Association of SLC12A1 and GLUR4 Ion Transporters with Neoadjuvant Chemoresistance in Luminal Locally Advanced Breast Cancer

Abstract

Chemoresistance to standard neoadjuvant treatment commonly occurs in locally advanced breast cancer, particularly in the luminal subtype, which is hormone receptor-positive and represents the most common subtype of breast cancer associated with the worst outcomes. Identifying the genes associated with chemoresistance is crucial for understanding the underlying mechanisms and discovering effective treatments. In this study, we aimed to identify genes linked to neoadjuvant chemotherapy resistance in 62 retrospectively included patients with luminal breast cancer. Whole RNA sequencing of 12 patient biopsies revealed 269 differentially expressed genes in chemoresistant patients. We further validated eight highly correlated genes associated with resistance. Among these, solute carrier family 12 member 1 (SLC12A1) and glutamate ionotropic AMPA type subunit 4 (GRIA4), both implicated in ion transport, showed the strongest association with chemoresistance. Notably, SLC12A1 expression was downregulated, while protein levels of glutamate receptor 4 (GLUR4), encoded by GRIA4, were elevated in patients with a worse prognosis. Our results suggest a potential link between SLC12A1 gene expression and GLUR4 protein levels with chemoresistance in luminal breast cancer. In particular, GLUR4 protein could serve as a potential target for drug intervention to overcome chemoresistance.

Keywords: chemoresistance; ion transport; locally advanced; luminal breast cancer; neoadjuvant chemotherapy.

Scheme 1

Method used to identify resistance markers in luminal breast cancer. We divided this method into three steps: (1) patients and tissue collection, (2) discovery, and (3) validation.

Figure 1

Analysis of differentially expressed genes and functional enrichment in luminal chemoresistant breast cancer patients. (A) Volcano plot of the up- and downregulated genes in chemoresistant patients (p-adjusted < 0.05, log2 fold change > 1 for upregulation and <1 for downregulation); Genes that did not meet the significance threshold are denoted as ‘NS’; (B) Disease enrichment analysis dot plot generated using the DOSE package in R, based on differential expression analysis with p-adjusted < 0.05 and log2 fold change ≤ −1 or ≥1. Dot color indicates the p-adjusted value of each term, and gene ratio indicates the proportion of genes in the term relative to the total number of genes in the dataset. The plot shows the top 16 most significant human diseases, revealing a significant enrichment of neoplasms in the downregulated genes. (C) Gene ontology analysis dot plot of enriched biological concepts, analyzed using the clusterProfiler package in R. The downregulated genes are associated with processes such as import and transport across the cell membrane, while the upregulated genes are involved in the formation and stabilization of microtubules; (D) Network of gene and biological concept linkages enriched in resistant patients, based on ConsensusPathDB pathways. Node size reflects the number of genes associated with each biological concept, and edges represent functional relationships between these genes; (E) Heatmap of differentially expressed genes that correlate with the residual cancer burden (RCB) classification (correlation ≥ 7 or ≤−7, log2 fold change ≥ 2 or ≤−2, and p-adjusted < 0.05). Rows represent genes, and columns represent tissue samples, with expression levels indicated by a color scale ranging from blue (downregulated) to red (upregulated).

Figure 2

Chemoresistant patients showed downregulation of the solute carrier family 12 member 1 (SLC12A1) gene. (A) Box plots showing the validation of eight resistance-correlated genes in 12 patients using real-time PCR. Outlying data points are considered as outliers. (B) box plots showing gene transcripts per million (TPM) of eight resistance-correlated genes in 102 breast cancer samples from dataset EGAD00001008269 (published by Sammut et al. [14]); (C) box plot showing SLC12A1 mRNA levels in 46 breast cancer samples, measured using real-time PCR; (D) Kaplan–Meier graph for distant relapse-free survival (DRFS) of luminal-ER+ patients with low or high expression of SLC12A1 determined using real-time PCR; (E) Kaplan–Meier graph for DRFS of luminal-ER+ breast cancer patients with low or high SLC12A1 expression from the microarray dataset GSE25066 (published by Hatzis et al. [15]). Median ± range are shown in box plots, and the Mann–Whitney test was used to analyze the data. * p < 0.05, ** p < 0.005 were considered significant.

Figure 3

Higher levels of glutamate ionotropic AMPA type subunit 4 (GRIA4) protein, GLUR4, were associated with worse outcomes in chemoresistant luminal breast cancer patients. (A) Detection of GLUR4 through immunohistochemistry (IHC) in normal colon tissue, utilized as a positive control. Representative magnified image highlights staining along the epithelial cells. Scale bars: 500 μm and 50 μm (zoomed in); (B) Representative images of tru-cut biopsies taken before neoadjuvant chemotherapy from breast cancer patients, illustrating the presence of GLUR4 staining. The magnified view highlights the staining within tumor cells. (C) Calculation of IHC scores by multiplying staining intensity with the proportion of positivity. (D) Scatter plot of IHC scores demonstrating a significant elevation in GLUR4 protein levels in chemoresistant tissues compared to chemosensitive tissues. Data presented as median ± range and analyzed using the Mann–Whitney test. (E) Distant relapse-free survival analysis (DRFS) of breast cancer patients based on low or high GLUR4 protein levels from tissue microarrays. (F) DRFS analysis of luminal breast cancer patients based on low or high GRIA4 gene expression from microarray dataset GSE25066. * p < 0.05 was considered statistically significant.