Introduction
Breast cancer is the most common cancer in American women in addition to skin cancers [
1]. The American Cancer Society estimates that approximately 268,600 new cases of invasive breast cancer will be diagnosed in women and approximately 41,760 women will die from breast cancer every year [
2]. In China, the incidence of breast cancer is growing rapidly and is approximately more than twice the global incidence rate [
1]. Breast cancer is now one of the most common cancers and the sixth leading cause of cancer-related deaths among Chinese women. Approximately 11% of all breast cancers worldwide occur in China [
3]. The most aggressive subtype of breast cancer is triple-negative breast cancer (TNBC). TNBC is characterized by a lack of progesterone receptors, estrogen receptors, and no ERBB2 gene expression. TNBC tends to have a higher histologic grade and elevated risk of recurrence and metastasis compared to other subtypes [
4].
Immunotherapy is a new strategy for the treatment of TNBC. Humans have two types of immune systems—innate and acquired [
5]. Recent studies found that the innate immune system plays a role in preventing relapse in women with breast cancer [
6]. The innate immune system, also known as the nonspecific immune system or inborn immunity system, is an important subsystem of the overall immune system that comprises the cells and mechanisms that defend the host from infection by other organisms. Toll-like receptors (TLRs) are a class of proteins that play a key role in the innate immune system. They are single, membrane-spanning, noncatalytic receptors usually expressed on sentinel cells such as macrophages and dendritic cells that recognize structurally conserved molecules derived from microbes [
7].
Ten different TLRs (TLR1–TLR10) have been identified in humans. Different TLRs recognize their specific ligands.
TLR3 is activated by double-stranded RNA or polyinosinic–polycytidylic acid (poly [I:C]).
TLR3 is expressed not only in immune system cells, such as dendritic cells and macrophages, but also in other cells, such as epithelial, fibroblast, and breast cancer cells [
8].
Toll-like receptor 3 (TLR3) is a member of the toll-like receptor family of pattern recognition receptors of the innate immune system [
9]. It recognizes dsRNA and initiates an innate immune response. Previous studies found that TLR3 is a biomarker for the therapeutic efficacy of double-stranded RNA in breast cancer [
8]. The activation of TLR3 enhances the anti-tumor effects of chimeric antigen receptor–modified T (CAR-T) cells [
10]. Furthermore, in situ activation of TLR3 leads to systemic clinical tumor regression and the potentiation of the PD1 blockade [
11]. We found that
TLR3 acts as a suppressor gene in breast cancer initiation and progression in our previous two-stage association studies and functional investigations [
12]. Furthermore, our study suggested that
TLR3 is less frequently expressed in breast cancer tissues. However, we only investigated 78 cases in the expression studies, and we did not study the influence of
TLR3 expression on survival rates in patients with triple-negative breast cancer.
The aim of the current study was to evaluate the prognostic value of
TLR3 expression in human breast cancer. First, using the multiomics data of our Fudan University Shanghai Cancer Center (FUSCC) TNBC cohort [
13], we investigated the expression of
TLR3 in TNBC tissues, the association of
TLR3 and immune cells, and the influence of
TLR3 expression on survival rates in TNBC. Moreover, we performed immunohistochemical staining to analyze
TLR3 expression in breast cancer tissue microarrays. Furthermore, we utilized the data obtained from the Cancer Genome Atlas (TCGA) to verify the results obtained from our FUSCC cohort.
Materials and methods
FUSCC-TNBC study cohort
This study included a cohort from the Fudan University Shanghai Cancer Center TNBC study [
13]. The research protocol was approved by the Clinical Research Ethics Committee of the Fudan University Shanghai Cancer Center. Informed consent was obtained from patients. Gene-set variation analysis (GSVA) was utilized to calculate the enrichment score of each pathway in each sample. TNBC was classified into four transcriptome-based subtypes: (1) LAR, (2) immunomodulatory, (3) BLIS, and (4) MES [
13]. The Gene expression datasets (GSE118527) were extracted from the Gene Expression Omnibus (GEO) database.
Survival analysis
Survival curves were constructed using the Kaplan–Meier method and compared with the log-rank test. Univariate and multivariate Cox proportional hazards models were used to explore independent prognostic variables. Age, the number of positive lymph nodes, tumor size, homologous recombination deficiency (HRD) scores, PAM50 subtypes, and TNBC immune subtypes were first analyzed using a univariate Cox proportional hazards model. Then, a multivariate analysis of all significant variables was performed using the Cox proportional hazard regression model.
