Research of SLC7A11 to estimate the prognosis and immune infiltration landscape for breast cancer

Research of SLC7A11 to estimate the prognosis and immune infiltration landscape for breast cancer | 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 Research of SLC7A11 to estimate the prognosis and immune infiltration landscape for breast cancer Xiangjie Xue, Yicheng Zhi, Lu Wang, Ye Shu, Jiaxin Chen, Ting Li, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5847099/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract SLC7A11, a key factor protecting cancer cells from oxidative stress, is upregulated and shows prognostic significance in amounts of malignant tumors largely while its role still remains indistinct in breast cancer. We conduct an explicit analysis of the gene SLC7A11 based on The Cancer Genome Atlas (TCGA) for breast cancer patients. Subsequently the co-expressed genes of SLC7A11 are identified. On account of the previous exploration of SLC7A11 thoroughly, we assess the immune infiltrating cell populations and immune checkpoints in breast cancer to unveil the complexity of tumor microenvironment (TME) by Cibersort and Tumor Infiltrating Estimation Resources (TIMER). We continue to convey a further preliminary investigation into the drug resistance of breast cancer. Comparing to the normal tissue, SLC7A11 is significantly expressed in breast cancer, and its differential expression is evidential in patients without distant metastasis (M0). Elevated expression of SLC7A11 is associated with notable changes in the tumor microenvironment (TME) for breast cancer patients, including a decreased presence of CD8 + T cells and activated natural killer (NK) cells. Additionally, there is an increase in immune checkpoint such as CD274, CTLA4, HAVCR2, LAG3, PDCD1LG2 and TIGIT. This modulation in the immune landscape corresponds with improved sensitivity to conventional breast cancer treatments. Our comprehensive analysis confirms that SLC7A11 is a dependable tumor biomarker, offering valuable insights for the development of targeted therapies in breast cancer. Breast cancer SLC7A11 Survival analysis Immune infiltrating cells (TILs) Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Cancer is a critical global health problem. The incidence of breast cancer (BC) is increasing rapidly(Sher et al. 2022 ). Different molecular subtypes, pathological types, TNM stages, and clinical stages provide accurate and comprehensive biology information for breast cancer patients, facilitating the development of treatments (Asleh et al. 2022 ). However, the unique biological heterogeneity of BC still presents formidable challenges for treatment, such as drug resistance and cancer recurrence. Tumor biomarkers play a vital role in identifying and predicting cancer prognosis and treatment. Common tumor biomarkers, including CEA and CA15-3, exhibit high sensitivity and specificity for cancer detection. However, their utility in guiding breast cancer treatment has been debated for more than three decades (Li et al. 2020 ; Hing et al. 2020 ). Therefore, the discovery of reliable and novel tumor biomarkers is urgently needed for breast cancer patients. The solute carrier family member SLC7A11 is the 11th member of the solute carrier family 7 (SLC7), functioning as a cystine/ glutamate antiporter primarily involved in amino acid transport across the plasma membrane (Luo et al. 2022 ). SLC7A11 is a substantial regulator of ferroptosis and is closely associated with malignant cancers. Downregulation of SLC7A11 could inhibit cystine metabolism, leading to decreased intracellular cystine levels and depletion of glutathione (GSH) biosynthesis. This effect indirectly inhibits the activity of GPX4, resulting in the accumulation of lipid peroxides and ultimately inducing ferroptosis at tumor sites (Koppula et al. 2021 ). The biological function of SLC7A11 is regulated by multiple factors and pathways. In KRAS-mutant lung adenocarcinoma (LUAD), treatment with an SLC7A11 inhibitor slows lung cancer growth and extends survival (Hu et al. 2020 ). miR-139-5p can regulate and affect the expression of PI3K and Akt, which are related to the phosphatidylinositol signaling pathway, by inhibiting SLC7A11, thus suppressing the growth of pancreatic cancer cells (Zhu et al. 2020 ). Recently, studies have indicated that SLC7A11 is overexpressed in various tumors, and patients with SLC7A11 overexpression have a poor prognosis, suggesting that SLC7A11 may be a potential prognostic biomarker in many cancer types (Lin et al. 2022 ). LUAD, renal cell carcinoma, and hepatocellular carcinoma patients with high SLC7A11 expression have lower overall survival rates than those with low SLC7A11 expression (Xu et al. 2021 ; Hu et al. 2020 ; Liang et al. 2022 ). Within the SLC7 family, SLC7A4 and SLC7A5 have been identified as potential prognostic markers for breast cancer (Zhao et al. 2022 ). However, the role of SLC7A11 in breast cancer has not been fully elucidated and requires further investigation. The main goal of this study was to utilize a bioinformatics technique to evaluate and investigate the relationship between SLC7A11 and breast cancer. There have been explicit outcomes in breast cancer patients utilizing bioinformatics techniques. First, we focused on breast cancer to analyze SLC7A11 expression and patient prognosis in the TCGA cohort. We subsequently constructed a protein‒protein interaction (PPI) network related to SLC7A11 and explored the genes co-expressed with SLC7A11 by enrichment analysis. Additionally, we investigated the relationships between positively correlated genes co-expressed with SLC7A11 and patient prognosis and investigated the associations of SLC7A11 with relevant signaling pathways, immune cell infiltration, immune checkpoints, and drug resistance in breast cancer. Given that SLC7A11 is an amino acid transporter and a key gene in ferroptosis, this study enhances our understanding of SLC7A11 in breast cancer and its importance in comprehensive therapy. 2. Materials and methods 2.1. Data collection and processing In this study, RNA-sequencing data and clinical information on SLC7A11 related to 33 human cancers were downloaded from The Cancer Genome Atlas (TCGA) and genotype tissue expression (GTEx) databases. SLC7A11 expression levels were examined across cancers via the TCGA and GTEx databases. Specific clinical information on SLC7A11 expression in breast cancer patients, including molecular subtypes, TNM stages, clinical stages and different racial types, was mined from the TCGA database only. 2.2. Prognostic value of SLC7A11 in breast cancer RNA-sequencing expression (level 3) profiles and corresponding clinical information for SLC7A11 were downloaded from TCGA-BRCA. OS, PFS, DFS, and DSS were compared between the high and low SLC7A11 expression groups via Kaplan‒Meier (KM) curves generated via the survival and Survminer packages in R by the log-rank test. The x-axis represents time (such as years, months, etc.), whereas the y-axis represents the cumulative risk of adverse prognostic events (such as death, disease recurrence, etc.). HR (High exp) represents the hazard ratio of the low-expression sample relative to the high-expression sample. HR > 1 indicates that the SLC7A11 gene is a risk factor, and HR < 1 indicates that the SLC7A11 gene is a protective factor. 95% CI represents the HR confidence interval. 2.3. PPI network construction and identification of genes co-expressed with SLC7A11 We constructed a protein‒protein interaction network (PPI) of SLC7A11 in breast cancer via String, a free biological database. SLC7A11 co-expressed genes were mined via cBioportal for cancer genomics via the TCGA. Furthermore, the correlation between SLC7A11 co-expressed genes and the prognosis of breast cancer patients was tested via Spearman analysis to detect whether the co-expressed genes affected the overall survival of cancer patients, and volcano plots and forest plots were generated. 2.4. Enrichment analysis Co-expressed genes whose expression was significantly positively correlated with that of the SLC7A11 gene were selected for Gene Ontology (GO) (biological process (BP), cellular component (CC) and molecular function (MF)) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment were performed on genes that were positively coexpressed with SLC7A11 via the R package cluster Profiler. The results were considered statistically significant if the p value was < 0.05. 