Bioinformatic analysis of the role of solute carrier-glutamine transporters in breast cancer

Introduction

Globally, breast cancer (BC) is the most common malignant tumor among women. Based on the latest global cancer burden data released by the International Agency for Research on Cancer of the World Health Organization, there will be 19.29 million new cases of cancer in the world in 2020, and 2.26 million new cases of BC worldwide; thus, BC will replace lung cancer as the world’s most prevalent cancer (1). BC is a highly heterogeneous disease. With the development of precision medicine, the classification of metabolic subtypes in the BC microenvironment is gradually developing.

Glutamine is the most abundant free amino acid in the body and is involved in a series of pathways, such as energy generation, macromolecular synthesis, and signal transmission in tumor cells. Additionally, certain tumor growth is dependent on glutamine transport uptake, a phenomenon known as “glutamine addiction” (2). Based on the above characteristics, glutamine transporter may play a key target role in tumor progression. However, little is known about the role of glutamine transporter in the occurrence and development of breast cancer.

Recent studies have shown that amino acid transport and metabolic pathways are crucial in the proliferation and development of BC (3,4). Based on the exploration of human genomes, about 430 solute carriers (SLCs) have been identified and classified by several classification systems (5). Many SLCs have been shown to function physiologically to transport amino acids (6). Glutamine is the most abundant free amino acid in the body and is involved in a series of pathways, such as energy generation, macromolecular synthesis, and signal transmission by a specific carrier in tumor cells (7). The tumorigenesis and progression of the glutamine transporters involved in SLCs mainly include SLC1A5, SLC3A2, SLC7A5, SLC7A8, SLC6A14, SLC38A1, and SLC38A2. However, further bioinformatics analyses of BC remain to be performed. A study has shown that amino acid transporter-mediated metabolic reprogramming is essential for the aggressive characterization of BC (8). The importance of glutamine for the proliferation and growth of tumor cells, and the role of these glutamine transporters in the occurrence and development of a variety of cancers has been partially confirmed (9). Research has shown that the co-expression of 3 transporters (i.e., SLC1A5, SLC7A5, and SLC3A2) is related to the poor prognosis of patients, especially in high-proliferative estrogen receptor positive (ER⁺)/luminal B subtype (10). Additionally, SLC7A5 may become a neo-target spot chemotherapeutic in luminal subtype of BC (11). In low proliferative ER⁺/luminal A subtype BC, the high expression of SLC7A8 indicates a good prognosis (12). The high expression of SLC6A14 promotes tumor cell proliferation in ER⁺ BC (13). Additionally, inhibiting the expression of SLC38A1 inhibits the growth of 4T1 cells (14). The increased expression of glutamine transporter SLC38A2 promotes a glutamine-dependent proliferative pathway and oxidative stress resistance, which suggests a poor prognosis for triple negative (TN) patients (15). However, no bioinformatics analyses have been published on the glutamine transporters of SLCs in BC.

In this study, we used various publicly available online databases to analyze the expression variations or copy numbers of thousands of genes published online. Additionally, the Gene Expression Profiling Interactive Analysis (GEPIA) online tool was used to examine the transcription and prognostic data of glutamine-related transporters of BC patients in the Oncomine databases and a gene expression profile interaction analysis was undertaken. The correlations between the SLCs and the infiltration of the immune cell markers were investigated using the Tumor Immune Estimation Resource (TIMER 2.0). We sought to understand the tumorigenesis and progression of the glutamine transporters involved in SLCs (i.e., SLC1A5, SLC3A2, SLC7A5, SLC7A8, SLC6A14, SLC38A1, and SLC38A2) to investigate their clinicopathological significance and predictive prognostic value for BC patients. We present the following article in accordance with the REMARK reporting checklist (available at https://atm.amegroups.com/article/view/10.21037/atm-22-2620/rc).

Methods

Ethical statement

The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013).

Dataset Acquisition and Bioinformatics Analyses

All the RNA-seq transcriptome data and corresponding clinicopathologic information were obtained from publicly available datasets (Oncomine; GEPIA; Kaplan-Meier; cBioPortal). Additionally, the Gene Expression Profiling Interactive Analysis (GEPIA) online tool was used to examine the transcription and prognostic data of glutamine-related transporters of BC patients and a gene expression profile interaction analysis was undertaken. The correlations between the SLCs and the infiltration of the immune cell markers were investigated using the Tumor Immune Estimation Resource (TIMER 2.0).

