Introduction

Breast cancer (BC) is currently the most common cancer affecting women, and its incidence has been increasing on an annual basis [1]. The tumor microenvironment, which consists of cancer cells, immune cells, fibroblasts, endothelial cells, and cytokines, is a key determinant of tumor growth, angiogenesis, invasion, and metastasis [2]. The heterogeneity and dynamics of these components influence tumorigenesis and progression, and represent potential diagnostic markers or therapeutic targets [3]. The CD3+CD4−CD8− double-negative T (DNT) cells comprise a rare population accounting for only 3–5% of the peripheral T lymphocytes, and have been shown to infiltrate solid tumors [4, 5]. Patients with chronic B cell lymphoid leukemia have significantly higher peripheral blood DNT cell counts compared to healthy controls, and the number of DNT cells is positively correlated with the number of CD3+ T cells [6]. Furthermore, thyroid tumors have significantly more DNT cells than benign nodules, and the percentage of DNT cells is a potential preoperative diagnostic indicator of thyroid cancer [7]. Zhang et al. reported successful outcomes of adoptive immunotherapy based on DNT cells against hematological malignancies [8,9,10]. In addition, several studies have reported significant anti-tumor effects of DNT cells in non-small cell lung cancer, pancreatic cancer, and triple-negative breast cancer [11,12,13]. These reports suggest that DNT cells likely function as immunomodulatory or immunosuppressive cells in various tumors. Through single-cell sequencing experiments, recent studies have shown that DNT cells are a functionally heterogeneous population consisting of cytotoxic T cells, helper T cells, and innate DNT cells [14].

The aim of this study was to explore the genetic and functional differences between the peripheral blood DNT cells of BC patients and healthy controls. To this end, we analyzed the proportion of DNT cells in both groups and screened the differentially expressed genes (DEGs) using the Smart-seq2 full-length transcriptome sequencing. The Smart-seq2 technique is suitable for analyzing specific cells and small numbers of samples, and offers high sensitivity, precision, flexibility, and coverage by improving reverse transcription, template switching, and preamplification [15, 16]. The putative functions of the DNT cells in BC were identified by bioinformatics analysis of the screened DEGs.

Methods

Clinical samples and ethics statement

Twenty-six BC patients and 22 healthy subjects were enrolled in February 2023 at the 960th Hospital of the PLA Joint Logistics Support Force, China. The age at diagnosis ranged from 30 to 80 years. Patients lacking a pathologically confirmed diagnosis of BC, and those with other malignancies, immunological disorders, or infections were excluded. The healthy subjects were examined at our hospital, and exhibited no clinical evidence of any disease. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013), and the protocol was approved by the Ethics Commission of the 960th Hospital of the PLA Joint Logistics Support Force, China (No. 2023107).

Flow cytometry

Blood samples (2 mL) were collected from all subjects, and the peripheral blood mononuclear cells (PBMCs) were isolated by Ficoll gradient centrifugation. The PBMCs were stained with fluorochrome-labeled monoclonal antibodies [BD Biosciences (San Jose, CA, USA) or BioLegend (San Diego, CA, USA)] specific for 7AAD, CD8a (Clone: HIT8a) CD3 (Clone: UCTHT1), CD4 (Clone: RPA-T4), and CD19 (Clone: SJ25C1). Flow cytometry was performed using the FACSAria III system (BD Biosciences) and FlowJo software.

SMART-Seq2 sequencing and identification of DEGs

Total RNA was extracted from the lysed PBMCs, reverse-transcribed into cDNA, and purified to construct a high-quality library. The cDNA library was sequenced on the Agilent 2100 platform (Agilent Technologies, CA, USA). The DEGs between the BC and control groups were screened by DESeq2 software using |log2FC|> 2 and P < 0.05 as the criteria. The Ggplot2 package was used to draw a volcano map to visualize the results.

Functional annotation of DEGs

Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using the clusterProfiler R package. The results were visualized in the form of bubble charts using R software (R Foundation for Statistical Computing, Vienna, Austria).