2.5. Analysis of immune infiltration in breast cancer associated with SLC7A11 Cibersort is a deconvolution method that characterizes the cell composition of complex tissues from their gene expression profiles. TIMER is an interactive web tool that enables comprehensive and flexible analysis and visualization of tumor-infiltrating immune cells to infer the abundance of tumor-infiltrating immune cells from the gene expression profiles of different cancer types in TCGA. We utilized these 2 tools to analyze immune infiltration levels in breast cancer. Eight commonly known immune checkpoints were used to investigate the relationships between immune checkpoints and SLC7A11 in breast cancer. 2.6. Drug sensitivity analysis in the cancer database Analysis of the relationship between SLC7A11 and the half-maximal inhibitory concentration (IC50) of drugs was performed through Genomics of Drug Sensitivity in Cancer 2 (GDSC2) ( https://www.cancerrxgene.org/ ) to obtain the IC50 differences and relevance between high and low SLC7A11 expression groups in breast cancer. The analytical process was implemented by R package pRophetic. 2.7. Statistical analysis All the statistical analysis methods and R packages were executed in R language (foundation for statistical computing 2020) version 4.0.3. Additionally, we defined the p value as follows: ns, not significant; *p < 0.05; **p < 0.01; ***p < 0.001; and ****p < 0.0001. Significant differences between the two groups were compared through the Wilcoxon test, and significant differences among the three groups were tested with the Kruskal‒Wallis test. 3. Results 3.1. Abnormal expression of SLC7A11 in breast cancer This study explored the relationship between breast cancer and the SLC7A11 gene through a detailed analysis of data from the TCGA and GTEx databases. We examined the differential expression of SLC7A11 across 33 types of human cancers. Notably, SLC7A11 expression was significantly elevated in 23 tumor types (ACC, BRCA, CESC, CHOL, COAD, DLBC, ESCA, GBM, HNSC, KICH, KIRC, KIRP, LGG, LIHC, LUAD, LUSC, OV, PAAD, PRAD, READ, STAD, UCEC, UCS). However, two cancers (PCPG and SARC) presented relatively high SLC7A11 expression, whereas the remaining ten cancers, such as BLCA and LAML, presented no significant differences compared with normal adjacent tissues (Fig. 1 A). TCGA database analysis revealed more pronounced differential expression of SLC7A11 than did combined database analysis. Interestingly, there were no differences in expression levels across the three different racial groups (Fig. 1 B, Supplementary Fig. 1, S1D). Moreover, SLC7A11 expression varies significantly among the three breast cancer subtypes but does not differ significantly across the four clinical characteristics (Fig. 1 C, Supplementary Fig. 1, S1A). Further analysis based on TNM staging revealed that SLC7A11 expression was not influenced by tumor size (T) or lymph node involvement (N). Nevertheless, a significant increase in SLC7A11 was observed in patients without distant metastasis (M0) compared with those with distant metastasis (M1) (Fig. 1 D, Supplementary Fig. 1, S1B-C). These findings highlight the potential importance of SLC7A11 in breast cancer. 3.2. Prognostic value analysis of SLC7A11 in breast cancer The Kaplan‒Meier (KM) platform was used to examine the prognostic value of the SLC7A11 gene in breast cancer. For patients with non-metastatic breast cancer (M0) who had high SLC7A11 expression, the Kaplan–Meier (KM) curves indicated poor outcomes in 4 survival indices: overall survival (OS, p = 0.0335), progression-free survival (PFS, p = 0.0293), disease-free survival (DFS, p = 0.0453), and disease-specific survival (DSS, p = 0.0204) (Fig. 2 A-D); however, there was no statistically significant difference in prognosis between patients with different SLC7A11 expression levels and patients with distant metastatic breast cancer (M1) (Supplementary Fig. 2, S2). In conclusion, SLC7A11 may serve as a predictive gene marker for the prognosis of patients without distant metastasis (M0) in breast cancer. 3.3. PPI network construction and genes co-expressed with SLC7A11 To explore the potential molecular mechanisms of SLC7A11 in breast cancer, we constructed a protein‒protein interaction (PPI) network between SLC7A11 and other protein-coding genes using the STRING. The PPI network revealed that SLC7A11 was strongly correlated with SLC3A1, SLC3A2, SLC7A5, SLC7A6, GPX4, CD44, BECN1, ATF4, NFE2L2 and OTUB1 (Supplementary Fig. 3, S3). Using data mining techniques in cBioPortal, we identified 12,683 co-expressed genes that were positively and negatively correlated with SLC7A11. We performed Spearman correlation analysis and identified the top 20 genes that were most positively and negatively correlated with SLC7A11 (Fig. 3 A- 3 B). Additionally, the associations of co-expressed genes with prognosis value were explored for breast cancer patients with high SLC7A11 expression. The results revealed 1,158 genes significantly associated with OS, 370 genes significantly associated with PFS, 372 genes significantly associated with DFS, and 568 genes significantly associated with DSS. Figure 4 presents the top 20 genes co-expressed with SLC7A11 related to the prognosis of breast cancer patients with volcano plots and forest plots, respectively (Fig. 4 ). Interestingly, two sets of co-expressed genes did not overlap, indicating that the genes most closely co-expressed with SLC7A11 differ from those most significantly associated with prognosis. This intriguing analysis suggested that while SLC7A11 is co-expressed with certain genes, those co-expressed genes might not directly influence the prognosis of breast cancer patients. 3.4. Enrichment analysis of genes co-expressed with SLC7A11 in breast cancer To further explore the biological significance of genes co-expressed with SLC7A11 in breast cancer, we used the R package Cluster Profiler to perform GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analyses on the previous data of genes positively correlated with SLC7A11. We filtered genes with FDR < 0.1 and p value < 0.05. The genes co-expressed with SLC7A11 were positively correlated with 1,000 GO biological process (BP), 300 cellular component (CC), 310 molecular function (MF), and 27 KEGG pathways. A bar chart displayed the top 5 BPs, CCs, and MFs (Fig. 3 C). 4 Bubble charts presenting the top 20 GO-BP, CC, MF, and KEGG terms pathways for the SLC7A11 positively correlated with co-expressed genes in breast cancer patients individually (Fig. 3 D- 3 G). GO analysis revealed that the main biological functions of the genes co-expressed with SLC7A11 were related to biosynthetic processes, including macromolecule biosynthesis, cellular macromolecule biosynthesis, and heterocycle biosynthesis. KEGG analysis revealed that the primary pathways enriched with genes co-expressed with SLC7A11 were related to RNA transport (Fig. 3 G). 3.5. Relevance analysis between pathways and SLC7A11 in breast cancer To investigate the correlation between SLC7A11 and various pathways in breast cancer, we conducted extensive pathway-related analyses. The first two results demonstrated that SLC7A11 is positively correlated with DNA replication (p = 7.86e-10) and tumor proliferation (p = 3.9e-14) (Fig. 5 A- 5 B). As a key molecule in ferroptosis, SLC7A11 displayed a significant positive correlation with the ferroptosis pathway, as expected (p = 3.06e-11) (Fig. 5 C). Additionally, as a valid member of the solute carrier transporter family, SLC7A11 was strongly correlated with common amino acid metabolism correlated with glutathione metabolism (p = 0.045), and histidine metabolism (p = 0.001) pathways, including D-glutamine and glutamate metabolism (p = 2.03e-05), cysteine and methionine metabolism (p = 4.91e-11) and lysine degradation (p = 2.04e-16) (Fig. 5 D- 5 E, Supplementary Fig. 4, S4A). Conversely, SLC7A11 was negatively and tyrosine metabolism (p = 2.42e-14) (Fig. 5 F, Supplementary Fig. 4, S4B- S4C). Further analysis revealed that SLC7A11 was positively correlated with the P53 (p = 0.001), MYC (p = 1.39e-13), and PI3K-AKT-mTOR (p = 2.09e-06) pathways. Finally, the associations between SLC7A11 and immune behavior in breast cancer patients suggested positive correlations with the inflammatory response (p = 0.001), the IL-10 anti-inflammatory signaling pathway (p = 0.001) (Fig. 5 J- 5 L), and the G2M immune checkpoint in breast cancer. 3.6. Association between SLC7A11 and immune cell infiltration in breast cancer CIBERSORT was used to analyze the infiltration of 22 immune cells in breast cancer samples with different SLC7A11 expression levels, and the results are presented in a heatmap (Fig. 6 A). The infiltration levels of immune cells that included B-cell plasma, CD8 + T-cells, CD4 + memory-activated T-cells, follicular helper T-cells, resting NK cells, NK cells activation, M0 macrophages, M1 macrophages, M2 macrophages, mast cells activation, and neutrophils, significantly differed between high and low SLC7A11 expression in breast cancer (Fig. 6 B). Additionally, we analyzed the TIMER database to determine the correlation between SLC7A11 expression and the infiltration of six immune infiltrating cell types. The results revealed that B cells and CD4 + T cells were negatively correlated, whereas the other four were relatively positively correlated with SLC7A11 expression in breast cancer (Fig. 6 C). 