high Aurora-A IHC positive tumor cell counts and high histologic grade, ER and PR negativity, HER2 positivity, and high Ki67 (in-house cohort; Fig. 1D–H, Table 1). No significant associations were observed between the levels of Aurora-A positivity and tumor size, nor lymph node status. In the in-house NanoString mRNA expression cohort and external METABRIC discovery and validation cohorts (< 50 cohorts, n = 368), we observed associations between high AURKA mRNA expression and high histologic grade, lymph node metastases, ER negativity, and high Ki67 (Fig. 1I–M; Supplementary Fig. 1A-H). In the METABRIC validation cohort, we also found an association between high AURKA mRNA and large tumor size (Supplementary Fig. 1I).

Table 1 Aurora-A positive tumor cell fraction (%, by IHC) across tumor characteristics and breast cancer subtypes
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When examining how Aurora-A protein and AURKA mRNA expression was distributed across molecular subtypes, we observed the highest levels of Aurora-A IHC positive tumor cells and AURKA mRNA expression in the triple negative and basal-like subtype in both the in-house and METABRIC cohorts (< 50 cohorts, n = 368), and high AURKA levels also in the luminal B and HER2 positive/enriched subtypes (Fig. 2A–C; Table 1). Moreover, when examining the distribution of Aurora-A IHC and mRNA levels across risk of recurrence groups, we found associations between high Aurora-A/AURKA levels and a high-risk score (Supplementary Fig. 2A-B).

Fig. 2
figure 2

Aurora kinase A (AURKA) expression associates with aggressive subtypes and young age. A Aurora kinase A (Aurora-A) expression across molecular subtypes (by IHC; in house cohort, n = 292), B molecular subtypes by PAM50 (in-house cohort, n = 116). C AURKA mRNA expression across molecular subtypes (by PAM50; METABRIC < 50 cohort, n = 204), D and age groups in METABRIC discovery cohort (n = 939), E METABRIC validation cohort (n = 845), F and METABRIC discovery cohort with luminal A cases only (n = 466). Data shown with error-bars representing 95% confidence interval of the mean, and P-values by Mann-Witney U-test

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When investigating how AURKA expression varied between age groups, we observed a successive decline in AURKA expression towards age-groups of increasing 10-year intervals. The largest stepwise difference in AURKA mRNA expression was observed comparing patients under 40 years to the patient group aged 40–49 years (Fig. 2D–E; METABRIC combined cohorts, all ages, n = 1784). Also, within the luminal A cases, we found significantly higher AURKA mRNA expression among patients below 40 years compared to those aged 40–49 (Fig. 2F; METABRIC discovery cohort). In the in-house cohort, we observed higher expression of AURKA mRNA in the below 40 group, however, the same result was not observed for Aurora-A IHC data (Supplementary Fig. 2C-D).

Aurora-A expression presents independent prognostic value

We found that high levels of Aurora-A IHC positive tumors cells and high AURKA mRNA expression were associated with shorter disease-specific survival (Fig. 3A–C). High AURKA expression also predicted recurrence-free survival in the breast cancer KMplotter cohort [24]; Fig. 3D). By adding the clinico-pathologic variables tumor diameter, histologic grade, and lymph node status to the multivariate analysis (METABRIC discovery cohort, all ages, n = 1784), AURKA mRNA demonstrated independent association with shorter disease-specific survival (P < 0.001, HR = 1.44 95% CI 1.26–1.65, Fig. 3E). When additionally including molecular subtypes to the Cox model, AURKA mRNA expression maintained independent association with reduced survival (P = 0.021, HR = 1.24 95% CI 1.03–1.48, Fig. 3F).

