Investigating RFTN1 as a Potential Immune System Inhibitor in the Tumor Microenvironment of Breast Cancer to Enhance Tumor Immune Escape

Investigating RFTN1 as a Potential Immune System Inhibitor in the Tumor Microenvironment of Breast Cancer to Enhance Tumor Immune Escape | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Investigating RFTN1 as a Potential Immune System Inhibitor in the Tumor Microenvironment of Breast Cancer to Enhance Tumor Immune Escape Hongbin Xin, Mingzhu Zhang, Linrui Miu, Lin Zhou, Zhenghang Li, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4437350/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Immune checkpoint inhibitors have been extensively utilized in treating breast cancer patients, leading to improved prognoses. For patients with negative checkpoint responses, there is a pressing need to identify alternative therapies to improve outcomes. Materials and Methods We used WGCNA in muti-place metastasis samples to find the lymph node metastasis related gene RFTN1 . Consensus cluster show the different subtype with significant pathway changes and immune cells differences. We used CellChat estimated the different interactions of cells in single cell data. We used hdWGCNA and irGSEA to identify the changes between different RFTN1 expression groups. Results We identified a gene, RFTN1 , that is closely associated with lymph node metastasis, a critical early step in breast cancer spread. Immune infiltration analysis suggested that RFTN1 might be involved in regulating the immune system. Single-cell RNA sequencing revealed that samples with higher RFTN1 expression had increased proportions of CD8+ and CD4+ T cells, albeit the overall proportions were lower. These samples also showed different interactions between T cells and other cells, indicating a greater reception of chemotactic factors (CFs) in samples with higher RFTN1 expression. It appears that RFTN1 may facilitate T cell receptor binding to CFs, thereby enhancing T cell activation in the tumor microenvironment (TME). Conclusion This study proposes a novel approach to modulating T cells in the TME and offers an alternative to traditional immune checkpoint inhibitor therapies for treating BC. RFTN1 is related to the CFs receptor transportation in CD4+ T cells and CD8+ T cells, which may activate the anti-tumor immunity system in TME. anti-tumor immunity chemotactic factors T cells protein transportations T cells immigration Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background Breast cancer (BC) remains the most common cancer diagnosed among women in the United States, accounting for 32% of all new cancer diagnoses in women. 1 The impact of BC is profound, underscoring the urgent need for innovative treatment strategies. 2 Although significant strides have been made in medical research, allowing for the curative potential of surgical intervention before metastasis, the evasion of the immune system by tumor cells remains a critical hurdle. 2 Tumor cells in TME employ various strategies to escape immune surveillance, such as inhibiting immune cell functions, including regulating the signaling of TCR, suppress the activity of T cells. 3 Without effective immune surveillance, BC cells can metastasize to lymph nodes and subsequently to multiple organs. Besides, checkpoint could restrain T cell immunity in tumor-drain lymph node. 4 That may improve the metastasis of tumor cells. Accumulating evidence indicates that both cellular and acellular components of the TME play crucial roles in reprogramming aspects of cancer such as initiation, growth, invasion, metastasis, and response to therapies. 5–7 Consequently, the focus of cancer research and treatment has shifted from a tumor-centric approach to one that emphasizes the TME, reflecting its growing recognized importance in the field of cancer biology. 6,7 However, despite this shift, the clinical outcomes of therapeutic strategies that target the TME—particularly those aimed at specific cells or pathways within it—have yet to meet expectations. A detailed classification of the chemopathological features of the TME and an understanding of the interactions between its various components could significantly advance the development of more effective treatment methods. 8 Postoperative treatments for BC encompass a range of options, including radiotherapy, chemotherapy, endocrine therapy, and the more recent addition of immunotherapy. 1 Immunotherapy has revolutionized cancer treatment paradigms, particularly through the use of agents that block the PD-1/PD-L1 axis, thereby reactivating the anti-tumor immune response by TCR signal transportation in T cells. 3 Microarray-based investigations of immune-related tumor gene expression showed that the immune signatures influence the clinical outcomes, particularly with HER2 + breast tumors and TNBC. 9 However, challenges remain for patients who develop resistance to PD-1/PD-L1 inhibitors, limiting the efficacy of current immunotherapeutic approaches. 10 Immune checkpoint inhibitors have been widely used in BC patients and brought longer life to such patients. 1 But limit exists in patients with negative immune checkpoint in patients to treat with immune checkpoint inhibitors. It is necessary to find a new way to help or replace the immune checkpoint inhibitors to gain better effects. This study seeks to explore novel avenues for modulating the immune response against BC, aiming to provide new hope and options for patients facing this challenging disease. Methods Data collection The Cancer Genome Atlas (TCGA) is an extensive web-based database that features a variety of datasets, including gene expression profiles, methylation patterns, and copy number variations, from over 11,000 tumors spanning 33 different types of cancer, along with their clinical data. We accessed RNA-sequencing data (Level 3) for the breast invasive cancer cohort (BRCA) from TCGA, which included tissue samples from 1109 cases and clinical features from 1097 patients, along with 113 matching adjacent normal tissue samples (data retrieved up to 1 January 2020). This data was obtained from the TCGA database accessible at https://portal.gdc.cancer.gov/. The RNA-sequencing data, originally presented as FPKM (Fragments Per Kilobase of exon model per Million mapped fragments) values, were converted to TPM (Transcripts Per Kilobase Million) values for analysis. Additionally, we acquired datasets from the Gene Expression Omnibus (GEO) repository. These include dataset GSE56493 11 from the GPL10379 platform and datasets GSE110590 12 from both GPL11154 and GPL16791 platforms. GEO, accessible at https//www.ncbi.nlm.nih.gov/geo/, also provided us with single-cell RNA sequencing datasets of BC. Specifically, datasets GSE176078 13 and GSE161529 14 , which utilized the Chromium system by 10X Genomics for scRNA-Seq analysis, were conducted on primary tissues from BC patients. Samples chose from GSE161529 and GSE 176078 were in number of 38 grouped by the average mean of RFTN1 expression in every sample. RNA sequence processing Anno the gene symbols of GSE56493 and GSE110590 from their platform GPL10379, GPL11154, and GPL16791. After integration of GSE56493 and GSE110590 ,we removed the batch effects by using the R-package sva. 15 Functional enrichment In our study, we employed the FindMarkers function to identify differentially expressed genes across various groups within single-cell datasets, adhering to the criterion of an adjusted p-value below 0.01. For pathway enrichment, we utilized the BioEnricher R-package alongside aPEAR 16 to elucidate the biological pathways involved. For the analysis of KEGG pathways, we focused on genes with varying expressions across groups, particularly those influenced by RFTN1 expression levels in the TCGA-BRCA dataset. We selected genes with an adjusted p-value of less than 0.05 and a log2 fold change exceeding 1. Pathway enrichment was facilitated through the HALLMARK gene sets, with visualizations generated via the online platform sangerbox.com. Only pathways with an FDR (adjusted p-value) under 0.1 were considered significant for this study. In the context of GSVA, our approach within the TCGA-BRCA samples involved the application of GSVA scoring to pathways using GO gene sets and TPM data. We set stringent criteria, including an adjusted p-value threshold of 0.01 and a minimum log2 fold change of 1.4, to pinpoint significantly enriched pathways. For the irGSEA analysis, pathway enrichment was assessed using HALLMARK gene sets across cells from each sample. To mitigate background noise, we opted for pathway enrichment methods tailored to single-cell expression data, such as AUCell, UCell, singscore, and ssGSEA. The Wilcoxon test was employed to analyze differences in pathway enrichment scores across clusters, setting the bar for differential expression at an adjusted p-value of less than 0.05. Integrating results from various analyses, we utilized the RobustRankAggreg R-package (version 1.1.0) for robust rank aggregation (RRA), which helped us isolate significantly enriched pathways across the majority of gene set enrichment methodologies. This comprehensive process was conducted through the irGSEA R-package. 17 Consensus clustering To analyze the variations between different RFTN1 expression groups, we employed consensus clustering 18 based on the differential genes identified among these groups. We chose the partitioning around medoids (pam) method as the clustering algorithm, given its effectiveness in grouping objects into clusters based on their similarities. For measuring the dissimilarity between the data points, we used the canberra distance, which is particularly useful for high-dimensional data like gene expression profiles due to its sensitivity to changes in data points. The optimal number of clusters (OPTK) was determined to be 2, indicating that the data can be most meaningfully divided into two distinct groups based on RFTN1 expression levels. This analysis was facilitated by the R package ConsensusClusterPlus (version 1.62.0). Kaplan-Meier analysis Using the Kaplan-Meier Plotter online analysis tool, 19 we estimated the survival differences in patients with varying levels of RFTN1 expression. This tool is accessible through the Kaplan-Meier plotter website for BC at kmplot.com. The groups were divided based on the median expression level as the cutoff. Estimation and immune score analysis The estimation and immune score analysis were visualized using the online analysis tool Sangerbox, available at sangerbox.com. 20 10x single cell RNA sequence data processing: We processed 10x single cell RNA sequence data by the Seurat (version 5.0.1). 