Gene Expression Patterns Associated with Tumor-Infiltrating CD4+ and CD8+ T Cells in Invasive Breast Carcinomas

Gene Expression Patterns Associated with Tumor-Infiltrating CD4+ and CD8+ T Cells in Invasive Breast Carcinomas | 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 Gene Expression Patterns Associated with Tumor-Infiltrating CD4+ and CD8+ T Cells in Invasive Breast Carcinomas zhanwei Wang, Jiamin Xu, Xi Yang, Yuefen Pan, Junjun Shen, Jin Liu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-42109/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 Apr, 2021 Read the published version in Human Immunology → Version 1 posted You are reading this latest preprint version Abstract Objective: This study investigated the gene expression patterns associated with tumor-infiltrating CD4+ and CD8+ T cells in invasive breast carcinomas. Methods: The gene expression data and corresponding clinical phenotype data from the Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) were downloaded. The stromal and immune score were calculated using ESTIMATE. The differentially expressed genes (DEGs) with a high vs. low stromal score and a high vs. low immune score were screened and then functionally enriched. The tumor-infiltrating immune cells were investigated using the Cibersort algorithm, and the CD4+ and CD8+ T cell-related genes were identified using a Spearman correlation test of infiltrating abundance with the DEGs. Moreover, the miRNA-mRNA pairs and lncRNA-miRNA pairs were predicted to construct the competing endogenous RNAs (ceRNA) network. Kaplan-Meier (K-M) survival curves were also plotted. Results: In total, 478 DEGs with a high vs. low stromal score and 796 DEGs with a high vs. low immune score were identified. In addition, 39 CD4+ T cell-related genes and 78 CD8+ T cell-related genes were identified; of these, 14 genes were significantly associated with the prognosis of BRCA patients. Moreover, for CD4+ T cell-related genes, the chr22-38_28785274-29006793.1-–miR-34a/c-5p–CAPN6 axis was identified from the ceRNA network, whereas the chr22-38_28785274-29006793.1–miR-494-3p–SLC9A7 axis was identified for CD8+ T cell-related genes. Conclusions: The chr22-38_28785274-29006793.1-–miR-34a/c-5p–CAPN6 axis and the chr22-38_28785274-29006793.1–miR-494-3p–SLC9A7 axis might regulate cellular activities associated with CD4+ and CD8+ T cell infiltration, respectively, in BRCA. Epigenetics & Genomics Invasive breast carcinomas CD4+ T cells CD8+ T cells Competing endogenous RNAs Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Highlights 1.In total, 478 and 796 genes were differentially expressed with a high vs. low stromal score and a high vs. low immune score, respectively. 2.MUC2 might be a biomarker to predict the prognosis of BRCA. 3.The chr22-38_28785274-29006793.1–miR-34a/c-5p–CAPN6 axis might regulate cellular activities associated with CD4 + T cell infiltration in BRCA. 4.The chr22-38_28785274-29006793.1–miR-494-3p–SLC9A7 axis might regulate cellular activities associated with CD8 + T cell infiltration in BRCA. Background Breast carcinoma is one of the most common tumors in women, accounting for approximately 25% of all cancers; it is the second leading cause of cancer-related death in women worldwide [ 1 ]. Breast carcinomas can be classified into two main categories, including in situ carcinomas and invasive carcinomas [ 2 ]. Invasive breast carcinoma (BRCA) refers to those tumors that show tumor cell invasion to adjacent tissues of the mammary ducts and display a trend of regional lymph node metastasis [ 2 ]. The clinical course and outcome of breast carcinoma differs with diverse immunohistochemical characteristics and histopathological subtypes [ 3 ]. Despite the great progress in diagnosis and therapy of breast carcinomas, clinical outcomes remain unsatisfactory, and the 5-year survival rate of female patients with metastatic breast cancer is 27% [ 4 ]. Recently, the therapeutic prospects of malignancies, including breast carcinomas, have been remarkably altered with a deeper understanding of tumor-immune interactions and the development of immunological checkpoint inhibitors [ 5 , 6 ]. Immune checkpoints are defined as various inhibitory pathways mediating self-tolerance and immune responses to limit collateral tissue damage [ 7 ]. These pathways can be utilized by tumor cells to escape detection and elimination by the immune system [ 8 ]. Programmed death 1 (PD-1) is an immune checkpoint; it suppresses the biological functions of effector T cells. Tumor cells can express PD-L1, a ligand of PD-1, which can inhibit the antitumor immune response by binding to PD-1 [ 9 , 10 ]. Tumor-infiltrating lymphocytes (TILs) are immune cells that have migrated to the tumor tissue microenvironment, indicating an antitumor immune response [ 6 , 11 ]. Shi et al. revealed that the distribution of TILs differs in diverse subtypes; patients with triple-negative breast carcinomas show more PD-1 + exhausted TILs, suggesting an immunosuppressive microenvironment [ 12 ]. CD4 + and CD8 + T cells are two major lymphocyte cell types. Matsumoto et al. suggested that increased CD4 + and CD8 + T cell infiltration indicates good prognosis in triple-negative breast carcinomas [ 13 ]. Additionally, Su et al. indicated that immunosuppression in breast carcinomas can be reversed by blocking the recruitment of naive CD4 + T cells, which might be a promising strategy for anticancer immunotherapy [ 14 ]. The growth and metastasis of breast carcinomas involves dynamic processes that are affected by the tumor-immune microenvironment, in which the gene expression of resident cells exhibit obvious alterations [ 8 , 15 ]. Genetic alterations in tumor progression facilitate the ectopic expression of normally silent genes in tumors, with potential carcinogenesis [ 16 ]. Masjedi et al. demonstrated that olfactory receptor genes are upregulated in BRCA and are implicated in the proliferation and progression of BRCA [ 17 ]. The role of tumor-immune interactions in breast carcinomas has received more attention. Nevertheless, the major molecular changes in the immune response accompanying breast carcinoma progression, especially in CD4 + and CD8 + T cells, is still largely unknown. Specific TIL subsets were indicated to be clinically significant and could be used to predict treatment responses. Cytotoxic CD8 + T lymphocytes could alternatively identify and eliminate tumor cells. While it could be deactivated by T reg cells, and the role of co-stimulatory proteins on antigen-presenting cells (APC) would subsequently decrease. That is to say, CD8 + T lymphocytes can be inhibited by T reg cells [ 18 ]. Breast carcinomas can generate substances that affect APCs and alter T cell types [ 19 ]. Disis et al. showed that the cause of robust TILs in triple-negative breast carcinomas might be the elevated mutations producing a neoantigen signature [ 20 ]. Therefore, we explored the gene expression pattern associated with tumor-infiltrating CD4 + and CD8 + T cells of invasive breast carcinomas. The results are expected to provide therapeutic targets for clinical treatment of BRCA. Materials And Methods Data acquisition The RNA-sequencing (RNA-seq) Fragments per Kilobase of transcript per Million mapped reads (FPKM) and corresponding clinical phenotype data from The Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) data collection were downloaded from the University of California Santa Cruz (UCSC, https://xenabrowser.net/ ) Genome Browser database. There were 1194 samples, including 1082 BRCA samples and 112 normal samples (data acquisition on 07-18-2019). Those data were analyzed according to the presupposed workflow (Fig. 1). Differentially expressed gene (DEG) screening and functional enrichment The probes were first annotated according to the annotation files, and were filtered based on whether they matched the gene symbol. The mean value was selected when multiple probes matched to the same gene. The stromal score and immune score of tumor tissue were calculated using the ESTIMATE (Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data) package [ 21 ]. Then, samples were split into high stromal/immune score and low stromal/immune score groups according to the median value of the stromal/immune score, respectively. Then, the DEGs with a high stromal/immune score vs. a low stromal/immune score were screened using the Limma package [ 22 ] (Version 3.10.3) with |log fold change (FC)| > 0.263 and P value < 0.05. Finally, the overlapping DEGs between the two groups were selected and used in the following analysis. Functional enrichment analysis The biological processes terms of the Gene Ontology annotation and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were analyzed to investigate the functions of the upregulated and downregulated overlapping DEGs using clusterProfiler [ 23 ] (Version 3.2.11) in R package. The number of enriched genes was set as: count ≥ 2. The terms with P < 0.05 were considered to be significant results. Identification of CD4 + and CD8 + T cell-related genes Based on the Cibersort algorithm [ 24 ], the tumor-infiltrating immune cells in BRCA were investigated to estimate the infiltrating abundance of six immune cell types, including B cells, CD4 + T cells, CD8 + T cells, neutrophils, macrophages, and dendritic cells. Then, the correlation coefficient (r) between overlapping DEGs and infiltrating abundance of immune cells (CD4 + and CD8 + T cells) was calculated by using the Spearman correlation test, and CD4 + and CD8 + T cell-related genes were identified with |r| > 0.15. Construction of the protein-protein interaction (PPI) network The interactions between CD4 + T cell-related genes were retrieved from the STRING database [ 25 ] with a PPI score setting as 0.15 (low confidence), and the species was set as human. Based on the retrieved PPIs, CD4 + T cell-related PPI network visualization was performed using Cytoscape [ 26 ] (version: 3.2.0). The CD8 + T cell-related PPI network was also constructed using the same method. Construction of the competing endogenous RNAs (ceRNA) network The