Fig. 3
figure 3

High AURKA expression associates with poor survival. A High Aurora-A expression associates with shorter disease-specific survival (n = 291; in-house cohort). B–C High AURKA mRNA expression associates with shorter disease-specific survival in METABRIC validation- (B; n = 843) and discovery cohort (C; n = 939) cohort. (D) Recurrence-free breast cancer survival according to AURKA mRNA in the cohorts from the online KM plotter database (n = 4929; www.kmplot.com). E–F When adjusting for traditional prognostic variables, AURKA mRNA demonstrated independent association with shorter disease-specific survival (E; Cox multivariate analysis) also when adding molecular subtypes to the analysis (F). The lines represent a hazard ratio (HR) of 1.0, and error bars represent 95% CI. G–H High AURKA expression was also significantly associated with shorter survival in luminal A tumors (METABRIC discovery and KM plotter cohorts, n = 466 and n = 2277 respectively). KM plots: numbers in brackets indicate number of patients/number of events

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Upon investigating the prognostic impact of AURKA expression in individual molecular breast cancer subtypes, we found that high AURKA expression was associated with reduced disease-specific and recurrence-free survival in luminal A tumors (Fig. 3G–H; METABRIC discovery (all ages, n = 1784) and KM-plotter data, respectively). Moreover, high AURKA mRNA expression also showed independently significant prognostic value in luminal tumors, when adjusting for the traditional clinico-pathologic variables and MKI67 mRNA expression (Table 2A; METABRIC discovery cohort). In luminal A tumors only, AURKA mRNA presented independent prognostic value in addition to MKI67 mRNA expression, when adjusting for the traditional clinico-pathologic variables (Table 2B).

Table 2 Uni- and multivariate analysis. Cox' proportional hazards regression with disease-specific death from breast cancer as end-point. METABRIC discovery cohort luminal A and B subtypes combined (n = 734) and luminal A subtype only (n = 466)
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When investigating the prognostic impact of Aurora-A protein and mRNA expression separately for patients below 40 and 40–49 years, we found that both high Aurora-A protein and AURKA mRNA expression associated with reduced survival in patients aged 40–49 (Fig. 4A–D) but did not show prognostic value in the patient group below 40 years of age (data not shown). To note, when adding the clinico-pathologic variables tumor diameter, histologic grade, lymph node status, and MKI67 to the multivariate analysis (< 50 METABRIC discovery and validation cohorts, n = 368), AURKA mRNA demonstrated independent association with shorter disease-specific survival (P < 0.001, HR = 2.25 95% CI 1.63–3.10 and HR = 2.21 95% CI 1.50–3.26 respectively) for patients aged 40–49 years (Supplementary Table 1).

Fig. 4
figure 4

Aurora kinase A IHC and AURKA mRNA associates with poor survival in patients aged 40–49, and AURKA mRNA correlates with BRCAness- and DNA damage score. A–B Aurora-A and AURKA mRNA expression associated with shorter disease-specific survival in patients aged 40–49 years (n = 219 and n = 93 respectively, in-house cohort). C–D High AURKA mRNA expression was also significantly associated with shorter survival in patients aged 40–49 years in METABRIC < 50 discovery (C; n = 151) and validation (D; n = 110) cohorts. E–H Correlation between BRCAness score and DNA damage score and AURKA mRNA (E–F; in-house cohort, G–H; METABRIC < 50 discovery cohort). Scatter plots are represented with P-values by Spearman's rank correlation and the corresponding coefficients (ρ). Gene expression values are Log2-transformed mRNA levels. KM plots: numbers in brackets indicate number of patients/number of events. BRCAness- and DNA damage score calculated from Log2 transformed mRNA

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Gene expression profiles in young breast cancer with high AURKA expression reflect pathways related to proliferation and DNA damage

To study the potential age-related differences of biological processes accompanying alterations in AURKA mRNA expression in breast cancer, we analyzed global gene expression data from primary tumors in breast cancer patients. We employed the combined METABRIC discovery and validation cohorts for this purpose (all ages, n = 1784).

When examining genes differentially expressed between AURKA expression high and low, we identified 178 upregulated and 173 downregulated genes in the AURKA-high group (Supplementary Table 2; fold change ≥ 1.5/ ≤  − 1.5, FDR < 0.008%; METABRIC cohorts). As expected, we observed multiple cell-cycle-related genes among the top-ranked upregulated genes: UBE2C, CDC20, AURKB, and CCNB2 (Supplementary Table 2). In line with this observation, signatures reflecting proliferation pathways were in gene set enrichment analyses repeatedly enriched in AURKA mRNA-high tumors (Supplementary Table 3; GSEA, MsigDB; FDR < 5%).