21 After reading data, quality control of every sample deleted the discrete values. We used DoubletFinder 22 to delete the double cells and integrated data of all samples by harmony. Then we identified all the cell category by their markers paired with what in CellMark 2.0. 23 Cell interaction analysis Using the R-package CellChat, we uncovered variations in cell-type interactions among different groups. This analysis allowed us to decode the complex communication patterns within cellular communities, highlighting distinctive interaction dynamics that may significantly influence observed biological behaviors. 24 Weighted Gene Co-expression Network Analysis To explore genes implicated in BC metastasis, we applied Weighted Gene Co-expression Network Analysis (WGCNA). 25 Initially, we removed low-quality samples (Extended Figure 2A) and set the CutHeight parameter to 175. The analysis proceeded with a soft threshold power of 3 (refer to Extended Figure 2B). We then identified key genes by evaluating Gene Significance (GS) and Module Membership (MM), setting thresholds at GS > 0.2 and MM > 0.8. This process led to the identification of RFTN1 as a pivotal gene. Further delving into RFTN1 ’s roles in BC, we employed high-dimensional Weighted Gene Co-expression Network Analysis (hdWGCNA) 26 with a soft threshold power of 12 (see Extended Figure 3A). This advanced approach revealed 15 distinct gene modules, offering deeper insights into the genetic underpinnings of the disease. Statistical analysis All the statistical analyses were performed using the R(version4.3.2). Nonparametric tests (Wilcoxon rank-sum test for independent groups and Wilcoxon signed-rank test for paired groups) were used to compare the cell proportion between different groups. p<0.05 was considered to indicate statistical significance, and all the statistical tests were two-sided. Flow diagram The hole flow chart could be seen in the flow diagram (extend figure 1A). Results Identification of RFTN1 as a potential regulator for BRCA metastasis in lymph nodes with better prognosis To investigate the genes that promote metastasis in BC, we performed WGCNA on bulk RNA sequencing data from BC primary tumor tissues and various metastatic sites like lymph node, lung, liver, bone, brain, skin and so on, using datasets GSE110590 and GSE56493 (Fig. 1 A). The heatmap revealed gene modules associated with metastatic sites. Given that lymph nodes are often the first site of metastasis for BC cells, we focused on gene modules related to lymph nodes. Our selection criteria for these modules were a gene significance (GS) score greater than 0.2 and a module membership (MM) score greater than 0.8, leading us to identify the gene RFTN1 (Fig. 1 B). RFTN1 expression showed a significant difference between BC tumors and normal tissues from TCGA-BRCA (Fig. 1 C). To understand RFTN1 ’s role in BC, we conducted survival analyses across multiple BC subtypes (Fig. 1 D-F). The results indicated significant difference in survival between groups when analyzing all BC samples with subtype basal like and HER2-positive using the median cutoff for all samples. However, no clear differences in survival were observed in Luminal A samples and Luminal B patients, suggesting that RFTN1 is significantly related to the prognosis of BC patients with basal like or HER2-positive subtype (extend Fig. 2 C-D). To determine the impact of RFTN1 on BC prognosis, it is essential to identify an optimal cutoff to categorize samples into distinct groups. For clarity, we identified differential genes between two groups grouped by the median of RFTN1 expression in TCGA-BRCA (Fig. 1 G), resulting in 899 upregulated genes and nine downregulated genes. Subsequent consensus clustering by these differential genes revealed that the optimal number of clusters (optK) is 2 (Fig. 1 H), allowing for the division of TCGA-BRCA samples into two groups (Fig. 1 I). A significant difference in RFTN1 expression was observed between the two groups (Fig. 1 J), with higher RFTN1 expression in the second cluster. Analysis of overall survival (OS) and progression-free interval (PFI) demonstrated the prognostic significance of RFTN1 between these groups (Fig. 1 K-L), suggesting that RFTN1 may be associated with a more favorable prognosis in BC patients. The results indicates that RFTN1 potentially acts as a protective factor in BC. RFTN1 is related to the changes of immuno system in BC The relationship between RFTN1 expression and the immune system in BC patients was highlighted by the immune score, which showed a clear correlation (Fig. 2 A-C). This suggests that RFTN1 may influence the immune system within TME. Analysis using Cibersort for the two clusters identified in BRCA revealed significant differences (Fig. 2 D), with anti-tumor immunity notably suppressed in cluster one, which had lower RFTN1 expression. Conversely, the presence of pro-tumor M2 macrophages was higher in cluster one compared to cluster two. To further understand RFTN1 ’s functions, we enriched pathways using the differentially expressed genes previously identified (Fig. 2 E). Notably, significant alterations in the epithelial-mesenchymal transition (EMT) pathway and allograft rejection pathway were observed, which could be mechanisms through which tumor cells metastasize. GSVA of these clusters revealed pathway differences that aligned with the Cibersort findings (Fig. 2 F), indicating that higher RFTN1 expression is associated with increased activation of anti-tumor immunity in BC. These findings suggest that RFTN1 may play a role in modulating the immune response in the TME, potentially affecting the progression and metastasis of BC. RFTN1 expressed in multi-kinds of cells and may relate to the active anti-tumor immunity To elucidate RFTN’ s role in TME, we analyzed single-cell RNA sequencing data from BC patients' tumor tissues, specifically from studies GSE176078 and GSE161529. We selected 38 samples that exhibited notable RFTN1 expression and divided them into two groups based on the mean RFTN1 expression across all samples. The expression markers for each cell type were visualized in a bubble plot (Fig. 3 A-B). Analysis revealed that RFTN1 is expressed in multiple cell types (Fig. 3 C). Our initial focus was at the cellular level, examining the proportions of various cell types. We compared these proportions between the RFTN1 Higher Expression Group (RHEG) and the RFTN1 lower expression group (RLEG) (Fig. 3 D-E). In the RHEG, we observed higher proportions of CD4 + T cells, CD8 + T cells, and plasmacytoid dendritic cells (pDCs), alongside fewer malignant cells and conventional dendritic cells (DCs). This suggests a more robust activation of the immune system in the RHEG, which may target malignant cells more effectively. To support this hypothesis, we employed CellChat to analyze intercellular communication. We found that both the number and strength of inferred interactions were greater in the RHEG compared to the RLEG (Fig. 3 F). Furthermore, molecular signals associated with inflammatory and immune activation, such as BAFF, GAS, MIF, and CXCL, were significantly more prevalent in the RHEG than in the RLEG (Fig. 3 G). These findings lead to the conclusion that anti-tumor immunity may be more activated in the RHEG compared to the RLEG, potentially due to the influence of RFTN1 . Correlations exist between RFTN1 and immune cells immigration To delve deeper into the alterations within various cell types, we utilized CellChat for further analysis. When comparing the RFTN1 Lower Expression Group (RLEG) to other group, we noticed that mesenchymal and endothelial cells exhibited a significant reduction in the number of interactions with other cells, while other cell types showed an increase in interaction numbers (Fig. 4 A-B). In the RHEG, there was a noticeable decrease in the strength of interactions received by CD4 + T cells, a trend that was also observed in CD8 + T cells. To understand these changes, we examined the signaling interactions, observing increased signaling in the RLEG and decreased signaling in the RHEG (Fig. 4 C-D). In the RLEG, CD4 + T cells and CD8 + T cells received increased signaling from various sources, including MIF signaling from B cells, CD4 + T cells, dendritic cells (DCs), plasmacytoid DCs (pDCs), and MDK from malignant cells, cycling cells, and DCs. This suggests a potential activation of cell-mediated immunity directed towards these cells (Fig. 4 C). Conversely, the decrease in signaling to CD4 + T cells and CD8 + T cells in the RLEG from multiple cell types involved molecules like MIF, LGALS9, and CXCL12 (Fig. 4 D). Given the overall trend of decreased CD4 + T cell and CD8 + T cell populations in the RLEG, it appears that the recruitment of these cells is downregulated in this group. Further analysis of chemotactic factors (CFs) across all cells between the two groups revealed higher expression levels of CFs (Fig. 4 F-G). This leads to the conclusion that RFTN1 is associated with increased recruitment of CD4 + T cells and CD8 + T cells, potentially enhancing the anti-tumor immune response in the TME. RFTN1 may brought higher immunity activation To explore RFTN1 's functions at the molecular level within the RHEG and RLEG, we applied hdWGCNA. This analysis identified 15 gene modules, with modules M6, M1, M4, M5, M3, M13, M14, M7, and M12 showing higher correlation with expression in the RLEG, whereas the remaining modules were more associated with the RHEG (Fig. 5 A-B). Notably, the M3 module was found to be highly expressed in CD4 + T cells and CD8 + T cells (Fig. 5 C). Focusing on the M3 module, we enriched pathways to understand its role (Fig. 5 D). The significantly altered pathways were related to the activation of the immune system. Further analysis, including immune response gene set enrichment analysis (irGSEA) of CD4 + T cells and CD8 + T cells, along with the M3 module, indicated a pronounced suppression in these T cell types (Fig. 5 E-F). This suppression could be attributed to the reduced chemotactic factor (CF) reception by CD4 + T cells and CD8 + T cells. With T cell activation, the programmed apoptosis of tumor cells is expected to follow. Indeed, the proportion of malignant cells in the RHEG was found to be lower than in the RLEG, as previously observed (Fig. 3 E). Subsequent irGSEA of tumor cells within these groups revealed that pathways related to cell apoptosis, such as those involving