Protein–protein interaction (PPI) network of the DEGs

The PPI network of the DEGs was constructed using the STRING (https://cn.string-db.org/) database [17] with confidence score ≥ 0.4 as the cutoff criterion. Cytoscape (version 3.9.1) [18] was used to visualize the PPI network. The top 10 hub genes in the network were identified using the CytoHubba plug-in of Cytoscape by applying the MCC algorithm. Significant modules in the PPI network were also screened by the MCODE plug-in using the following parameter settings: degree of cutoff = 2, node score cutoff = 0.2, k-core = 2, and maximum depth = 100.

Survival analysis

The Kaplan–Meier plotter (https://kmplot.com/analysis/) is routinely used for evaluating and visualizing the overall survival (OS), relapse-free survival (RFS), distant metastases-free survival (DMFS), and post-progression survival (PPS) of cancer patients [19]. Survival analysis was performed based on the expression levels of common genes screened by MCODE and CytoHubba. The log-rank test was used to calculate the P-values, and P < 0.05 was considered statistically significant.

Immune infiltration analysis

The correlation between the expression levels of core genes and immune infiltration was estimated using TIMER2.0 (http://timer.cistrome.org) [20]. The TISIDB database (http://cis.hku.hk/TISIDB) [21] was used to determine the correlation between the expression levels of core genes and immune checkpoint genes. Scatter plots were drawn to demonstrate the relationship between each immune checkpoint and core gene.

DNA methylation analysis

The expression profiles and promoter methylation status of five genes (TMEM176B, C1QB, C1QC, RASD2 and IFIT3) were generated using the UALCAN portal (https://ualcan.path.uab.edu/index.html), which is based on The Cancer Genome Atlas (TCGA) database [22]. MethSurv (https://biit.cs.ut.ee/methsurv/) was used to analyze relationship between gene methylation profile (CpGs located in or around the proximity of the gene) and prognosis of BC patients [23]. P < 0.05 was considered statistically significant.

RT-qPCR

Total RNA was extracted from the DNT cells using TRIzol reagent (Thermo Fisher Scientific, Waltham, MA, USA) and reverse-transcribed using the FastKing gDNA Dispelling RT SuperMix (Tiangen Biotech, Beijing, China) according to the manufacturers’ instructions. The cDNA was amplified by RT-qPCR using SYBR Green on a LightCycler 480 instrument (Roche Diagnostics, Basel, Switzerland). The thermal cycling conditions were as follows: initial denaturation at 95 °C for 15 min followed by 40 cycles of denaturation at 95 °C for 10 s, annealing at 60 °C for 20 s, and extension at 72 °C for 30 s. GAPDH was used as the internal reference gene. The relative expression of TMEM176B, EGR1, C1QB, C1QC, RASD2 and IFIT3 was calculated by the 2−ΔΔCt method [24]. The genes and primer sequences are presented in Table 1.

Table 1 Primer sequences used for RT-qPCR
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Statistical analysis

Data analysis was performed using GraphPad Prism 9.4.1 and R v.4.2.1. The Shapiro–Wilk test was used to assess the normality of data. The two-tailed unpaired Student’s t-test was used to compare two groups. P < 0.05 was considered statistically significant.

Results

The proportion of peripheral DNT cells is increased in BC patients

The preoperative percentage of DNT cells in peripheral blood samples from BC patients and healthy controls was analyzed by flow cytometry. As shown in Fig. 1, the percentage of DNT cells was significantly higher in the BC patients compared to the controls (P < 0.05). There were no significant differences between the two groups in terms of demographic parameters like age and gender.

Fig. 1
figure 1

Gating strategies and expression profiles of double-negative T cells (DNT cells). DNT cells express CD3 but lack CD4 and CD8. A FSC and SSC were used for the lymphocyte gating. B FSC-A and FSC-H were used to distinguish cell aggregates from single cells. C Non-viable cells were excluded through 7-AAD staining. D The B cells were excluded on the basis of CD19 and CD3 expression. E Representative flow cytometry dot plots of peripheral blood mononuclear cells (PBMCs) isolated from a patient with breast cancer (BC). F Representative dot plots of PBMCs isolated from a healthy control (HC). G Percentage of DNT cells in BC patients and HCs. P values were calculated by two-tailed unpaired Student’s t-test (**, P < 0.01)

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Identification and functional characterization of DEGs

The DEGs in the DNT cells isolated from BC patients were screened using Smart-seq2. Using |log2FC|> 2 and P < 0.05 as the criteria, we obtained 289 DEGs, of which 137 were upregulated and 152 were downregulated in the BC group. The volcano map of the DEGs is shown in Fig. 2.

