Double-negative T cells with a distinct transcriptomic profile are abundant in the peripheral blood of patients with breast cancer

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This study characterized peripheral DNT cells in individuals diagnosed with breast cancer (BC). Methods Peripheral blood DNT cells were collected from patients with BC and healthy controls by flow cytometry. The sorted DNT cells were analyzed by Smart-seq2 for single-cell full-length transcriptome profiling. Conducting bioinformatics analysis to pinpoint pivotal genes and investigate potential underlying mechanisms. RT -PCR was used to measure the relative expression of TMEM176B, EGR1, C1QB and C1QC. Result The percentage of DNT cells was higher in patients with BC than in healthy controls. In total, 289 differentially expressed genes (DEGs) were identified (|log 2 FC| > 2, P < 0.05). Gene enrichment analysis indicated that the DEGs were significantly associated with complement activation, and B cell receptor signaling. We identified 2 module-related and 10 hub genes, including IFIT1, IFI27, RSAD2, IFIT3, EGR1, IFI44L, C1QB, C1QC, TMEM176A, TMEM176B, NGFR, and VCAM1. The results of RT-qPCR showed significant differential expression of TMEM176B, EGR1, C1QB and C1QC between the DNT cells of BC patients and healthy controls (P < 0.05). Conclusions DNT cells are abundant in patients with BC, and they might exert anti-tumor immune responses by regulating genes such as TMEM176B and EGR1 . Breast cancer Double-negative T cells Smart-seq2 Differentially expressed genes Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. 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 malignant growth, the induction of angiogenesis, and tumor invasion and metastasis [ 2 ]. The heterogeneity and dynamics of these components influence tumorigenesis and progression and represent potential diagnostic markers or therapeutic targets [ 3 ]. CD3 + CD4 − CD8 − double-negative T (DNT) cells comprise a rare population accounting for only 3–5% of peripheral T lymphocytes, and these cells have been demonstrated to infiltrate solid tumors [ 4 , 5 ]. Patients with chronic B cell lymphoid leukemia have significantly higher peripheral blood DNT cell counts than 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 – 10 ]. In addition, several studies observed the significant anti-tumor effects of DNT cells in non-small cell lung cancer, pancreatic cancer, and triple-negative breast cancer [ 11 – 13 ]. These reports suggest that DNT cells function as immunomodulatory or immunosuppressive cells in various tumors. Through single-cell sequencing experiments, recent studies suggested that DNT cells comprise a functionally heterogeneous population consisting of subtypes such as cytotoxic, helper, and innate DNT cells [ 14 ]. This study aimed to explore the genetic and functional differences of peripheral blood DNT cells between patients with BC and healthy controls. To this end, we analyzed the proportion of DNT cells in both groups and screened the differentially expressed genes (DEGs) between the DNT cell populations of patients with BC and healthy controls using the Smart-seq2 technique. Smart-seq2 full-length transcriptome sequencing technology is suitable for analyzing specific cells and small numbers of samples. By improving reverse transcription, template switching, and preamplification, Smart-seq2 provides complete mRNA information with high sensitivity, precision, flexibility, and coverage [ 15 , 16 ]. The putative functions of the DNT cells in BC were identified by bioinformatics analysis. 2. Methods 2.1 Clinical samples and ethics statement Forty-eight participants, including 26 patients with BC 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 they 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). 2.2 Flow cytometry Peripheral blood samples (2 mL) were collected from the subjects, and peripheral blood mononuclear cells (PBMCs) were isolated by Ficoll gradient centrifugation. The PBMCs were stained with fluorochrome-labeled monoclonal antibodies against the following proteins [BD Biosciences (San Jose, CA, USA) or BioLegend (San Diego, CA, USA)]: 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. 2.3 SMART-Seq2 sequencing and identification of DEGs Total RNA was obtained 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 |log 2 FC| > 2 and P < 0.05 as the criteria. The Ggplot2 package was used to draw a volcano map to visualize the results. 2.4 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). 2.5 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. 2.6 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 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 , and C1QC was calculated by the 2 −ΔΔCt method [ 19 ]. The genes and primer sequences are presented in Table 1 . Table 1 Primer sequences used for RT-qPCR Gene name Sequence (5′→3′) GAPDH Forward: GGAGCGAGATCCCTCCAAAAT Reverse: GGCTGTTGTCATACTTCTCATGG TMEM176B Forward: TACAGATGGATGCGGCGAAGT Reverse: TCAAGACACAGACAGCCAGGAA EGR1 Forward: TACGAGCACCTGACCGCAG Reverse: AGTGGTTTGGCTGGGGTAACT C1QB Forward: TCTCTGCCACAAGAACCATCAAC Reverse: GGCGTGGTAGGTGAAGTAGTAGA C1QC Forward: AAGGTAGGGTACGACGGACTG Reverse: GTTCTCCCTTCTGCCCTTTGG 2.7 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. 