3.7. Associations between SLC7A11 and immune checkpoints in breast cancer To understand the immune landscape of breast cancer with different SLC7A11 expression level, we examined ten commonly immune checkpoints, namely, CD274, CTLA4, HAVCR2, IGSF8, ITPRIPL1, LAG3, PDCD1, PDCD1LG2, SIGLEC15 and TIGIT, to investigate the potential role of SLC7A11 in tumor immunity. The outcomes displayed that CD274, CTLA4, HAVCR2, LAG3, PDCD1LG2 and TIGIT expression levels were accumulated with high SLC7A11 group, whereas the expression of IGSF8 and SIGLEC15 decreased (Fig. 7 A). We further conducted a correlation analysis between SLC7A11 and the eight immune checkpoints with statistically significant differences. When SLC7A11 expression was increased, the expression of six immune checkpoints (CD274, CTLA4, HAVCR2, LAG3, PDCD1LG2 and TIGIT) was positively correlated with SLC7A11 expression (Fig. 7 B- 7 G). In contrast, as SLC7A11 level increased, IGSF8 and SIGLEC15 were negatively correlated with SLC7A11 expression (Fig. 7 H- 7 I). The correlation and differential expression results were consistent, suggesting a meaningful and potentially biological relationship between SLC7A11 and immune checkpoints in breast cancer. 3.8. IC50 analysis of SLC7A11 in breast cancer Finally, the relationship between SLC7A11 and six commonly used drugs for the treatment of breast cancer were explored using chemotherapy sensitivity data from the GDSC database. The IC50 results demonstrated that higher SLC7A11 expression was associated with lower IC50 values, indicating higher target affinity and better therapeutic efficacy (Fig. 8 A- 8 F). To assess drug resistance in breast cancer, the IC50 of six drugs was validated in triple-negative breast cancer (TNBC) with differential expression of SLC7A11. The results revealed that the IC50 values of tamoxifen, docetaxel and paclitaxel were significantly lower in the high SLC7A11 expression group than in the low SLC7A11 expression group, while 5-fluorouracil and doxorubicin had relatively lower IC50 values in the high SLC7A11 expression group (Fig. 8 G- 8 K). The IC50 of one chemotherapeutic drug, rapamycin, was not significantly different between the two cohorts (Fig. 8 I). These findings suggested that commonly used drugs are comparably effective in treating TNBC patients with high SLC7A11 expression. SLC7A11 might be a promising target for reducing drug resistance in breast cancer patients. 4. Discussion In this study, we explored SLC7A11 expression, prognosis, potential co-expressed genes, enrichment analysis, pathway relevance, immune cell infiltration and preliminary IC50 analysis of traditional drugs for the treatment of breast cancer. Current studies have demonstrated that SLC7A11 varies greatly across various cancer types and was associated with poor prognosis, indicating that SLC7A11 has significant prognostic value. At present, the results of the pan-cancer analysis of SLC7A11 aligned with those of previous reports in the literature (Asleh et al. 2022 ). The KM results for breast cancer prognosis also remained consistent with the expression levels, indicating poor OS, PFS, DFS, and DSS with high levels of SLC7A11. Whether in pancreatic carcinoma, hepatocellular carcinoma, colon cancer or lung adenocarcinoma, patients with high SLC7A11 expression have similar poor clinical prognoses (Zhu et al. 2020 ; Liang et al. 2022 ; Huang et al. 2021 ; Qian et al. 2022 ; Han et al. 2022 ; Koppula et al. 2021 ). On the basis of the results of this in-depth study, SLC7A11 might serve as a prognostic biomarker for breast cancer, particularly patients without distant metastasis (M0). Ferroptosis, a form of programmed cell death that depends on iron and is distinct from apoptosis, is characterized by lipid peroxidation and the accumulation of reactive oxygen species (ROS) due to the inactivation of GPX4 (Jiang et al. 2021 ). Compared with normal tissues, cancer cells present distinct metabolic processes, with metabolic alterations being a hallmark of these cells, exemplified by the Warburg effect (Brunner and Finley 2023 ). Glutamine metabolism is particularly important in breast cancer, as changes in glutamine synthesis and degradation significantly impact amino acid transporters (Yang et al. 2021 ). Inhibiting glutamine metabolism selectively can enhance antitumor T-cell activity, particularly in triple-negative breast cancer, thereby increasing the immune system's antitumor response (Edwards et al. 2021 ). When the expression of glutamine synthetase (GS) is silenced, the metabolism of glutamine is reprogrammed, which leads to drug resistance in the ovarian cancer cell line A2780 (Guo et al. 2021 ). Furthermore, the results revealed that SLC7A11 was positively correlated with the biosynthesis of glutamine and glutamate (Fig. 5 D). Glutathione, which is synthesized from glycine, glutamate, and cysteine, relies on cysteine as the rate-limiting precursor. Glutathione converts lipid hydroperoxides into lipid alcohols, thereby reducing lipid peroxidation and preventing ferroptosis (Koppula et al. 2021 ; Luo et al. 2022 ). Our research consistently revealed that SLC7A11 is associated with ferroptosis and related amino acid metabolism pathways. The tumor microenvironment (TME) consists of various cell types recruited to tumor sites and influenced by the tumor, forming a peculiar environment distinct from that of normal tissues (Elhanani et al. 2023 ). Immune cells within the TME, especially lymphocytes, also known as tumor-infiltrating lymphocytes (TILs), have a significant impact on tumor biological behaviors, including proliferation, migration, epithelial‒mesenchymal transition (EMT), apoptosis, and distant metastasis (Zheng et al. 2021 ). Further analysis revealed that SLC7A11 was linked to immune responses and IL-10 expression (Fig. 5 J- 5 K). T cells, especially CD8 + T cells, are the fighters in the immune system (St Paul and Ohashi 2020 ). Targeting ferroptosis-related genes in gastric cancer patients could deactivate CD4 + T cells and enhance their immunotherapeutic effects (Yao et al. 2021 ). Upregulation of SLC7A11 in breast cancer was associated with not only a decrease in CD8 + T cells but also in activated NK cells and B-cell plasma, whereas activated CD4 + T memory cells clearly increased, which suggested that SLC7A11 acted as a crucial player at the crossroads of tumor metabolic reprogramming and immunity. Its influence on immune cell populations, particularly the reduction in CD8 + T cells and NK cells, highlighted its role in fostering immune evasion. Macrophages, the most prevalent immune cells in the TME, could differentiate into either M1 (proinflammatory and antitumor) or M2 (anti-inflammatory and protumor) phenotypes, depending on various stimuli (Ma et al. 2022 ; Hu et al. 2022 ). In the immunological field of macrophage polarization, many advanced studies have confirmed that the repolarization of M2 macrophages can be accomplished, especially in the TME (Locati et al. 2020 ). A recent study revealed that certain microtubule-targeting agents, including vinblastine, colchicine, and paclitaxel, can convert the M2 phenotype into a phenotype resembling M1 macrophages (Wang et al. 2023b ). Sun Hwa Kim et al. also demonstrated that M1 macrophage-derived exosomes could reprogram M2 macrophages into functional M1-like macrophages, resulting in dynamic changes among macrophages in the TME (Kim et al. 2023 ). An in vitro study indicated that Moringa oleifera leaf polysaccharide (MOLP) could induce the conversion of M2 macrophages to M1 macrophages by targeting TLR4 (Wang et al. 2023a ). Our CIBERSORT and TIMER results revealed that with elevated SLC7A11 levels, macrophages exhibit characteristics of both M1 and M2 types rather than predominantly presenting as M2 in breast cancer. This dynamic polarization and repolarization indicated that SLC7A11 might play a regulatory role in macrophage behavior, influencing their function and contributing to the complexity of immune responses within the TME. This unexpected observation revealed the potential role of SLC7A11 in modulating macrophage polarization in breast cancer, leading to diverse immune responses and tumor interactions. CD274 encoded programmed death-ligand 1 (PD-L1), which was often highly expressed in tumors, allowing them to evade immune detection through the PD-1/PD-L1 signaling pathway. Blocking this pivotal pathway has been shown to significantly reduce tumor growth and enhance the natural antitumor immune