Oncomine database

Oncomine (https://www.oncomine.org/resource/login.html) is currently the largest oncogene microarray database platform in the world. Oncomine possesses the most complete tumor gene mutation spectrum, gene expression, and relevant clinical data, and can be used to explore novel biomarkers or therapeutic targets for cancer. Oncomine microarray data sets were used to analyze the transcription levels of SLC1A5, SLC3A2, SLC7A5, SLC7A8, SLC6A14, SLC38A1, and SLC38A2 in various types of cancer.

GEPIA online analysis

GEPIA is a web-based tool that provides fast and customizable functionality based on data from The Cancer Genome Atlas (TCGA) and the GenotypeTissue Expression (GTEx) (16). GEPIA has key interactive and customizable analytical functions, including differential expression, gene mapping, correlation, patient survival, similar gene detection, and dimension reduction analyses.

Survival analysis

Kaplan-Meier (K-M) plotter (http://kmplot.com/analysis) assesses correlations between genes (mRNAs, micro RNAs, and proteins) and survival outcomes in samples from 21 tumor types including, breast, ovarian, lung, and gastric cancer. Overall survival (OS), relapse-free survival (RFS), distant metastasis-free survival (DMFS), and post-progress survival (PPS) were used as prognostic indicators for BC patients based on the hazard ratios (HRs) with 95% confidence intervals (CIs) and log rank P values.

TCGA Data and cBioPortal

TCGA comprises both sequencing and pathological data for 30 different cancers (17). The breast invasive carcinoma (TCGA, PanCancer Atlas) data set, comprises data from 1084 cases and was selected for the further analysis of SLCs using cBioPortal (http://www.cbioportal.org/index.do). The genomic profiles included mutations, putative copy number alterations from the genomic identification of significant targets in cancer, mRNA expression Z scores (RNA-seq v.2 RSEM), and protein expression Z scores (reverse phase protein arrays). According to the cBioPortal’s online instructions, a co-expression module was used to obtain the top 100 genes with the strongest correlations with SLC1A5. The top 100 genes with the strongest correlations with other SLCs were obtained in the same way. The web tool Venn (http://bioinformatics.psb.ugent.be/webtools/Venn/) was used to intersect these 700 genes for the subsequent analysis.

PPI network and functional enrichment analysis

The Search Tool for the Retrieval of Interacting Genes (STRING) database was used to retrieve known proteins and establish the protein-protein interaction network (PPI) (18). Functional Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using Database for Annotation, Visualization, and Integrated Discovery (DAVID) software (https://david.ncifcrf.gov) as described previously (19).

Infiltration of immune cells

TIMER is a web server for the comprehensive analysis of tumor infiltrating immune cells (20). The abundances of 6 immune infiltrates, including B cells, cluster of differentiation 4 positive (CD4⁺) T cells, CD8⁺ T cells, neutrophils, macrophages, and dendritic cells (DCs) were estimated by the TIMER2.0 algorithm. TIMER also allows the user to select any gene of interest and visualize the correlation between its expression and the level of immune infiltration of different cancer types (21).

Statistical analysis

The messenger ribonucleic acid (mRNA) expression levels of these SLCs in clinical cancer specimens were compared to those of normal controls, and the Student’s t test was used to generate a P value. The cutoffs of the P value and fold change (FC) value were defined as 0.01 and 2, respectively. P<0.05 were defined as significant different.

Results

Transcriptional levels of SLCs in human BC

The main glutamine transporters involved in SLCs include SLC1A5, SLC3A2, SLC7A5, SLC7A8, SLC6A14, SLC38A1, and SLC38A2. These 7 glutamine transporter genes have been identified in mammalian cells. In summary, SLC1A5, SLC3A2, SLC7A5, SLC38A1, and SLC38A2 are highly expressed in most tumors, while SLC7A8 is lowly expressed in most tumors (see Figure 1).

Figure 1 The transcription levels of SLCs in different types of cancers (Oncomine). SLCs, solute carriers.