Next, we investigated enriched gene sets within AURKA-high tumors, age-stratified, comparing output from GSEA analysis on AURKA high vs low between patients aged below 40 years (n = 58) and patients aged 40–49 (n = 151) (METABRIC discovery < 50 cohort, n = 204); MSigDB/Hallmark gene sets; KEGG; GO/Biological processes). Gene sets reflecting proliferation were top enriched in both age groups (Supplementary Table 4). We age-stratified the analyses of genes differentially expressed between AURKA high and low tumors, searching for AURKA-high associated genes uniquely up- and downregulated in the young and older patients (METABRIC < 50 cohorts, n = 368), fold change ± 1.5, FDR < 5%). Within the patient group aged below 40 years, we identified 75 unique upregulated genes and 72 unique downregulated genes in the AURKA-high subset. For patients 40–49 years, 51 genes were uniquely upregulated, and 86 genes uniquely downregulated in AURKA-high tumors (Supplementary Fig. 3, Supplementary Table 5).

When exploring functional enrichment from the gene list associated with high AURKA expression in the young, using the g:Profiler analysis tool, the top ranked GO/biological processes uniquely enriched in the AURKA-high/young group related to DNA replication, cell cycle, and DNA duplex unwinding (Supplementary Table 6). Moreover, gene sets reflecting activity in DNA replication and the cell cycle were top ranked pathways activated within the KEGG and Reactome databases, respectively, and were uniquely enriched in the AURKA-high/young group. The unique DEGs downregulated in the AURKA-high/young group showed enrichment of functions related to extracellular matrix organization, and extracellular structure organization (Supplementary Table 6).

Based on the uniquely up-regulated DEGs within AURKA- high tumors from patients aged below 40 years, we constructed a protein–protein interaction network assessing the overrepresentation of gene ontology categories (GO: Biological processes) among these genes. We demonstrated enrichment of GO categories reflecting cell-cycle, DNA replication, double strand break repair and DNA repair (Supplementary Fig. 4; Supplementary Table 7; P < 0.001).

Due to our results demonstrating AURKA mRNA being independent in multivariate analysis against Ki67, we wanted to explore whether Ki67 and AURKA mRNA shared common categorical gene set enrichment, and whether there were any unique enrichments associated with AURKA mRNA. In analyses on gene sets enriched in Ki67-high tumors, the results were much alike the output for AURKA-high tumors—gene sets reflecting proliferation dominated the top-ranked list (Supplementary Table 8). When comparing genes differentially expressed between Ki67-high and -low tumors (Supplementary Table 9) with genes differentially expressed between AURKA high and low cases (Supplementary Table 2), we found nine and seven uniquely differentially up- and downregulated genes in Ki67-high tumors (Supplementary Fig. 5). Several of the uniquely upregulated genes are previously shown to be involved in tumor cell proliferation such as UBE2C [39], GABRP [40], and FOXC1 [41].

Patients with high AURKA expression have increased DNA damage activation and BRCAness score

To further investigate the enrichment of gene sets reflecting DNA damage and repair in the young AURKA-high group, we investigated how AURKA mRNA expression related to DNA damage- and BRCAness scores (see MM section), stratified by age. We found strong correlations between AURKA mRNA expression and both signature scores (Fig. 4E-H; Supplementary Fig. 2H-I; In-house and METABRIC < 50 cohorts, n = 368), also when examined independently in the age groups < 40 and 40–49 (Supplementary Fig. 6A-L). When investigating the DNA damage score and BRCAness score across these age groups, and stratified for molecular subtypes, we found higher expression of both scores in luminal A subtypes (METABRIC discovery cohort; Supplementary Fig. 6M-N).

Discussion

Studies based on Aurora-A protein- and AURKA mRNA expression in breast cancer have implied that high Aurora kinase A expression is a strong and independent prognostic marker [42,43,44,45]. However, data on whether Aurora-A/AURKA expression is associated with prognosis specifically in young breast cancer has been lacking. In this study, we aimed to characterize Aurora-A/AURKA in young breast cancer patients and evaluate its prognostic significance. To our knowledge, this is the first study describing the expression and prognostic value of Aurora-A protein in patients aged below 50 years, paired with well-characterized clinico-pathologic variables and long and complete follow-up information.