response, particularly through the activation of CD8 + T cells (Yamaguchi et al. 2022 ; de Vries et al. 2023 ). Similarly, CTLA4, which interacted with B7, played a role in preventing the activation of naive T cells, while the PD-1/PD-L1 (B7-H1) interaction could deplete effector T cells within the TME. Recent in vitro experiments have demonstrated that blocking CTLA-4 could increase immune cell infiltration and improve metabolic activity, especially in tumors with low glycolysis (Zappasodi et al. 2021 ). Our study revealed that CD274 and CTLA4 levels were significantly elevated in tumors with high SLC7A11 expression, along with varying expression levels of other immune checkpoints. Furthermore, our correlation analysis between tumor mutational burden (TMB) and SLC7A11 highlighted the critical role of SLC7A11 in the TME and its potential impact on immunotherapy outcomes (Supplementary Fig. 5, S5). These findings suggested that SLC7A11 might serve as a sensitive biomarker for assessing the effectiveness of immunotherapy in breast cancer patients, although the efficacy of targeting SLC7A11 remains to be validated. The current treatment options for breast cancer are abundant and include systematic therapy, localized therapy, endocrine therapy, monoclonal therapy, immunotherapy, chemotherapy, and other novel treatments, which are usually used in combination with surgery (Rodrigues-Ferreira and Nahmias 2022 ). Despite enormous amendments in overall survival rates for patients with breast cancer, patients with poorly differentiated breast cancer, particularly triple-negative breast cancer (TNBC) patients, still face problems such as poor prognosis and drug resistance (Leon-Ferre and Goetz 2023 ). TNBC patients were highly insensitive to most treatments, but in the TNBC cohort with high SLC7A11 expression, the IC50 values of tamoxifen and paclitaxel were significantly lower than those in the low-expression group (Fig. 8 ). These interesting findings suggested that the presence of SLC7A11 might improve the resistance of TNBC to tamoxifen or paclitaxel. 5. Limitation This exploration deepened our understanding of the relationship between SLC7A11 and breast cancer, but its limitations are also distinct. First, all the results were solely based on bioinformatic analysis of public databases, and in vitro or in vivo experiments to support the results are lacking. Second, we did not explore the upstream genes or mRNAs of SLC7A11, leaving the upstream regulatory mechanisms of SLC7A11 unclear. Third, while our bioinformatic analysis suggested dynamic changes in macrophages within the tumor microenvironment (TME), these changes have not been experimentally validated, indicating the need for further exploration of macrophages in breast cancer. Finally, additional immune-related or clinical experiments are necessary to validate the relationship between SLC7A11 and immunotherapy. 6. Conclusion Taken together, we cautiously demonstrated the overexpression and unfavorable prognostic value of SLC7A11 in multiple ways for breast cancer. Gathering results indicated that SLC7A11 was highly expressed in breast cancer and was associated with poor prognosis, particularly in patients without distant metastasis (M0). Additionally, we proposed that SLC7A11 played a significant role in regulating immune cells in the tumor microenvironment (TME), suggesting its potential as a tumor biomarker for effective immunotherapy. Specifically, combined therapies targeting CD274 and CTLA4 could offer new targeting points and directions for breast cancer immunotherapy. Abbreviations ACC, adrenocortical carcinoma; BLCA, Bladder urothelial carcinoma; BRCA, Breast invasive carcinoma; CESC, cervical squamous cell carcinoma and endocervical adenocarcinoma; CHOL, cholangiocarcinoma; COAD, Colon adenocarcinoma; DLBC, Diffuse large B-cell lymphoma; ESCA, Esophageal carcinoma; GBM, Glioblastoma multiforme; HNSC, head and neck squamous cell carcinoma; KICH, Kidney chromophobe; KIRC, Kidney renal clear cell carcinoma; KIRP, kidney renal papillary cell carcinoma; LAML, acute myeloid leukemia; LGG, Brain lower grade glioma; LIHC, Liver hepatocellular carcinoma; LUAD, Lung adenocarcinoma; ­­­­LUSC, Lung squamous cell carcinoma; MESO, Mesothelioma; OV, ovarian serous cystadenocarcinoma; PAAD, Pancreatic adenocarcinoma; PCPGs, pheochromocytomas and paragangliomas; PRAD, prostate adenocarcinoma; READ, rectum adenocarcinoma; SARC, Sarcoma; STAD, stomach adenocarcinoma; TGCTs, testicular germ cell tumors; THCA, Thyroid carcinoma; THYM, Thymoma; UCEC, uterine corpus endometrial carcinoma; UCS, uterine carcinosarcoma; UVM, uveal melanoma. Declarations Funding This work was supported by research grants from the National Natural Science Foundation of China (82103102 to C.Y.Y.; 82203602 to J.W.), the Zhejiang Provincial Natural Science Foundation of China under Grant No. LQ22H160020 to J.W. This work was also supported by start-up funding from Zhejiang Provincial People’s Hospital (ZRY2021A001 to J.W.), and the Basic Scientific Research Funds of the Department of Education of Zhejiang Province (KYQN202109 to J.W.). Author contributions X.J.X. wrote the original manuscript and prepared the figures. Y.C.Z. and L.W. wrote the methodology. Y.S., J.X.C., and T.L. organized the bioinformatic data. Y.Y.H., P.H.J. and Q.H.G. collected the bioinformatic data. C.Y.Y. and J.W. supervised the study and revised the manuscript. Consent for publication All authors have read and approved the article for publication. Availability of data and material The data presented in this article are available in this study. Compliance with Ethical Standards Disclosure of potential conflicts of interest All author states that there is no conflict of interest. Research involving human participants and/or animals Not applicable. Acknowledgements Not applicable. References Asleh K, Riaz N, Nielsen TO (2022) Heterogeneity of triple negative breast cancer: Current advances in subtyping and treatment implications. J Exp Clin Cancer Res 41 (1):265. doi:10.1186/s13046-022-02476-1 Brunner JS, Finley LWS (2023) Metabolic determinants of tumour initiation. Nat Rev Endocrinol 19 (3):134-150. doi:10.1038/s41574-022-00773-5 de Vries NL, van de Haar J, Veninga V, Chalabi M, Ijsselsteijn ME, van der Ploeg M, van den Bulk J, Ruano D, van den Berg JG, Haanen JB, Zeverijn LJ, Geurts BS, de Wit GF, Battaglia TW, Gelderblom H, Verheul HMW, Schumacher TN, Wessels LFA, Koning F, de Miranda N, Voest EE (2023) gammadelta T cells are effectors of immunotherapy in cancers with HLA class I defects. Nature 613 (7945):743-750. doi:10.1038/s41586-022-05593-1 Edwards DN, Ngwa VM, Raybuck AL, Wang S, Hwang Y, Kim LC, Cho SH, Paik Y, Wang Q, Zhang S, Manning HC, Rathmell JC, Cook RS, Boothby MR, Chen J (2021) Selective glutamine metabolism inhibition in tumor cells improves antitumor T lymphocyte activity in triple-negative breast cancer. J Clin Invest 131 (4). doi:10.1172/JCI140100 Elhanani O, Ben-Uri R, Keren L (2023) Spatial profiling technologies illuminate the tumor microenvironment. Cancer Cell 41 (3):404-420. doi:10.1016/j.ccell.2023.01.010 Guo J, Satoh K, Tabata S, Mori M, Tomita M, Soga T (2021) Reprogramming of glutamine metabolism via glutamine synthetase silencing induces cisplatin resistance in A2780 ovarian cancer cells. BMC Cancer 21 (1):174. doi:10.1186/s12885-021-07879-5 Han L, Yan Y, Fan M, Gao S, Zhang L, Xiong X, Li R, Xiao X, Wang X, Ni L, Tong D, Huang C, Cao Y, Yang J (2022) Pt3R5G inhibits colon cancer cell proliferation through inducing ferroptosis by down-regulating SLC7A11. Life Sci 306:120859. doi:10.1016/j.lfs.2022.120859 Hing JX, Mok CW, Tan PT, Sudhakar SS, Seah CM, Lee WP, Tan SM (2020) Clinical utility of tumour marker velocity of cancer antigen 15-3 (CA 15-3) and carcinoembryonic antigen (CEA) in breast cancer surveillance. Breast 52:95-101. doi:10.1016/j.breast.2020.05.005 Hu B, Yin G, Sun X (2022) Identification of specific role of SNX family in gastric cancer prognosis evaluation. Sci Rep 12 (1):10231. doi:10.1038/s41598-022-14266-y Hu K, Li K, Lv J, Feng J, Chen J, Wu H, Cheng F, Jiang W, Wang J, Pei H, Chiao PJ, Cai Z, Chen Y, Liu M, Pang X (2020) Suppression of the SLC7A11/glutathione axis causes synthetic lethality in KRAS-mutant lung adenocarcinoma. J Clin Invest 130 (4):1752-1766. doi:10.1172/JCI124049 Huang H, Liu J, Wu H, Liu F, Zhou X (2021) Ferroptosis-associated gene SLC7A11 is upregulated in NSCLC and correlated with patient’s poor prognosis: An integrated bioinformatics analysis. Pteridines 32 (1):106-116. doi:10.1515/pteridines-2020-0034 Jiang X, Stockwell BR, Conrad M (2021) Ferroptosis: mechanisms, biology and role in disease. Nat Rev Mol Cell Biol 22 (4):266-282. doi:10.1038/s41580-020-00324-8 Kim H, Park HJ, Chang HW, Back JH, Lee