The significant changes of SLC expression in transcription levels between different types of BC tissues and normal tissues showed that the mRNA expression level of SLC1A5 was significantly downregulated in BC patients in the Finak Breast Statistics data set (22). In the Finak Breast Statistics data set, SLC1A5 was more downregulated in invasive breast carcinoma tissues than normal tissue (FC: 7.248), as was SLC3A2 (FC: 8.545) (22). In TCGA breast data set, SLC7A5 was overexpressed in invasive breast carcinoma (FC: 2.683) and invasive ductal breast carcinoma (FC: 3.443) (see Table 1). In the Curtis Breast Statistics (FC: 3.239) (23). In the Richardson Breast 2 Statistics data set, SLC7A5 was more upregulated in ductal breast carcinoma tissue than normal tissue (FC: 3.620) (24). In the Finak Breast Statistics data set, SLC7A8 was found to be highly expressed in invasive breast carcinoma (FC: 4.259) (22). SLC7A8 was found to be highly expressed in mixed lobular and ductal breast carcinoma (FC: 3.448) in TCGA, and mucinous breast carcinoma (FC: 2.323) in Curtis Breast data set versus normal samples (23). While in the Richardson Breast 2 Statistics data set, SLC7A8 was found to be lowly expressed in ductal breast carcinoma (FC: 2.135) (24). The mRNA expression level of SLC6A14 was significantly downregulated in BC patients in the Ma Breast 4 Statistics data set (25), TCGA Breast Statistics data set, and the Richardson Breast 2 Statistics data set (24). In the Ma Breast 4 Statistics data set, SLC6A14 was more downregulated in the invasive ductal breast carcinoma epithelia (FC: 55.571), invasive ductal breast carcinoma stroma (FC: 8.683), ductal breast carcinoma in-situ stroma (FC: 6.807), ductal breast carcinoma in situ epithelia (FC: 21.600) tissues than the normal tissues. In TCGA Breast Statistics data set, SLC6A14 was more lowly expressed in the mixed lobular and ductal breast carcinoma (FC: 6.465), invasive lobular breast carcinoma (FC: 5.429), and intraductal cribriform breast adenocarcinoma samples than the normal samples (FC: 15.810). In the Richardson Breast 2 Statistics data set, SLC6A14 was significantly downregulated in patients with ductal breast carcinoma (FC: 4.958) (24). In the Finak’s data set (22), SLC38A2 was more lowly expressed in the ductal breast carcinoma samples (FC: 23.038) than the normal samples (see Table 1).

Expression of SLCs and their correlations with tumor stages in BC patients

The GEPIA data set (http://gepia.cancer-pku.cn/) was used to analyze the mRNA expression levels of SLC1A5, SLC3A2, SLC7A5, SLC7A8, SLC6A14, SLC38A1, and SLC38A2 between BC tissues and normal tissues. The results showed that the expression levels of SLC1A5, SLC3A2, SLC7A5, SLC7A8, and SLC38A1 were higher in the BC tissues than the normal tissues. Notably, the expression of SLC7A5 and SLC38A1 were more highly expressed in the BC tissues than the normal tissues. Compared to the normal tissues, SLC6A14 and SLC38A2 were lowly expressed in the BC tissues. Additionally, the expression level of SLC6A14 was significantly lower in the BC tissues than the normal tissues (see Figure 2).

Figure 2 The expression of SLCs in BC [GEPIA; (A) scatter diagram; (B) box plot]. The “*” indicates a statistically significant difference between the two groups. SLCs, solute carriers; BC, breast cancer; BRCA, breast cancer.

We also analyzed the expression of glutamine transporters (i.e., SLC1A5, SLC3A2, SLC7A5, SLC7A8, SLC6A14, SLC38A1, and SLC38A2) in different stages of BC. The results showed that the differential expression of the SLC7A5, SLC7A8, SLC6A14, and SLC38A2 genes differed significantly based on the clinical stage of BC. There was no significant difference in relation to the SLC1A5, SLC3A2 and SLC38A1 genes (see Figure 3).

Figure 3 Correlation between SLCs expression and tumor stage in BC patients (GEPIA). SLCs, solute carriers; BC, breast cancer.