We demonstrated higher expression of Aurora-A in tumors from the youngest breast cancer patients (below 40 years) compared to the older (40-49 years), emphasizing that high levels of Aurora-A kinase protein and AURKA mRNA expression point to aggressive tumor features and associate with reduced survival in breast cancer patients below 50 years. The Aurora-A/AURKA expression levels demonstrated independent prognostic impact also when adjusting for the traditional clinico-pathologic markers and molecular subtypes, also in the age group 40–49 years, as has been demonstrated in studies by others, but not specifically in young breast cancer cohorts [16, 44]. Of note, a previous study on Ki67 from our group showed lower prognostic impact of Ki67 in the young compared to older [46].

Overexpression of Aurora-A may lead to tumorigenic transformation and DNA instability [11,12,13]. Notably, increased expression of Aurora-A also promotes cell cycle progression despite abnormal chromosomal segregation, even when DNA is damaged, which is known as a hallmark of malignant tumors [47]. By multiple analytical approaches, our results provide evidence for increased tumor cell proliferation in breast cancer in AURKA-high tumors, in line with previous studies from our group and others [21, 48,49,50,51,52].

When comparing differentially expressed genes and enriched gene sets between AURKA-high and Ki67-high tumors, we observed very similar outputs, with gene sets reflecting proliferation dominating the top-ranked enriched list. Among the uniquely differentially up- and downregulated genes in Ki67-high tumors, several of these are previously shown to be involved in tumor cell proliferation, suggesting that both Ki67 and AURKA are contributes to tumor cell proliferation, potentially with involvement in different pathways and biological processes contributing to tumor growth.

Due to the crucial functions of aurora kinases in the cell cycle, particularly in the G2-M phases, it is expected that their effects will be affected following DNA damage, aiming to maintain the DNA checkpoint functionality [4]. Studies have shown that overexpression of Aurora-A can invalidate the G2 DNA damage checkpoint, and that high Aurora-A expression may lead to initiation of G2-M transition via the CDC25, p53 and PLK1 pathways [53,54,55]. Also, abnormal expression of Aurora-A may cause aneuploidy, which in turn can lead to an accumulation of defect or abnormal cells which ultimately contributes to malignancy [56]. These studies demonstrate a link between high levels of Aurora-A and increased DNA damage in cancer. Our data supports this, suggesting AURKA as a marker of increased DNA damage and concurrently deficient DNA repair, also observed in the luminal A subset in the young (age below 40 years).

In conclusion, our results demonstrate associations between high Aurora-A/AURKA expression and young age, as well as with aggressive tumor features including increased tumor cell proliferation. Also, increased DNA damage and DNA repair deficiency in AURKA-high tumors is indicated. Our findings point to AURKA as a biomarker relevant for young breast cancer patients.

Availability of data and materials

The METABRIC gene expression datasets are available at https://ega-archive.org/studies/EGAS00000000083 (METABRIC).

References

  1. Du R, Huang C, Liu K, Li X, Dong Z. Targeting AURKA in Cancer: molecular mechanisms and opportunities for Cancer therapy. Mol Cancer. 2021;20(1):15.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Tang A, Gao K, Chu L, Zhang R, Yang J, Zheng J. Aurora kinases: novel therapy targets in cancers. Oncotarget. 2017;8(14):23937–54.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Siggelkow W, Boehm D, Gebhard S, Battista M, Sicking I, Lebrecht A, et al. Expression of aurora kinase A is associated with metastasis-free survival in node-negative breast cancer patients. BMC Cancer. 2012;12:562.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Ma HT, Poon RYC. Aurora kinases and DNA damage response. Mutat Res. 2020;821:111716.