3. Results 3.1 The proportion of peripheral DNT cells is increased in BC patients The preoperative percentage of DNT cells in peripheral blood samples from patients with BC and healthy controls was analyzed by flow cytometry. As presented in Fig. 1 , the percentage of DNT cells was significantly higher in patients with BC ( P < 0.05). There were no significant differences between the two groups in terms of demographic parameters like age and gender. 3.2 Identification and functional characterization of DEGs The DEGs in the DNT cells isolated from patients with BC were screened using Smart-seq2. Using |log 2 FC| > 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 presented in Fig. 2 . 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 ). 3.3 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 presented in Fig. 4 A, 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. 4 B), and the second module included C1QB , TMEM176A , C1QC , and TMEM176B (Fig. 4 C). The top 10 hub genes were identified by cytoHubba (ranked in MCC), and they included nine upregulated genes ( IFIT1 , RSAD2 , IFI27 , IFIT3 , EGR1 , IFI44L , C1QB , C1QC , NGFR ) and one downregulated gene ( VCAM1 , Fig. 4 D). 3.4 Gene expression validation by RT-qPCR To confirm the results of Smart seq-2 RNA-seq, the expression of TMEM176B , EGR1 , C1QB , and C1QC was further analyzed by RT-qPCR using GAPDH as the internal control. Compared to the controls, TMEM176B was significantly downregulated, whereas EGR1 , C1QB , and C1QC were upregulated in the BC samples ( P < 0.05, Fig. 5 ). 4. Discussion DNT cells account for only 3–5% of peripheral blood T-lymphocytes, and they play crucial roles in both innate and adaptive immune responses [ 20 ]. In addition, DNT cells are closely related to autoimmune diseases, inflammatory infections, and tumor progression [ 21 ]. 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 [ 22 ]. In our study, we similarly found that the percentage of peripheral DNT cells was higher in patients with BC than in healthy subjects. There is considerable ambiguity regarding the function of DNT cells in different types of tumors. For example, although DNT cells have been revealed to exert immunosuppressive functions in mouse melanoma and glioma models, they have the ability to inhibit the growth of malignant cells in most tumors [ 23 ]. In vitro -expanded DNT cells induced cytotoxic effects in co-cultured triple-negative BC cells through the classic cell-killing mechanisms, including direct interaction via NKG2D and DNAM-1 and secretion of perforin and granzyme B. Therefore, we further 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 DNT cells from patients with BC. 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. Taken together, these results suggest a close association between DNT cells and B cells. DNT cells are key coordinators of the immune response, and they might play a role in tumor immunity by regulating B cells. 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 they were highly expressed in the BC group. The complement system is an important branch of innate immunity [ 24 ], and complement activation has been revealed to play a role in tumor progression [ 25 ]. C1Q promotes T cell activation and IFN-γ production and facilitates the subsequent immune response by activating the complement classical pathway [ 26 , 27 ]. IFN-γ stimulates cellular immune responses and mediates the anti-tumor effects of DNT cells [ 21 , 28 ]. Therefore, we hypothesized that C1Q activates DNT cells in the breast tumor microenvironment and triggers an anti-tumor immune response. The PPI network comprised 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 was significantly downregulated in patients with BC compared to the healthy controls. It encodes a cation channel protein of the MS4A transmembrane 4A family, and it is mainly expressed on lymphocytes and hematopoietic cells [ 29 ]. 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 [ 30 ]. Ion channels also play an important role in enhancing anti-tumor immunity as regulatory checkpoints and therapeutic targets [ 31 ]. Therefore, TMEM176B might influence the differentiation and immunomodulatory function of DNT cells, and the higher percentage of DNT cells in patients with BC relative to healthy controls could be related to difference in TMEM176B expression between the two groups. In addition, TMEM176B directly promotes the growth of triple-negative BC cells by activating the AKT/mTOR signaling pathway, and TMEM176B blockade using monoclonal antibodies significantly inhibited cancer cell proliferation. Taken together, DNT cells might exert an anti-tumor effect in BC by inhibiting TMEM176B . EGR1 , a transcriptional regulator containing a zinc-finger DNA-binding domain, is poorly expressed in resting T cells, and its expression rapidly increases upon the induction of TCR signaling [ 32 ]. 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 [ 33 ]. Furthermore, EGR1 can activate TNF-α and FasL transcription in response to TCR signaling [ 34 , 35 ]. Several studies revealed that DNT cells exert anti-tumor effects by promoting the secretion of TNF-α, IFN-γ, and FasL [ 21 ]. 