IFN-α, IFN-γ, and apoptosis processes, were significantly enhanced in the RHEG (Fig. 5 G). Further examination of the Gene Ontology (GO) enriched pathways within the M3 module highlighted significant roles in signal and protein transportations, shedding light on RFTN1 ’s function at the molecular level. In summary, the increased activation of T cells in the presence of higher RFTN1 expression may lead to the programmed apoptosis of breast tumor cells, highlighting RFTN1 ’s potential role in enhancing the anti-tumor immune response. RFTN1 may regulate immunity system by controlling transform function in cell RFTN1 is known to be associated with receptor internalization 27 , suggesting it may have similar roles in the BC TME. To understand its function, we identified significant gene expression differences between the RHEG and RLEG, finding 610 upregulated and 544 downregulated genes (Fig. 6 A). Pathway enrichment analysis of these differentially expressed genes highlighted functions related to protein transport, endocytic vesicle membrane, and organelle inner membrane in the Gene Ontology Cellular Component (GO CC) category, as well as passive transmembrane transporter activity in the Gene Ontology molecular function (GOMF) category (Fig. 6 B-C). These findings suggest RFTN1 ’s involvement in molecular signaling processes. GSEA analysis of the differentially expressed genes between the RHEG and RLEG (Fig. 6 D-F) revealed that upregulated pathways in the RHEG are indicative of immune activation. These pathways include increased recognition of signaling factors and transmembrane signaling, while downregulated pathways are associated with reduced energy metabolism. This pattern supports the hypothesis that RFTN1 may function as a receptor transfer carrier, potentially activating the immune system in the TME by facilitating the transfer of receptors in immune cells. Discussion Immunotherapy has gained widespread attention for its effectiveness in treating various cancers, especially for patients who are resistant to chemotherapy. 28 However, there remains a gap in treatment options for BC patients with low PD-1 expression. This study proposes new direction for treating such patients by focusing on the role of RFTN1 in TME. T cells was highly related to the anti-tumor immunity . 29 CD8 + T cell is the most prominent anti-tumor immune cell with strong efficient anti-tumor attack. 30,31 CD4 + T cell is related to the activation of anti-tumor anti-tumor cell. 32–34 Considering the negative correlation between numbers of T cell and tumor cell in peripheral blood 35 , we prospect the activation of T cell could suppress the metastasis of tumor cell in BC patients. If we find a way to raise the activated T cell in TME, may be the metastasis of tumor cell could be significantly suppressed in early BC. But with the development of tumor cell and TME, the functions of T cell could be changed and the anti-tumor immunity may lost its functions. 29 New way to raise activated T cell and activate anti-tumor immunity was extremely needed . Our study suggests that RFTN1 may enhance anti-tumor immunity. Using Weighted Gene Co-expression Network Analysis (WGCNA), we identified a factor related to lymphatic metastasis in BC. Stratification of samples via consensus clustering based on RFTN1 expression revealed two subtypes, with pathway changes akin to those observed in single-cell sequencing analyses. Notably, a higher enrichment of CD8 + T cells and CD4 + T cells was observed in samples with elevated RFTN1 expression, as determined by Cibersort. Interestingly, cluster two showed a higher prevalence of M2 macrophages, which are known to suppress anti-tumor immunity. The lower expression of RFTN1 in tumor tissues compared to normal tissues, coupled with the better prognosis observed in the high RFTN1 expressing cluster, suggests that RFTN1 could serve as a protective factor for BC patients. Further exploration into how RFTN1 influences BC revealed that pathway differences between varying levels of RFTN1 expression—both in bulk and single-cell sequencing—are closely related to immune system activation. At the cellular level, samples with higher RFTN1 expression showed increased chemotactic factor (CF) reception in CD4 + T cells and CD8 + T cells, aligning with the higher proportions of these cells in the RFTN1 Higher Expression Group (RHEG), as corroborated by Cibersort. RFTN1 ’s molecular function, associated with receptor internalization, 27 could enhance signaling pathways when RFTN1 expression is increased. It is similar how RFTN1 work in macrophage 35 that RFTN1 could mediates internalization of TLR4 to endosomes in dendritic cells and macrophages; and internalization of poly(I:C) to TLR3-positive endosomes in myeloid dendritic cells and epithelial cells; resulting in activation of TICAM1-mediated signaling and subsequent IFNB1 production upon bacterial lipopolysaccharide stimulation. 35 We hypothesize that RFTN1 may facilitate the internalization of CF receptors like MIF or MDK receptor, allowing immune cells to migrate to tumor sites and eliminate cancer cells especially T cells. 36 With more T cells raised in where tumor cell antigen exist, CD4 + T cells would cause the activation of anti-tumor cells and CD8 + T cells would exert an efficient anti-tumoral attack through the exocytosis of perforin- and granzyme-containing granules, 30,31 resulting the decreased number of tumor cells. Enhancing RFTN1 expression in BC patients could potentially activate anti-tumor immunity, leading to the significant elimination of tumor cells. This approach may offer surgical opportunities for patients with large tumors and potentially extend the lifespan and reduce pain for those with metastatic disease, especially those resistant to PD-1 inhibitors, by inhibiting tumor cell metastasis through the activation of anti-tumor immunity. Besides, it offers us a new set of fresh sight to regulate the T cells in TME. Different from the immune checkpoint inhibitor, we were going to find a way to activate T cells by regulating inside cell transportations rather than work on the receptors and ligands, which means a new way to combine with or replace those patients with resistance to current therapy of BC. However, our study has limitations. The overwhelming impact of RFTN1 on immune cells within the TME hindered our exploration of its effects on other cell types. Additionally, our inability to determine the optimal threshold for grouping in single-cell sequence analysis might introduce bias into our findings. Conclusion These findings shed light on a potential mechanism by which RFTN1 influences TME in BC. It is suggested that RFTN1 may enhance anti-tumor immunity by modulating CF receptor trafficking within CD4 + and CD8 + T cells. Elevated levels of RFTN1 expression in BC patients are associated with improved survival rates and more favorable prognoses, highlighting its potential as a novel target for immunotherapy development. However, our data also underscore the complexity of this pathway and the need for extensive further research. Future studies should delve deeper into the regulatory mechanisms of RFTN1 and its specific interactions with T cells, both in vivo and in vitro, to fully understand its therapeutic potential and application in cancer treatment. Declarations Acknowledgments We would like to thank TCGA and GEO databases for their support for the original data. Funding The First Clinical College Clinical medicine first-class discipline construction project to department of Breast and Thyroid Surgery to Lingfeng Tang (CYYY-BSYJSCXXW-202322). Competing Interest The authors declare that they have no competing interests. Author Contributions Conceptualization, Lingfeng Tang and Hongbin Xin; Data curation, Hongbin Xin; Formal analysis, Hongbin Xin and Zhenghang Li; Investigation, Lingfeng Tang and Hongbin Xin; Methodology, Lingfeng Tang, Mingzhu Zhang and Hongbin Xin; Resources, Hongbin Xin, Lin Zhou, Linrui Miu; Software, Lingfeng Tang; Supervision, Lingfeng Tang and zhengxing Li; Writing-original draft, Lingfeng Tang, Zhenghang Li and Hongbin Xin. Data Availability The RNA-sequencing data for the breast invasive carcinoma (BRCA) cohort utilized in this study are available in The Cancer Genome Atlas (TCGA) repository, accessible at https://portal.gdc.cancer.gov/. Further supporting datasets are available in the Gene Expression Omnibus (GEO) repository under accession number GSE56493, GSE110590, GSE176078, and GSE161529. Ethics approval Not applicable. The study is based on secondary, deidentified publicly available datasets. Consent for publish Not applicable. The study is based on secondary, deidentified publicly available datasets. Acknowledgments The authors wish to thank all the study participants, research staff and students who participated in this work. References Siegel RL, Giaquinto AN, Jemal A. Cancer statistics, 2024. CA A Cancer J Clinicians . 2024;74(1):12-49. doi:10.3322/caac.21820 Harbeck N, Penault-Llorca F, Cortes J, et al. Breast cancer. 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IFNg and lymphocytes prevent primary tumour development and shape tumour immunogenicity. 2001;410. Tatematsu M, Yoshida R, Morioka Y, et al. Raftlin Controls Lipopolysaccharide-Induced TLR4 Internalization and TICAM-1 Signaling in a Cell Type–Specific Manner. The Journal of Immunology . 2016;196(9):3865-3876. doi:10.4049/jimmunol.1501734 Van Der Vorst EPC, Döring Y, Weber C. Chemokines and their receptors in Atherosclerosis. J Mol Med . 2015;93(9):963-971. doi:10.1007/s00109-015-1317-8 Additional Declarations No competing interests reported. Supplementary Files extendfigure1.zip extendfigure2.zip extendfigure3.zip Cite Share Download PDF Status: Posted Version 1 posted 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. 