SJ, Park YE, Kim EH, Hong Y, Kwak G, Kwon IC, Lee JE, Lee YS, Kim SY, Yang Y, Kim SH (2023) Exosome-guided direct reprogramming of tumor-associated macrophages from protumorigenic to antitumorigenic to fight cancer. Bioact Mater 25:527-540. doi:10.1016/j.bioactmat.2022.07.021 Koppula P, Zhuang L, Gan B (2021) Cystine transporter SLC7A11/xCT in cancer: ferroptosis, nutrient dependency, and cancer therapy. Protein Cell 12 (8):599-620. doi:10.1007/s13238-020-00789-5 Leon-Ferre RA, Goetz MP (2023) Advances in systemic therapies for triple negative breast cancer. BMJ 381:e071674. doi:10.1136/bmj-2022-071674 Li J, Liu L, Feng Z, Wang X, Huang Y, Dai H, Zhang L, Song F, Wang D, Zhang P, Ma B, Li H, Zheng H, Song F, Chen K (2020) Tumor markers CA15-3, CA125, CEA and breast cancer survival by molecular subtype: a cohort study. Breast Cancer 27 (4):621-630. doi:10.1007/s12282-020-01058-3 Liang Y, Su S, Lun Z, Zhong Z, Yu W, He G, Wang Q, Wang J, Huang S (2022) Ferroptosis regulator SLC7A11 is a prognostic marker and correlated with PD-L1 and immune cell infiltration in liver hepatocellular carcinoma. Front Mol Biosci 9:1012505. doi:10.3389/fmolb.2022.1012505 Lin Y, Dong Y, Liu W, Fan X, Sun Y (2022) Pan-Cancer Analyses Confirmed the Ferroptosis-Related Gene SLC7A11 as a Prognostic Biomarker for Cancer. Int J Gen Med 15:2501-2513. doi:10.2147/IJGM.S341502 Locati M, Curtale G, Mantovani A (2020) Diversity, Mechanisms, and Significance of Macrophage Plasticity. Annu Rev Pathol 15:123-147. doi:10.1146/annurev-pathmechdis-012418-012718 Luo T, Wang Y, Wang J (2022) Ferroptosis assassinates tumor. J Nanobiotechnology 20 (1):467. doi:10.1186/s12951-022-01663-8 Ma RY, Black A, Qian BZ (2022) Macrophage diversity in cancer revisited in the era of single-cell omics. Trends Immunol 43 (7):546-563. doi:10.1016/j.it.2022.04.008 Qian L, Wang F, Lu SM, Miao HJ, He X, Feng J, Huang H, Shi RF, Zhang JG (2022) A Comprehensive Prognostic and Immune Analysis of Ferroptosis-Related Genes Identifies SLC7A11 as a Novel Prognostic Biomarker in Lung Adenocarcinoma. J Immunol Res 2022:1951620. doi:10.1155/2022/1951620 Rodrigues-Ferreira S, Nahmias C (2022) Predictive biomarkers for personalized medicine in breast cancer. Cancer Lett 545:215828. doi:10.1016/j.canlet.2022.215828 Sher G, Salman NA, Khan AQ, Prabhu KS, Raza A, Kulinski M, Dermime S, Haris M, Junejo K, Uddin S (2022) Epigenetic and breast cancer therapy: Promising diagnostic and therapeutic applications. Semin Cancer Biol 83:152-165. doi:10.1016/j.semcancer.2020.08.009 St Paul M, Ohashi PS (2020) The Roles of CD8(+) T Cell Subsets in Antitumor Immunity. Trends Cell Biol 30 (9):695-704. doi:10.1016/j.tcb.2020.06.003 Wang S, Hu Q, Chang Z, Liu Y, Gao Y, Luo X, Zhou L, Chen Y, Cui Y, Wang Z, Wang B, Huang Y, Liu Y, Liu R, Zhang L (2023a) Moringa oleifera leaf polysaccharides exert anti-lung cancer effects upon targeting TLR4 to reverse the tumor-associated macrophage phenotype and promote T-cell infiltration. Food Funct 14 (10):4607-4620. doi:10.1039/d2fo03685a Wang YN, Wang YY, Wang J, Bai WJ, Miao NJ, Wang J (2023b) Vinblastine resets tumor-associated macrophages toward M1 phenotype and promotes antitumor immune response. J Immunother Cancer 11 (8). doi:10.1136/jitc-2023-007253 Xu F, Guan Y, Xue L, Zhang P, Li M, Gao M, Chong T (2021) The roles of ferroptosis regulatory gene SLC7A11 in renal cell carcinoma: A multi-omics study. Cancer Med 10 (24):9078-9096. doi:10.1002/cam4.4395 Yamaguchi H, Hsu JM, Yang WH, Hung MC (2022) Mechanisms regulating PD-L1 expression in cancers and associated opportunities for novel small-molecule therapeutics. Nat Rev Clin Oncol 19 (5):287-305. doi:10.1038/s41571-022-00601-9 Yang WH, Qiu Y, Stamatatos O, Janowitz T, Lukey MJ (2021) Enhancing the Efficacy of Glutamine Metabolism Inhibitors in Cancer Therapy. Trends Cancer 7 (8):790-804. doi:10.1016/j.trecan.2021.04.003 Yao F, Zhan Y, Pu Z, Lu Y, Chen J, Deng J, Wu Z, Chen B, Chen J, Tian K, Ni Y, Mou L (2021) LncRNAs Target Ferroptosis-Related Genes and Impair Activation of CD4(+) T Cell in Gastric Cancer. Front Cell Dev Biol 9:797339. doi:10.3389/fcell.2021.797339 Zappasodi R, Serganova I, Cohen IJ, Maeda M, Shindo M, Senbabaoglu Y, Watson MJ, Leftin A, Maniyar R, Verma S, Lubin M, Ko M, Mane MM, Zhong H, Liu C, Ghosh A, Abu-Akeel M, Ackerstaff E, Koutcher JA, Ho PC, Delgoffe GM, Blasberg R, Wolchok JD, Merghoub T (2021) CTLA-4 blockade drives loss of T(reg) stability in glycolysis-low tumours. Nature 591 (7851):652-658. doi:10.1038/s41586-021-03326-4 Zhao X, Jin L, Liu Y, Liu Z, Liu Q (2022) Bioinformatic analysis of the role of solute carrier-glutamine transporters in breast cancer. Ann Transl Med 10 (14):777. doi:10.21037/atm-22-2620 Zheng L, Qin S, Si W, Wang A, Xing B, Gao R, Ren X, Wang L, Wu X, Zhang J, Wu N, Zhang N, Zheng H, Ouyang H, Chen K, Bu Z, Hu X, Ji J, Zhang Z (2021) Pan-cancer single-cell landscape of tumor-infiltrating T cells. Science 374 (6574):abe6474. doi:10.1126/science.abe6474 Zhu JH, De Mello RA, Yan QL, Wang JW, Chen Y, Ye QH, Wang ZJ, Tang HJ, Huang T (2020) MiR-139-5p/SLC7A11 inhibits the proliferation, invasion and metastasis of pancreatic carcinoma via PI3K/Akt signaling pathway. Biochim Biophys Acta Mol Basis Dis 1866 (6):165747. doi:10.1016/j.bbadis.2020.165747 Additional Declarations No competing interests reported. Supplementary Files Supplementaryfigure.pdf Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 04 Mar, 2025 Reviews received at journal 01 Mar, 2025 Reviewers agreed at journal 26 Feb, 2025 Reviews received at journal 18 Feb, 2025 Reviewers agreed at journal 12 Feb, 2025 Reviewers invited by journal 06 Feb, 2025 Editor assigned by journal 03 Feb, 2025 Submission checks completed at journal 30 Jan, 2025 First submitted to journal 17 Jan, 2025 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-5847099","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":409053730,"identity":"4ca9d4bb-2e5c-4d0f-bb90-fb17fd1dad0e","order_by":0,"name":"Xiangjie Xue","email":"","orcid":"","institution":"Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College","correspondingAuthor":false,"prefix":"","firstName":"Xiangjie","middleName":"","lastName":"Xue","suffix":""},{"id":409053733,"identity":"ef43fcd6-83a7-475d-a076-b17dd538ae33","order_by":1,"name":"Yicheng Zhi","email":"","orcid":"","institution":"Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College","correspondingAuthor":false,"prefix":"","firstName":"Yicheng","middleName":"","lastName":"Zhi","suffix":""},{"id":409053735,"identity":"6458f66f-148c-4dfd-8905-9f3ad0afa056","order_by":2,"name":"Lu Wang","email":"","orcid":"","institution":"Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College","correspondingAuthor":false,"prefix":"","firstName":"Lu","middleName":"","lastName":"Wang","suffix":""},{"id":409053736,"identity":"ace295cf-8081-4690-a5d5-91507b8b66a4","order_by":3,"name":"Ye Shu","email":"","orcid":"","institution":"Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College","correspondingAuthor":false,"prefix":"","firstName":"Ye","middleName":"","lastName":"Shu","suffix":""},{"id":409053737,"identity":"39c5d951-4e74-47c3-9699-6895ff25d64a","order_by":4,"name":"Jiaxin Chen","email":"","orcid":"","institution":"Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College","correspondingAuthor":false,"prefix":"","firstName":"Jiaxin","middleName":"","lastName":"Chen","suffix":""},{"id":409053738,"identity":"2a6686b2-d1f6-497d-b4bc-13d0ba8b726a","order_by":5,"name":"Ting Li","email":"","orcid":"","institution":"Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College","correspondingAuthor":false,"prefix":"","firstName":"Ting","middleName":"","lastName":"Li","suffix":""},{"id":409053740,"identity":"734ada62-30b5-4f7e-b377-843c04b98408","order_by":6,"name":"Yanyan Hu","email":"","orcid":"","institution":"Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College","correspondingAuthor":false,"prefix":"","firstName":"Yanyan","middleName":"","lastName":"Hu","suffix":""},{"id":409053744,"identity":"b61a3776-3baf-4ee1-919c-65f48e336b1c","order_by":7,"name":"Peihong Jin","email":"","orcid":"","institution":"Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College","correspondingAuthor":false,"prefix":"","firstName":"Peihong","middleName":"","lastName":"Jin","suffix":""},{"id":409053746,"identity":"3025a272-24ca-4eb5-bdd0-ade4d3eb100a","order_by":8,"name":"Qinhao Gu","email":"","orcid":"","institution":"Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College","correspondingAuthor":false,"prefix":"","firstName":"Qinhao","middleName":"","lastName":"Gu","suffix":""},{"id":409053748,"identity":"16b9e233-7bbe-443e-9d34-bed049a17c85","order_by":9,"name":"Chenyang Ye","email":"","orcid":"","institution":"the Second Affiliated Hospital of Zhejiang