    Article  CAS  PubMed  Google Scholar 

  5. Fu J, Bian M, Jiang Q, Zhang C. Roles of Aurora kinases in mitosis and tumorigenesis. Mol Cancer Res. 2007;5(1):1–10.

    Article  CAS  PubMed  Google Scholar 

  6. Jalalirad M, Haddad TC, Salisbury JL, Radisky D, Zhang M, Schroeder M, et al. Aurora-A kinase oncogenic signaling mediates TGF-β-induced triple-negative breast cancer plasticity and chemoresistance. Oncogene. 2021;40(14):2509–23.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Staff S, Isola J, Jumppanen M, Tanner M. Aurora-A gene is frequently amplified in basal-like breast cancer. Oncol Rep. 2010;23(2):307–12.

    PubMed  Google Scholar 

  8. Guo M, Lu S, Huang H, Wang Y, Yang MQ, Yang Y, et al. Increased AURKA promotes cell proliferation and predicts poor prognosis in bladder cancer. BMC Syst Biol. 2018;12(Suppl 7):118.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Bodvarsdottir SK, Hilmarsdottir H, Birgisdottir V, Steinarsdottir M, Jonasson JG, Eyfjord JE. Aurora-A amplification associated with BRCA2 mutation in breast tumours. Cancer Lett. 2007;248(1):96–102.

    Article  CAS  PubMed  Google Scholar 

  10. Sen S, Zhou H, White RA. A putative serine/threonine kinase encoding gene BTAK on chromosome 20q13 is amplified and overexpressed in human breast cancer cell lines. Oncogene. 1997;14(18):2195–200.

    Article  CAS  PubMed  Google Scholar 

  11. Wang X, Zhou YX, Qiao W, Tominaga Y, Ouchi M, Ouchi T, et al. Overexpression of aurora kinase A in mouse mammary epithelium induces genetic instability preceding mammary tumor formation. Oncogene. 2006;25(54):7148–58.

    Article  CAS  PubMed  Google Scholar 

  12. Hu W, Kavanagh JJ, Deaver M, Johnston DA, Freedman RS, Verschraegen CF, et al. Frequent overexpression of STK15/Aurora-A/BTAK and chromosomal instability in tumorigenic cell cultures derived from human ovarian cancer. Oncol Res. 2005;15(1):49–57.

    Article  CAS  PubMed  Google Scholar 

  13. Chou CH, Yang NK, Liu TY, Tai SK, Hsu DS, Chen YW, et al. Chromosome instability modulated by BMI1-AURKA signaling drives progression in head and neck cancer. Cancer Res. 2013;73(2):953–66.

    Article  CAS  PubMed  Google Scholar 

  14. Cirak Y, Furuncuoglu Y, Yapicier O, Aksu A, Cubukcu E. Aurora A overexpression in breast cancer patients induces taxane resistance and results in worse prognosis. J Buon. 2015;20(6):1414–9.

    PubMed  Google Scholar 

  15. Eterno V, Zambelli A, Villani L, Tuscano A, Manera S, Spitaleri A, et al. AurkA controls self-renewal of breast cancer-initiating cells promoting wnt3a stabilization through suppression of miR-128. Sci Rep. 2016;6:28436.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Lykkesfeldt AE, Iversen BR, Jensen MB, Ejlertsen B, Giobbie-Hurder A, Reiter BE, et al. Aurora kinase A as a possible marker for endocrine resistance in early estrogen receptor positive breast cancer. Acta Oncol. 2018;57(1):67–73.

    Article  CAS  PubMed  Google Scholar 

  17. Yan M, Wang C, He B, Yang M, Tong M, Long Z, et al. Aurora-A kinase: a potent oncogene and target for cancer therapy. Med Res Rev. 2016;36(6):1036–79.

    Article  PubMed  Google Scholar 

  18. Ramani P, Nash R, Rogers CA. Aurora kinase A is superior to Ki67 as a prognostic indicator of survival in neuroblastoma. Histopathology. 2015;66(3):370–9.

    Article  PubMed  Google Scholar 

  19. Heilmann T, Dittmann L, van Mackelenbergh M, Mundhenke C, Weimer J, Arnold N, et al. Head-to-head comparison of the impact of Aurora A, Aurora B, Repp86, CDK1, CDK2 and Ki67 expression in two of the most relevant gynaecological tumor entities. Arch Gynecol Obstet. 2016;294(4):813–23.