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 [ 36 ], and they belonged to module 1 in the PPI network. ISG-encoded proteins are induced by type I IFN signaling, and they regulate innate and adaptive immune responses involved in the development of multiple autoimmune diseases and tumors [ 37 ]. Type I promotes the survival of CD8 + T cells and enhances their anti-tumor capacity [ 38 ]. 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 patients with BC, and they 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. Declarations Funding This study was supported by the Natural Science Foundation of Shandong Province of China (No. ZR2021MC137). Author Contributions Xiaofei Liu : Conceptualization, Methodology, Resources, Writing - Review & Editing, Funding acquisition. Huiru Zhu : Validation, Writing - Original Draft, Visualization. Jiaqi Guo : Formal analysis. Yunbo Wei : Conceptualization, Resources. Data Availability All data that support the findings are included in this manuscript. Declaration of Competing Interest The authors declare that they have no conflicts of interest. 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Cite Share Download PDF Status: Published Journal Publication published 10 Sep, 2024 Read the published version in Breast Cancer Research and Treatment → Version 1 posted Editorial decision: Revision requested 31 Jul, 2024 Reviews received at journal 29 Jul, 2024 Reviewers agreed at journal 10 Jul, 2024 Reviewers invited by journal 10 Jul, 2024 Editor assigned by journal 10 Jul, 2024 Submission checks completed at journal 10 Jul, 2024 First submitted to journal 09 Jul, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4714931","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":334321821,"identity":"37d48c93-1471-4bfa-a992-fff7a974c88a","order_by":0,"name":"Huiru Zhu","email":"","orcid":"","institution":"Shandong Second Medical University","correspondingAuthor":false,"prefix":"","firstName":"Huiru","middleName":"","lastName":"Zhu","suffix":""},{"id":334321822,"identity":"2ae38e44-fd34-4e6b-bde7-6c8f753672e2","order_by":1,"name":"Yunbo Wei","email":"","orcid":"","institution":"Qilu University of Technology (Shandong Academy of Sciences)","correspondingAuthor":false,"prefix":"","firstName":"Yunbo","middleName":"","lastName":"Wei","suffix":""},{"id":334321823,"identity":"ab8a29ad-1d44-42e5-a258-fe33c2b397a8","order_by":2,"name":"Jiaqi Guo","email":"","orcid":"","institution":"Shandong Second Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jiaqi","middleName":"","lastName":"Guo","suffix":""},{"id":334321824,"identity":"f8d85ff6-8e17-4290-ac73-cdd76ebe2c67","order_by":3,"name":"Xiaofei Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA70lEQVRIie3Rv2oCMRzA8Rw/iMuvZk2gD/E7DhThqK8SKdzkIHQtJXDgqOv5HAXnHAFHfQCXe4TcUgot4lnclFy7dchnSAjkC/nDWBT9Q0PoBs2YYJAY0pSjECac8GuiDIBd+EXxqCrbk1xnZRjXbeVdTkb3JAMcyeY1l2KA6TvSAYnZxLfz0MEwI70rpCoxy5COOAYDarMNJmmjuXsjh6OfZGIsh4dwQlafnJw6HH8h7S/L3iRtZksnCbhOK7K/SfgLzVaFlA4seXpGVdVl8C5CuK36/OhebF13X/n9NBWirH0bSO5JzN/2R1EURTfOvdxIgy0yHuIAAAAASUVORK5CYII=","orcid":"","institution":"the 960th Hospital of the PLA Joint Logistics Support Force","correspondingAuthor":true,"prefix":"","firstName":"Xiaofei","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2024-07-10 02:12:55","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4714931/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4714931/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10549-024-07477-6","type":"published","date":"2024-09-10T15:58:22+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":62183517,"identity":"309ced74-bc1e-4b6e-9663-de3b495fd9f5","added_by":"auto","created_at":"2024-08-10 11:36:10","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":717353,"visible":true,"origin":"","legend":"\u003cp\u003eFlow cytometry of double-negative T (DNT) cells. \u003cstrong\u003eA-D\u003c/strong\u003e Gating strategy for DNT cells. \u003cstrong\u003eE\u003c/strong\u003e Representative flow cytometry dot plots of peripheral blood mononuclear cells (PBMCs) isolated from a patient with breast cancer (BC). \u003cstrong\u003eF\u003c/strong\u003eRepresentative dot plots of PBMCs isolated from a healthy control (HC). \u003cstrong\u003eG \u003c/strong\u003ePercentage of DNT cells in patients with BC and HCs.\u003c/p\u003e","description":"","filename":"Fig.1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4714931/v1/cd717bd5fb5cc4556e38658b.jpg"},{"id":62184467,"identity":"03b554d8-e9a5-4fdf-9d71-3749fa9fab78","added_by":"auto","created_at":"2024-08-10 11:44:10","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":127680,"visible":true,"origin":"","legend":"\u003cp\u003eVolcano plot presenting the differentially expressed genes. Red dots represent upregulated genes, turquoise dots represent downregulated genes, and grey dots indicate genes without significantly altered expression.\u003c/p\u003e","description":"","filename":"Fig.2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4714931/v1/f4eff4755b00d2282ab7d4a9.jpg"},{"id":62183513,"identity":"a88ee253-457b-42f8-bbe9-deaee1701df6","added_by":"auto","created_at":"2024-08-10 11:36:10","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":249974,"visible":true,"origin":"","legend":"\u003cp\u003eGene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of the differentially expressed genes. \u003cstrong\u003eA \u003c/strong\u003eThe top 10 enriched biological process (BP), cellular component (CC), and molecular function (MF) terms. \u003cstrong\u003eB\u003c/strong\u003e Significantly enriched KEGG pathways.