Fig. 2
figure 2

Volcano plot showing the differentially expressed genes. The criteria for the DEGs were |log2FC|> 2 and P < 0.05. Red dots represent upregulated genes, turquoise dots represent downregulated genes, and grey dots indicate genes without significantly altered expression

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To further explore the potential biological functions of these DEGs, we performed GO and KEGG enrichment analyses using the clusterProfiler package of R software. The categories of biological processes (BP), molecular functions (MF), and cellular components (CC) were included in the GO analysis. The significantly enriched BP terms for the DEGs included immunoglobulin mediated immune response, complement activation, classical pathway, humoral immune response mediated by circulating immunoglobulin, complement activation, and B cell receptor signaling pathway. In addition, the DEGs were significantly associated with CC terms including external side of plasma membrane, immunoglobulin complex, and postsynaptic membrane. Regarding MF terms, DEGs were enriched in antigen binding and immunoglobulin receptor binding. Finally, KEGG analysis revealed that the DEGs were mainly enriched in pathways related to protein digestion and absorption, hematopoietic cell lineage, B cell receptor signaling, ATP-binding cassette transporters, and complement and coagulation cascades (Fig. 3).

Fig. 3
figure 3

Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of the differentially expressed genes (DEGs). A The top 10 enriched biological process (BP), cellular component (CC), and molecular function (MF) terms. B Significantly enriched KEGG pathways. The sizes of the dots represent the number of genes in each term

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Identification of core genes related to the DNT cells

To investigate possible associations between the DEGs, we constructed a PPI network using the STRING website. As shown in Fig. 4A, the PPI network consisted of 183 nodes with 121 edges. Using the MCODE plug-in of Cytoscape, we filtered five modules and obtained two pivotal modules. One module included IFIT1, IFIT3, IFI27, IFI44L, RSAD2, and EGR1 (Fig. 4B), and the second module included C1QB, TMEM176A, C1QC, and TMEM176B (Fig. 4C). The top 10 hub genes were identified by CytoHubba (ranked in MCC), and included nine upregulated genes (IFIT1, RSAD2, IFI27, IFIT3, EGR1, IFI44L, C1QB, C1QC, NGFR) and one downregulated gene (VCAM1, Fig. 4D).

Fig. 4
figure 4

Protein–protein interaction (PPI) network and modular analysis of differentially expressed genes (DEGs). A PPI network. B–C The top two modules of the DEGs according to MCODE. D Selection of hub genes using the CytoHubba plug-in of Cytoscape

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Survival analysis of the identified core genes

We identified the potential core genes as those screened by both MCODE and CytoHubba. The prognostic values of these genes were estimated through survival analysis using the Kaplan–Meier method. As shown in Fig. 5, IFIT3, RSAD2, TMEM176B, C1QB and C1QC were significantly associated with survival rates, indicating their potential as prognostic indicators based on expression level.

Fig. 5
figure 5

Prognostic significance of the 10 core genes in BC. A–J Kaplan–Meier curves showing the overall survival of BC patient subgroups based on the expression of IFIT1, IFII27, RSAD2, IFIT3, EGR1, IFI44L, TMEM176A, TMEM176B, C1QB, C1QC, and EGR1. The prognostic significance of each gene was determined on the basis of the hazard ratio (HR) and P value. P < 0.05 indicated statistical significance

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Immune infiltration analysis

To further explore the role of DNT cells in tumor immunity and the potential mechanisms, we analyzed the correlation between the core genes and immune cells and immune checkpoints using the TIMER 2.0 and TISIDB databases. Interestingly, TMEM167B (r = 0.307, P = 3.90e-23), C1QB (r = 0.435, P = 3.20e-47), C1QC (r = 0.4, P = 2.14e-39), RSAD2 (r = 0.194, P = 7.58e-10) and IFIT3(r = 0.259, P = 9.46e-17) were positively correlated with the infiltration of CD8+ T cells. In addition, TMEM176B (r = 0.163, P = 2.36e-07), C1QB (r = 0.277, P = 6.36e-19), C1QC (r = 0.254, P = 3.80e-16), RSAD2(r = 0.09, P = 4.54e-03) and IFIT3 (r = 0.13, P = 3.94e-0.5; Fig. 6A–E) correlated significantly with the NK cells. The expression levels of three core genes were closely associated with immune checkpoints such as PDCD1, CD274, LAG3 and CTLA4 (Fig. 7A–E).