prediction of miRNA-mRNA interactions was conducted for CD4 + T cell-related genes using miRWalk 3.0 [ 27 ], and the species was set as human. The miRNA-mRNA interactions with a score > 0.95, and those that existed in both the TargetScan and miRDB databases were selected. Next, based on DIANA-LncBase v.2 [ 28 ], the prediction of lncRNA-miRNA interactions was conducted and the interactions with score = 1 were selected. The construction of a CD4 + T cell-related ceRNA network was completed by integrating the obtained lncRNA-miRNA interactions and miRNA-mRNA interactions. Similarly, a CD8 + T cell-related ceRNA network was also constructed using the same method. Construction of a chemical-target network The genes and chemicals associated with breast neoplasms were explored from the comparative toxicogenomics database (CTD) [ 29 ] using “breast neoplasms” as the search keywords. Then, the overlapping genes between breast neoplasm-associated genes and genes in the CD4 + T cell-related ceRNA network were selected and used to screen chemical-target pairs. Then, the CD4 + T cell-related chemical-target network was visualized using Cytoscape. Similarly, the CD8 + T cell-related chemical-target network was also constructed using the same method. Survival analysis The overall survival (OS) and OS status in the clinical phenotype data were used to perform survival analysis. Briefly, the immune cell-related genes (CD4 + and CD8 + T cell-related genes) were split into high-expression and low-expression groups based on the median expression value, together with a log-rank statistical test. The cut-off was set as a P value < 0.05 to select the genes significantly associated with prognosis, and then Kaplan-Meier (K-M) survival curves were plotted. Results Differences in gene expression between high and low stromal scores The stromal score was calculated to predict the non-tumor cell infiltration [ 21 ]. Based on the stromal score, the samples were split into high and low stromal score groups, and a total of 478 genes were significantly differentially expressed in the two groups, including 104 upregulated and 374 downregulated genes (Fig. 2A). These genes were considered to be associated with the stroma in tumor tissue. Differences in gene expression between high and low immune scores The immune score refers to the infiltration of immune cells in tumor tissue, which is considered an available indicator of prognosis in tumors [ 30 ]. Based on the immune score, the samples were split into high and low immune score groups; 796 genes were found to be significantly differentially expressed in the 2 groups (Fig. 2B). Of these, 503 genes were upregulated whereas 293 genes were downregulated, suggesting that these genes might be implicated in the immune status in the tumor microenvironment. Overlapping genes between the stroma score-related and immune score-related genes The overlapping genes between the stroma score-related and immune score-related genes were identified by VENN analysis; 167 genes were obtained, including 58 upregulated and 109 downregulated genes (Fig. 2C). The functions of these overlapping genes were further investigated, and the downregulated genes were found to be significantly implicated in one KEGG pathway (hsa05146, amoebiasis) and five biological process terms, for example: GO:0006885 ~ regulation of pH (Table 1 ). Table 1 The significantly enriched GO terms and KEGG pathways ID Term Count P value Genes GO:0034220 ion transmembrane transport 7 0.0061074 GRIK1, AQP8, ATP1A3, ATP6V0A4, ATP12A, FXYD6, FXYD7 GO:0006885 regulation of pH 3 0.0067471 SLC9A7, ATP6V0A4, ATP12A GO:0015991 ATP hydrolysis coupled proton transport 3 0.0257158 ATP1A3, ATP6V0A4, ATP12A GO:0008625 extrinsic apoptotic signaling pathway via death domain receptors 3 0.0353638 TNFRSF10C, DEDD2, CD27 GO:0006355 regulation of transcription, DNA-templated 19 0.044434 ZNF44, ZBTB21, ZBTB8B, ZNF132, ZNF284, ZKSCAN1, VENTX, ZNF34, CITED1, YBX2, ZNF439, MEOX2, POU5F1, PERM1, ZNF850, DMRTC1, MTERF3, ZNF319, ATOH7 hsa05146 Amoebiasis 4 0.033452 C8A, GNAL, MUC2, GNA15 ID: The ID of the enriched GO annotation or KEGG pathway terms; Term: the name of the enriched GO annotation or KEGG pathway; Count: the number of genes enriched in GO or KEGG term; P value: the significance of the enriched terms; Genes: the genes enriched in GO or KEGG term. Immune cell infiltration in BRCA The infiltration abundance of six immune cell types in BRCA was analyzed using the Cibersort algorithm. From the bar charts of immune cell subset proportions (Fig. 3), CD8 + T cells and activated memory CD4 + T cells accounted for a large proportion of immune cell infiltration in BRCA. Therefore, CD8 + and CD4 T + cells were selected in the following analysis. Identification of CD4 + and CD8 + T cell-related genes The infiltrating abundance of CD4 + and CD8 + T cells is related to prognosis in BRCA [ 13 ]. The infiltrating abundance of CD4 + and CD8 + T cells was calculated using the Cibersort algorithm. Then, the immune cell-related genes were identified using the Spearman correlation test between overlapping genes and the infiltrating abundance of immune cells. A total of 39 CD4 + T cell-related genes and 78 CD8 + T cell-related genes were identified. These genes were regarded as crucial genes involved in CD4 + and CD8 + T cell infiltration. PPI network of CD4 + and CD8 + T cell-related genes Proteins and their functional interactions form the backbone of cellular machinery, and connectivity networks are conducive to fully understand biological phenomena [ 31 ]. For CD4 + T cell-related genes, the PPI network contained 13 genes and 11 interactions (Fig. 4A). Of the 13 genes, 5 genes were upregulated whereas 8 genes were downregulated. For CD8 + T cell-related genes, the PPI network consisted of 56 genes (16 upregulated and 40 downregulated) and 76 interactions (Fig. 4B). ceRNA network of CD4 + and CD8 + T cell-related genes For CD4 + T cell-related genes, 17 miRNA-mRNA interactions (Supplemental Fig. 1A), and 13 lncRNA-mRNA interactions (Supplemental Table 1) were finally predicted. The ceRNA network is shown in Fig. 5A, and contains 2 lncRNAs, 12 miRNAs, and 5 mRNAs, consisting of 25 ceRNA regulatory axes. For example: the chr22-38_28785274-29006793.1–miR-34a/c-5p–CAPN6 axis was identified. For CD8 + T cell-related genes, a total of 51 miRNA-mRNA pairs (Supplemental Fig. 1B), and 43 lncRNA-mRNA pairs were predicted (Supplemental Table 2). The ceRNA network contained 10 lncRNAs, 32 miRNAs, and 14 mRNAs, consisting of 81 ceRNA regulatory axes (Fig. 5B). For example: the chr22-38_28785274-29006793.1–miR-494-3p–SLC9A7 axis was identified. Breast neoplasm-related chemicals target mRNAs in the ceRNA network The etiology of many diseases involves the interactions between environmental chemicals and genes that regulate physiological processes [ 32 ]. The CTD provides information about chemical–gene/protein-disease relationships [ 32 ]. In this study, the chemical–gene interactions involving breast neoplasms were identified from the CTD, and were filtered using the mRNAs in the ceRNA network. A total of 31 chemicals were found to interact with the five genes in the CD4 + T cell-related ceRNA network (Fig. 6A, Supplemental Table 3). For example, atrazine (CAS, 1912-24-9) was predicted to result in the increased expression of CAPN6 mRNA. Similarly, 57 chemicals were found to interact with the 12 genes in the CD8 + T cell-related ceRNA network (Fig. 6B, Supplemental Table 4). For example, arsenic (CAS, 7440-38-2) was predicted to affect the methylation of the SLC9A7 gene. CD4 + and CD8 + T cell-related genes associated with prognosis of BRCA To explore the prognostic value of CD4 + and CD8 + T cell-related genes, survival analysis was performed. In total, 14 genes were found to be significantly related to prognosis of BRCA patients; of these, eight genes were commonly related to both CD4 + and CD8 + T cells (Fig. 7, Table 2 ), for example, MUC2. Among the 14 genes, 6 genes were found to be specific to CD4 + or CD8 + T cells; METTL5 and MSTN were CD4 + T cell-related genes, whereas NMNAT3, GNAL, ZBED4, and RDH12 were CD8 + T cell-related genes. Table 2 The genes significantly associated with survival of BRCA patients Genes P value High.median Low.median (a) CD4 + T cells related genes MRPS21 0.003368158 148.5333333 113.6333333 GNG8 0.011001026 131.3666667 131.9666667 METTL5 0.011379507 215.2 115.7333333 FAM129C 0.013358227 122.3 148.5333333 RNASE12 0.013759917 122.3 142.2333333 NCOA4 0.021167879 142.2333333 131.5 MSTN 0.032129699 131.9666667 115.4 MUC2 0.03310499 131.5 131.3666667 DNAI2 0.036270816 131.5 122.3 KRTAP7-1 0.038981646 142.2333333 131.5 (b) CD8 + T cells related genes NMNAT3 0.001104224 148.5333333 115.4 MRPS21 0.003368158 148.5333333 113.6333333 GNAL 0.00887105 148.5333333 130.8666667 GNG8 0.011001026 131.3666667 131.9666667 FAM129C 0.013358227 122.3 148.5333333 RNASE12 0.013759917 122.3 142.2333333 NCOA4 0.021167879 142.2333333 131.5 ZBED4 0.028738163 131.3666667 131.5 MUC2 0.03310499 131.5 131.3666667 DNAI2 0.036270816 131.5 122.3 KRTAP7-1 0.038981646 142.2333333 131.5 RDH12 0.042798068 215.2 129.1 (a) Prognosis associated genes from the CD4 + T cells related genes; (b) Prognosis associated genes from the CD8 + T cells related genes; The genes marked in red represent the overlapped genes from both CD4 + T cells related genes and CD8 + T cells related genes. Discussion In this study, the RNA-seq data and clinical phenotype data from TCGA-BRCA were used to investigate the gene expression pattern in the immune response in BRCA progression. A total of 478 DEGs with a high vs. low stromal score, and 796 DEGs with a high vs. low immune score were identified, and the overlapping DEGs were found to be implicated in ion transmembrane transport, regulation of pH, and extrinsic apoptotic signaling pathways. In addition, a total of 39 CD4 + T cell-related genes and 78 CD8 + T cell-related genes were identified, of which 14 genes were related to prognosis of BRCA patients, for example, MUC2. Moreover, for CD4 + T cell-related genes, the