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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-4437350","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":307888005,"identity":"c9172d42-de15-4292-8ebe-43f658cd3e07","order_by":0,"name":"Hongbin Xin","email":"","orcid":"","institution":"The First Affiliated Hospital of Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Hongbin","middleName":"","lastName":"Xin","suffix":""},{"id":307888006,"identity":"ae62c298-522d-4c57-9b36-0400ac238075","order_by":1,"name":"Mingzhu Zhang","email":"","orcid":"","institution":"The First Affiliated Hospital of Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Mingzhu","middleName":"","lastName":"Zhang","suffix":""},{"id":307888007,"identity":"a22a1986-df2a-433b-bec5-79ff232f79ce","order_by":2,"name":"Linrui Miu","email":"","orcid":"","institution":"The First Affiliated Hospital of Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Linrui","middleName":"","lastName":"Miu","suffix":""},{"id":307888008,"identity":"3dac3757-53d1-4461-b970-a162ec162af3","order_by":3,"name":"Lin Zhou","email":"","orcid":"","institution":"Chengdu Jinjiang Maternity and Child Health Hospital","correspondingAuthor":false,"prefix":"","firstName":"Lin","middleName":"","lastName":"Zhou","suffix":""},{"id":307888009,"identity":"5067c3a7-228c-494e-873d-cb9ea2bd5169","order_by":4,"name":"Zhenghang Li","email":"","orcid":"","institution":"The First Affiliated Hospital of Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhenghang","middleName":"","lastName":"Li","suffix":""},{"id":307888010,"identity":"fe060c76-2f68-491d-8391-260c5bc19cfb","order_by":5,"name":"Lingfeng Tang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIiWNgGAWjYFAC5gYGxgYwi/EBgwERGnhA6qFamA1I1sImQZSz7NkbGx8X7rDJ4xc7fKzyR8EdeQb2w0c34LWF52Cz8cwzacWSs9PSbvMYPDNs4ElLu4FXi0RimzRv2+HEDbdzzG4zGBxmbJDgMcOvRf5h+2+Qlv23878V/jA4bE9YiwRjGzPYFukcNgYeg8OJhLWcSWyWntmWljjjdpqxNFBLchshv7C3Hz74ubDNJrF/dvLDjz/+HLbtZz98DK8WEGBG4bERUo6pZRSMglEwCkYBOgAAJCtLYCoHp8AAAAAASUVORK5CYII=","orcid":"","institution":"The First Affiliated Hospital of Chongqing Medical University","correspondingAuthor":true,"prefix":"","firstName":"Lingfeng","middleName":"","lastName":"Tang","suffix":""}],"badges":[],"createdAt":"2024-05-17 14:31:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4437350/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4437350/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":57727766,"identity":"25808a5b-bd7a-4182-953b-9085470b3f35","added_by":"auto","created_at":"2024-06-04 21:39:52","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":558384,"visible":true,"origin":"","legend":"\u003cp\u003eThrough WGCNA, we identified genes closely associated with lymph node metastasis. Among them, \u003cem\u003eRFTN1\u003c/em\u003e emerged as a key gene, notably expressed at lower levels in tumor tissues. Kaplan-Meier plots suggest that \u003cem\u003eRFTN1\u003c/em\u003e might be indicative of a more favorable prognosis for BC patients. By conducting a consensus analysis among BC patients based on gene expression differences in \u003cem\u003eRFTN1\u003c/em\u003e expression groups, we identified two distinct clusters. It was evident that the cluster with higher \u003cem\u003eRFTN1\u003c/em\u003eexpression had a more favorable prognosis. (A) A heatmap generated by WGCNA showcases gene associations in cases of multiple metastases. (B) A scatter plot illustrates the correlation between the MEmagenta module and gene significance, with a correlation coefficient (r) of 0.55 and a p-value of 1.2e-18. (C) A box plot compares \u003cem\u003eRFTN1\u003c/em\u003e expression levels between tumor and normal tissues within the TCGA-BRCA dataset. (D) A Kaplan-Meier plot for overall survival rates in BC patients is stratified by median \u003cem\u003eRFTN1\u003c/em\u003e expression levels. (E) A Kaplan-Meier plot for patients with the basal-like subtype. (F)A Kaplan-Meier plot for patients with the Her-2 subtype. (G) A volcano plot, augmented with Protein-Protein Interaction (PPI) data, displays the differential gene expression within the TCGA-BRCA dataset, categorized by the median \u003cem\u003eRFTN1\u003c/em\u003e expression levels. The comparison between higher and lower \u003cem\u003eRFTN1\u003c/em\u003eexpression groups revealed 899 upregulated genes and nine downregulated genes. (H) The consensus Cumulative Distribution Function (CDF) indicated the optimal number of clusters (optK) to be 2. (I) Consensus clustering revealed that TCGA-BRCA samples could be bifurcated into two groups based on gene expression differences. (J) A box plot depicted the variance in \u003cem\u003eRFTN1\u003c/em\u003e expression levels between the two identified groups within the TCGA-BRCA dataset. (K) A Kaplan-Meier plot for overall survival within the TCGA-BRCA cohort, stratified by the consensus clusters. (L) A Kaplan-Meier plot for the progression-free interval within the TCGA-BRCA cohort, grouped by consensus clusters, provided insights into the potential impact of \u003cem\u003eRFTN1\u003c/em\u003e expression on disease progression.\u003c/p\u003e","description":"","filename":"floatimage1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4437350/v1/ae7d9bd22ee18d0777252bdd.jpg"},{"id":57727768,"identity":"7e9e66cc-af78-4f0f-b65c-092bd37866c9","added_by":"auto","created_at":"2024-06-04 21:39:52","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":896388,"visible":true,"origin":"","legend":"\u003cp\u003eThe immune score underscores a significant association between \u003cem\u003eRFTN1\u003c/em\u003e expression and immune activity. Both Cibersort analysis and pathway enrichment investigations highlight \u003cem\u003eRFTN1\u003c/em\u003e’s substantial role in immune activation. (A) The immunoscore associated with \u003cem\u003eRFTN1\u003c/em\u003e in the TCGA-BRCA dataset reveals a strong correlation (p=8.6e-72, r=0.51), indicating \u003cem\u003eRFTN1\u003c/em\u003e’s relevance to immune phenomena within the BC context. (B) The stromal score (stormescore) for \u003cem\u003eRFTN1\u003c/em\u003ein TCGA-BRCA further supports its significance, with a p-value of 2.6e-182 and a correlation coefficient of 0.59. (C) The estimate score for \u003cem\u003eRFTN1\u003c/em\u003e in TCGA-BRCA strengthens the evidence for its association with immune activity, showcasing a p-value of 2.9e-113 and a correlation coefficient of 0.62. (D) Cibersort analysis comparing consensus clusters in TCGA-BRCA provides insights into the differential immune landscapes influenced by \u003cem\u003eRFTN1\u003c/em\u003e expression levels. (E) Pathway enrichment analysis, utilizing hallmark gene sets and focusing on genes differentially expressed in relation to \u003cem\u003eRFTN1\u003c/em\u003e levels in TCGA-BRCA. (F) GSVA scores in the TCGA-BRCA dataset, categorized by consensus clusters, further delineate the influence of \u003cem\u003eRFTN1\u003c/em\u003eon gene expression patterns related to immune functions.\u003c/p\u003e","description":"","filename":"floatimage2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4437350/v1/2983a2c96e10828286714a85.jpg"},{"id":57727769,"identity":"ed45564c-1f90-4f4f-98dc-9e60874669fe","added_by":"auto","created_at":"2024-06-04 21:39:52","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":665577,"visible":true,"origin":"","legend":"\u003cp\u003eThe single-cell analysis revealed that samples with higher \u003cem\u003eRFTN1\u003c/em\u003eexpression are characterized by a reduced presence of malignant cells and an increased proportion of CD4+ and CD8+ T cells. This shift is associated with a higher number of interactions and stronger interaction strengths within the TME, as quantified by CellChat. (A) UMAP visualization of all cells within the BC TME, categorized by cell types, offers a comprehensive overview of the cellular landscape. (B) Verification of cell types within the TME was conducted, confirming the diverse cellular composition. (C) UMAP analysis specifically highlighting \u003cem\u003eRFTN1\u003c/em\u003e expression across the TME. (D) The composition of all samples, including group-specific breakdowns, provides insight into the variance in cellular makeup. (E) A differential test on cell proportions between the two groups, conducted via T-test, underscores significant variances in cell representation. (F) Analysis of cell-to-cell interaction numbers and strengths between groups reveals the impact of \u003cem\u003eRFTN1\u003c/em\u003e expression on cellular communication within the TME. (G) Assessment of relative information flow between the groups further elucidates the role of \u003cem\u003eRFTN1\u003c/em\u003e in modulating TME dynamics.\u003c/p\u003e","description":"","filename":"floatimage3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4437350/v1/02bf27fcdd12abbae7b95b72.jpg"},{"id":57727770,"identity":"0cc8adca-1291-4127-bc60-e1b71a50e4e2","added_by":"auto","created_at":"2024-06-04 21:39:52","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":740718,"visible":true,"origin":"","legend":"\u003cp\u003eIn TME, the quantity and intensity of cellular interactions are more pronounced in RHEG compared to RLEG. Specifically, the signals received by CD4+ and CD8+ T cells are diminished in RLEG. (A) A comparative analysis reveals distinct differences in the numbers and strengths of cellular interactions between RHEG and RLEG.(B) A heatmap illustrates the variance in interaction numbers and strengths between RHEG and RLEG, providing a visual representation of the differential signaling landscape.(C) A depiction of the decreased signaling pathways in RLEG as compared to RHEG, emphasizing the reduced communicative activity among immune cells.(D) An overview of the signaling pathways that are intensified in RLEG relative to RHEG.\u003c/p\u003e","description":"","filename":"floatimage4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4437350/v1/ea14ee58457e0be0e99c9668.jpg"},{"id":57727771,"identity":"2375eb18-2baa-4107-ac60-8bc9bf74da97","added_by":"auto","created_at":"2024-06-04 21:39:52","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":725738,"visible":true,"origin":"","legend":"\u003cp\u003ehdWGCNA identified 15 gene modules differentiating between groups with RHEG and RLEG. Module M3, which is positively associated with RLEG, is particularly noteworthy for its correlation with CD4+ and CD8+ T cells, and its enriched pathways highlight immune system modifications. (A) hdWGCNA revealed distinct gene modules associated with RHEG and RLEG, offering insights into the molecular distinctions between these groups. (B) Differential Module Expression Lollipop plots (DMEsLollipop) visually represent the comparison of gene module expressions between RHEG and RLEG, facilitating the identification of significantly altered modules. (C) The analysis explored the associations between gene modules and specific cell types, clarifying the relevance of each module to particular cellular constituents of the TME. (D) Pathway enrichment analysis of the M3 module’s genes with FDR less than 0.1. (E) irGSEA for CD4+ T cells between RHEG and RLEG. (F) irGSEA for CD8+ T cells between RHEG and RLEG. (G) irGSEA for malignant cells within RHEG. (H) The enrichment analysis of M3 module genes by GO-CC and GO-MF gene sets.