University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Chenyang","middleName":"","lastName":"Ye","suffix":""},{"id":409053749,"identity":"81e5fa2d-ba7f-49c8-9902-ba5790db93f4","order_by":10,"name":"Ji Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8UlEQVRIiWNgGAWjYBACxmYwJcHDxnD4AIMEiH2AOC0WcvyMxxKI0wIFFcaSzWcMIGxCWpjbeQw/F/ySSNxw7MznD5ZtDHJ8NxIYPxfgdRiPsfTMPqCWM2c3GEi2MRhL3khglp6BVwvvBmneHqCWG2c3JAC1ABkJbMw8+LVs/g3Wcv/NgwNALfXEaNkmzfNDwliy4QxjA1BLggFhLfzfrHkbJOT4GY4ZM0ickzCceeZhszQ+LYb9x5Jv8/ypA0Xl488SZTbyfMeTD37Gq6UBZFUbhMMsAY5MxgY8GhgY5MHkH6grP+BVOwpGwSgYBSMVAADb402k/2ECUQAAAABJRU5ErkJggg==","orcid":"","institution":"Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College","correspondingAuthor":true,"prefix":"","firstName":"Ji","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-01-17 07:53:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5847099/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5847099/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":75409272,"identity":"57dcc0c6-ceab-4b90-a129-79a1e2ef921e","added_by":"auto","created_at":"2025-02-04 09:03:17","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":453671,"visible":true,"origin":"","legend":"\u003cp\u003eSLC7A11 expression in breast cancer. A. Pan-cancer expression of SLC7A11 in the TCGA and GTEx databases. B. High and low SLC7A11 expression levels in tumor and adjacent normal tissues in the TCGA-BRCA cohort. C. Correlations between SLC7A11 expression and subtypes of breast cancer. D. Comparison of SLC7A11 expression and metastasis levels (M) in breast cancer. (P value, ns, not significant, *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001, ****p \u0026lt; 0.0001)\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5847099/v1/a452229781817ac560e801d3.png"},{"id":75409267,"identity":"cac5803f-c2f0-4f0a-8dec-feffc11272f5","added_by":"auto","created_at":"2025-02-04 09:03:17","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":409045,"visible":true,"origin":"","legend":"\u003cp\u003ePrognostic value of SLC7A11 in breast cancer. A-D. Kaplan–Meier curves (KM) for nonmetastatic breast cancer patients (M0) with high SLC7A11 expression suggested poor OS, PFS, DFS and DSS. (P value, ns, not significant; *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001, ****p \u0026lt; 0.0001)\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5847099/v1/f9bb97834653ab42aa84f673.png"},{"id":75409266,"identity":"f00cd626-a71f-4fad-9143-30668c35df3e","added_by":"auto","created_at":"2025-02-04 09:03:17","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":334055,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of genes co-expressed with SLC7A11. A-B. Top 20 positively and negatively co-expressed genes of SLC7A11 in breast cancer. C. GO analysis of SLC7A11-positively co-expressed genes revealed the top 5 GO-BP, CC, and MF terms in a bar chart. D-F. GO-BP, CC, and MF bubble drawings. G. KEGG analysis of SLC7A11 positively co-expressed genes revealed 20 meaningful pathways in a bubble chart. (P value, ns, not significant; *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001, ****p \u0026lt; 0.0001)\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5847099/v1/75986ef2b8687af411cdc897.png"},{"id":75411556,"identity":"8f5ed012-1fed-499e-a68b-9372bc164875","added_by":"auto","created_at":"2025-02-04 09:11:18","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":462540,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of genes co-expressed with SLC7A11 for the prognosis of breast cancer patients. A, C, E, G: Volcano plot of the top 20 genes highly related to the OS, PFS, DFS, and DSS of breast cancer patients, respectively. B, D, F, H: Forest plots displaying the top 20 genes highly related to the OS, PFS, DFS, and DSS of breast cancer patients,respectively. (P value, ns, not significant; *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001, ****p \u0026lt; 0.0001)\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-5847099/v1/74a4875ed96ec0176c2d4ba1.png"},{"id":75409316,"identity":"9d70a476-1542-4b67-8d2e-8e67f2605cee","added_by":"auto","created_at":"2025-02-04 09:03:19","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":435779,"visible":true,"origin":"","legend":"\u003cp\u003eRelevance analysis of SLC7A11 and related gene pathways in breast cancer. A. DNA replication (p= 7.86e-10). B. Tumor proliferation (p= 3.9e-14). C. Ferroptosis (p= 3.06e-11). D. D-Glutamine and glutamate metabolism (p= 2.03e‒05). E. Cysteine and methionine metabolism (p= 4.91e-11). F. Glutathione metabolism (p= 0.045). G. P53 pathway (p= 0.001). H. PI3K-AKT-mTOR pathway (p= 2.09e-06). I. MYC targets (p= 1.39e-13). J. Inflammatory response (p= 0.001). K. IL-10 anti-inflammatory signaling pathway (p= 0.001). L. G2M immune checkpoint (p= 1.15e-22). (P value, ns, not significant; *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001, ****p \u0026lt; 0.0001)\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-5847099/v1/dfae08c2365dff2d97d4aaa9.png"},{"id":75411559,"identity":"b45d8012-257e-4b7a-b9f8-c0f84a30e0be","added_by":"auto","created_at":"2025-02-04 09:11:19","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":421642,"visible":true,"origin":"","legend":"\u003cp\u003eImmune cell infiltration in breast cancer with differential SLC7A11 expression. A. Heatmap showing the association between SLC7A11 and immune cell infiltrates in breast cancer. B. Twenty-two immune cells in the high- and low-SLC7A11 expression groups of breast cancer patients in the TCGA database. C. Correlation analysis between immune cells and SLC7A11 in breast cancer. (P value, ns, not significant; *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001, ****p \u0026lt; 0.0001)\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-5847099/v1/71e85527b52fba147db8f0a8.png"},{"id":75411558,"identity":"2672da83-0fbd-45bd-a9e9-61ad8e668222","added_by":"auto","created_at":"2025-02-04 09:11:18","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":644289,"visible":true,"origin":"","legend":"\u003cp\u003eRelevance to immune checkpoints of SLC7A11 in breast cancer. A. Correlations between SLC7A11 expression and the expression of 10 immune checkpoint genes in the TCGA database. B-I. Relevance analysis of CD274, CTLA4, HAVCR2, LAG3, PDCD1LG2, TIGIT, SIGLEC15, IGSF8 and SLC7A11 in breast cancer. G1 represents the SLC7A11 high-expression group, and G2 represents the SLC7A11 low-expression group. (P value, ns, not significant; *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001, ****p \u0026lt; 0.0001)\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-5847099/v1/c38fb7625d4fac2e86c46347.png"},{"id":75409317,"identity":"66b65cf8-fa6f-4d94-b8cd-2b999155bd3b","added_by":"auto","created_at":"2025-02-04 09:03:19","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":332211,"visible":true,"origin":"","legend":"\u003cp\u003eDrug sensitivity analysis. A-F. IC50 values for tamoxifen, 5-fluorouracil, docetaxel, doxorubicin, paclitaxel, and rapamycin in breast cancer. G‒L. Drug sensitivity of 6 drugs (tamoxifen, 5-fluorouracil, docetaxel, doxorubicin, paclitaxel, and rapamycin) in TNBC. G1 represents the SLC7A11 high-expression group, and G2 represents the SLC7A11 low-expression group. (P value, ns, not significant; *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001, ****p \u0026lt; 0.0001)\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-5847099/v1/b51b3d1f8c9ef77b98e6dc48.png"},{"id":75411582,"identity":"98455545-75ba-4b30-99d1-96da15190a62","added_by":"auto","created_at":"2025-02-04 09:11:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4414143,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5847099/v1/7b29290e-5f7f-41a5-8ac6-60ed2fd4a426.pdf"},{"id":75409263,"identity":"622e17d8-8146-4524-8f3c-7119e7b0e1bf","added_by":"auto","created_at":"2025-02-04 09:03:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1193243,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfigure.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5847099/v1/d3e729d8b77a67d615d15200.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Research of SLC7A11 to estimate the prognosis and immune infiltration landscape for breast cancer","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eCancer is a critical global health problem. The incidence of breast cancer (BC) is increasing rapidly(Sher et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Different molecular subtypes, pathological types, TNM stages, and clinical stages provide accurate and comprehensive biology information for breast cancer patients, facilitating the development of treatments (Asleh et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, the unique biological heterogeneity of BC still presents formidable challenges for treatment, such as drug resistance and cancer recurrence. Tumor biomarkers play a vital role in identifying and predicting cancer prognosis and treatment. Common tumor biomarkers, including CEA and CA15-3, exhibit high sensitivity and specificity for cancer detection. However, their utility in guiding breast cancer treatment has been debated for more than three decades (Li et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Hing et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Therefore, the discovery of reliable and novel tumor biomarkers is urgently needed for breast cancer patients.