chr22-38_28785274-29006793.1–miR-34a/c-5p–CAPN6 axis was identified in the ceRNA network and for CD8 + T cell-related genes, the chr22-38_28785274-29006793.1–miR-494-3p–SLC9A7 axis was identified. MUC2, also termed mucin-2, belongs to the mucin protein family, which are high molecular weight glycoproteins secreted to form an insoluble mucous barrier that protects the gut lumen [ 33 ]. MUC2 was found to be expressed in breast mucinous carcinomas, and Matsukita et al. indicated that overexpression of MUC2 in mucinous carcinoma might serve as a barrier to attenuate tumor aggressiveness [ 34 ]. Astashchanka et al. showed that MUC2 participates in the regulation of proliferation, apoptosis, and metastasis of breast carcinoma cells, indicating roles of MUC2 in therapy and in clinical outcome prediction in breast carcinoma [ 35 ]. In our study, the expression of MUC2 was correlated with both CD4 + and CD8 + T cell infiltration and was associated with prognosis of BRCA patients. Therefore, we speculated that MUC2 might regulate the aggressiveness of BRCA; this process is probably accompanied by T cell infiltration. For CD4 + T cell-related genes, the chr22-38_28785274-29006793.1–miR-34a/c-5p–CAPN6 axis was identified from the ceRNA network. The miR-34 family (miR-34a/b/c) is composed of tumor suppressors, functioning in inhibiting proliferation, migration and inducing apoptosis, and are also important mediators of the p53 signaling pathway [ 36 , 37 ]. It has been reported that miR-34a serves as a tumor suppressor in triple-negative breast carcinomas by targeting the proto-oncogene c-SRC [ 38 ]. Similarly, Xiao et al. found that miR-34a can inhibit glycolysis and proliferation of breast carcinoma cells in breast carcinomas by directly targeting lactate dehydrogenase A, whose overexpression is associated with tumor growth and metastasis [ 39 ]. CAPN6, also termed calpain-6, is a member of an intracellular cysteine protease family, and this family is found to be abnormally expressed in malignant tumors [ 40 ]. Calpains are reported to be involved in various cellular activities in breast carcinomas, including cellular survival, apoptosis and migration [ 41 ]. MacLeod et al. suggested that calpain 1 and 2 play a pro-tumorigenic role in HER2 + breast cancer, whereas tumorigenesis can be delayed by disrupting calpain 1 and 2 expression [ 42 ]. Calpain 6 has been reported to relate to tumorigenesis and unfavorable prognosis in head and neck squamous cell carcinoma [ 40 ], and is considered to be a possible target in the treatment of sarcomas [ 43 ]. The role of calpain 6 in breast carcinomas was rarely reported. A previous study showed that calpain 1 and miR-34a/c were associated with kanamycin-induced inner ear cell apoptosis [ 44 ]. In our study, CAPN6/calpain 6 was predicted to be a target of miR-34a/c-5p, which was regulated by the lncRNA chr22-38_28785274-29006793.1. The role of the lncRNA chr22-38_28785274-29006793.1 has not yet been reported. We speculated that the chr22-38_28785274-29006793.1–miR-34a/c-5p–CAPN6 axis might regulate cellular activities associated with CD4 + T cell infiltration in BRCA. The abnormal expression of miR-494 has been reported in various cancers. However, the role of miR-494 in carcinogenesis is contradictory, including a tumor suppressor role [ 45 , 46 ] and an oncogenic role [ 47 , 48 ]. Zhan et al. revealed that miR-494 can inhibit the progression and metastasis of breast carcinomas by targeting P21 (RAC1) activated kinase 1 [ 49 ]. The proliferation and migration of MDA‑MB‑231 and MDA‑MB‑468 breast carcinoma cells can be promoted by highly expressing miR‑183 or miR‑494 [ 50 ]. SLC9A7, also termed NHE7, is a (Na+, K+)/H + exchanger, functioning in regulating cellular pH and ion homeostasis [ 51 ]. pH has a crucial role in regulating cell motility and metastasis. The metastatic potential of breast carcinoma cells can be enhanced by exposure to alkaline pH [ 52 ]. Onishi et al. found that SLC9A7/NHE7 can promote adhesion, invasion, and oncogenesis of MDA-MB-231 breast carcinoma cells [ 53 ]. In our study, the chr22-38_28785274-29006793.1–miR-494-3p–SLC9A7 axis was identified from the CD8 + T cell-related ceRNA network. Hence, we suggested that the chr22-38_28785274-29006793.1–miR-494-3p–SLC9A7 axis might regulate cellular activities associated with CD8 + T cell infiltration in BRCA. Although several novel findings were found in this study, there were some limitations. (1) Our study preliminarily analyzed the gene expression pattern of tumor-infiltrating CD4 + and CD8 + T cells of BRCA. However, further experiments are needed to confirm the expression of the DEGs and the predicted ceRNA axes. (2) The correlations between prognosis and the 14 identified genes should be further investigated using clinical trials. (3) The predicted chemical–gene interactions should be confirmed to provide research topics for the treatment of BRCA. Conclusions In conclusion, the gene expression patterns of tumor-infiltrating CD4 + and CD8 + T cells in BRCA were identified. MUC2 might be a biomarker to predict the prognosis of BRCA. The chr22-38_28785274-29006793.1–miR-34a/c-5p–CAPN6 axis and the chr22-38_28785274-29006793.1–miR-494-3p–SLC9A7 axis might regulate the cellular activities associated with CD4 + and CD8 + T cell infiltration, respectively, in BRCA. Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Availability of data and materials The Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) data collection were downloaded from the University of California Santa Cruz (UCSC, https://xenabrowser.net/) and Comparative Toxicogenomics Database (http://ctdbase.org/) are available by contacting the author. Competing Interests The authors declare that no conflicts of interest exist. Funding This work was supported by the Medical and Health Projects of Zhejiang Province (NO.2020KY301) and Public Welfare Technology Application Research Program of Huzhou(NO.2019GY17). Author’s Contributions All authors participated in the conception and design of the study; Conceived and drafted the manuscript: Wang Zhanwei and Xu Jiamin; Analyzed data: Yang Xi, Zhou Qing and Liu Jin; Collated and proofread the literature: Yang Xi and Zhou Qing; Wrote the paper: Liu Jin and Han Shuwen; All authors read and approved the paper. Acknowledgements The authors gratefully acknowledge the multiple databases, which made the data available. References C. DeSantis, J. Ma, L. Bryan, A. Jemal, Breast cancer statistics, 2013, CA: A Cancer Journal for Clinicians, 64 (2014) 52-62. C.E. Bacchi, C.R. Viana, Histopathological and Immunohistochemical Classification of Invasive Breast Carcinomas, in: Breast Diseases, Springer, 2019, pp. 237-247. J. 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Acunzo, M. Garofalo, G. Di Leva, L. Cascione, C. Zanca, B. Bolon, G. Condorelli, C.M. Croce, MiR-494 is regulated by ERK1/2 and modulates TRAIL-induced apoptosis in non–small-cell lung cancer through BIM down-regulation, Proceedings of the National Academy of Sciences, 109 (2012) 16570-16575. H.B. Sun, X. Chen, H. Ji, T. Wu, H.W. Lu, Y. Zhang, H. Li, Y.M. Li, miR‑494 is an independent prognostic factor and promotes cell migration and invasion in colorectal cancer by directly targeting PTEN, International journal of oncology, 45 (2014) 2486-2494. M.-N. Zhan, X.-T. Yu, J. Tang, C.-X. Zhou, C.-L. Wang, Q.-Q. Yin, X.-F. Gong, M. He, J.-R. He, G.-Q. Chen, Q. Zhao, MicroRNA-494 inhibits breast cancer progression by directly targeting PAK1, Cell Death & Disease, 8 (2018) e2529-e2529. T. Macedo, R.J. Silva‑Oliveira, V.A. Silva, D.O. Vidal, A.F. Evangelista, M. Marques, Overexpression of mir-183 and mir-494 promotes proliferation and migration in human breast cancer cell lines, Oncology letters, 14 (2017) 1054-1060. N. Milosavljevic, M. Monet, I. Léna, F. Brau, S. Lacas-Gervais, S. Feliciangeli, L. Counillon, M. Poët, The intracellular Na+/H+ exchanger NHE7 effects a Na+-coupled, but not K+-coupled proton-loading mechanism in endocytosis, Cell reports, 7 (2014) 689-696. M.A. Khajah, I. Almohri, P.M. Mathew, Y.A. Luqmani, Extracellular alkaline pH leads to increased metastatic potential of estrogen receptor silenced endocrine resistant breast cancer cells, PLoS One, 8 (2013) e76327. I. Onishi, P.J. Lin, Y. Numata, P. Austin, J. Cipollone, M. Roberge, C.D. Roskelley, M. Numata, Organellar (Na+, K+)/H+ exchanger NHE7 regulates cell adhesion, invasion and anchorage-independent growth of breast cancer MDA-MB-231 cells, Oncology reports, 27 (2012) 311-317. Supplementary Files SupplementalTable4.xlsx SupplementalTable3.xlsx SupplementalTable2.xlsx SupplementalTable1.xls Supplementalfiles.doc SupplementalFigure1.tif Figurelegends.doc Cite Share Download PDF Status: Published Journal Publication published 01 Apr, 2021 Read the published version in Human Immunology → 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-42109","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research article","associatedPublications":[],"authors":[{"id":973162,"identity":"ca49532f-69ad-47e3-9343-4840a63f21bd","order_by":0,"name":"zhanwei Wang","email":"","orcid":"","institution":"Huzhou central hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"zhanwei","middleName":"","lastName":"Wang","suffix":""},{"id":973163,"identity":"193a3e13-2b2d-4d4c-99c0-ff4b2f796eea","order_by":1,"name":"Jiamin Xu","email":"","orcid":"","institution":"Huzhou University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jiamin","middleName":"","lastName":"Xu","suffix":""},{"id":973164,"identity":"2389a3b3-a9bc-4ef8-8918-2706104320ee","order_by":2,"name":"Xi Yang","email":"","orcid":"","institution":"Huzhou Central Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xi","middleName":"","lastName":"Yang","suffix":""},{"id":973165,"identity":"e9d219cc-ec24-421e-a211-e99c2db1d956","order_by":3,"name":"Yuefen Pan","email":"","orcid":"","institution":"Huzhou Central Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yuefen","middleName":"","lastName":"Pan","suffix":""},{"id":973166,"identity":"860306d1-bdc6-4623-9ccd-7b81f6343d33","order_by":4,"name":"Junjun Shen","email":"","orcid":"","institution":"Huzhou