Tissue microarrays
The tissue microarray (TMA) chips were obtained from the Shanghai Zhuoli Biotechnology Company Ltd. Tissues were obtained from 90 patients. Formalin-fixed, paraffin-embedded core cylinders were punched and then deposited into a recipient paraffin block. A tissue-arraying instrument (Beecher Instruments®, Silver Spring, MD, USA) was used. Ninety paired sections of breast cancer tissues and matched adjacent tissues were cut and placed on slides.
Immunohistochemistry
The tissue microarray was incubated at 59 °C for 60 min. After being dewaxed in xylene three times and dehydrated in a series of graded alcohols, the chips were treated with 3% hydrogen peroxide and NaN3 to exhaust the endogenous peroxidase activity. The chips were microwaved in 10 mM citrate buffer pH 6.0 to unmask the epitopes. Rabbit polyclonal anti-human TLR3 antibody (Abcam, # ab62566), the primary antibody, was diluted 1:50 in phosphate-buffered saline (PBS). The chips were incubated with diluted primary antibody overnight at 4 °C. After PBS wash, the chips were incubated with anti-rabbit IgG secondary antibody at 37 °C for 35 min. Horseradish peroxidase (HRP) conjugate was then applied to the chips. After washing, the chips were incubated with peroxidase substrate diaminobenzidine for two minutes and counterstained with hematoxylin.
The clinical information and gene expression data of TNBC patients and normal controls were downloaded from the official TCGA website. In addition, there are thousands of breast cancer cases in the METABRIC database, and we have used these data for our analysis. Boxplots were used to visualize expression differences for discrete variables. The RNA-seq gene expression level 3 HTSeq-Counts data of breast cancer patients with TNBC and clinical data were retained and further analyzed.
A Kaplan–Meier plotter (
https://kmplot.com) database, a meta-analysis based discovery, and the validation of an online survival biomarkers tool were used to investigate the survival of TNBC in the GEO, the European Genome-phenome Archive (EGA), the TCGA databases, and the METABRIC database.
Evaluation of tumor-infiltrating immune cells (TIIC)
The CIBERSORT algorithm (
http://cibersort.stanford.edu/) was utilized to calculate the tumor-infiltrating immune cell composition [
14]. The TIIC immune cells included B cells, plasma cells, T cells, NK cells, macrophages, monocytes, mast cells, dendritic cells, neutrophils, and eosinophils. Perl (Perl Programming Language, version 5.28.1,
http://www.perl.org) was conducted to convert IDs and group samples. The Limma package of R (R Project for Statistical Computing, version 3.5.3,
https://www.r-project.org) was used to normalize the gene expression data. The TIIC,
P-value, root mean square error, and Pearson’s correlation coefficient were quantified for each sample. Furthermore, we applied CIBERSORT to calculate the
P-value for the deconvolution.
Gene-set enrichment analysis
GSEA was performed to detect whether an a priori defined set of genes showed statistically significant differential expression between the TLR3 expression groups to identify potential mechanisms underlying the influence of TLR3 expression on TCGA. GSEA is a computational method that determines whether an a priori defined set of genes shows statistically significant, concordant differences between two biological states. The expression level of TLR3 was used as a phenotype label. Gene sets with a normal P-value < 0.05 and false discovery rate (FDR) < 0.25 were considered to be significantly enriched.
Statistical analysis
All statistical analyses were conducted using R (v.3.6.0). The expression of TLR3 in TNBC patients in the TCGA-BRCA dataset was evaluated. The chi-square test and Fisher’s exact test were applied to identify correlations between TLR3 mRNA expression and the clinical features of breast cancer. The relationship between clinicopathological features and TLR3 expression was analyzed with the Wilcoxon signed-rank test and logistic regression. The associations of clinical and pathological characteristics with overall survival in TCGA patients were assessed using Cox regression and the Kaplan–Meier method. The cut-off value of TLR3 expression was determined by its median value. P < 0.05 was considered statistically significant.