    Article  CAS  PubMed  Google Scholar 

  20. Ali HR, Dawson SJ, Blows FM, Provenzano E, Pharoah PD, Caldas C. Aurora kinase A outperforms Ki67 as a prognostic marker in ER-positive breast cancer. Br J Cancer. 2012;106(11):1798–806.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Svanøe AA, Humlevik RO, Knutsvik G, Sæle AK, Askeland C, Ingebriktsen LM, Hugaas U, Kvamme AB, Tegnander AF, Krüger K, Davidsen B. Age-related phenotypes in breast cancer: a population-based study. https://doi.org/10.1101/2023.05.22.541427 (2023).

  22. Curtis C, Shah SP, Chin SF, Turashvili G, Rueda OM, Dunning MJ, et al. The genomic and transcriptomic architecture of 2000 breast tumours reveals novel subgroups. Nature. 2012;486(7403):346–52.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA. 2005;102(43):15545–50.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Györffy B, Lanczky A, Eklund AC, Denkert C, Budczies J, Li Q, et al. An online survival analysis tool to rapidly assess the effect of 22,277 genes on breast cancer prognosis using microarray data of 1,809 patients. Breast Cancer Res Treat. 2010;123(3):725–31.

    Article  PubMed  Google Scholar 

  25. Győrffy B. Survival analysis across the entire transcriptome identifies biomarkers with the highest prognostic power in breast cancer. Comput Struct Biotechnol J. 2021;19:4101–9.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Tusher VG, Tibshirani R, Chu G. Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci USA. 2001;98(9):5116–21.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Maere S, Heymans K, Kuiper M. BiNGO: a Cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks. Bioinformatics. 2005;21(16):3448–9.

    Article  CAS  PubMed  Google Scholar 

  28. Croft D, O’Kelly G, Wu G, Haw R, Gillespie M, Matthews L, et al. Reactome: a database of reactions, pathways and biological processes. Nucl Acids Res. 2011;39:D691–7.

    Article  CAS  PubMed  Google Scholar 

  29. Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucl Acids Res. 2000;28(1):27–30.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Kanehisa M, Sato Y, Furumichi M, Morishima K, Tanabe M. New approach for understanding genome variations in KEGG. Nucl Acids Res. 2019;47(D1):D590–5.

    Article  CAS  PubMed  Google Scholar 

  31. Raudvere U, Kolberg L, Kuzmin I, Arak T, Adler P, Peterson H, et al. g:Profiler: a web server for functional enrichment analysis and conversions of gene lists (2019 update). Nucl Acids Res. 2019;47(W1):W191–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Ingebriktsen LM, Svanøe AA, Myrmel Sæle AK, Humlevik ROC, Toska K, Kalvenes MB, et al. Age-related clusters and favorable immune phenotypes in breast cancer of the young patients. Mod Pathol. 2024;37(8):100529.

    Article  PubMed  Google Scholar 

  33. Technologies N. nCounter® Breast Cancer 360TM Panel 2021 [cited 28.04.2021. Available from: https://www.nanostring.com/products/ncounter-assays-panels/oncology/breast-cancer-360/.

  34. NanoString Technologies® I. nCounter Advanced Analysis 2.0 Plugin for nSolver Software. User Manual. NanoString2018.

  35. Ferreira JA. The Benjamini-Hochberg method in the case of discrete test statistics. Int J Biostat. 2007;3(1):11.

    Article  PubMed  Google Scholar 

  36. Parker JS, Mullins M, Cheang MC, Leung S, Voduc D, Vickery T, et al. Supervised risk predictor of breast cancer based on intrinsic subtypes. J Clin Oncol. 2009;27(8):1160–7.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Wallden B, Storhoff J, Nielsen T, Dowidar N, Schaper C, Ferree S, et al. Development and verification of the PAM50-based Prosigna breast cancer gene signature assay. BMC Med Genomics. 2015;8:54.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Nielsen TO, Parker JS, Leung S, Voduc D, Ebbert M, Vickery T, et al. A comparison of PAM50 intrinsic subtyping with immunohistochemistry and clinical prognostic factors in tamoxifen-treated estrogen receptor-positive breast cancer. Clin Cancer Res. 2010;16(21):5222–32.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Kim YJ, Lee G, Han J, Song K, Choi JS, Choi YL, et al. UBE2C Overexpression aggravates patient outcome by promoting estrogen-dependent/independent cell proliferation in early hormone receptor-positive and HER2-negative breast cancer. Front Oncol. 2019;9:1574.