\u003c/p\u003e \u003cp\u003eThe solute carrier family member SLC7A11 is the 11th member of the solute carrier family 7 (SLC7), functioning as a cystine/ glutamate antiporter primarily involved in amino acid transport across the plasma membrane (Luo et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). SLC7A11 is a substantial regulator of ferroptosis and is closely associated with malignant cancers. Downregulation of SLC7A11 could inhibit cystine metabolism, leading to decreased intracellular cystine levels and depletion of glutathione (GSH) biosynthesis. This effect indirectly inhibits the activity of GPX4, resulting in the accumulation of lipid peroxides and ultimately inducing ferroptosis at tumor sites (Koppula et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The biological function of SLC7A11 is regulated by multiple factors and pathways. In KRAS-mutant lung adenocarcinoma (LUAD), treatment with an SLC7A11 inhibitor slows lung cancer growth and extends survival (Hu et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). miR-139-5p can regulate and affect the expression of PI3K and Akt, which are related to the phosphatidylinositol signaling pathway, by inhibiting SLC7A11, thus suppressing the growth of pancreatic cancer cells (Zhu et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Recently, studies have indicated that SLC7A11 is overexpressed in various tumors, and patients with SLC7A11 overexpression have a poor prognosis, suggesting that SLC7A11 may be a potential prognostic biomarker in many cancer types (Lin et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). LUAD, renal cell carcinoma, and hepatocellular carcinoma patients with high SLC7A11 expression have lower overall survival rates than those with low SLC7A11 expression (Xu et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Hu et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Liang et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Within the SLC7 family, SLC7A4 and SLC7A5 have been identified as potential prognostic markers for breast cancer (Zhao et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, the role of SLC7A11 in breast cancer has not been fully elucidated and requires further investigation.\u003c/p\u003e \u003cp\u003eThe main goal of this study was to utilize a bioinformatics technique to evaluate and investigate the relationship between SLC7A11 and breast cancer. There have been explicit outcomes in breast cancer patients utilizing bioinformatics techniques. First, we focused on breast cancer to analyze SLC7A11 expression and patient prognosis in the TCGA cohort. We subsequently constructed a protein‒protein interaction (PPI) network related to SLC7A11 and explored the genes co-expressed with SLC7A11 by enrichment analysis. Additionally, we investigated the relationships between positively correlated genes co-expressed with SLC7A11 and patient prognosis and investigated the associations of SLC7A11 with relevant signaling pathways, immune cell infiltration, immune checkpoints, and drug resistance in breast cancer. Given that SLC7A11 is an amino acid transporter and a key gene in ferroptosis, this study enhances our understanding of SLC7A11 in breast cancer and its importance in comprehensive therapy.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Data collection and processing\u003c/h2\u003e \u003cp\u003eIn this study, RNA-sequencing data and clinical information on SLC7A11 related to 33 human cancers were downloaded from The Cancer Genome Atlas (TCGA) and genotype tissue expression (GTEx) databases. SLC7A11 expression levels were examined across cancers via the TCGA and GTEx databases. Specific clinical information on SLC7A11 expression in breast cancer patients, including molecular subtypes, TNM stages, clinical stages and different racial types, was mined from the TCGA database only.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Prognostic value of SLC7A11 in breast cancer\u003c/h2\u003e \u003cp\u003eRNA-sequencing expression (level 3) profiles and corresponding clinical information for SLC7A11 were downloaded from TCGA-BRCA. OS, PFS, DFS, and DSS were compared between the high and low SLC7A11 expression groups via Kaplan‒Meier (KM) curves generated via the survival and Survminer packages in R by the log-rank test. The x-axis represents time (such as years, months, etc.), whereas the y-axis represents the cumulative risk of adverse prognostic events (such as death, disease recurrence, etc.). HR (High exp) represents the hazard ratio of the low-expression sample relative to the high-expression sample. HR\u0026thinsp;\u0026gt;\u0026thinsp;1 indicates that the SLC7A11 gene is a risk factor, and HR\u0026thinsp;\u0026lt;\u0026thinsp;1 indicates that the SLC7A11 gene is a protective factor. 95% CI represents the HR confidence interval.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. PPI network construction and identification of genes co-expressed with SLC7A11\u003c/h2\u003e \u003cp\u003eWe constructed a protein‒protein interaction network (PPI) of SLC7A11 in breast cancer via String, a free biological database. SLC7A11 co-expressed genes were mined via cBioportal for cancer genomics via the TCGA. Furthermore, the correlation between SLC7A11 co-expressed genes and the prognosis of breast cancer patients was tested via Spearman analysis to detect whether the co-expressed genes affected the overall survival of cancer patients, and volcano plots and forest plots were generated.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Enrichment analysis\u003c/h2\u003e \u003cp\u003eCo-expressed genes whose expression was significantly positively correlated with that of the SLC7A11 gene were selected for Gene Ontology (GO) (biological process (BP), cellular component (CC) and molecular function (MF)) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment were performed on genes that were positively coexpressed with SLC7A11 via the R package cluster Profiler. The results were considered statistically significant if the p value was \u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Analysis of immune infiltration in breast cancer associated with SLC7A11\u003c/h2\u003e \u003cp\u003eCibersort is a deconvolution method that characterizes the cell composition of complex tissues from their gene expression profiles. TIMER is an interactive web tool that enables comprehensive and flexible analysis and visualization of tumor-infiltrating immune cells to infer the abundance of tumor-infiltrating immune cells from the gene expression profiles of different cancer types in TCGA. We utilized these 2 tools to analyze immune infiltration levels in breast cancer. Eight commonly known immune checkpoints were used to investigate the relationships between immune checkpoints and SLC7A11 in breast cancer.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Drug sensitivity analysis in the cancer database\u003c/h2\u003e \u003cp\u003eAnalysis of the relationship between SLC7A11 and the half-maximal inhibitory concentration (IC50) of drugs was performed through Genomics of Drug Sensitivity in Cancer 2 (GDSC2) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cancerrxgene.org/\u003c/span\u003e\u003cspan address=\"https://www.cancerrxgene.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to obtain the IC50 differences and relevance between high and low SLC7A11 expression groups in breast cancer. The analytical process was implemented by R package pRophetic.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7. Statistical analysis\u003c/h2\u003e \u003cp\u003eAll the statistical analysis methods and R packages were executed in R language (foundation for statistical computing 2020) version 4.0.3. Additionally, we defined the p value as follows: ns, not significant; *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; and ****p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001. Significant differences between the two groups were compared through the Wilcoxon test, and significant differences among the three groups were tested with the Kruskal‒Wallis test.