Central Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Junjun","middleName":"","lastName":"Shen","suffix":""},{"id":973167,"identity":"d78ef9dc-41d3-4899-bb8a-2f70cb60178c","order_by":5,"name":"Jin Liu","email":"","orcid":"","institution":"Huzhou Central Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jin","middleName":"","lastName":"Liu","suffix":""},{"id":973168,"identity":"f263ae0d-3322-44a5-995c-dd3411c7ad0a","order_by":6,"name":"Shuwen Han","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIiWNgGAWjYDACCTB5gIGBvfn4hw8GNnYkaOE5lsY4oyAtmQQtEjlmzDwfDjE2ENIhP7vHTOLjjjvy5hJpaY9tDA4wM7AfProBnxaDO2fMJGeeeWa4s+fxceMcgzt8DDxpaTfwagG65zZv22HGDcfTEqRzDJ4xM0jwmOHVIj8DqOVv22H7DQdyDKQtDA4zNhDSwnADqIWx7XDihhM5ZtIMxGgxuJFW/rO37XDyhjPHkg17DNKS2Qj5RX5G8maDn22HbTccbz744McfGzt+9sPH8DsMA7CRpnwUjIJRMApGATYAAM98U/dwCQLCAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0001-6180-9565","institution":"Huzhou Cent Hosp, Affiliated Cent Hops HuZhou University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Shuwen","middleName":"","lastName":"Han","suffix":""}],"badges":[],"createdAt":"2020-07-13 18:52:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-42109/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-42109/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1016/j.humimm.2021.02.001","type":"published","date":"2021-04-01T21:17:54+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":1661759,"identity":"fc547094-dde6-4070-a751-833b18f75001","added_by":"auto","created_at":"2020-07-23 15:08:21","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":55907,"visible":true,"origin":"","legend":"The workflow of the study.","description":"","filename":"Figure1.JPG","url":"https://assets-eu.researchsquare.com/files/rs-42109/v1/Figure1.JPG"},{"id":1661760,"identity":"47c8d748-7aba-45e4-aef7-28a3a12be224","added_by":"auto","created_at":"2020-07-23 15:08:21","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":109329,"visible":true,"origin":"","legend":"Expression profiles of genes in BRCA.\nVolcano plot showing the expression profile of genes with a high vs. low stromal score (A) and with a high vs. low immune score (B). Red and green dots in the volcano plot represent upregulated and downregulated genes, respectively. VENN diagram (C) showing the overlapping genes in the stromal and immune score-related genes.","description":"","filename":"Figure2.JPG","url":"https://assets-eu.researchsquare.com/files/rs-42109/v1/Figure2.JPG"},{"id":1661761,"identity":"84e7f401-3552-4b6e-b62c-fc5f0e4ac05b","added_by":"auto","created_at":"2020-07-23 15:08:22","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":153516,"visible":true,"origin":"","legend":"The landscape of immune infiltration in BRCA.\nBar charts of immune cell subset proportions showing the infiltration abundance of different immune cells. (A) Bar charts of immune cell subset proportions of all BRCA samples, and (B) bar charts of immune cell subset proportions of BRCA samples with P \u003c 0.3. The Y-axis represents the relative percent of immune cell subset infiltration; X-axis represents BRCA samples.","description":"","filename":"Figure3.JPG","url":"https://assets-eu.researchsquare.com/files/rs-42109/v1/Figure3.JPG"},{"id":1661762,"identity":"e5f7cc3e-f88d-495d-92d5-dc70acbd42d3","added_by":"auto","created_at":"2020-07-23 15:08:22","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":73261,"visible":true,"origin":"","legend":"Protein-protein interaction networks\nProtein-protein interaction network of CD4+ T cell-related genes (A) and CD8+ T cell-related genes (B). Red and green nodes represent upregulated and downregulated genes, respectively.","description":"","filename":"Figure4.JPG","url":"https://assets-eu.researchsquare.com/files/rs-42109/v1/Figure4.JPG"},{"id":1661763,"identity":"51bbbd8e-c438-44b0-be19-2aad8b020bae","added_by":"auto","created_at":"2020-07-23 15:08:22","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":119135,"visible":true,"origin":"","legend":"Competing endogenous RNAs (ceRNA) networks\nThe ceRNA network of CD4+ T cell-related genes (A) and CD8+ T cell-related genes (B). Red and green nodes represent upregulated genes and downregulated genes, respectively. Yellow triangles represent microRNAs; purple nodes represent long non-coding RNAs.","description":"","filename":"Figure5.JPG","url":"https://assets-eu.researchsquare.com/files/rs-42109/v1/Figure5.JPG"},{"id":1661764,"identity":"5bbc4b76-4119-428c-9050-dc65cc8357e6","added_by":"auto","created_at":"2020-07-23 15:08:22","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":139858,"visible":true,"origin":"","legend":"Chemical–gene interaction networks\nChemical–gene interactions are predicted for the genes in the competing endogenous RNAs network based on the CTD. Chemical–gene interaction network of genes in the CD4+ T cell-related ceRNA network (A) and the CD8+ T cell-related ceRNA network (B). Red and green nodes represent upregulated and downregulated genes, respectively. Blue squares represent chemicals.","description":"","filename":"Figure6.JPG","url":"https://assets-eu.researchsquare.com/files/rs-42109/v1/Figure6.JPG"},{"id":1661765,"identity":"644e3c61-365a-483d-b18d-13071d0e5986","added_by":"auto","created_at":"2020-07-23 15:08:22","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":142024,"visible":true,"origin":"","legend":"Kaplan-Meier curves of overall survival in patients with BRCA\nKaplan-Meier curves of overall survival showing the prognosis value of NMNAT3 (A), MRPS21 (B), GNAL (C), GNG8 (D), METTL5 (E), and MUC2 (F).","description":"","filename":"Figure7.JPG","url":"https://assets-eu.researchsquare.com/files/rs-42109/v1/Figure7.JPG"},{"id":13559307,"identity":"ab2ece3b-59b6-4128-8dde-47e7d509082f","added_by":"auto","created_at":"2021-09-17 03:00:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1208240,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-42109/v1/904895e1-d973-44e8-aad7-38ff56a24d42.pdf"},{"id":1661767,"identity":"a149d84f-52ce-4a23-bc9e-bedab98f6d84","added_by":"auto","created_at":"2020-07-23 15:08:24","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":23290,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTable4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-42109/v1/SupplementalTable4.xlsx"},{"id":1661768,"identity":"9a884f26-1ba5-4b81-a93c-fafc76ba4bdd","added_by":"auto","created_at":"2020-07-23 15:08:24","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":14888,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTable3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-42109/v1/SupplementalTable3.xlsx"},{"id":1661769,"identity":"88ab1261-8a1a-4d44-83bc-80e25b2ab13b","added_by":"auto","created_at":"2020-07-23 15:08:25","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":11988,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTable2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-42109/v1/SupplementalTable2.xlsx"},{"id":1661770,"identity":"c22ac32d-0e9b-4487-bb0c-7de21af9bb25","added_by":"auto","created_at":"2020-07-23 15:08:25","extension":"xls","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":19456,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTable1.xls","url":"https://assets-eu.researchsquare.com/files/rs-42109/v1/SupplementalTable1.xls"},{"id":1661771,"identity":"833800e5-4c3e-41ca-bb4c-83b09f642dd8","added_by":"auto","created_at":"2020-07-23 15:08:25","extension":"doc","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":11776,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementalfiles.doc","url":"https://assets-eu.researchsquare.com/files/rs-42109/v1/Supplementalfiles.doc"},{"id":1661772,"identity":"6dd728b0-c261-45f8-88ac-d4a8aaf33352","added_by":"auto","created_at":"2020-07-23 15:08:25","extension":"tif","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":1484120,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalFigure1.tif","url":"https://assets-eu.researchsquare.com/files/rs-42109/v1/SupplementalFigure1.tif"},{"id":1661773,"identity":"4f14016a-b096-49b2-b55a-fc3144d6ee51","added_by":"auto","created_at":"2020-07-23 15:08:25","extension":"doc","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":16384,"visible":true,"origin":"","legend":"","description":"","filename":"Figurelegends.doc","url":"https://assets-eu.researchsquare.com/files/rs-42109/v1/Figurelegends.doc"}],"financialInterests":"","formattedTitle":"\u003cp\u003eGene Expression Patterns Associated with Tumor-Infiltrating CD4+ and CD8+ T Cells in Invasive Breast Carcinomas\u003c/p\u003e","fulltext":[{"header":"Highlights","content":" \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e1.In total, 478 and 796 genes were differentially expressed with a high vs. low stromal score and a high vs. low immune score, respectively.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e2.MUC2 might be a biomarker to predict the prognosis of BRCA.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e3.The chr22-38_28785274-29006793.1\u0026ndash;miR-34a/c-5p\u0026ndash;CAPN6 axis might regulate cellular activities associated with CD4\u0026thinsp;+\u0026thinsp;T cell infiltration in BRCA.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e4.The chr22-38_28785274-29006793.1\u0026ndash;miR-494-3p\u0026ndash;SLC9A7 axis might regulate cellular activities associated with CD8\u0026thinsp;+\u0026thinsp;T cell infiltration in BRCA.