\u003c/p\u003e","description":"","filename":"floatimage5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4437350/v1/a5ce1002d10911869b67320e.jpg"},{"id":57727773,"identity":"5c4a5dd5-918c-4666-9fe8-c2d7eb2f4cec","added_by":"auto","created_at":"2024-06-04 21:39:53","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":450739,"visible":true,"origin":"","legend":"\u003cp\u003eUtilizing the FindMarkers function, we discerned the differentially expressed genes between RHEG and RLEG, subsequently enriching pathways with these identified genes. The enriched pathways prominently feature receptor activation and molecular transport in RHEG, highlighting key functional disparities between the groups. (A) A volcano plot delineates the differentially expressed genes between RHEG and RLEG, providing a visual representation of the magnitude and significance of gene expression changes. (B) The network of enriched pathways, derived from the differentially expressed genes and based on GO-CC.(C) The network of enriched pathways based on GO-MF gene sets further elucidates the molecular functions predominantly active in RHEG. (D) Gene Set Enrichment Analysis (GSEA) of differentially expressed genes using GO-MF showcases the specific molecular functions that are upregulated in RHEG, providing insights into the mechanistic underpinnings of the observed phenotypic differences. (E) A significant test for GSEA, focusing on positively regulated genes within RHEG according to GO-MF, identifies key functional activations that contribute to the distinctive characteristics of RHEG. (F)A significant test for GSEA of negatively regulated genes in RHEG, based on GO-MF, reveals the molecular functions that are downregulated.\u003c/p\u003e","description":"","filename":"floatimage6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4437350/v1/8724e14a9fd8016a3a9e7a3d.jpg"},{"id":63754833,"identity":"e625cb5d-4b7a-489d-8bff-2060530356fa","added_by":"auto","created_at":"2024-09-02 04:50:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4653132,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4437350/v1/65bed051-6804-4319-a8a0-b3d167ca36c9.pdf"},{"id":57728649,"identity":"7c6a6f9f-fd19-4bf4-b022-ad71e2a44aa5","added_by":"auto","created_at":"2024-06-04 21:47:52","extension":"zip","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":41714,"visible":true,"origin":"","legend":"","description":"","filename":"extendfigure1.zip","url":"https://assets-eu.researchsquare.com/files/rs-4437350/v1/48a82f75bdaecc6ab25612eb.zip"},{"id":57727774,"identity":"b36ecae2-094e-4638-9131-e710a348b17c","added_by":"auto","created_at":"2024-06-04 21:39:53","extension":"zip","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":787704,"visible":true,"origin":"","legend":"","description":"","filename":"extendfigure2.zip","url":"https://assets-eu.researchsquare.com/files/rs-4437350/v1/b02c912173f78721b91dfa44.zip"},{"id":57727775,"identity":"ceb5bc71-d3d7-4571-bb89-ed890321b738","added_by":"auto","created_at":"2024-06-04 21:39:53","extension":"zip","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":407703,"visible":true,"origin":"","legend":"","description":"","filename":"extendfigure3.zip","url":"https://assets-eu.researchsquare.com/files/rs-4437350/v1/cdd855ee0fa30b2ccc2c1efb.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"Investigating RFTN1 as a Potential Immune System Inhibitor in the Tumor Microenvironment of Breast Cancer to Enhance Tumor Immune Escape","fulltext":[{"header":"Background","content":"\u003cp\u003eBreast cancer (BC) remains the most common cancer diagnosed among women in the United States, accounting for 32% of all new cancer diagnoses in women.\u003csup\u003e1\u003c/sup\u003e The impact of BC is profound, underscoring the urgent need for innovative treatment strategies.\u003csup\u003e2\u003c/sup\u003e Although significant strides have been made in medical research, allowing for the curative potential of surgical intervention before metastasis, the evasion of the immune system by tumor cells remains a critical hurdle. \u003csup\u003e2\u003c/sup\u003e Tumor cells in TME employ various strategies to escape immune surveillance, such as inhibiting immune cell functions, including regulating the signaling of TCR, suppress the activity of T cells.\u003csup\u003e3\u003c/sup\u003e Without effective immune surveillance, BC cells can metastasize to lymph nodes and subsequently to multiple organs. Besides, checkpoint could restrain T cell immunity in tumor-drain lymph node.\u003csup\u003e4\u003c/sup\u003e That may improve the metastasis of tumor cells.\u003c/p\u003e \u003cp\u003eAccumulating evidence indicates that both cellular and acellular components of the TME play crucial roles in reprogramming aspects of cancer such as initiation, growth, invasion, metastasis, and response to therapies. \u003csup\u003e5\u0026ndash;7\u003c/sup\u003e Consequently, the focus of cancer research and treatment has shifted from a tumor-centric approach to one that emphasizes the TME, reflecting its growing recognized importance in the field of cancer biology.\u003csup\u003e6,7\u003c/sup\u003e However, despite this shift, the clinical outcomes of therapeutic strategies that target the TME\u0026mdash;particularly those aimed at specific cells or pathways within it\u0026mdash;have yet to meet expectations. A detailed classification of the chemopathological features of the TME and an understanding of the interactions between its various components could significantly advance the development of more effective treatment methods.\u003csup\u003e8\u003c/sup\u003e\u003c/p\u003e \u003cp\u003ePostoperative treatments for BC encompass a range of options, including radiotherapy, chemotherapy, endocrine therapy, and the more recent addition of immunotherapy.\u003csup\u003e1\u003c/sup\u003e Immunotherapy has revolutionized cancer treatment paradigms, particularly through the use of agents that block the PD-1/PD-L1 axis, thereby reactivating the anti-tumor immune response by TCR signal transportation in T cells. \u003csup\u003e3\u003c/sup\u003e Microarray-based investigations of immune-related tumor gene expression showed that the immune signatures influence the clinical outcomes, particularly with HER2\u0026thinsp;+\u0026thinsp;breast tumors and TNBC. \u003csup\u003e9\u003c/sup\u003eHowever, challenges remain for patients who develop resistance to PD-1/PD-L1 inhibitors, limiting the efficacy of current immunotherapeutic approaches.\u003csup\u003e10\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eImmune checkpoint inhibitors have been widely used in BC patients and brought longer life to such patients.\u003csup\u003e1\u003c/sup\u003e But limit exists in patients with negative immune checkpoint in patients to treat with immune checkpoint inhibitors. It is necessary to find a new way to help or replace the immune checkpoint inhibitors to gain better effects. This study seeks to explore novel avenues for modulating the immune response against BC, aiming to provide new hope and options for patients facing this challenging disease.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eData collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Cancer Genome Atlas (TCGA) is an extensive web-based database that features a variety of datasets, including gene expression profiles, methylation patterns, and copy number variations, from over 11,000 tumors spanning 33 different types of cancer, along with their clinical data. We accessed RNA-sequencing data (Level 3) for the breast invasive cancer cohort (BRCA) from TCGA, which included tissue samples from 1109 cases and clinical features from 1097 patients, along with 113 matching adjacent normal tissue samples (data retrieved up to 1 January 2020). This data was obtained from the TCGA database accessible at\u0026nbsp;https://portal.gdc.cancer.gov/.\u0026nbsp;The RNA-sequencing data, originally presented as FPKM (Fragments Per Kilobase of exon model per Million mapped fragments) values, were converted to TPM (Transcripts Per Kilobase Million) values for analysis.\u003c/p\u003e\n\u003cp\u003eAdditionally, we acquired datasets from the Gene Expression Omnibus (GEO) repository. These include dataset GSE56493\u003csup\u003e11\u003c/sup\u003e from the GPL10379 platform and datasets GSE110590\u003csup\u003e12\u003c/sup\u003e from both GPL11154 and GPL16791 platforms. GEO, accessible at https//www.ncbi.nlm.nih.gov/geo/, also provided us with single-cell RNA sequencing datasets of BC. Specifically, datasets GSE176078\u003csup\u003e13\u003c/sup\u003e and GSE161529\u003csup\u003e14\u003c/sup\u003e, which utilized the Chromium system by 10X Genomics for scRNA-Seq analysis, were conducted on primary tissues from BC patients.\u0026nbsp;Samples chose from GSE161529 and GSE 176078 were in number of 38 grouped by the average mean of \u003cem\u003eRFTN1\u003c/em\u003e expression in every sample.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRNA sequence processing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnno the gene symbols of GSE56493 and GSE110590 from their platform GPL10379, GPL11154, and GPL16791. After integration of GSE56493 and GSE110590 ,we removed the batch effects by using the R-package sva.\u003csup\u003e15\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunctional enrichment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn our study, we employed the FindMarkers function to identify differentially expressed genes across various groups within single-cell datasets, adhering to the criterion of an adjusted p-value below 0.01. For pathway enrichment, we utilized the BioEnricher R-package alongside aPEAR\u003csup\u003e16\u003c/sup\u003e to elucidate the biological pathways involved.\u003c/p\u003e\n\u003cp\u003eFor the analysis of KEGG pathways, we focused on genes with varying expressions across groups, particularly those influenced by \u003cem\u003eRFTN1\u003c/em\u003e expression levels in the TCGA-BRCA dataset. We selected genes with an adjusted p-value of less than 0.05 and a log2 fold change exceeding 1. Pathway enrichment was facilitated through the HALLMARK gene sets, with visualizations generated via the online platform sangerbox.com. Only pathways with an FDR (adjusted p-value) under 0.1 were considered significant for this study.\u003c/p\u003e\n\u003cp\u003eIn the context of GSVA, our approach within the TCGA-BRCA samples involved the application of GSVA scoring to pathways using GO gene sets and TPM data. We set stringent criteria, including an adjusted p-value threshold of 0.01 and a minimum log2 fold change of 1.4, to pinpoint significantly enriched pathways.