\u003c/p\u003e","description":"","filename":"Fig.3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4714931/v1/8302642cff4d6e755ff17ae4.jpg"},{"id":62183514,"identity":"f417daf2-e9e8-435b-b63e-b90d74c230e9","added_by":"auto","created_at":"2024-08-10 11:36:10","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":369544,"visible":true,"origin":"","legend":"\u003cp\u003eProtein–protein interaction (PPI) network and modular analysis of differentially expressed genes (DEGs). \u003cstrong\u003eA\u003c/strong\u003e PPI network. \u003cstrong\u003eB-C\u003c/strong\u003e The top two modules of the DEGs according to MCODE. \u003cstrong\u003eD\u003c/strong\u003e Selection of hub genes using the cytoHubba plug-in of Cytoscape.\u003c/p\u003e","description":"","filename":"Fig.4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4714931/v1/a757a1b5405b38aab34add8a.jpg"},{"id":62183516,"identity":"c3246ca7-dd36-4c00-8229-4d48d3925c79","added_by":"auto","created_at":"2024-08-10 11:36:10","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":70658,"visible":true,"origin":"","legend":"\u003cp\u003eVerification of the expression of 4 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.\u003c/p\u003e","description":"","filename":"Fig.5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4714931/v1/64a83d4cc40e9b67c8050272.jpg"},{"id":64620038,"identity":"5e62b4f6-f2ca-4ddb-81ec-780e9a0e850d","added_by":"auto","created_at":"2024-09-16 16:17:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2035923,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4714931/v1/7a7064d3-5e96-4774-a522-2577ea93cea0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Double-negative T cells with a distinct transcriptomic profile are abundant in the peripheral blood of patients with breast cancer","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eBreast cancer (BC) is currently the most common cancer affecting women, and its incidence has been increasing on an annual basis [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The tumor microenvironment, which consists of cancer cells, immune cells, fibroblasts, endothelial cells, and cytokines, is a key determinant of malignant growth, the induction of angiogenesis, and tumor invasion and metastasis [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The heterogeneity and dynamics of these components influence tumorigenesis and progression and represent potential diagnostic markers or therapeutic targets [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. CD3\u003csup\u003e+\u003c/sup\u003eCD4\u003csup\u003e\u0026minus;\u003c/sup\u003eCD8\u003csup\u003e\u0026minus;\u003c/sup\u003e double-negative T (DNT) cells comprise a rare population accounting for only 3\u0026ndash;5% of peripheral T lymphocytes, and these cells have been demonstrated to infiltrate solid tumors [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Patients with chronic B cell lymphoid leukemia have significantly higher peripheral blood DNT cell counts than healthy controls, and the number of DNT cells is positively correlated with the number of CD3\u003csup\u003e+\u003c/sup\u003e T cells [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Zhang \u003cem\u003eet al.\u003c/em\u003e reported successful outcomes of adoptive immunotherapy based on DNT cells against hematological malignancies [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR9\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In addition, several studies observed the significant anti-tumor effects of DNT cells in non-small cell lung cancer, pancreatic cancer, and triple-negative breast cancer [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR12\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. These reports suggest that DNT cells function as immunomodulatory or immunosuppressive cells in various tumors. Through single-cell sequencing experiments, recent studies suggested that DNT cells comprise a functionally heterogeneous population consisting of subtypes such as cytotoxic, helper, and innate DNT cells [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study aimed to explore the genetic and functional differences of peripheral blood DNT cells between patients with BC and healthy controls. To this end, we analyzed the proportion of DNT cells in both groups and screened the differentially expressed genes (DEGs) between the DNT cell populations of patients with BC and healthy controls using the Smart-seq2 technique. Smart-seq2 full-length transcriptome sequencing technology is suitable for analyzing specific cells and small numbers of samples. By improving reverse transcription, template switching, and preamplification, Smart-seq2 provides complete mRNA information with high sensitivity, precision, flexibility, and coverage [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The putative functions of the DNT cells in BC were identified by bioinformatics analysis.