Association between the mRNA expression of SLC1A5, SLC3A2, SLC7A5, SLC7A8, SLC6A14, SLC38A1, and SLC38A2 and the prognosis of BC patients

To investigate the expression of SLC1A5, SLC3A2, SLC7A5, SLC7A8, SLC6A14, SLC38A1, and SLC38A2 in relation to the prognosis of BC patients, we performed a prognosis analysis of SLC1A5, SLC3A2, SLC7A5, SLC7A8, SLC6A14, SLC38A1, and SLC38A2 in BC patients using publicly available data sets (26). In the BC patients, the OS, RFS, DMFS, and PPS of the 2 groups with high expression and low expression levels of the target genes were used as prognostic indicators. The K-M curves revealed that patients with high SLC1A5 expression had a poor prognosis (OS HR =1.28, 95% CI: 1.06–1.54; P=0.01). The high expression of SLC3A2 was significantly correlated with a poor prognosis (DMFS HR =1.19, 95% CI: 1.02–1.39; P=0.027). Increased SLC7A5 mRNA levels and decreased SLC7A8 mRNA levels were significantly associated with a poor prognosis in terms of OS, RFS, DMFS and PPS. (SLC7A5: OS HR =1.64, 95% CI: 1.36–1.99; P=2.4e-07; RFS HR =1.58, 95% CI: 1.42–1.75; P<1e-16; DMFS HR =1.98, 95% CI: 1.69–2.32; P<1e-16; PPS HR =1.29, 95% CI: 1.02–1.62; P=0.032; SLC7A8: OS HR =0.77, 95% CI: 0.64–0.93; P = 0.0057; RFS HR =0.61, 95% CI: 0.55–0.67; P<1e-16; DMFS HR =0.67, 95% CI: 0.58–0.79; P=6e-07). The high expression of SLC6A14 was significantly correlated with a poor prognosis (PPS HR =1.35, 95% CI: 1.07–1.7; P=0.011). The high expression of SLC38A1 was correlated with a better prognosis than low expression of SLC38A1 (RFS HR =0.84, 95% CI: 0.76–0.93; P=0.00077; DMFS HR =0.78, 95% CI: 0.67–0.91; P=0.0013) (see Figure 4).

Figure 4 The prognostic value of the mRNA levels of SLCs in BC patients (K-M plots). SLCs, solute carriers; BC, breast cancer; OS, overall survival; RFS, relapse-free survival; DMFS, distant metastasis-free survival; PPS, post-progress survival.

Network analysis of SLC co-expressed genes and identification of potential “Hub” genes with functions and pathways prediction

We used the cBioPortal online tool to analyze the genomic characteristics of the glutamine transporter-related genes (i.e., SLC1A5, SLC3A2, SLC7A5, SLC7A8, SLC6A14, SLC38A1, and SLC38A2) in invasive BC (cbioportal.org/) (27). About 8% of the patients with invasive breast carcinoma had the related SLC mutations (see Figure 5A). We then analyzed the correlations among related SLCs mRNA expression using the Pearson’s correction in the cBioPortal online tool [mRNA Expression, RSEM (Batch normalized from Illumina HiSeq_RNASeqV2)].

Figure 5 SLCs identified in the gene expression and mutation analysis of BC patients (cBioPortal). (A) SLC gene expression and mutation analysis of BC (cBioPortal). (B) Correlation between different SLCs in BC (cBioPortal). (C) Network analysis for SLCs and the 100 most frequently altered neighbor genes. (D) The functions of the SLCs and the genes significantly associated with the SLC alterations were predicted by an analysis of the GO by DAVID (https://david.ncifcrf.gov/summary.jsp). The GO enrichment analysis predicted the functional roles of the target host genes in relation to 3 aspects (i.e., BPs, CCs, and MFs). (E) The functions of SLCs and genes significantly associated with SLC alterations were predicted by the analysis of KEGG by DAVID tools (https://david.ncifcrf.gov/summary.jsp). SLCs, solute carriers; BC, breast cancer. BP, biological process; CC, cellular component; MF, molecular function; KEGG, Kyoto Encyclopedia of Genes and Genomes.