\u003c/p\u003e\n\u003cp\u003eFor the irGSEA analysis, pathway enrichment was assessed using HALLMARK gene sets across cells from each sample. To mitigate background noise, we opted for pathway enrichment methods tailored to single-cell expression data, such as AUCell, UCell, singscore, and ssGSEA. The Wilcoxon test was employed to analyze differences in pathway enrichment scores across clusters, setting the bar for differential expression at an adjusted p-value of less than 0.05. Integrating results from various analyses, we utilized the RobustRankAggreg R-package (version 1.1.0) for robust rank aggregation (RRA), which helped us isolate significantly enriched pathways across the majority of gene set enrichment methodologies. This comprehensive process was conducted through the irGSEA R-package.\u003csup\u003e17\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsensus clustering\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo analyze the variations between different \u003cem\u003eRFTN1\u003c/em\u003e expression groups, we employed consensus clustering\u003csup\u003e18\u003c/sup\u003e based on the differential genes identified among these groups. We chose the partitioning around medoids (pam) method as the clustering algorithm, given its effectiveness in grouping objects into clusters based on their similarities. For measuring the dissimilarity between the data points, we used the canberra distance, which is particularly useful for high-dimensional data like gene expression profiles due to its sensitivity to changes in data points.\u003c/p\u003e\n\u003cp\u003eThe optimal number of clusters (OPTK) was determined to be 2, indicating that the data can be most meaningfully divided into two distinct groups based on \u003cem\u003eRFTN1\u003c/em\u003e expression levels. This analysis was facilitated by the R package ConsensusClusterPlus (version 1.62.0).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eKaplan-Meier analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUsing the Kaplan-Meier Plotter online analysis tool,\u003csup\u003e19\u003c/sup\u003e we estimated the survival differences in patients with varying levels of \u003cem\u003eRFTN1\u003c/em\u003e expression. This tool is accessible through the Kaplan-Meier plotter website for BC at\u0026nbsp;kmplot.com. The groups were divided based on the median expression level as the cutoff.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEstimation and immune score analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe estimation and immune score analysis were visualized using the online analysis tool Sangerbox, available at\u0026nbsp;sangerbox.com.\u003csup\u003e20\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e10x single cell RNA sequence data processing:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe processed 10x single cell RNA sequence data by the Seurat (version 5.0.1).\u0026nbsp;\u003csup\u003e21\u003c/sup\u003e After reading data, quality control of every sample deleted the discrete values. We used \u0026nbsp;DoubletFinder\u003csup\u003e22\u003c/sup\u003e to delete the double cells and integrated data of all samples by harmony. Then we identified all the cell category by their markers paired with what in CellMark 2.0.\u003csup\u003e23\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCell interaction analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUsing the R-package CellChat, we uncovered variations in cell-type interactions among different groups. This analysis allowed us to decode the complex communication patterns within cellular communities, highlighting distinctive interaction dynamics that may significantly influence observed biological behaviors.\u003csup\u003e24\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWeighted Gene Co-expression Network Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo explore genes implicated in BC metastasis, we applied Weighted Gene Co-expression Network Analysis (WGCNA).\u003csup\u003e25\u003c/sup\u003e Initially, we removed low-quality samples (Extended Figure 2A) and set the CutHeight parameter to 175. The analysis proceeded with a soft threshold power of 3 (refer to Extended Figure 2B). We then identified key genes by evaluating Gene Significance (GS) and Module Membership (MM), setting thresholds at GS \u0026gt; 0.2 and MM \u0026gt; 0.8. This process led to the identification of \u003cem\u003eRFTN1\u003c/em\u003e as a pivotal gene.\u003c/p\u003e\n\u003cp\u003eFurther delving into \u003cem\u003eRFTN1\u003c/em\u003e’s roles in BC, we employed high-dimensional Weighted Gene Co-expression Network Analysis (hdWGCNA)\u003csup\u003e26\u003c/sup\u003e with a soft threshold power of 12 (see Extended Figure 3A). This advanced approach revealed 15 distinct gene modules, offering deeper insights into the genetic underpinnings of the disease.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the statistical analyses were performed using the R(version4.3.2). Nonparametric tests (Wilcoxon rank-sum test for independent groups and Wilcoxon signed-rank test for paired groups) were used to compare the cell proportion between different groups. p\u0026lt;0.05 was considered to indicate statistical significance, and all the statistical tests were two-sided.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFlow diagram\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe hole flow chart could be seen in the flow diagram (extend figure 1A).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cb\u003eIdentification of\u003c/b\u003e \u003cb\u003eRFTN1\u003c/b\u003e \u003cb\u003eas a potential regulator for BRCA metastasis in lymph nodes with better prognosis\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo investigate the genes that promote metastasis in BC, we performed WGCNA on bulk RNA sequencing data from BC primary tumor tissues and various metastatic sites like lymph node, lung, liver, bone, brain, skin and so on, using datasets GSE110590 and GSE56493 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). The heatmap revealed gene modules associated with metastatic sites. Given that lymph nodes are often the first site of metastasis for BC cells, we focused on gene modules related to lymph nodes. Our selection criteria for these modules were a gene significance (GS) score greater than 0.2 and a module membership (MM) score greater than 0.8, leading us to identify the gene \u003cem\u003eRFTN1\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). \u003cem\u003eRFTN1\u003c/em\u003e expression showed a significant difference between BC tumors and normal tissues from TCGA-BRCA (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). To understand \u003cem\u003eRFTN1\u003c/em\u003e\u0026rsquo;s role in BC, we conducted survival analyses across multiple BC subtypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003eD-F). The results indicated significant difference in survival between groups when analyzing all BC samples with subtype basal like and HER2-positive using the median cutoff for all samples. However, no clear differences in survival were observed in Luminal A samples and Luminal B patients, suggesting that \u003cem\u003eRFTN1\u003c/em\u003e is significantly related to the prognosis of BC patients with basal like or HER2-positive subtype (extend Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eC-D). To determine the impact of \u003cem\u003eRFTN1\u003c/em\u003e on BC prognosis, it is essential to identify an optimal cutoff to categorize samples into distinct groups. For clarity, we identified differential genes between two groups grouped by the median of \u003cem\u003eRFTN1\u003c/em\u003e expression in TCGA-BRCA (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003eG), resulting in 899 upregulated genes and nine downregulated genes. Subsequent consensus clustering by these differential genes revealed that the optimal number of clusters (optK) is 2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003eH), allowing for the division of TCGA-BRCA samples into two groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003eI). A significant difference in \u003cem\u003eRFTN1\u003c/em\u003e expression was observed between the two groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003eJ), with higher \u003cem\u003eRFTN1\u003c/em\u003e expression in the second cluster. Analysis of overall survival (OS) and progression-free interval (PFI) demonstrated the prognostic significance of \u003cem\u003eRFTN1\u003c/em\u003e between these groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003eK-L), suggesting that \u003cem\u003eRFTN1\u003c/em\u003e may be associated with a more favorable prognosis in BC patients. The results indicates that \u003cem\u003eRFTN1\u003c/em\u003e potentially acts as a protective factor in BC.\u003c/p\u003e \u003cp\u003e \u003cb\u003eRFTN1\u003c/b\u003e \u003cb\u003eis related to the changes of immuno system in BC\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe relationship between \u003cem\u003eRFTN1\u003c/em\u003e expression and the immune system in BC patients was highlighted by the immune score, which showed a clear correlation (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-C). This suggests that \u003cem\u003eRFTN1\u003c/em\u003e may influence the immune system within TME. Analysis using Cibersort for the two clusters identified in BRCA revealed significant differences (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eD), with anti-tumor immunity notably suppressed in cluster one, which had lower \u003cem\u003eRFTN1\u003c/em\u003e expression. Conversely, the presence of pro-tumor M2 macrophages was higher in cluster one compared to cluster two.\u003c/p\u003e \u003cp\u003eTo further understand \u003cem\u003eRFTN1\u003c/em\u003e\u0026rsquo;s functions, we enriched pathways using the differentially expressed genes previously identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). Notably, significant alterations in the epithelial-mesenchymal transition (EMT) pathway and allograft rejection pathway were observed, which could be mechanisms through which tumor cells metastasize. GSVA of these clusters revealed pathway differences that aligned with the Cibersort findings (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eF), indicating that higher \u003cem\u003eRFTN1\u003c/em\u003e expression is associated with increased activation of anti-tumor immunity in BC. These findings suggest that \u003cem\u003eRFTN1\u003c/em\u003e may play a role in modulating the immune response in the TME, potentially affecting the progression and metastasis of BC.