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Clinical samples and ethics statement\u003c/h2\u003e \u003cp\u003e Forty-eight participants, including 26 patients with BC 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 they 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).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Flow cytometry\u003c/h2\u003e \u003cp\u003ePeripheral blood samples (2 mL) were collected from the subjects, and peripheral blood mononuclear cells (PBMCs) were isolated by Ficoll gradient centrifugation. The PBMCs were stained with fluorochrome-labeled monoclonal antibodies against the following proteins [BD Biosciences (San Jose, CA, USA) or BioLegend (San Diego, CA, USA)]: 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.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 SMART-Seq2 sequencing and identification of DEGs\u003c/h2\u003e \u003cp\u003eTotal RNA was obtained 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 |log\u003csub\u003e2\u003c/sub\u003eFC| \u0026gt; 2 and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 as the criteria. The Ggplot2 package was used to draw a volcano map to visualize the results.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Functional annotation of DEGs\u003c/h2\u003e \u003cp\u003eGene 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).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Protein\u0026ndash;protein interaction (PPI) network of the DEGs\u003c/h2\u003e \u003cp\u003eThe PPI network of the DEGs was constructed using the STRING (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cn.string-db.org/\u003c/span\u003e\u003cspan address=\"https://cn.string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) database [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e17\u003c/span\u003e] with confidence score\u0026thinsp;\u0026ge;\u0026thinsp;0.4 as the cutoff criterion. Cytoscape (version 3.9.1) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e18\u003c/span\u003e] 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\u0026thinsp;=\u0026thinsp;2, node score cutoff\u0026thinsp;=\u0026thinsp;0.2, k-core\u0026thinsp;=\u0026thinsp;2, and maximum depth\u0026thinsp;=\u0026thinsp;100.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 RT-qPCR\u003c/h2\u003e \u003cp\u003eTotal 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\u0026rsquo; 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 initial denaturation at 95\u0026deg;C for 15 min followed by 40 cycles of denaturation at 95\u0026deg;C for 10 s, annealing at 60\u0026deg;C for 20 s, and extension at 72\u0026deg;C for 30 s. GAPDH was used as the internal reference gene. The relative expression of \u003cem\u003eTMEM176B\u003c/em\u003e, \u003cem\u003eEGR1\u003c/em\u003e, \u003cem\u003eC1QB\u003c/em\u003e, and \u003cem\u003eC1QC\u003c/em\u003e was calculated by the 2\u003csup\u003e\u0026minus;ΔΔCt\u003c/sup\u003e method [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The genes and primer sequences are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrimer sequences used for RT-qPCR\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSequence (5\u0026prime;\u0026rarr;3\u0026prime;)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGAPDH\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward: GGAGCGAGATCCCTCCAAAAT\u003c/p\u003e \u003cp\u003eReverse: GGCTGTTGTCATACTTCTCATGG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTMEM176B\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward: TACAGATGGATGCGGCGAAGT\u003c/p\u003e \u003cp\u003eReverse: TCAAGACACAGACAGCCAGGAA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEGR1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward: TACGAGCACCTGACCGCAG\u003c/p\u003e \u003cp\u003eReverse: AGTGGTTTGGCTGGGGTAACT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eC1QB\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward: TCTCTGCCACAAGAACCATCAAC\u003c/p\u003e \u003cp\u003eReverse: GGCGTGGTAGGTGAAGTAGTAGA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eC1QC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward: AAGGTAGGGTACGACGGACTG\u003c/p\u003e \u003cp\u003eReverse: GTTCTCCCTTCTGCCCTTTGG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Statistical analysis\u003c/h2\u003e \u003cp\u003eData analysis was performed using GraphPad Prism 9.4.1 and R v.4.2.1. The Shapiro\u0026ndash;Wilk test was used to assess the normality of data. The two-tailed unpaired Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e-test was used to compare two groups. \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 The proportion of peripheral DNT cells is increased in BC patients\u003c/h2\u003e \u003cp\u003eThe preoperative percentage of DNT cells in peripheral blood samples from patients with BC and healthy controls was analyzed by flow cytometry. As presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the percentage of DNT cells was significantly higher in patients with BC (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). There were no significant differences between the two groups in terms of demographic parameters like age and gender.