Discussion
In this study, we found that the mRNA expression of TLR3 was lower in TNBC tissue than in the adjacent normal tissue in the FUSCC datasets. Therefore, we suspect that TLR3 low expression is associated with TNBC. We performed this study to explore how TLR3 affects TNBC. We analyzed the expression of TLR3 in different types of TNBC. The TLR3 had high expression in IM and MES subtypes and low expression in LAR and BLIS subtypes. To explore the relationship between TLR3 expression and patient prognosis, we performed a survival analysis. We found high expression of TLR3 in TNBC predicted better prognosis in the FUSCC TNBC cohort. To verify TLR3 expression at the protein level, we performed immunohistochemistry on the FUSCC cohort. Immunohistochemical staining of tissue microarrays showed that TLR3 had lower expression in breast cancer tissues than in the adject normal tissues. Furthermore, We all know that the immune microenvironment of tumors has a very important impact on tumors, so we also investigated the relationship between TLR3 expression and immune cells, We found that the TLR3 expression was positively associated with B cell, CD4 + T cells, CD8 + T cells, neutrophils, macrophages, and myeloid dendritic cells. To verify the findings of the FUSCC database, we downloaded additional data from the public databases TCGA and METABRIC for analysis. Bioinformatic analysis using high-throughput RNA-sequencing data from TCGA and METABRIC demonstrated that the reduced expression of TLR3 in breast cancer was associated with survival time, and poor prognosis.
TLRs are biomolecules expressed on the surface of macrophages, dendritic cells, and epithelial cells that recognize multiple types of pathogen-associated molecular patterns (PAMPs) or damage-associated molecular patterns (DAMPs). TLRs can initiate intracellular signaling, leading to the expression of effector molecules and the secretion of receptors. Recently, many studies have found that aberrant expression and regulation of TLR2,4,7,9 can promote immune escape and angiogenesis of cancer cells [
15]. A number of TLRs antagonists are available for the treatment of human cancers. However, TLR3 appears to have a tumor suppressive function.
We previously performed a two-stage association study and found that two SNPs were associated with the breast cancer incidence risk and tumor size. Furthermore,
TLR3 directly inhibited cell proliferation both in vitro and in vivo. TLR3 plays a negative regulatory role in the initiation and progression of human breast cancer cells, at least in part by downregulating the EGFR/PI3K/AKT pathway [
12].
Recent studies have suggested that
TLR3 has therapeutic potency in breast cancer. The activation of
TLR3 using a poly (I:C) glycopeptide vaccine induced a pro-inflammatory environment and elicited a strong cellular immune response crucial for breast cancer elimination [
16]. Ultimo et al. found that targeting
TLR3 with dsRNA-conjugated mesoporous silica nanoparticles promotes antitumor effects on breast cancer cells [
17]. The TLR3 adjuvant, poly-ICLC, plus breast cancer vaccine have been found to provide modest immune stimulation [
18]. TLR3 regulates inflammatory factors in the breast cancer microenvironment [
19]. However, Schwartz et al. reported that the inhibition of TLR3 signaling using phenyl methimazole (C10) in combination with tamoxifen may increase the effectiveness of the current treatments of breast cancer [
20].
To further investigate the functions of
TLR3 in breast cancer, we performed GSEA using TCGA data. GSEA showed that Toll-like receptors, the JAK/STAT pathway, the TGF-beta pathway, apoptosis, focal adhesion, and cell adhesion molecules are differentially enriched in the
TLR3 low-expression phenotype. This finding suggests that
TLR3 may serve as a potential prognostic marker of prognosis and as a therapeutic target in breast cancer. TLR3 is a member of the Toll-like receptors [
21]. A recent study found that the JAK-STAT pathway was required for TLR3 signaling to induce synergistic responses for subsequent TLR7 stimulation [
22].
TLR3 stimulation with dsRNA is considered to directly promote tumor-cell apoptosis in many types of cancer, such as breast, melanoma, prostate, cervical, colon, and hepatocellular carcinoma [
23‐
25]. Poly (I:C), a
TLR3 activator, directly causes apoptosis in cancer cells via a caspase-dependent pathway [
26,
27].
The results of the gene-set enrichment analysis and the association of
TLR3 expression with clinicopathological characteristics were in accordance with one another. Logistic regression showed that
TLR3 expression was associated with distant metastasis (stage 4 breast cancer). The gene set enrichment analysis suggested that
TLR3 is related to genes that control metastasis, including the TGF-beta pathway, focal adhesion, and cell adhesion molecules. Zagory et al. reported that TLR3 mediates the cell expansion of cells expressing the stem cell marker PROMININ-1 via TGF-beta [
28]. Montazeri et al. reported that an interleukin-1 receptor antagonist mediates the Toll-like receptor 3-induced inhibition of cell adhesion [
29]. However, most of the above mechanisms were studied in other cells, not in breast cancer cells. The regulatory mechanism needs to be further elucidated in breast cancer cells.
In conclusion, TLR3 has low expression in TNBC tissue. High expression of TLR3 in triple-negative breast cancer predicts better prognosis. TLR3 expression may be a potential prognostic molecular marker of poor survival in breast cancer.
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