\u003c/p\u003e \u003cp\u003e \u003cb\u003eRFTN1\u003c/b\u003e \u003cb\u003eexpressed in multi-kinds of cells and may relate to the active anti-tumor immunity\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo elucidate \u003cem\u003eRFTN\u0026rsquo;\u003c/em\u003es role in TME, we analyzed single-cell RNA sequencing data from BC patients' tumor tissues, specifically from studies GSE176078 and GSE161529. We selected 38 samples that exhibited notable \u003cem\u003eRFTN1\u003c/em\u003e expression and divided them into two groups based on the mean \u003cem\u003eRFTN1\u003c/em\u003e expression across all samples. The expression markers for each cell type were visualized in a bubble plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eA-B). Analysis revealed that \u003cem\u003eRFTN1\u003c/em\u003e is expressed in multiple cell types (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003eOur initial focus was at the cellular level, examining the proportions of various cell types. We compared these proportions between the \u003cem\u003eRFTN1\u003c/em\u003e Higher Expression Group (RHEG) and the \u003cem\u003eRFTN1\u003c/em\u003e lower expression group (RLEG) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eD-E). In the RHEG, we observed higher proportions of CD4\u0026thinsp;+\u0026thinsp;T cells, CD8\u0026thinsp;+\u0026thinsp;T cells, and plasmacytoid dendritic cells (pDCs), alongside fewer malignant cells and conventional dendritic cells (DCs). This suggests a more robust activation of the immune system in the RHEG, which may target malignant cells more effectively.\u003c/p\u003e \u003cp\u003eTo support this hypothesis, we employed CellChat to analyze intercellular communication. We found that both the number and strength of inferred interactions were greater in the RHEG compared to the RLEG (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eF). Furthermore, molecular signals associated with inflammatory and immune activation, such as BAFF, GAS, MIF, and CXCL, were significantly more prevalent in the RHEG than in the RLEG (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eG). These findings lead to the conclusion that anti-tumor immunity may be more activated in the RHEG compared to the RLEG, potentially due to the influence of \u003cem\u003eRFTN1\u003c/em\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCorrelations exist between\u003c/b\u003e \u003cb\u003eRFTN1\u003c/b\u003e \u003cb\u003eand immune cells immigration\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo delve deeper into the alterations within various cell types, we utilized CellChat for further analysis. When comparing the \u003cem\u003eRFTN1\u003c/em\u003e Lower Expression Group (RLEG) to other group, we noticed that mesenchymal and endothelial cells exhibited a significant reduction in the number of interactions with other cells, while other cell types showed an increase in interaction numbers (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-B). In the RHEG, there was a noticeable decrease in the strength of interactions received by CD4\u0026thinsp;+\u0026thinsp;T cells, a trend that was also observed in CD8\u0026thinsp;+\u0026thinsp;T cells.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo understand these changes, we examined the signaling interactions, observing increased signaling in the RLEG and decreased signaling in the RHEG (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC-D). In the RLEG, CD4\u0026thinsp;+\u0026thinsp;T cells and CD8\u0026thinsp;+\u0026thinsp;T cells received increased signaling from various sources, including MIF signaling from B cells, CD4\u0026thinsp;+\u0026thinsp;T cells, dendritic cells (DCs), plasmacytoid DCs (pDCs), and MDK from malignant cells, cycling cells, and DCs. This suggests a potential activation of cell-mediated immunity directed towards these cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Conversely, the decrease in signaling to CD4\u0026thinsp;+\u0026thinsp;T cells and CD8\u0026thinsp;+\u0026thinsp;T cells in the RLEG from multiple cell types involved molecules like MIF, LGALS9, and CXCL12 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003eGiven the overall trend of decreased CD4\u0026thinsp;+\u0026thinsp;T cell and CD8\u0026thinsp;+\u0026thinsp;T cell populations in the RLEG, it appears that the recruitment of these cells is downregulated in this group. Further analysis of chemotactic factors (CFs) across all cells between the two groups revealed higher expression levels of CFs (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF-G). This leads to the conclusion that \u003cem\u003eRFTN1\u003c/em\u003e is associated with increased recruitment of CD4\u0026thinsp;+\u0026thinsp;T cells and CD8\u0026thinsp;+\u0026thinsp;T cells, potentially enhancing the anti-tumor immune response in the TME.\u003c/p\u003e \u003cp\u003e \u003cb\u003eRFTN1\u003c/b\u003e \u003cb\u003emay brought higher immunity activation\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo explore \u003cem\u003eRFTN1\u003c/em\u003e's functions at the molecular level within the RHEG and RLEG, we applied hdWGCNA. This analysis identified 15 gene modules, with modules M6, M1, M4, M5, M3, M13, M14, M7, and M12 showing higher correlation with expression in the RLEG, whereas the remaining modules were more associated with the RHEG (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA-B). Notably, the M3 module was found to be highly expressed in CD4\u0026thinsp;+\u0026thinsp;T cells and CD8\u0026thinsp;+\u0026thinsp;T cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFocusing on the M3 module, we enriched pathways to understand its role (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). The significantly altered pathways were related to the activation of the immune system. Further analysis, including immune response gene set enrichment analysis (irGSEA) of CD4\u0026thinsp;+\u0026thinsp;T cells and CD8\u0026thinsp;+\u0026thinsp;T cells, along with the M3 module, indicated a pronounced suppression in these T cell types (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE-F). This suppression could be attributed to the reduced chemotactic factor (CF) reception by CD4\u0026thinsp;+\u0026thinsp;T cells and CD8\u0026thinsp;+\u0026thinsp;T cells.\u003c/p\u003e \u003cp\u003eWith T cell activation, the programmed apoptosis of tumor cells is expected to follow. Indeed, the proportion of malignant cells in the RHEG was found to be lower than in the RLEG, as previously observed (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). Subsequent irGSEA of tumor cells within these groups revealed that pathways related to cell apoptosis, such as those involving IFN-α, IFN-γ, and apoptosis processes, were significantly enhanced in the RHEG (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eG).\u003c/p\u003e \u003cp\u003eFurther examination of the Gene Ontology (GO) enriched pathways within the M3 module highlighted significant roles in signal and protein transportations, shedding light on \u003cem\u003eRFTN1\u003c/em\u003e\u0026rsquo;s function at the molecular level. In summary, the increased activation of T cells in the presence of higher \u003cem\u003eRFTN1\u003c/em\u003e expression may lead to the programmed apoptosis of breast tumor cells, highlighting \u003cem\u003eRFTN1\u003c/em\u003e\u0026rsquo;s potential role in enhancing the anti-tumor immune response.\u003c/p\u003e \u003cp\u003e \u003cb\u003eRFTN1\u003c/b\u003e \u003cb\u003emay regulate immunity system by controlling transform function in cell\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003eRFTN1\u003c/em\u003e is known to be associated with receptor internalization\u003csup\u003e27\u003c/sup\u003e, suggesting it may have similar roles in the BC TME. To understand its function, we identified significant gene expression differences between the RHEG and RLEG, finding 610 upregulated and 544 downregulated genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). Pathway enrichment analysis of these differentially expressed genes highlighted functions related to protein transport, endocytic vesicle membrane, and organelle inner membrane in the Gene Ontology Cellular Component (GO CC) category, as well as passive transmembrane transporter activity in the Gene Ontology molecular function (GOMF) category (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB-C). These findings suggest \u003cem\u003eRFTN1\u003c/em\u003e\u0026rsquo;s involvement in molecular signaling processes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eGSEA analysis of the differentially expressed genes between the RHEG and RLEG (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD-F) revealed that upregulated pathways in the RHEG are indicative of immune activation. These pathways include increased recognition of signaling factors and transmembrane signaling, while downregulated pathways are associated with reduced energy metabolism. This pattern supports the hypothesis that \u003cem\u003eRFTN1\u003c/em\u003e may function as a receptor transfer carrier, potentially activating the immune system in the TME by facilitating the transfer of receptors in immune cells.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eImmunotherapy has gained widespread attention for its effectiveness in treating various cancers, especially for patients who are resistant to chemotherapy.\u003csup\u003e28\u003c/sup\u003e However, there remains a gap in treatment options for BC patients with low PD-1 expression. This study proposes new direction for treating such patients by focusing on the role of \u003cem\u003eRFTN1\u003c/em\u003e in TME. T cells was highly related to the anti-tumor immunity .\u003csup\u003e29\u003c/sup\u003e CD8\u0026thinsp;+\u0026thinsp;T cell is the most prominent anti-tumor immune cell with strong efficient anti-tumor attack. \u003csup\u003e30,31\u003c/sup\u003e CD4\u0026thinsp;+\u0026thinsp;T cell is related to the activation of anti-tumor anti-tumor cell.\u003csup\u003e32\u0026ndash;34\u003c/sup\u003e Considering the negative correlation between numbers of T cell and tumor cell in peripheral blood\u003csup\u003e35\u003c/sup\u003e, we prospect the activation of T cell could suppress the metastasis of tumor cell in BC patients. If we find a way to raise the activated T cell in TME, may be the metastasis of tumor cell could be significantly suppressed in early BC. But with the development of tumor cell and TME, the functions of T cell could be changed and the anti-tumor immunity may lost its functions.