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e "},{"header":"Background","content":" \u003cp\u003eBreast carcinoma is one of the most common tumors in women, accounting for approximately 25% of all cancers; it is the second leading cause of cancer-related death in women worldwide [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Breast carcinomas can be classified into two main categories, including \u003cem\u003ein situ\u003c/em\u003e carcinomas and invasive carcinomas [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Invasive breast carcinoma (BRCA) refers to those tumors that show tumor cell invasion to adjacent tissues of the mammary ducts and display a trend of regional lymph node metastasis [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The clinical course and outcome of breast carcinoma differs with diverse immunohistochemical characteristics and histopathological subtypes [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Despite the great progress in diagnosis and therapy of breast carcinomas, clinical outcomes remain unsatisfactory, and the 5-year survival rate of female patients with metastatic breast cancer is 27% [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRecently, the therapeutic prospects of malignancies, including breast carcinomas, have been remarkably altered with a deeper understanding of tumor-immune interactions and the development of immunological checkpoint inhibitors [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Immune checkpoints are defined as various inhibitory pathways mediating self-tolerance and immune responses to limit collateral tissue damage [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. These pathways can be utilized by tumor cells to escape detection and elimination by the immune system [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Programmed death 1 (PD-1) is an immune checkpoint; it suppresses the biological functions of effector T cells. Tumor cells can express PD-L1, a ligand of PD-1, which can inhibit the antitumor immune response by binding to PD-1 [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Tumor-infiltrating lymphocytes (TILs) are immune cells that have migrated to the tumor tissue microenvironment, indicating an antitumor immune response [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Shi et al. revealed that the distribution of TILs differs in diverse subtypes; patients with triple-negative breast carcinomas show more PD-1\u0026thinsp;+\u0026thinsp;exhausted TILs, suggesting an immunosuppressive microenvironment [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. CD4\u0026thinsp;+\u0026thinsp;and CD8\u0026thinsp;+\u0026thinsp;T cells are two major lymphocyte cell types. Matsumoto et al. suggested that increased CD4\u0026thinsp;+\u0026thinsp;and CD8\u0026thinsp;+\u0026thinsp;T cell infiltration indicates good prognosis in triple-negative breast carcinomas [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Additionally, Su et al. indicated that immunosuppression in breast carcinomas can be reversed by blocking the recruitment of naive CD4\u0026thinsp;+\u0026thinsp;T cells, which might be a promising strategy for anticancer immunotherapy [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe growth and metastasis of breast carcinomas involves dynamic processes that are affected by the tumor-immune microenvironment, in which the gene expression of resident cells exhibit obvious alterations [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Genetic alterations in tumor progression facilitate the ectopic expression of normally silent genes in tumors, with potential carcinogenesis [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Masjedi et al. demonstrated that olfactory receptor genes are upregulated in BRCA and are implicated in the proliferation and progression of BRCA [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The role of tumor-immune interactions in breast carcinomas has received more attention. Nevertheless, the major molecular changes in the immune response accompanying breast carcinoma progression, especially in CD4\u0026thinsp;+\u0026thinsp;and CD8\u0026thinsp;+\u0026thinsp;T cells, is still largely unknown. Specific TIL subsets were indicated to be clinically significant and could be used to predict treatment responses. Cytotoxic CD8\u0026thinsp;+\u0026thinsp;T lymphocytes could alternatively identify and eliminate tumor cells. While it could be deactivated by T reg cells, and the role of co-stimulatory proteins on antigen-presenting cells (APC) would subsequently decrease. That is to say, CD8\u0026thinsp;+\u0026thinsp;T lymphocytes can be inhibited by T reg cells [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Breast carcinomas can generate substances that affect APCs and alter T cell types [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Disis et al. showed that the cause of robust TILs in triple-negative breast carcinomas might be the elevated mutations producing a neoantigen signature [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Therefore, we explored the gene expression pattern associated with tumor-infiltrating CD4\u0026thinsp;+\u0026thinsp;and CD8\u0026thinsp;+\u0026thinsp;T cells of invasive breast carcinomas. The results are expected to provide therapeutic targets for clinical treatment of BRCA.\u003c/p\u003e "},{"header":"Materials And Methods","content":" \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eData acquisition\u003c/h2\u003e \u003cp\u003eThe RNA-sequencing (RNA-seq) Fragments per Kilobase of transcript per Million mapped reads (FPKM) and corresponding clinical phenotype data from The Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) data collection were downloaded from the University of California Santa Cruz (UCSC, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://xenabrowser.net/\u003c/span\u003e\u003c/span\u003e) Genome Browser database. There were 1194 samples, including 1082 BRCA samples and 112 normal samples (data acquisition on 07-18-2019). Those data were analyzed according to the presupposed workflow (Fig.\u0026nbsp;1).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eDifferentially expressed gene (DEG) screening and functional enrichment\u003c/h2\u003e \u003cp\u003eThe probes were first annotated according to the annotation files, and were filtered based on whether they matched the gene symbol. The mean value was selected when multiple probes matched to the same gene. The stromal score and immune score of tumor tissue were calculated using the ESTIMATE (Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data) package [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Then, samples were split into high stromal/immune score and low stromal/immune score groups according to the median value of the stromal/immune score, respectively. Then, the DEGs with a high stromal/immune score vs. a low stromal/immune score were screened using the Limma package [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] (Version 3.10.3) with |log fold change (FC)| \u0026gt; 0.263 and \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Finally, the overlapping DEGs between the two groups were selected and used in the following analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eFunctional enrichment analysis\u003c/h2\u003e \u003cp\u003eThe biological processes terms of the Gene Ontology annotation and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were analyzed to investigate the functions of the upregulated and downregulated overlapping DEGs using clusterProfiler [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] (Version 3.2.11) in R package. The number of enriched genes was set as: count\u0026thinsp;\u0026ge;\u0026thinsp;2. The terms with \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered to be significant results.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of CD4\u0026thinsp;+\u0026thinsp;and CD8\u0026thinsp;+\u0026thinsp;T cell-related genes\u003c/h2\u003e \u003cp\u003eBased on the Cibersort algorithm [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], the tumor-infiltrating immune cells in BRCA were investigated to estimate the infiltrating abundance of six immune cell types, including B cells, CD4\u0026thinsp;+\u0026thinsp;T cells, CD8\u0026thinsp;+\u0026thinsp;T cells, neutrophils, macrophages, and dendritic cells. Then, the correlation coefficient (r) between overlapping DEGs and infiltrating abundance of immune cells (CD4\u0026thinsp;+\u0026thinsp;and CD8\u0026thinsp;+\u0026thinsp;T cells) was calculated by using the Spearman correlation test, and CD4\u0026thinsp;+\u0026thinsp;and CD8\u0026thinsp;+\u0026thinsp;T cell-related genes were identified with |r| \u0026gt; 0.15.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eConstruction of the protein-protein interaction (PPI) network\u003c/h2\u003e \u003cp\u003eThe interactions between CD4\u0026thinsp;+\u0026thinsp;T cell-related genes were retrieved from the STRING database [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] with a PPI score setting as 0.15 (low confidence), and the species was set as human. Based on the retrieved PPIs, CD4\u0026thinsp;+\u0026thinsp;T cell-related PPI network visualization was performed using Cytoscape [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] (version: 3.2.0). The CD8\u0026thinsp;+\u0026thinsp;T cell-related PPI network was also constructed using the same method.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eConstruction of the competing endogenous RNAs (ceRNA) network\u003c/h2\u003e \u003cp\u003eThe prediction of miRNA-mRNA interactions was conducted for CD4\u0026thinsp;+\u0026thinsp;T cell-related genes using miRWalk 3.0 [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], and the species was set as human. The miRNA-mRNA interactions with a score\u0026thinsp;\u0026gt;\u0026thinsp;0.95, and those that existed in both the TargetScan and miRDB databases were selected. Next, based on DIANA-LncBase v.2 [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], the prediction of lncRNA-miRNA interactions was conducted and the interactions with score\u0026thinsp;=\u0026thinsp;1 were selected. The construction of a CD4\u0026thinsp;+\u0026thinsp;T cell-related ceRNA network was completed by integrating the obtained lncRNA-miRNA interactions and miRNA-mRNA interactions. Similarly, a CD8\u0026thinsp;+\u0026thinsp;T cell-related ceRNA network was also constructed using the same method.