Fig. 6
figure 6

Correlation analysis of gene expression and infiltration of CD8.+ T cells and NK cells in BC (TIMER database). A TMEM176B, B C1QB, C C1QC, D RSAD2, E IFIT3

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Fig. 7
figure 7

Correlation analysis of gene expression and immune checkpoints (PDCD1, CD274, LAG3 and CTLA4) in BC. A TMEM176B, B C1QB, C C1QC, D RSAD2, E IFIT3

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DNA methylation analysis

We next examined the correlation between gene expression and promoter methylation using the UALCAN database to determine the impact of epigenetic alterations on breast tumorigenesis and development. The methylation levels of TMEM176B, RSAD2 and IFIT3 were higher in the BC samples compared to that in the normal samples (Fig. 8A, D–E; P < 0.05), whereas C1QB and C1QC showed lower promoter methylation in the BC samples (Fig. 8B–C; P < 0.05). Moreover, analysis of the MerthSuv database showed that C1QB hypomethylation in 3’UTR-open-sea-cg18763854, RSAD2 hypomethylation in TSS1500-open-sea-cg15346781, and C1QC hypermethylation in TSS1500-open-sea-cg17097874 located in the CpG islands were associated with a favorable prognosis in BC patients (Fig. 8F–J). In contrast, the methylation status of TMEM176B and IFIT3 were not significant associated with prognosis (P < 0.05).

Fig. 8
figure 8

Association between promoter methylation and prognosis in BC. A–E Methylation level of the five prognostic genes in BC patients and healthy controls. F–J Kaplan–Meier curves showing the impact of promoter methylation (from the MethSurv database) on survival

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Gene expression validation by RT-qPCR

To confirm the results of Smart seq-2 RNA-seq, the expression of TMEM176B, EGR1, C1QB, C1QC, RASD2 and IFIT3 were further analyzed by RT-qPCR using GAPDH as the internal control. Compared to the controls, TMEM176B was significantly downregulated, whereas EGR1, C1QB, C1QC, RASD2 and IFIT3 were upregulated in the BC samples (P < 0.05, Fig. 9).

Fig. 9
figure 9

Verification of the expression levels of six genes. HC healthy control, BC breast cancer. RNA was extracted from peripheral blood DNT cells of BC (n = 5) and HC (n = 5) groups respectively. The gene expression was analyzed using the SYBR green RT-qPCR method. Data are presented as the mean ± SD. P values were calculated by two-tailed unpaired Student’s t-test (*, P < 0.05; **, P < 0.01)

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Discussion

DNT cells account for only 3–5% of the peripheral blood T lymphocytes, and play crucial roles in both innate and adaptive immune responses [25]. In addition, DNT cells are closely related to autoimmune diseases, inflammatory infections, and tumor progression [26]. Some studies reported an increased percentage of DNT cells in hematological and solid tumors. For instance, the proportion of TCR-αβ+ DNT cells is significantly higher in the peripheral blood of patients with acquired aplastic anemia than in healthy controls, and these patients are more responsive to immunosuppressive therapy [27]. In our study as well, the percentage of peripheral DNT cells was higher in BC patients compared to the healthy subjects. There is considerable ambiguity regarding the function of DNT cells in different types of tumors. Although DNT cells have demonstrated an immunosuppressive role in murine melanoma and glioma models, they have the ability to inhibit the growth of malignant cells in most tumors [28]. For example, in vitro-expanded DNT cells inhibited the growth of co-cultured triple-negative BC cells through the classic cell-killing mechanisms, i.e., direct interaction via NKG2D and DNAM-1, and secretion of perforin and granzyme B. Therefore, we explored the function and potential mechanism of DNT cells in BC by full-length transcriptome sequencing and bioinformatic analysis.