    Article  PubMed  Google Scholar 

  40. Meng Y, Li R, Geng S, Chen W, Jiang W, Li Z, et al. GABRP promotes the metastasis of pancreatic cancer by activation of the MEK/ERK signaling pathway. Biochem Genet. 2024;62(1):242–53.

    Article  CAS  PubMed  Google Scholar 

  41. Johnson J, Choi M, Dadmanesh F, Han B, Qu Y, Yu-Rice Y, et al. FOXC1 identifies basal-like breast cancer in a hereditary breast cancer cohort. Oncotarget. 2016;7(46):75729–38.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Aradottir M, Reynisdottir ST, Stefansson OA, Jonasson JG, Sverrisdottir A, Tryggvadottir L, et al. Aurora A is a prognostic marker for breast cancer arising in BRCA2 mutation carriers. J Pathol Clin Res. 2015;1(1):33–40.

    Article  CAS  PubMed  Google Scholar 

  43. Yamamoto S, Yamamoto-Ibusuki M, Yamamoto Y, Fujiwara S, Iwase H. A comprehensive analysis of Aurora A; transcript levels are the most reliable in association with proliferation and prognosis in breast cancer. BMC Cancer. 2013;13:217.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Miligy IM, Toss MS, Gorringe KL, Ellis IO, Green AR, Rakha EA. Aurora Kinase A Is an independent predictor of invasive recurrence in breast ductal carcinoma in situ. Pathobiology. 2022;89(6):382–92.

    Article  CAS  PubMed  Google Scholar 

  45. Nadler Y, Camp RL, Schwartz C, Rimm DL, Kluger HM, Kluger Y. Expression of Aurora A (but not Aurora B) is predictive of survival in breast cancer. Clin Cancer Res. 2008;14(14):4455–62.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Svanøe AA, Humlevik ROC, Knutsvik G, Sæle AKM, Askeland C, Ingebriktsen LM, et al. Age-related phenotypes in breast cancer: A population-based study. Int J Cancer. 2023;154(11):2014–24.

    Article  Google Scholar 

  47. Mou PK, Yang EJ, Shi C, Ren G, Tao S, Shim JS. Aurora kinase A, a synthetic lethal target for precision cancer medicine. Exp Mol Med. 2021;53(5):835–47.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Azim HA Jr, Partridge AH. Biology of breast cancer in young women. Breast Cancer Res. 2014;16(4):427.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Azim HA Jr, Michiels S, Bedard PL, Singhal SK, Criscitiello C, Ignatiadis M, et al. Elucidating prognosis and biology of breast cancer arising in young women using gene expression profiling. Clin Cancer Res. 2012;18(5):1341–51.

    Article  CAS  PubMed  Google Scholar 

  50. Azim HA Jr, Nguyen B, Brohée S, Zoppoli G, Sotiriou C. Genomic aberrations in young and elderly breast cancer patients. BMC Med. 2015;13:266.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Fredholm H, Magnusson K, Lindström LS, Tobin NP, Lindman H, Bergh J, et al. Breast cancer in young women and prognosis: how important are proliferation markers? Eur J Cancer. 2017;84:278–89.

    Article  PubMed  Google Scholar 

  52. Ingebriktsen LM, Finne K, Akslen LA, Wik E. A novel age-related gene expression signature associates with proliferation and disease progression in breast cancer. Br J Cancer. 2022;127(10):1865–75.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Liu Q, Kaneko S, Yang L, Feldman RI, Nicosia SV, Chen J, et al. Aurora-A abrogation of p53 DNA binding and transactivation activity by phosphorylation of serine 215. J Biol Chem. 2004;279(50):52175–82.