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eConstruction of a chemical-target network\u003c/h2\u003e \u003cp\u003eThe genes and chemicals associated with breast neoplasms were explored from the comparative toxicogenomics database (CTD) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] using \u0026ldquo;breast neoplasms\u0026rdquo; as the search keywords. Then, the overlapping genes between breast neoplasm-associated genes and genes in the CD4\u0026thinsp;+\u0026thinsp;T cell-related ceRNA network were selected and used to screen chemical-target pairs. Then, the CD4\u0026thinsp;+\u0026thinsp;T cell-related chemical-target network was visualized using Cytoscape. Similarly, the CD8\u0026thinsp;+\u0026thinsp;T cell-related chemical-target network was also constructed using the same method.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSurvival analysis\u003c/h2\u003e \u003cp\u003eThe overall survival (OS) and OS status in the clinical phenotype data were used to perform survival analysis. Briefly, the immune cell-related genes (CD4\u0026thinsp;+\u0026thinsp;and CD8\u0026thinsp;+\u0026thinsp;T cell-related genes) were split into high-expression and low-expression groups based on the median expression value, together with a log-rank statistical test. The cut-off was set as a \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 to select the genes significantly associated with prognosis, and then Kaplan-Meier (K-M) survival curves were plotted.\u003c/p\u003e \u003c/div\u003e "},{"header":"Results","content":" \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eDifferences in gene expression between high and low stromal scores\u003c/h2\u003e \u003cp\u003eThe stromal score was calculated to predict the non-tumor cell infiltration [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Based on the stromal score, the samples were split into high and low stromal score groups, and a total of 478 genes were significantly differentially expressed in the two groups, including 104 upregulated and 374 downregulated genes (Fig.\u0026nbsp;2A). These genes were considered to be associated with the stroma in tumor tissue.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eDifferences in gene expression between high and low immune scores\u003c/h2\u003e \u003cp\u003eThe immune score refers to the infiltration of immune cells in tumor tissue, which is considered an available indicator of prognosis in tumors [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Based on the immune score, the samples were split into high and low immune score groups; 796 genes were found to be significantly differentially expressed in the 2 groups (Fig.\u0026nbsp;2B). Of these, 503 genes were upregulated whereas 293 genes were downregulated, suggesting that these genes might be implicated in the immune status in the tumor microenvironment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eOverlapping genes between the stroma score-related and immune score-related genes\u003c/h2\u003e \u003cp\u003eThe overlapping genes between the stroma score-related and immune score-related genes were identified by VENN analysis; 167 genes were obtained, including 58 upregulated and 109 downregulated genes (Fig.\u0026nbsp;2C). The functions of these overlapping genes were further investigated, and the downregulated genes were found to be significantly implicated in one KEGG pathway (hsa05146, amoebiasis) and five biological process terms, for example: GO:0006885\u0026thinsp;~\u0026thinsp;regulation of pH (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe significantly enriched GO terms and KEGG pathways\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTerm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCount\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGenes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:0034220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eion transmembrane transport\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0061074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGRIK1, AQP8, ATP1A3, ATP6V0A4, ATP12A, FXYD6, FXYD7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:0006885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eregulation of pH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0067471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSLC9A7, ATP6V0A4, ATP12A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:0015991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eATP hydrolysis coupled proton transport\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0257158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eATP1A3, ATP6V0A4, ATP12A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:0008625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eextrinsic apoptotic signaling pathway via death domain receptors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0353638\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTNFRSF10C, DEDD2, CD27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:0006355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eregulation of transcription, DNA-templated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.044434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eZNF44, ZBTB21, ZBTB8B, ZNF132, ZNF284, ZKSCAN1, VENTX, ZNF34, CITED1, YBX2, ZNF439, MEOX2, POU5F1, PERM1, ZNF850, DMRTC1, MTERF3, ZNF319, ATOH7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa05146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAmoebiasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.033452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC8A, GNAL, MUC2, GNA15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eID: The ID of the enriched GO annotation or KEGG pathway terms; Term: the name of the enriched GO annotation or KEGG pathway; Count: the number of genes enriched in GO or KEGG term; P value: the significance of the enriched terms; Genes: the genes enriched in GO or KEGG term.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eImmune cell infiltration in BRCA\u003c/h2\u003e \u003cp\u003eThe infiltration abundance of six immune cell types in BRCA was analyzed using the Cibersort algorithm. From the bar charts of immune cell subset proportions (Fig.\u0026nbsp;3), CD8\u0026thinsp;+\u0026thinsp;T cells and activated memory CD4\u0026thinsp;+\u0026thinsp;T cells accounted for a large proportion of immune cell infiltration in BRCA. Therefore, CD8\u0026thinsp;+\u0026thinsp;and CD4 T\u0026thinsp;+\u0026thinsp;cells were selected in the following analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of CD4\u0026thinsp;+\u0026thinsp;and CD8\u0026thinsp;+\u0026thinsp;T cell-related genes\u003c/h2\u003e \u003cp\u003eThe infiltrating abundance of CD4\u0026thinsp;+\u0026thinsp;and CD8\u0026thinsp;+\u0026thinsp;T cells is related to prognosis in BRCA [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The infiltrating abundance of CD4\u0026thinsp;+\u0026thinsp;and CD8\u0026thinsp;+\u0026thinsp;T cells was calculated using the Cibersort algorithm. Then, the immune cell-related genes were identified using the Spearman correlation test between overlapping genes and the infiltrating abundance of immune cells. A total of 39 CD4\u0026thinsp;+\u0026thinsp;T cell-related genes and 78 CD8\u0026thinsp;+\u0026thinsp;T cell-related genes were identified. These genes were regarded as crucial genes involved in CD4\u0026thinsp;+\u0026thinsp;and CD8\u0026thinsp;+\u0026thinsp;T cell infiltration.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003ePPI network of CD4\u0026thinsp;+\u0026thinsp;and CD8\u0026thinsp;+\u0026thinsp;T cell-related genes\u003c/h2\u003e \u003cp\u003eProteins and their functional interactions form the backbone of cellular machinery, and connectivity networks are conducive to fully understand biological phenomena [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. For CD4\u0026thinsp;+\u0026thinsp;T cell-related genes, the PPI network contained 13 genes and 11 interactions (Fig.\u0026nbsp;4A). Of the 13 genes, 5 genes were upregulated whereas 8 genes were downregulated. For CD8\u0026thinsp;+\u0026thinsp;T cell-related genes, the PPI network consisted of 56 genes (16 upregulated and 40 downregulated) and 76 interactions (Fig.\u0026nbsp;4B).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eceRNA network of CD4\u0026thinsp;+\u0026thinsp;and CD8\u0026thinsp;+\u0026thinsp;T cell-related genes\u003c/h2\u003e \u003cp\u003eFor CD4\u0026thinsp;+\u0026thinsp;T cell-related genes, 17 miRNA-mRNA interactions (Supplemental Fig.\u0026nbsp;1A), and 13 lncRNA-mRNA interactions (Supplemental Table\u0026nbsp;1) were finally predicted. The ceRNA network is shown in Fig.\u0026nbsp;5A, and contains 2 lncRNAs, 12 miRNAs, and 5 mRNAs, consisting of 25 ceRNA regulatory axes. For example: the chr22-38_28785274-29006793.1\u0026ndash;miR-34a/c-5p\u0026ndash;CAPN6 axis was identified.\u003c/p\u003e \u003cp\u003eFor CD8\u0026thinsp;+\u0026thinsp;T cell-related genes, a total of 51 miRNA-mRNA pairs (Supplemental Fig.\u0026nbsp;1B), and 43 lncRNA-mRNA pairs were predicted (Supplemental Table\u0026nbsp;2). The ceRNA network contained 10 lncRNAs, 32 miRNAs, and 14 mRNAs, consisting of 81 ceRNA regulatory axes (Fig.\u0026nbsp;5B). For example: the chr22-38_28785274-29006793.1\u0026ndash;miR-494-3p\u0026ndash;SLC9A7 axis was identified.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eBreast neoplasm-related chemicals target mRNAs in the ceRNA network\u003c/h2\u003e \u003cp\u003eThe etiology of many diseases involves the interactions between environmental chemicals and genes that regulate physiological processes [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The CTD provides information about chemical\u0026ndash;gene/protein-disease relationships [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. In this study, the chemical\u0026ndash;gene interactions involving breast neoplasms were identified from the CTD, and were filtered using the mRNAs in the ceRNA network. A total of 31 chemicals were found to interact with the five genes in the CD4\u0026thinsp;+\u0026thinsp;T cell-related ceRNA network (Fig.\u0026nbsp;6A, Supplemental Table\u0026nbsp;3). For example, atrazine (CAS, 1912-24-9) was predicted to result in the increased expression of CAPN6 mRNA.\u003c/p\u003e \u003cp\u003eSimilarly, 57 chemicals were found to interact with the 12 genes in the CD8\u0026thinsp;+\u0026thinsp;T cell-related ceRNA network (Fig.