We found positive correlations in the following related SLCs: SLC3A2 with SLC1A5 and SLC7A5; SLC7A5 with SLC3A2 and SCL6A14; SLC7A8 with SLC38A1; SLC6A14 with SLC7A5; SLC38A1 with SLC7A8 and SLC38A2; and SLC38A2 with SLC38A1. Conversely, SLC7A5 was found to be negatively correlated with SLC7A8 (see Figure 5B). A Venn diagram was constructed of the top 100 frequently altered neighbor genes of each SLC, and a PPI analysis was conducted for those that were related to at least 2 or more SLCs at the same time (see Figure 5C). The functional annotation of the SLCs and the genes significantly associated with SLC alterations were predicted by analyzing GO and the KEGG in the DAVID database (https://david.ncifcrf.gov/summary.jsp). The GO analysis predicted the function of the target genes in relation to the following 3 aspects: biological process (BP), cellular component (CC), and molecular function (MF) (see Figure 5D). In relation to the BPs, the GO analysis indicated that the target genes were significantly enriched in the positive/negative regulation of transcription from RNA polymerase II promoter, cell division, and mitotic nuclear division. In relation to the CCs, the genes were significantly enriched in the nucleus and nucleoplasm. In relation to the MFs, the genes were significantly enriched in protein binding, deoxyribonucleic acid (DNA) binding, and metal-ion binding. The KEGG analysis defined the pathways related to the functions of the SLCs and their frequently altered neighbor genes, and the results showed that the cell-cycle pathway may significantly involve in the development of cancer (see Figure 5E).

Correlation of SLC expression with the infiltration of immune cells in BC and immune marker genes

Tumor-infiltrating lymphocytes are closely related to the chemotherapy reaction and survival prognosis of BC patients (28,29). To further explore the SLCs and immune cell infiltration in BC, we used the TIMER database to examine whether the expression of the related SLCs was correlated to the degree of immune cell infiltration in BC (see Figure 6). Among the seven SLC genes, we found that SLC1A5 expression was correlated with the infiltration levels of CD8⁺ T cells and macrophages, SLC3A2 expression was correlated with the infiltration levels of neutrophils and DCs, SLC7A5 expression was correlated with the infiltration levels of macrophages, neutrophils, and DCs, SLC7A8 expression was correlated with the infiltration levels of B cells, macrophages, neutrophils, and DCs, SLC6A14 expression was correlated with the infiltration levels of B cells, CD8⁺ T cells, CD4⁺ T cells, neutrophils, and DCs, SLC38A1 expression was correlated with the infiltration levels of CD8⁺ T cells and macrophages, and SLC38A2 expression was correlated with the infiltration levels of CD8⁺ T cells, CD4⁺ T cells, macrophages, neutrophils, and DCs. These results strongly suggested that the expression of the related SLC genes is closely correlated with the degree of immune cell infiltration in BC, especially in macrophages, neutrophils, and DCs.

Figure 6 SLC expression is correlated with the infiltration of immune cells in BC; a correlation analysis of immune marker genes. SLC, solute carrier; BC, breast cancer.

Macrophages, the most plastic cells in the hematopoietic system, commonly exist in various tissues of the body and play an important role in homeostasis, tissue repair, and disease (30). Tumor-associated macrophages (TAMs) are the key regulatory factors in the tumor microenvironment (31). In the tumor microenvironment, under the influence of signals, such as chemokines, monocytes are actively recruited to the tumor site and differentiate into macrophages with specific phenotypes and functions. The expression of C-C motif chemokine ligand-2 (CCL2), interleukin (IL) 10, and CD68 in TAMs, the interferon regulatory factor-5 of the M1 phenotype, and membrane-spanning 4-domains subfamily A4A (MS4A4A) and CD163 of the M2 phenotype were significantly correlated with SLC7A5 expression in BC. The expression of CCL2 and IL10 in TAMs, the interferon regulatory factor-5, nitric oxide synthase-2 (NOS2), and prostaglandin-endoperoxide synthase 2 (PTGS2) of the M1 phenotype, and MS4A4A and CD163 of the M2 phenotype were significantly correlated with SLC7A8 expression in BC. The expression of CCL2 in TAMs and interferon regulatory factor-5 of the M1 phenotype were significantly correlated with SLC38A1 expression in BC. The expression of CD68 and IL10 in TAMs, NOS2 and PTGS2 of the M1 phenotype, and VSIG4, MS4A4A, and CD163 of the M2 phenotype were significantly correlated with SLC38A2 expression in BC (see Figure 7). Thus, the glutamine transporter-related SLCs of SLC1A5, SLC3A2, SLC7A5, SLC7A8, SLC6A14, SLC38A1, and SLC38A2 may affect the occurrence and development of BC by inducing and regulating the infiltration of immune cells with different phenotypes. Notably, SLC7A5, SLC7A8, SLC38A1, and SLC38A2 have the potential to regulate the polarization of TAMs in the microenvironment.