    Article  CAS  PubMed  Google Scholar 

  54. Seki A, Coppinger JA, Jang CY, Yates JR, Fang G. Bora and the kinase Aurora a cooperatively activate the kinase Plk1 and control mitotic entry. Science. 2008;320(5883):1655–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Chan EH, Santamaria A, Silljé HH, Nigg EA. Plk1 regulates mitotic Aurora A function through betaTrCP-dependent degradation of hBora. Chromosoma. 2008;117(5):457–69.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Anand S, Penrhyn-Lowe S, Venkitaraman AR. AURORA-A amplification overrides the mitotic spindle assembly checkpoint, inducing resistance to Taxol. Cancer Cell. 2003;3(1):51–62.

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

We thank Ingeborg Winge, Randi Hope Lavik, and Bendik Nordanger for their excellent technical assistance. The work was supported by the University of Bergen and the Research Council of Norway through its Center of Excellence funding scheme (project number 223250), and through the Helse Vest Research Fund (F-12143).

Funding

Open access funding provided by University of Bergen. The research was supported by grants from the University of Bergen, Helse Vest research fund (F-12143), and the Research Council of Norway through its Centres of Excellence funding scheme (project number 223250) The results are in part based on data generated by the METABRIC consortium. The funding sources were not involved in the study other than financial support.

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Author notes

  1. E. A. Hoivik and L. A. Akslen has shared authorship.

Authors and Affiliations

  1. Department of Clinical Medicine, Section for Pathology, Centre for Cancer Biomarkers CCBIO, University of Bergen, Bergen, Norway

    L. M. Ingebriktsen, R. O. C. Humlevik, A. A. Svanøe, A. K. M. Sæle, I. Winge, M. B. Kalvenes, G. Knutsvik, C. Askeland, I. M. Stefansson, E. A. Hoivik, L. A. Akslen & E. Wik

  2. Department of Pathology, Haukeland University Hospital, Bergen, Norway

    G. Knutsvik, C. Askeland, I. M. Stefansson, E. A. Hoivik, L. A. Akslen & E. Wik

  3. Section for Cancer Genomics, Haukeland University Hospital, Bergen, Norway

    K. Toska

  4. Department of Surgery, Section for Breast and Endocrine Surgery, Haukeland University Hospital, Bergen, Norway

    B. Davidsen & A. Heie

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  1. L. M. Ingebriktsen
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Contributions

E.W. conceived and designed the study, and contributed to pathology review of tumor sections, Aurora kinase A IHC scoring, data analysis and interpretation, and writing the manuscript. L.M.I. contributed to the study design, data collection, analyses and interpretation, and to writing the manuscript. E.A.H. and L.A.A. contributed to the study design, data analyses and interpretation, and to writing the manuscript. R.O.C.H. and A.A.S. contributed with data collection, including molecular characterization of the in-house cohort, and data interpretation. I.W. contributed with the Aurora kinase A IHC lab work. L.M.I., A.K.M.S., K.T., M.B.K., B.D., A.H., G.K., C.A., I.M.S., E.A.H., L.A.A., and E.W. participated in data collection and interpretation. L.M.I., E.A.H., L.A.A., and E.W. wrote the manuscript with critical input from all co-authors. All authors read and approved the final manuscript.

Corresponding author

Correspondence to E. Wik.

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Ethics approval and consent to participate

The study was approved by the Western Regional Committee for Medical and Health Research Ethics, REC West (2014/1984/REK vest). Written informed consent was waived by the ethics committee. The national identification numbers of all patients were checked with the Registry of Withdrawal from Biological Research Consent by the Norwegian Institute of Public Health. None of the cases were listed in the registry.

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The authors declare no competing interests.

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Ingebriktsen, L.M., Humlevik, R.O.C., Svanøe, A.A. et al. Elevated expression of Aurora-A/AURKA in breast cancer associates with younger age and aggressive features. Breast Cancer Res 26, 126 (2024). https://doi.org/10.1186/s13058-024-01882-x

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Breast Cancer Research

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