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

doi:10.21203/rs.3.rs-4437350/v1

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Research Article

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

https://doi.org/10.21203/rs.3.rs-4437350/v1

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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 RFTN1expression 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

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. ⁹However, 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.

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 GSE56493¹¹ from the GPL10379 platform and datasets GSE110590¹² 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 GSE176078¹³ and GSE161529¹⁴, 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 aPEAR¹⁶ 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 clustering¹⁸ 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 DoubletFinder²² 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).

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. 1A). 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. 1B). RFTN1 expression showed a significant difference between BC tumors and normal tissues from TCGA-BRCA (Fig. 1C). To understand RFTN1’s role in BC, we conducted survival analyses across multiple BC subtypes (Fig. 1D-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. 2C-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. 1G), 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. 1H), allowing for the division of TCGA-BRCA samples into two groups (Fig. 1I). A significant difference in RFTN1 expression was observed between the two groups (Fig. 1J), 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. 1K-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. 2A-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. 2D), 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. 2E). 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. 2F), 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. 3A-B). Analysis revealed that RFTN1 is expressed in multiple cell types (Fig. 3C).

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. 3D-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. 3F). 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. 3G). 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. 4A-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. 4C-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. 4C). 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. 4D).

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. 4F-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. 5A-B). Notably, the M3 module was found to be highly expressed in CD4 + T cells and CD8 + T cells (Fig. 5C).

Focusing on the M3 module, we enriched pathways to understand its role (Fig. 5D). 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. 5E-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. 3E). 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. 5G).

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 internalization²⁷, 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. 6A). 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. 6B-C). These findings suggest RFTN1’s involvement in molecular signaling processes.

GSEA analysis of the differentially expressed genes between the RHEG and RLEG (Fig. 6D-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.

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 blood³⁵, 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.

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.

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.

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

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