Figure 7 Scatterplots of correlations between SLC expression and the gene markers of monocytes, TAMs, M1 macrophages, and M2 macrophages in BC. Monocyte markers: CD86 and CSF1R. TAM markers: CCL2, CD68, and IL10. M1 macrophages markers: NOS2, IRF5, and PTGS2. M2 macrophages markers: CD163, VSIG4, and MS4A4A. SLC, solute carrier; TAMs, tumor-associated macrophages; BC, breast cancer; CCL2, C-C motif chemokine ligand-2; IL10, interleukin 10.

Discussion

The SLC groups of membrane transport proteins, which comprise 65 families, control the influx of zinc, and are responsible for the transport of amino acids, participate in a range of physiological processes and may provide novel therapeutic targets for human malignances (9,32). In this study, we explored for the first time the mRNA expression and conducted K-M survival analyses of glutamine-associated transporters (i.e., SLC1A5, SLC3A2, SLC7A5, SLC7A8, SLC6A14, SLC38A1, and SLC38A2) in BC. We also analyzed the genomic characteristics and examined correlations between related SLCs and the infiltration of immune cells to examine how glutamine-associated transporters participate in the evolution and progression of BC. We found that glutamine transporters may have great potential as therapeutic targets for BC in the future.

SLC1A5, also called alanine-serine-cysteine transporter 2, is one of the most important and most studied transporters of glutamine in tumors (33). The treatment of two cells (i.e., luminal type and basal-like type) with a high expression of SLC1A5 with an inhibitor of SLC1A5-GPNA inhibited the growth of 1806 HCC cells and decreased the activity of rapamycin (mTOR) complex 1 (mTORC1). The inhibition of SLC1A5 significantly increased the number of apoptotic HCCl806 cells but did not affect the cell viability of MCF7. Thus, BC cells with different molecular subtypes have different degrees of dependence on SLC1A5 activity, and only the Basal-1ike type BC cell line requires SLC1A5 to absorb glutamine for growth (34). The effective inhibition of SLC1A5-mediated glutamine uptake may be a therapeutic strategy for the treatment of type 2 diabetes mellitus (T2DM) BC patients (35). A proteomic analysis of BC metabolism identified serine hydroxymethyltransferase 2 (SHMT2) and SLC1A5 as independent prognostic factors, and the high protein expression of SLC1A5 was significantly found to be correlated with poor RFS (HR =1.31, 95% CI: 1.01–1.71; P≤0.05) (36). These findings are consistent with our analysis; that is, we found that the expression of SLC1A5 is higher in BC tissues than normal tissues. The K-M curves revealed that high SLC1A5 expression was correlated with poor OS.

SLC3A2, also known as CD98hc, is a transmembrane protein that, as a chaperone, dimerizes with amino acid transporters (such as SLC7A5 and SLC7A11) to achieve functional expression in the plasma membrane (37,38). A large BC cohort study revealed a significant correlation between high SLC3A2 protein expression and poor prognostic clinicopathological parameters, additionally, the high expression of SLC3A2 has been shown to be significantly correlated with proliferation (39). We found that the expression of SLC3A2 is significantly higher in BC tissues than normal tissues. The K-M curves revealed that patients with high SLC3A2 expression had poor DMFS.

SLC7A5, also referred to as L-type amino acid transporter 1, is thought to regulate tumor metabolism and be related to tumor proliferation. Multiple studies have shown that the amino acid transporter SLC7A5 is highly expressed in highly proliferating BC subtypes and indicates a poor prognosis for patients; thus, it is a key therapeutic target for ER⁺ BC (11,40-42). Additionally, SLC7A5 regulates the expression of Myc and constitutes a positive feedback loop mechanism that promotes essential amino acid transport and tumorigenesis (43,44). SLC7A5 has also been shown to confer endocrine resistance (45). SLC7A5 needs to be covalently bound to the SLC3A2 heavy chain to achieve its functional expression in the plasma membrane (46). Further, the co-operative expression of SLC1A5, SLC7A5, and SLC3A2 has been shown to be associated with poor prognostic characteristics and poor patient outcomes, particularly in the ER⁺ high proliferation/luminal B subtype (10). In the present study, the expression level of SLC7A5 in tumor tissues was higher than that in normal tissues, and the differential expression was significantly correlated with the clinical staging of BC. The high expression of SLC7A5 was significantly associated with poor OS, RFS, DMFS, and PPS in all BC patients.