\u003csup\u003e29\u003c/sup\u003e New way to raise activated T cell and activate anti-tumor immunity was extremely needed .\u003c/p\u003e \u003cp\u003eOur study suggests that \u003cem\u003eRFTN1\u003c/em\u003e may enhance anti-tumor immunity. Using Weighted Gene Co-expression Network Analysis (WGCNA), we identified a factor related to lymphatic metastasis in BC. Stratification of samples via consensus clustering based on \u003cem\u003eRFTN1\u003c/em\u003e expression revealed two subtypes, with pathway changes akin to those observed in single-cell sequencing analyses. Notably, a higher enrichment of CD8\u0026thinsp;+\u0026thinsp;T cells and CD4\u0026thinsp;+\u0026thinsp;T cells was observed in samples with elevated \u003cem\u003eRFTN1\u003c/em\u003e expression, as determined by Cibersort. Interestingly, cluster two showed a higher prevalence of M2 macrophages, which are known to suppress anti-tumor immunity. The lower expression of \u003cem\u003eRFTN1\u003c/em\u003e in tumor tissues compared to normal tissues, coupled with the better prognosis observed in the high \u003cem\u003eRFTN1\u003c/em\u003e expressing cluster, suggests that \u003cem\u003eRFTN1\u003c/em\u003e could serve as a protective factor for BC patients.\u003c/p\u003e \u003cp\u003eFurther exploration into how \u003cem\u003eRFTN1\u003c/em\u003e influences BC revealed that pathway differences between varying levels of \u003cem\u003eRFTN1\u003c/em\u003e expression\u0026mdash;both in bulk and single-cell sequencing\u0026mdash;are closely related to immune system activation. At the cellular level, samples with higher \u003cem\u003eRFTN1\u003c/em\u003e expression showed increased chemotactic factor (CF) reception in CD4\u0026thinsp;+\u0026thinsp;T cells and CD8\u0026thinsp;+\u0026thinsp;T cells, aligning with the higher proportions of these cells in the \u003cem\u003eRFTN1\u003c/em\u003e Higher Expression Group (RHEG), as corroborated by Cibersort. \u003cem\u003eRFTN1\u003c/em\u003e\u0026rsquo;s molecular function, associated with receptor internalization, \u003csup\u003e27\u003c/sup\u003e could enhance signaling pathways when \u003cem\u003eRFTN1\u003c/em\u003e expression is increased. It is similar how \u003cem\u003eRFTN1\u003c/em\u003e work in macrophage \u003csup\u003e35\u003c/sup\u003e that \u003cem\u003eRFTN1\u003c/em\u003e could mediates internalization of TLR4 to endosomes in dendritic cells and macrophages; and internalization of poly(I:C) to TLR3-positive endosomes in myeloid dendritic cells and epithelial cells; resulting in activation of TICAM1-mediated signaling and subsequent IFNB1 production upon bacterial lipopolysaccharide stimulation.\u003csup\u003e35\u003c/sup\u003e We hypothesize that \u003cem\u003eRFTN1\u003c/em\u003e may facilitate the internalization of CF receptors like MIF or MDK receptor, allowing immune cells to migrate to tumor sites and eliminate cancer cells especially T cells.\u003csup\u003e36\u003c/sup\u003e With more T cells raised in where tumor cell antigen exist, CD4\u0026thinsp;+\u0026thinsp;T cells would cause the activation of anti-tumor cells and CD8\u0026thinsp;+\u0026thinsp;T cells would exert an efficient anti-tumoral attack through the exocytosis of perforin- and granzyme-containing granules,\u003csup\u003e30,31\u003c/sup\u003e resulting the decreased number of tumor cells.\u003c/p\u003e \u003cp\u003eEnhancing \u003cem\u003eRFTN1\u003c/em\u003e expression in BC patients could potentially activate anti-tumor immunity, leading to the significant elimination of tumor cells. This approach may offer surgical opportunities for patients with large tumors and potentially extend the lifespan and reduce pain for those with metastatic disease, especially those resistant to PD-1 inhibitors, by inhibiting tumor cell metastasis through the activation of anti-tumor immunity. Besides, it offers us a new set of fresh sight to regulate the T cells in TME. Different from the immune checkpoint inhibitor, we were going to find a way to activate T cells by regulating inside cell transportations rather than work on the receptors and ligands, which means a new way to combine with or replace those patients with resistance to current therapy of BC.\u003c/p\u003e \u003cp\u003eHowever, our study has limitations. The overwhelming impact of \u003cem\u003eRFTN1\u003c/em\u003e on immune cells within the TME hindered our exploration of its effects on other cell types. Additionally, our inability to determine the optimal threshold for grouping in single-cell sequence analysis might introduce bias into our findings.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThese findings shed light on a potential mechanism by which \u003cem\u003eRFTN1\u003c/em\u003e influences TME in BC. It is suggested that \u003cem\u003eRFTN1\u003c/em\u003e may enhance anti-tumor immunity by modulating CF receptor trafficking within CD4\u0026thinsp;+\u0026thinsp;and CD8\u0026thinsp;+\u0026thinsp;T cells. Elevated levels of \u003cem\u003eRFTN1\u003c/em\u003e expression in BC patients are associated with improved survival rates and more favorable prognoses, highlighting its potential as a novel target for immunotherapy development. However, our data also underscore the complexity of this pathway and the need for extensive further research. Future studies should delve deeper into the regulatory mechanisms of \u003cem\u003eRFTN1\u003c/em\u003e and its specific interactions with T cells, both in vivo and in vitro, to fully understand its therapeutic potential and application in cancer treatment.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank TCGA and GEO databases for their support for the original data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe First Clinical College Clinical medicine first-class discipline construction project to department of Breast and Thyroid Surgery to Lingfeng Tang (CYYY-BSYJSCXXW-202322).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, Lingfeng Tang and Hongbin Xin; Data curation, Hongbin Xin; Formal analysis, Hongbin Xin and Zhenghang Li; Investigation, Lingfeng Tang and Hongbin Xin; Methodology, Lingfeng Tang, Mingzhu Zhang and Hongbin Xin; Resources, Hongbin Xin, Lin Zhou, Linrui Miu; Software, Lingfeng Tang; Supervision, Lingfeng Tang and zhengxing Li; Writing-original draft, Lingfeng Tang, Zhenghang Li and Hongbin Xin.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe RNA-sequencing data for the breast invasive carcinoma (BRCA) cohort utilized in this study are available in The Cancer Genome Atlas (TCGA) repository, accessible at https://portal.gdc.cancer.gov/. Further supporting datasets are available in the Gene Expression Omnibus (GEO) repository under accession number GSE56493, GSE110590, GSE176078, and GSE161529.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. The study is based on secondary, deidentified publicly available datasets.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. The study is based on secondary, deidentified publicly available datasets.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors wish to thank all the study participants, research staff and students who participated in this work.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSiegel RL, Giaquinto AN, Jemal A. 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Chemokines and their receptors in Atherosclerosis. \u003cem\u003eJ Mol Med\u003c/em\u003e. 2015;93(9):963-971. doi:10.1007/s00109-015-1317-8\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"anti-tumor immunity, chemotactic factors, T cells, protein transportations, T cells immigration","lastPublishedDoi":"10.21203/rs.3.rs-4437350/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4437350/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eImmune checkpoint inhibitors have been extensively utilized in treating breast cancer patients, leading to improved prognoses. For patients with negative checkpoint responses, there is a pressing need to identify alternative therapies to improve outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMaterials and Methods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used WGCNA in muti-place metastasis samples to find the lymph node metastasis related gene \u003cem\u003eRFTN1\u003c/em\u003e. Consensus cluster show the different subtype with significant pathway changes and immune cells differences. We used CellChat estimated the different interactions of cells in single cell data. We used hdWGCNA and irGSEA to identify the changes between different \u003cem\u003eRFTN1\u003c/em\u003eexpression groups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe identified a gene, \u003cem\u003eRFTN1\u003c/em\u003e, that is closely associated with lymph node metastasis, a critical early step in breast cancer spread. Immune infiltration analysis suggested that \u003cem\u003eRFTN1\u003c/em\u003e might be involved in regulating the immune system. Single-cell RNA sequencing revealed that samples with higher \u003cem\u003eRFTN1\u003c/em\u003e expression had increased proportions of CD8+ and CD4+ T cells, albeit the overall proportions were lower. These samples also showed different interactions between T cells and other cells, indicating a greater reception of chemotactic factors (CFs) in samples with higher \u003cem\u003eRFTN1\u003c/em\u003e expression. It appears that \u003cem\u003eRFTN1\u003c/em\u003e may facilitate T cell receptor binding to CFs, thereby enhancing T cell activation in the tumor microenvironment (TME).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study proposes a novel approach to modulating T cells in the TME and offers an alternative to traditional immune checkpoint inhibitor therapies for treating BC. \u003cem\u003eRFTN1\u003c/em\u003e is related to the CFs receptor transportation in CD4+ T cells and CD8+ T cells, which may activate the anti-tumor immunity system in TME.\u003c/p\u003e","manuscriptTitle":"Investigating RFTN1 as a Potential Immune System Inhibitor in the Tumor Microenvironment of Breast Cancer to Enhance Tumor Immune Escape","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-04 21:39:47","doi":"10.21203/rs.3.rs-4437350/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"af7938c2-426b-4c25-a040-6292eafac3b1","owner":[],"postedDate":"June 4th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-09-02T04:42:44+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-04 21:39:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4437350","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4437350","identity":"rs-4437350","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}