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Identification and functional characterization of DEGs\u003c/h2\u003e \u003cp\u003eThe DEGs in the DNT cells isolated from patients with BC were screened using Smart-seq2. Using |log\u003csub\u003e2\u003c/sub\u003eFC| \u0026gt; 2 and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;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 presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo 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.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Identification of core genes related to the DNT cells\u003c/h2\u003e \u003cp\u003eTo investigate possible associations between the DEGs, we constructed a PPI network using the STRING website. As presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, 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 \u003cem\u003eIFIT1\u003c/em\u003e, \u003cem\u003eIFIT3\u003c/em\u003e, \u003cem\u003eIFI27\u003c/em\u003e, \u003cem\u003eIFI44L\u003c/em\u003e, \u003cem\u003eRSAD2\u003c/em\u003e, and \u003cem\u003eEGR1\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB), and the second module included \u003cem\u003eC1QB\u003c/em\u003e, \u003cem\u003eTMEM176A\u003c/em\u003e, \u003cem\u003eC1QC\u003c/em\u003e, and \u003cem\u003eTMEM176B\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). The top 10 hub genes were identified by cytoHubba (ranked in MCC), and they included nine upregulated genes (\u003cem\u003eIFIT1\u003c/em\u003e, \u003cem\u003eRSAD2\u003c/em\u003e, \u003cem\u003eIFI27\u003c/em\u003e, \u003cem\u003eIFIT3\u003c/em\u003e, \u003cem\u003eEGR1\u003c/em\u003e, \u003cem\u003eIFI44L\u003c/em\u003e, \u003cem\u003eC1QB\u003c/em\u003e, \u003cem\u003eC1QC\u003c/em\u003e, \u003cem\u003eNGFR\u003c/em\u003e) and one downregulated gene (\u003cem\u003eVCAM1\u003c/em\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Gene expression validation by RT-qPCR\u003c/h2\u003e \u003cp\u003eTo confirm the results of Smart seq-2 RNA-seq, the expression of \u003cem\u003eTMEM176B\u003c/em\u003e, \u003cem\u003eEGR1\u003c/em\u003e, \u003cem\u003eC1QB\u003c/em\u003e, and \u003cem\u003eC1QC\u003c/em\u003e was further analyzed by RT-qPCR using GAPDH as the internal control. Compared to the controls, \u003cem\u003eTMEM176B\u003c/em\u003e was significantly downregulated, whereas \u003cem\u003eEGR1\u003c/em\u003e, \u003cem\u003eC1QB\u003c/em\u003e, and \u003cem\u003eC1QC\u003c/em\u003e were upregulated in the BC samples (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eDNT cells account for only 3\u0026ndash;5% of peripheral blood T-lymphocytes, and they play crucial roles in both innate and adaptive immune responses [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In addition, DNT cells are closely related to autoimmune diseases, inflammatory infections, and tumor progression [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Some studies reported an increased percentage of DNT cells in hematological and solid tumors. For instance, the proportion of TCR-αβ\u003csup\u003e+\u003c/sup\u003e 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 [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In our study, we similarly found that the percentage of peripheral DNT cells was higher in patients with BC than in healthy subjects. There is considerable ambiguity regarding the function of DNT cells in different types of tumors. For example, although DNT cells have been revealed to exert immunosuppressive functions in mouse melanoma and glioma models, they have the ability to inhibit the growth of malignant cells in most tumors [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. \u003cem\u003eIn vitro\u003c/em\u003e-expanded DNT cells induced cytotoxic effects in co-cultured triple-negative BC cells through the classic cell-killing mechanisms, including direct interaction via NKG2D and DNAM-1 and secretion of perforin and granzyme B. Therefore, we further explored the function and potential mechanism of DNT cells in BC by full-length transcriptome sequencing and bioinformatic analysis.\u003c/p\u003e \u003cp\u003eWe identified 289 DEGs, including 137 upregulated and 152 downregulated genes, in DNT cells from patients with BC. 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. Taken together, these results suggest a close association between DNT cells and B cells. DNT cells are key coordinators of the immune response, and they might play a role in tumor immunity by regulating B cells.\u003c/p\u003e \u003cp\u003eThe results of GO and KEGG enrichment analyses also demonstrated the involvement of DEGs in the classical complement activation pathway. In addition, the complement components \u003cem\u003eC1QB\u003c/em\u003e and \u003cem\u003eC1QC\u003c/em\u003e were identified as hub genes, and they were highly expressed in the BC group. The complement system is an important branch of innate immunity [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e24\u003c/span\u003e], and complement activation has been revealed to play a role in tumor progression [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. C1Q promotes T cell activation and IFN-γ production and facilitates the subsequent immune response by activating the complement classical pathway [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. IFN-γ stimulates cellular immune responses and mediates the anti-tumor effects of DNT cells [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Therefore, we hypothesized that C1Q activates DNT cells in the breast tumor microenvironment and triggers an anti-tumor immune response.