Previous research has shown that the amino acid transporter SLC7A8 is overexpressed in the ER⁺ BC. SLC7A8 is a good prognostic marker of ER⁺ low-proliferation invasive BC (12). In this data analysis, the expression of SLC7A8 in tumor tissues was higher than that in normal tissues, but the difference was not statistically significant. The K-M curves revealed that the high expression of SLC7A8 is prognostic and favorable in BC.

SLC6A14, also referred to as amino acid transporter B^0,+ (ATB^0,+), is an amino acid transporter with unique properties (47). Research has shown that SLC6A14 is upregulated in ER⁺ BC, and this has been confirmed in human BC tissues and human BC cell lines; thus, SLC6A14 represents a novel effective drug target for the treatment of BC (13,48). In our study, the expression level of SLC6A14 in tumor tissues was higher than that in normal tissues, and the differential expression was significantly correlated with the clinical stage. Further, the high expression of SLC6A14 was significantly associated with poor PPS in BC.

The amino acid transporters SNAT1 and SNAT2 are encoded by SLC38A1 and SLC38A2, both members of the SLC38 gene family (49,50). The high expression of SLC38A1 is closely related to tumor size, lymph node metastasis, disease stage, Ki-67, and ER negative (ER^–) expression. Thus, SLC38A1 appears to be particularly important in the progression of BC. The specific short-hairpin RNA knockdown of SLC38A1 can cause cell cycle arrest and the apoptosis of 4T1 cells by activating Serotonin N-acetyltransferase1 (SNAT1) pathway (14). Persistent hypermethylation and the downregulation of SLC38A1 are associated with trastuzumab resistance in human epidermal growth factor receptor 2-positive BC patients (51). Paclitaxel-induced endoplasmic reticulum stress promotes the ubiquitination associated with the ubiquitin ligase Ring finger protein 5 (RNF5) and promotes the degradation of SLC1A5 and SLC38A2, which suggests that chemotherapeutic drugs have different effects on the expression of SLC1A5 and SLC38A2 in BC cells, and that the expression of RNF5 and/or SLC1A5/ 38A2 may be useful markers for patient stratification and prognosis (52). However, no research appears to have been conducted on SLC38A2 in relation to the survival and prognosis of BC patients.

In the present study, the expression of SLC38A1 in tumor tissues was higher than that in normal tissues, but this expression was not correlated with tumor stage in BC. Conversely, the low expression of SLC38A1 was significantly associated with poor RFS and DMFS in BC. We also found that SLC38A2 is highly expressed in normal tissues compared to tumor tissues, but the difference was not statistically significant. No valuable results were obtained from the survival analysis of BC in relation to SLC38A2. At present, the relevant research is still limited; thus, the biological function of SLC38A1, and SLC38A2 in BC remains unclear in terms of disease prognosis, and further basic and clinical studies need to be conducted to clarify their value.

Alterations in the glutamine pathway in BC also play a role in the production of specific immune cell infiltrates. A bioinformatics analysis of the association between SLC1A5 and the immune invasion of various cancers indicated that in liver cancer and low-grade glioma, the expression of SLC1A5 is positively correlated with the number of tumor-infiltrating B cells, CD4⁺ T cells, CD8⁺ T cells, macrophages, neutrophils, and DCs (53). The regulation of the immune system by SLC1A5 may be a potential mechanism by which SLC1A5 can become a new target for the treatment of a variety of cancers (54). Enhanced glutamine uptake has been shown to affect immune cell infiltration in BC, and the expression of SLCs, including SLC1A5, SLC7A5 and SLC3A2, have been shown to be significantly associated with programmed cell death-1/programmed cell death ligand 1 (PD-1⁺/PD-L1), forkhead lineage-transcription factor (FOXP3⁺), CD68⁺, and CD20⁺ infiltrates, and associated with an unfavorable prognosis in BC (55).