\u0026nbsp;6B, Supplemental Table\u0026nbsp;4). For example, arsenic (CAS, 7440-38-2) was predicted to affect the methylation of the SLC9A7 gene.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eCD4\u0026thinsp;+\u0026thinsp;and CD8\u0026thinsp;+\u0026thinsp;T cell-related genes associated with prognosis of BRCA\u003c/h2\u003e \u003cp\u003eTo explore the prognostic value of CD4\u0026thinsp;+\u0026thinsp;and CD8\u0026thinsp;+\u0026thinsp;T cell-related genes, survival analysis was performed. In total, 14 genes were found to be significantly related to prognosis of BRCA patients; of these, eight genes were commonly related to both CD4\u0026thinsp;+\u0026thinsp;and CD8\u0026thinsp;+\u0026thinsp;T cells (Fig.\u0026nbsp;7, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), for example, MUC2. Among the 14 genes, 6 genes were found to be specific to CD4\u0026thinsp;+\u0026thinsp;or CD8\u0026thinsp;+\u0026thinsp;T cells; METTL5 and MSTN were CD4\u0026thinsp;+\u0026thinsp;T cell-related genes, whereas NMNAT3, GNAL, ZBED4, and RDH12 were CD8\u0026thinsp;+\u0026thinsp;T cell-related genes.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe genes significantly associated with survival of BRCA patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh.median\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow.median\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e(a) CD4\u0026thinsp;+\u0026thinsp;T cells related genes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMRPS21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.003368158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e148.5333333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e113.6333333\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGNG8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.011001026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e131.3666667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e131.9666667\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMETTL5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.011379507\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e215.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e115.7333333\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFAM129C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.013358227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e122.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e148.5333333\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRNASE12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.013759917\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e122.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e142.2333333\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNCOA4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.021167879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e142.2333333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e131.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMSTN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.032129699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e131.9666667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e115.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMUC2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.03310499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e131.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e131.3666667\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDNAI2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.036270816\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e131.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e122.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKRTAP7-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.038981646\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e142.2333333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e131.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e(b) CD8\u0026thinsp;+\u0026thinsp;T cells related genes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNMNAT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.001104224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e148.5333333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e115.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMRPS21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.003368158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e148.5333333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e113.6333333\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGNAL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00887105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e148.5333333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e130.8666667\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGNG8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.011001026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e131.3666667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e131.9666667\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFAM129C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.013358227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e122.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e148.5333333\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRNASE12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.013759917\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e122.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e142.2333333\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNCOA4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.021167879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e142.2333333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e131.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZBED4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.028738163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e131.3666667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e131.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMUC2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.03310499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e131.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e131.3666667\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDNAI2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.036270816\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e131.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e122.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKRTAP7-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.038981646\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e142.2333333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e131.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRDH12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.042798068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e215.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e129.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e(a) Prognosis associated genes from the CD4\u0026thinsp;+\u0026thinsp;T cells related genes; (b) Prognosis associated genes from the CD8\u0026thinsp;+\u0026thinsp;T cells related genes; The genes marked in red represent the overlapped genes from both CD4\u0026thinsp;+\u0026thinsp;T cells related genes and CD8\u0026thinsp;+\u0026thinsp;T cells related genes.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e "},{"header":"Discussion","content":" \u003cp\u003eIn this study, the RNA-seq data and clinical phenotype data from TCGA-BRCA were used to investigate the gene expression pattern in the immune response in BRCA progression. A total of 478 DEGs with a high vs. low stromal score, and 796 DEGs with a high vs. low immune score were identified, and the overlapping DEGs were found to be implicated in ion transmembrane transport, regulation of pH, and extrinsic apoptotic signaling pathways. In addition, a total of 39 CD4\u0026thinsp;+\u0026thinsp;T cell-related genes and 78 CD8\u0026thinsp;+\u0026thinsp;T cell-related genes were identified, of which 14 genes were related to prognosis of BRCA patients, for example, MUC2. Moreover, for CD4\u0026thinsp;+\u0026thinsp;T cell-related genes, the chr22-38_28785274-29006793.1\u0026ndash;miR-34a/c-5p\u0026ndash;CAPN6 axis was identified in the ceRNA network and for CD8\u0026thinsp;+\u0026thinsp;T cell-related genes, the chr22-38_28785274-29006793.1\u0026ndash;miR-494-3p\u0026ndash;SLC9A7 axis was identified.\u003c/p\u003e \u003cp\u003eMUC2, also termed mucin-2, belongs to the mucin protein family, which are high molecular weight glycoproteins secreted to form an insoluble mucous barrier that protects the gut lumen [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. MUC2 was found to be expressed in breast mucinous carcinomas, and Matsukita et al. indicated that overexpression of MUC2 in mucinous carcinoma might serve as a barrier to attenuate tumor aggressiveness [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Astashchanka et al. showed that MUC2 participates in the regulation of proliferation, apoptosis, and metastasis of breast carcinoma cells, indicating roles of MUC2 in therapy and in clinical outcome prediction in breast carcinoma [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. In our study, the expression of MUC2 was correlated with both CD4\u0026thinsp;+\u0026thinsp;and CD8\u0026thinsp;+\u0026thinsp;T cell infiltration and was associated with prognosis of BRCA patients. Therefore, we speculated that MUC2 might regulate the aggressiveness of BRCA; this process is probably accompanied by T cell infiltration.