We identified 289 DEGs, including 137 upregulated and 152 downregulated genes, in the DNT cells isolated from BC patients. As per the results of GO enrichment analysis, the DEGs are likely involved in immunoglobulin mediated humoral immune responses, the classical pathway of complement activation, and the B cell receptor signaling pathway. KEGG analysis also revealed significant enrichment of the B cell receptor signaling pathway and the complement and coagulation cascade cell signaling pathways among these DEGs. Furthermore, ten core genes were identified in the PPI network, of which five exhibited prognostic significance. The latter were closely associated with cytotoxic cells, including CD8+T cells and NK cells. Moreover, the methylation status of three genes was associated with the prognosis of BC patients.

The results of GO and KEGG enrichment analyses also demonstrated the involvement of DEGs in the classical complement activation pathway. In addition, the complement components C1QB and C1QC were identified as hub genes, and were highly expressed in the BC group. The complement system is an important branch of innate immunity [29], and complement activation plays a key role in tumor progression [30]. C1Q promotes T cell activation and IFN-γ production, and facilitates the subsequent immune response by activating the complement classical pathway [31, 32]. IFN-γ stimulates cellular immune responses and mediates the anti-tumor effects of DNT cells [26, 33]. To further explore the potential mechanism of C1QB and C1QC in tumor immunity, we investigated the relationship between their expression levels and immune cell infiltration. Both C1QB and C1QC showed a positive correlation with CD8+T cells and NK cells, along with PD1, PDL1, LAG3, CTLA-4 in the BC samples. This suggested that C1Q activates DNT cells in the breast tumor microenvironment and triggers an anti-tumor immune response, and that BC patients with high C1QB and C1QC may benefit more from immunotherapy.

The PPI network consisted of 2 modules and 10 hub genes, of which TMEM176A and TMEM176B displayed the most significant differences in expression between the BC and control groups. TMEM176B encodes a cation channel protein of the MS4A transmembrane 4A family that is mainly expressed on lymphocytes and hematopoietic cells [34], and was significantly downregulated in the BC patients compared to the healthy controls. Segovia et al. found that TMEM176B controls the pH in phagosomes by modulating cation currents, which in turn affects antigen cross-presentation by dendritic cells [35]. Ion channels also play an important role in enhancing anti-tumor immunity as regulatory checkpoints and therapeutic targets [36]. Therefore, TMEM176B might influence the differentiation and immunomodulatory function of DNT cells, and the higher percentage of DNT cells in BC patients relative to healthy controls could be related to the differential TMEM176B expression. Furthermore, TMEM176B was identified as a protective factor in BC patients. TMEM176B is also a potential diagnostic and prognostic marker for skin cutaneous melanoma (SKCM), and its higher expression is associated with favorable prognosis [37]. Taken together, TMEM176B plays an important role in the tumor immune microenvironment.

EGR1, a transcriptional regulator containing a zinc-finger DNA-binding domain, is expressed at low levels in resting T cells , but increases rapidly upon the induction of TCR signaling [38]. The TCR signaling cascade regulates T cell proliferation, survival, and differentiation. EGR1 binds to the T-bet promoter homeostatic element and induces T-bet transcription, thereby activating and synergizing the TCR signaling pathway [39]. Furthermore, EGR1 can activate TNF-α and FasL transcription in response to TCR signaling [40, 41]. Several studies have shown that DNT cells exert anti-tumor effects by promoting the secretion of TNF-α, IFN-γ, and FasL [26]. In this study, EGR1 was highly expressed in the BC group compared to the healthy controls. This led us to hypothesize that EGR1 promotes the growth and differentiation of DNT cells via the TCR signaling pathway, which in turn elicits an anti-tumor immune response.

IFIT1, IFIT3, IFI27, IFI44L, and RSAD2 are interferon-stimulated genes (ISGs) that are highly expressed in a subset of patients with BC [42], and belonged to module 1 in the PPI network. ISG-encoded proteins are induced by type I IFN signaling, and regulate innate and adaptive immune responses involved in the development of multiple autoimmune diseases and tumors [43]. Type I promotes the survival of CD8+ T cells and enhances their anti-tumor capacity [44]. Thus, DNT cells might regulate the immune system by activating the IFN signaling pathway, thereby enhancing the anti-tumor immune response.

Conclusion

DNT cells are present in higher numbers in BC patients and exhibit a distinct transcriptomic profile. TMEM176B and EGR1 were identified as likely factors regulating the differentiation and functions of DNT cells. Our findings provide new insights into the function of DNT cells and novel immunotherapeutic strategies against BC.