SLC7A5-mediated leucine influx contributes to pro-inflammatory cytokine production via mTORC1-induced glycolytic reprograming in activated human monocytes/macrophages (56). Multi-omics and a clinical data analysis of 13 BC cell lines and 2898 BC patients in a public database showed that the SLC7A5 to SLC7A8 ratio was significantly correlated with the essential amino acid (EAA) level and EAA-metabolic activity in BC (57). Further, these patients had a shorter OS time, higher PD-L1 expression, and higher T regulatory cell infiltration, which indicates that a high level EAA metabolism is related to a poor prognosis and immune suppression in BC (57).

The human airway cell Calu-3 expresses a variety of amino acid transporters, including SLC3A2 and SLC6A14, and when inhibitors are used, they can inhibit the production of nitric oxide by activated mouse macrophages (58). A study has shown that the high expression of SLC38A1 in hepatocellular carcinoma is associated with an unfavorable prognosis and immune infiltration defects, and the high expression of SLC38A1 is inversely proportional to CD8⁺ T cells and directly proportional to macrophages M0, neutrophils, PD-1/PD-L1, and cytotoxic T lymphocyte-associated protein 4 (59). Both SLC22A5/OCTN2 and SLC38A2/SNAT2 are induced at the gene and protein levels during the differentiation of human monocytes into macrophages (60). We found that SLC1A5 expression was correlated with the infiltration levels of CD8⁺ T cells and macrophages, SLC3A2 expression was correlated with the infiltration levels of neutrophils and DCs, SLC7A5 expression was correlated with the infiltration levels of macrophages, neutrophils, and DCs, SLC7A8 expression was correlated with the infiltration levels of B cells, macrophages, neutrophils, and DCs, SLC6A14 expression was correlated with the infiltration levels of B cells, CD8⁺ T cells, CD4⁺ T cells, neutrophils, and DCs, SLC38A1 expression was correlated with the infiltration levels of CD8⁺ T cells and macrophages, and SLC38A2 expression was correlated with the infiltration levels of CD8⁺ T cells, CD4⁺ T cells, macrophages, neutrophils, and DCs.

Macrophages are the largest proportion of immune cells in the tumor microenvironment, so it is not difficult to understand that most SLCs are related to macrophage infiltration. We also assessed the expression of SLCs and the macrophage markers in the TIMER database. Our results suggested that SLC7A5, SLC7A8, SLC38A1, and SLC38A2 may regulate macrophage polarization in the BC microenvironment. However, some biological functions and mechanisms have not yet been examined. Thus, further research and verification of their relationship will have profound clinical significance in the exploration of novel targets for the treatment of BC.

In this study, we used bioinformatics methods to systematically analyze the expression of glutamine transporter SLCs in BC and their prognostic value. Our findings provide a basis for an in-depth understanding of the heterogeneity and complexity of BC. The high expression of SLC1A5, SLC3A2, and SLC7A5 was associated with a poor prognosis and immune cell infiltration. Our results suggest that the increased expression of SLC1A5, SLC3A2, SLC7A5, and SLC6A14 in BC tissues may play an important role in the development of BC cancer. Notably, SLC7A8 and SLC38A1 were lowly expressed in BC tissues and were associated with a poor prognosis. Additionally, the correlation between the SLCs and immune cell infiltration suggests that the interaction between glutamine transporters and immune cell infiltration plays a very important role in the occurrence and development of tumors. We believe that SLCs may become promising biomarkers in the diagnosis and prognosis of BC and may provide new directions and strategies for its treatment. The specific functional mechanism of SLCs is worth further exploration.

Acknowledgments

Funding: This work was funded by Natural Science Foundation of China grants (82061148016, 81630074, 81872141, 81702630, 81672622); Guangzhou Science and Technology Plan Key Projects (201804020076); Natural Science Foundation of Guangdong (2019A1515010146); and Joint Construction Project of Medical Science and Technology of Henan Province (LHGJ20190635).

Footnote

Reporting Checklist: The authors have completed the REMARK reporting checklist. Available at https://atm.amegroups.com/article/view/10.21037/atm-22-2620/rc

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://atm.amegroups.com/article/view/10.21037/atm-22-2620/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013).

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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