\u003c/p\u003e \u003cp\u003eFor CD4\u0026thinsp;+\u0026thinsp;T cell-related genes, the chr22-38_28785274-29006793.1\u0026ndash;miR-34a/c-5p\u0026ndash;CAPN6 axis was identified from the ceRNA network. The miR-34 family (miR-34a/b/c) is composed of tumor suppressors, functioning in inhibiting proliferation, migration and inducing apoptosis, and are also important mediators of the p53 signaling pathway [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. It has been reported that miR-34a serves as a tumor suppressor in triple-negative breast carcinomas by targeting the proto-oncogene c-SRC [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Similarly, Xiao et al. found that miR-34a can inhibit glycolysis and proliferation of breast carcinoma cells in breast carcinomas by directly targeting lactate dehydrogenase A, whose overexpression is associated with tumor growth and metastasis [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. CAPN6, also termed calpain-6, is a member of an intracellular cysteine protease family, and this family is found to be abnormally expressed in malignant tumors [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Calpains are reported to be involved in various cellular activities in breast carcinomas, including cellular survival, apoptosis and migration [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. MacLeod et al. suggested that calpain 1 and 2 play a pro-tumorigenic role in HER2\u0026thinsp;+\u0026thinsp;breast cancer, whereas tumorigenesis can be delayed by disrupting calpain 1 and 2 expression [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Calpain 6 has been reported to relate to tumorigenesis and unfavorable prognosis in head and neck squamous cell carcinoma [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], and is considered to be a possible target in the treatment of sarcomas [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. The role of calpain 6 in breast carcinomas was rarely reported. A previous study showed that calpain 1 and miR-34a/c were associated with kanamycin-induced inner ear cell apoptosis [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. In our study, CAPN6/calpain 6 was predicted to be a target of miR-34a/c-5p, which was regulated by the lncRNA chr22-38_28785274-29006793.1. The role of the lncRNA chr22-38_28785274-29006793.1 has not yet been reported. We speculated that the chr22-38_28785274-29006793.1\u0026ndash;miR-34a/c-5p\u0026ndash;CAPN6 axis might regulate cellular activities associated with CD4\u0026thinsp;+\u0026thinsp;T cell infiltration in BRCA.\u003c/p\u003e \u003cp\u003eThe abnormal expression of miR-494 has been reported in various cancers. However, the role of miR-494 in carcinogenesis is contradictory, including a tumor suppressor role [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] and an oncogenic role [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Zhan et al. revealed that miR-494 can inhibit the progression and metastasis of breast carcinomas by targeting P21 (RAC1) activated kinase 1 [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. The proliferation and migration of MDA‑MB‑231 and MDA‑MB‑468 breast carcinoma cells can be promoted by highly expressing miR‑183 or miR‑494 [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. SLC9A7, also termed NHE7, is a (Na+, K+)/H\u0026thinsp;+\u0026thinsp;exchanger, functioning in regulating cellular pH and ion homeostasis [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. pH has a crucial role in regulating cell motility and metastasis. The metastatic potential of breast carcinoma cells can be enhanced by exposure to alkaline pH [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Onishi et al. found that SLC9A7/NHE7 can promote adhesion, invasion, and oncogenesis of MDA-MB-231 breast carcinoma cells [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. In our study, the chr22-38_28785274-29006793.1\u0026ndash;miR-494-3p\u0026ndash;SLC9A7 axis was identified from the CD8\u0026thinsp;+\u0026thinsp;T cell-related ceRNA network. Hence, we suggested that the chr22-38_28785274-29006793.1\u0026ndash;miR-494-3p\u0026ndash;SLC9A7 axis might regulate cellular activities associated with CD8\u0026thinsp;+\u0026thinsp;T cell infiltration in BRCA.\u003c/p\u003e \u003cp\u003eAlthough several novel findings were found in this study, there were some limitations. (1) Our study preliminarily analyzed the gene expression pattern of tumor-infiltrating CD4\u0026thinsp;+\u0026thinsp;and CD8\u0026thinsp;+\u0026thinsp;T cells of BRCA. However, further experiments are needed to confirm the expression of the DEGs and the predicted ceRNA axes. (2) The correlations between prognosis and the 14 identified genes should be further investigated using clinical trials. (3) The predicted chemical\u0026ndash;gene interactions should be confirmed to provide research topics for the treatment of BRCA.\u003c/p\u003e "},{"header":"Conclusions","content":" \u003cp\u003eIn conclusion, the gene expression patterns of tumor-infiltrating CD4\u0026thinsp;+\u0026thinsp;and CD8\u0026thinsp;+\u0026thinsp;T cells in BRCA were identified. MUC2 might be a biomarker to predict the prognosis of BRCA. The chr22-38_28785274-29006793.1\u0026ndash;miR-34a/c-5p\u0026ndash;CAPN6 axis and the chr22-38_28785274-29006793.1\u0026ndash;miR-494-3p\u0026ndash;SLC9A7 axis might regulate the cellular activities associated with CD4\u0026thinsp;+\u0026thinsp;and CD8\u0026thinsp;+\u0026thinsp;T cell infiltration, respectively, in BRCA.\u003c/p\u003e "},{"header":"Declarations","content":"\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e\n\u003cp\u003eThe Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) data collection were downloaded from the University of California Santa Cruz (UCSC, https://xenabrowser.net/) and Comparative Toxicogenomics Database (http://ctdbase.org/) are available by contacting the author.\u003c/p\u003e\n\u003ch2\u003eCompeting Interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare that no conflicts of interest exist.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis work was supported by the Medical and Health Projects of Zhejiang Province (NO.2020KY301) and Public Welfare Technology Application Research Program of Huzhou(NO.2019GY17).\u003c/p\u003e\n\u003ch2\u003eAuthor\u0026rsquo;s Contributions\u003c/h2\u003e\n\u003cp\u003eAll authors participated in the conception and design of the study;\u003c/p\u003e\n\u003cp\u003eConceived and drafted the manuscript: Wang Zhanwei and Xu Jiamin;\u003c/p\u003e\n\u003cp\u003eAnalyzed data: Yang Xi, Zhou Qing and Liu Jin;\u003c/p\u003e\n\u003cp\u003eCollated and proofread the literature: Yang Xi and Zhou Qing;\u003c/p\u003e\n\u003cp\u003eWrote the paper: Liu Jin and Han Shuwen;\u003c/p\u003e\n\u003cp\u003eAll authors read and approved the paper.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eThe authors gratefully acknowledge the multiple databases, which made the data available.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eC. 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Numata, Organellar (Na+, K+)/H+ exchanger NHE7 regulates cell adhesion, invasion and anchorage-independent growth of breast cancer MDA-MB-231 cells, Oncology reports, 27 (2012) 311-317.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"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":"Invasive breast carcinomas, CD4+ T cells, CD8+ T cells, Competing endogenous RNAs","lastPublishedDoi":"10.21203/rs.3.rs-42109/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-42109/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective: \u003c/strong\u003eThis study investigated the gene expression patterns associated with tumor-infiltrating CD4+ and CD8+ T cells in invasive breast carcinomas.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eThe gene expression data and corresponding clinical phenotype data from the Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) were downloaded. The stromal and immune score were calculated using ESTIMATE. The differentially expressed genes (DEGs) with a high vs. low stromal score and a high vs. low immune score were screened and then functionally enriched. The tumor-infiltrating immune cells were investigated using the Cibersort algorithm, and the CD4+ and CD8+ T cell-related genes were identified using a Spearman correlation test of infiltrating abundance with the DEGs. Moreover, the miRNA-mRNA pairs and lncRNA-miRNA pairs were predicted to construct the competing endogenous RNAs (ceRNA) network. Kaplan-Meier (K-M) survival curves were also plotted.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e In total, 478 DEGs with a high vs. low stromal score and 796 DEGs with a high vs. low immune score were identified. In addition, 39 CD4+ T cell-related genes and 78 CD8+ T cell-related genes were identified; of these, 14 genes were significantly associated with the prognosis of BRCA patients. Moreover, for CD4+ T cell-related genes, the chr22-38_28785274-29006793.1-–miR-34a/c-5p–CAPN6 axis was identified from the ceRNA network, whereas the chr22-38_28785274-29006793.1–miR-494-3p–SLC9A7 axis was identified for CD8+ T cell-related genes.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e The chr22-38_28785274-29006793.1-–miR-34a/c-5p–CAPN6 axis and the chr22-38_28785274-29006793.1–miR-494-3p–SLC9A7 axis might regulate cellular activities associated with CD4+ and CD8+ T cell infiltration, respectively, in BRCA.\u0026nbsp;\u003c/p\u003e","manuscriptTitle":"Gene Expression Patterns Associated with Tumor-Infiltrating CD4+ and CD8+ T Cells in Invasive Breast Carcinomas","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2020-07-23 15:08:20","doi":"10.21203/rs.3.rs-42109/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":"6eedbddf-0e21-4835-85ad-4557a60e0ff6","owner":[],"postedDate":"July 23rd, 2020","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":192516,"name":"Epigenetics \u0026 Genomics"}],"tags":[],"updatedAt":"2021-07-27T21:17:54+00:00","versionOfRecord":{"articleIdentity":"rs-42109","link":"https://doi.org/10.1016/j.humimm.2021.02.001","journal":{"identity":"human-immunology","isVorOnly":true,"title":"Human Immunology"},"publishedOn":"2021-04-01 21:17:54","publishedOnDateReadable":"April 1st, 2021"},"versionCreatedAt":"2020-07-23 15:08:20","video":"","vorDoi":"10.1016/j.humimm.2021.02.001","vorDoiUrl":"https://doi.org/10.1016/j.humimm.2021.02.001","workflowStages":[]},"version":"v1","identity":"rs-42109","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-42109","identity":"rs-42109","version":["v1"]},"buildId":"FbvkV6FR0MCFSLy54lSbu","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}