\u003c/p\u003e \u003cp\u003eThe PPI network comprised 2 modules and 10 hub genes, of which \u003cem\u003eTMEM176A\u003c/em\u003e and \u003cem\u003eTMEM176B\u003c/em\u003e displayed the most significant differences in expression between the BC and control groups. \u003cem\u003eTMEM176B\u003c/em\u003e was significantly downregulated in patients with BC compared to the healthy controls. It encodes a cation channel protein of the MS4A transmembrane 4A family, and it is mainly expressed on lymphocytes and hematopoietic cells [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Segovia \u003cem\u003eet al.\u003c/em\u003e found that \u003cem\u003eTMEM176B\u003c/em\u003e controls the pH in phagosomes by modulating cation currents, which in turn affects antigen cross-presentation by dendritic cells [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Ion channels also play an important role in enhancing anti-tumor immunity as regulatory checkpoints and therapeutic targets [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Therefore, \u003cem\u003eTMEM176B\u003c/em\u003e might influence the differentiation and immunomodulatory function of DNT cells, and the higher percentage of DNT cells in patients with BC relative to healthy controls could be related to difference in \u003cem\u003eTMEM176B\u003c/em\u003e expression between the two groups. In addition, \u003cem\u003eTMEM176B\u003c/em\u003e directly promotes the growth of triple-negative BC cells by activating the AKT/mTOR signaling pathway, and \u003cem\u003eTMEM176B\u003c/em\u003e blockade using monoclonal antibodies significantly inhibited cancer cell proliferation. Taken together, DNT cells might exert an anti-tumor effect in BC by inhibiting \u003cem\u003eTMEM176B\u003c/em\u003e.\u003c/p\u003e \u003cp\u003e \u003cem\u003eEGR1\u003c/em\u003e, a transcriptional regulator containing a zinc-finger DNA-binding domain, is poorly expressed in resting T cells, and its expression rapidly increases upon the induction of TCR signaling [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The TCR signaling cascade regulates T cell proliferation, survival, and differentiation. \u003cem\u003eEGR1\u003c/em\u003e binds to the T-bet promoter homeostatic element and induces T-bet transcription, thereby activating and synergizing the TCR signaling pathway [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Furthermore, \u003cem\u003eEGR1\u003c/em\u003e can activate TNF-α and FasL transcription in response to TCR signaling [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Several studies revealed that DNT cells exert anti-tumor effects by promoting the secretion of TNF-α, IFN-γ, and FasL [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In this study, \u003cem\u003eEGR1\u003c/em\u003e was highly expressed in the BC group compared to the healthy controls. This led us to hypothesize that \u003cem\u003eEGR1\u003c/em\u003e promotes the growth and differentiation of DNT cells via the TCR signaling pathway, which in turn elicits an anti-tumor immune response.\u003c/p\u003e \u003cp\u003e \u003cem\u003eIFIT1\u003c/em\u003e, \u003cem\u003eIFIT3\u003c/em\u003e, \u003cem\u003eIFI27\u003c/em\u003e, \u003cem\u003eIFI44L\u003c/em\u003e, and \u003cem\u003eRSAD2\u003c/em\u003e are interferon-stimulated genes (ISGs) that are highly expressed in a subset of patients with BC [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e36\u003c/span\u003e], and they belonged to module 1 in the PPI network. ISG-encoded proteins are induced by type I IFN signaling, and they regulate innate and adaptive immune responses involved in the development of multiple autoimmune diseases and tumors [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Type I promotes the survival of CD8\u003csup\u003e+\u003c/sup\u003e T cells and enhances their anti-tumor capacity [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Thus, DNT cells might regulate the immune system by activating the IFN signaling pathway, thereby enhancing the anti-tumor immune response.\u003c/p\u003e "},{"header":"Conclusion","content":"\u003cp\u003eDNT cells are present in higher numbers in patients with BC, and they exhibit a distinct transcriptomic profile. \u003cem\u003eTMEM176B\u003c/em\u003e and \u003cem\u003eEGR1\u003c/em\u003e 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.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Natural Science Foundation of Shandong Province of China (No.\u0026nbsp;ZR2021MC137).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eXiaofei Liu\u003c/strong\u003e: Conceptualization, Methodology, Resources, Writing - Review \u0026amp; Editing, Funding acquisition. \u003cstrong\u003eHuiru Zhu\u003c/strong\u003e: Validation, Writing - Original Draft, Visualization. \u003cstrong\u003eJiaqi Guo\u003c/strong\u003e: Formal analysis. \u003cstrong\u003eYunbo Wei\u003c/strong\u003e: Conceptualization, Resources.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data that support the findings are included in this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Competing Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Commission of the 960th Hospital of the PLA Joint Logistics Support Force, China (No. 2023107).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSiegel RL, Giaquinto AN, Jemal A. 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Trends Immunol. 38(8) (2017) 542-57.https://doi.dog/10.1016/j.it.2017.05.005.\u003c/li\u003e\n\u003cli\u003eHervas-Stubbs S, Riezu-Boj JI, Gonzalez I, Mancheno U, Dubrot J, Azpilicueta A, et al. Effects of IFN-alpha as a signal-3 cytokine on human naive and antigen-experienced CD8(+) T cells. Eur J Immunol. 40(12) (2010) 3389-402.https://doi.dog/10.1002/eji.201040664.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"breast-cancer-research-and-treatment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"brea","sideBox":"Learn more about [Breast Cancer Research and Treatment](https://www.springer.com/journal/10549)","snPcode":"10549","submissionUrl":"https://submission.nature.com/new-submission/10549/3","title":"Breast Cancer Research and Treatment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Breast cancer, Double-negative T cells, Smart-seq2, Differentially expressed genes","lastPublishedDoi":"10.21203/rs.3.rs-4714931/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4714931/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eDouble-negative T (DNT) cells comprise a distinctive subset of T lymphocytes that play a significant role in the immune system. This study characterized peripheral DNT cells in individuals diagnosed with breast cancer (BC).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003ePeripheral blood DNT cells were collected from patients with BC and healthy controls by flow cytometry. The sorted DNT cells were analyzed by Smart-seq2 for single-cell full-length transcriptome profiling. Conducting bioinformatics analysis to pinpoint pivotal genes and investigate potential underlying mechanisms. RT -PCR was used to measure the relative expression of TMEM176B, EGR1, C1QB and C1QC.\u003c/p\u003e\u003ch2\u003eResult\u003c/h2\u003e \u003cp\u003eThe percentage of DNT cells was higher in patients with BC than in healthy controls. In total, 289 differentially expressed genes (DEGs) were identified (|log\u003csub\u003e2\u003c/sub\u003eFC| \u0026gt; 2, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Gene enrichment analysis indicated that the DEGs were significantly associated with complement activation, and B cell receptor signaling. We identified 2 module-related and 10 hub genes, including IFIT1, IFI27, RSAD2, IFIT3, EGR1, IFI44L, C1QB, C1QC, TMEM176A, TMEM176B, NGFR, and VCAM1. The results of RT-qPCR showed significant differential expression of TMEM176B, EGR1, C1QB and C1QC between the DNT cells of BC patients and healthy controls (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eDNT cells are abundant in patients with BC, and they might exert anti-tumor immune responses by regulating genes such as \u003cem\u003eTMEM176B\u003c/em\u003e and \u003cem\u003eEGR1\u003c/em\u003e.\u003c/p\u003e","manuscriptTitle":"Double-negative T cells with a distinct transcriptomic profile are abundant in the peripheral blood of patients with breast cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-10 11:36:05","doi":"10.21203/rs.3.rs-4714931/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-07-31T14:43:58+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-30T03:00:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"199581330487698684077610927749630432859","date":"2024-07-11T02:03:18+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-10T18:27:46+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-10T12:22:29+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-10T12:21:37+00:00","index":"","fulltext":""},{"type":"submitted","content":"Breast Cancer Research and Treatment","date":"2024-07-10T02:11:38+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"breast-cancer-research-and-treatment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"brea","sideBox":"Learn more about [Breast Cancer Research and Treatment](https://www.springer.com/journal/10549)","snPcode":"10549","submissionUrl":"https://submission.nature.com/new-submission/10549/3","title":"Breast Cancer Research and Treatment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"06b23c6d-701b-49d8-a16d-c3152da12597","owner":[],"postedDate":"August 10th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-09-16T16:13:36+00:00","versionOfRecord":{"articleIdentity":"rs-4714931","link":"https://doi.org/10.1007/s10549-024-07477-6","journal":{"identity":"breast-cancer-research-and-treatment","isVorOnly":false,"title":"Breast Cancer Research and Treatment"},"publishedOn":"2024-09-10 15:58:22","publishedOnDateReadable":"September 10th, 2024"},"versionCreatedAt":"2024-08-10 11:36:05","video":"","vorDoi":"10.1007/s10549-024-07477-6","vorDoiUrl":"https://doi.org/10.1007/s10549-024-07477-6","workflowStages":[]},"version":"v1","identity":"rs-4714931","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4714931","identity":"rs-4714931","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}