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Abnormal expression of SLC7A11 in breast cancer\u003c/h2\u003e \u003cp\u003eThis study explored the relationship between breast cancer and the SLC7A11 gene through a detailed analysis of data from the TCGA and GTEx databases. We examined the differential expression of SLC7A11 across 33 types of human cancers. Notably, SLC7A11 expression was significantly elevated in 23 tumor types (ACC, BRCA, CESC, CHOL, COAD, DLBC, ESCA, GBM, HNSC, KICH, KIRC, KIRP, LGG, LIHC, LUAD, LUSC, OV, PAAD, PRAD, READ, STAD, UCEC, UCS). However, two cancers (PCPG and SARC) presented relatively high SLC7A11 expression, whereas the remaining ten cancers, such as BLCA and LAML, presented no significant differences compared with normal adjacent tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). TCGA database analysis revealed more pronounced differential expression of SLC7A11 than did combined database analysis. Interestingly, there were no differences in expression levels across the three different racial groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB, Supplementary Fig.\u0026nbsp;1, S1D). Moreover, SLC7A11 expression varies significantly among the three breast cancer subtypes but does not differ significantly across the four clinical characteristics (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC, Supplementary Fig.\u0026nbsp;1, S1A). Further analysis based on TNM staging revealed that SLC7A11 expression was not influenced by tumor size (T) or lymph node involvement (N). Nevertheless, a significant increase in SLC7A11 was observed in patients without distant metastasis (M0) compared with those with distant metastasis (M1) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD, Supplementary Fig.\u0026nbsp;1, S1B-C). These findings highlight the potential importance of SLC7A11 in breast cancer.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Prognostic value analysis of SLC7A11 in breast cancer\u003c/h2\u003e \u003cp\u003eThe Kaplan‒Meier (KM) platform was used to examine the prognostic value of the SLC7A11 gene in breast cancer. For patients with non-metastatic breast cancer (M0) who had high SLC7A11 expression, the Kaplan\u0026ndash;Meier (KM) curves indicated poor outcomes in 4 survival indices: overall survival (OS, p\u0026thinsp;=\u0026thinsp;0.0335), progression-free survival (PFS, p\u0026thinsp;=\u0026thinsp;0.0293), disease-free survival (DFS, p\u0026thinsp;=\u0026thinsp;0.0453), and disease-specific survival (DSS, p\u0026thinsp;=\u0026thinsp;0.0204) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-D); however, there was no statistically significant difference in prognosis between patients with different SLC7A11 expression levels and patients with distant metastatic breast cancer (M1) (Supplementary Fig.\u0026nbsp;2, S2). In conclusion, SLC7A11 may serve as a predictive gene marker for the prognosis of patients without distant metastasis (M0) in breast cancer.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3. PPI network construction and genes co-expressed with SLC7A11\u003c/h2\u003e \u003cp\u003eTo explore the potential molecular mechanisms of SLC7A11 in breast cancer, we constructed a protein‒protein interaction (PPI) network between SLC7A11 and other protein-coding genes using the STRING. The PPI network revealed that SLC7A11 was strongly correlated with SLC3A1, SLC3A2, SLC7A5, SLC7A6, GPX4, CD44, BECN1, ATF4, NFE2L2 and OTUB1 (Supplementary Fig.\u0026nbsp;3, S3). Using data mining techniques in cBioPortal, we identified 12,683 co-expressed genes that were positively and negatively correlated with SLC7A11. We performed Spearman correlation analysis and identified the top 20 genes that were most positively and negatively correlated with SLC7A11 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA-\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Additionally, the associations of co-expressed genes with prognosis value were explored for breast cancer patients with high SLC7A11 expression. The results revealed 1,158 genes significantly associated with OS, 370 genes significantly associated with PFS, 372 genes significantly associated with DFS, and 568 genes significantly associated with DSS. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the top 20 genes co-expressed with SLC7A11 related to the prognosis of breast cancer patients with volcano plots and forest plots, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Interestingly, two sets of co-expressed genes did not overlap, indicating that the genes most closely co-expressed with SLC7A11 differ from those most significantly associated with prognosis. This intriguing analysis suggested that while SLC7A11 is co-expressed with certain genes, those co-expressed genes might not directly influence the prognosis of breast cancer patients.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Enrichment analysis of genes co-expressed with SLC7A11 in breast cancer\u003c/h2\u003e \u003cp\u003eTo further explore the biological significance of genes co-expressed with SLC7A11 in breast cancer, we used the R package Cluster Profiler to perform GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analyses on the previous data of genes positively correlated with SLC7A11. We filtered genes with FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.1 and p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The genes co-expressed with SLC7A11 were positively correlated with 1,000 GO biological process (BP), 300 cellular component (CC), 310 molecular function (MF), and 27 KEGG pathways. A bar chart displayed the top 5 BPs, CCs, and MFs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). 4 Bubble charts presenting the top 20 GO-BP, CC, MF, and KEGG terms pathways for the SLC7A11 positively correlated with co-expressed genes in breast cancer patients individually (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD-\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG). GO analysis revealed that the main biological functions of the genes co-expressed with SLC7A11 were related to biosynthetic processes, including macromolecule biosynthesis, cellular macromolecule biosynthesis, and heterocycle biosynthesis. KEGG analysis revealed that the primary pathways enriched with genes co-expressed with SLC7A11 were related to RNA transport (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Relevance analysis between pathways and SLC7A11 in breast cancer\u003c/h2\u003e \u003cp\u003eTo investigate the correlation between SLC7A11 and various pathways in breast cancer, we conducted extensive pathway-related analyses. The first two results demonstrated that SLC7A11 is positively correlated with DNA replication (p\u0026thinsp;=\u0026thinsp;7.86e-10) and tumor proliferation (p\u0026thinsp;=\u0026thinsp;3.9e-14) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA-\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). As a key molecule in ferroptosis, SLC7A11 displayed a significant positive correlation with the ferroptosis pathway, as expected (p\u0026thinsp;=\u0026thinsp;3.06e-11) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). Additionally, as a valid member of the solute carrier transporter family, SLC7A11 was strongly correlated with common amino acid metabolism correlated with glutathione metabolism (p\u0026thinsp;=\u0026thinsp;0.045), and histidine metabolism (p\u0026thinsp;=\u0026thinsp;0.001) pathways, including D-glutamine and glutamate metabolism (p\u0026thinsp;=\u0026thinsp;2.03e-05), cysteine and methionine metabolism (p\u0026thinsp;=\u0026thinsp;4.91e-11) and lysine degradation (p\u0026thinsp;=\u0026thinsp;2.04e-16) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD-\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE, Supplementary Fig.\u0026nbsp;4, S4A). Conversely, SLC7A11 was negatively and tyrosine metabolism (p\u0026thinsp;=\u0026thinsp;2.42e-14) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF, Supplementary Fig.\u0026nbsp;4, S4B- S4C). Further analysis revealed that SLC7A11 was positively correlated with the P53 (p\u0026thinsp;=\u0026thinsp;0.001), MYC (p\u0026thinsp;=\u0026thinsp;1.39e-13), and PI3K-AKT-mTOR (p\u0026thinsp;=\u0026thinsp;2.09e-06) pathways. Finally, the associations between SLC7A11 and immune behavior in breast cancer patients suggested positive correlations with the inflammatory response (p\u0026thinsp;=\u0026thinsp;0.001), the IL-10 anti-inflammatory signaling pathway (p\u0026thinsp;=\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eJ-\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eL), and the G2M immune checkpoint in breast cancer.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.6. Association between SLC7A11 and immune cell infiltration in breast cancer\u003c/h2\u003e \u003cp\u003eCIBERSORT was used to analyze the infiltration of 22 immune cells in breast cancer samples with different SLC7A11 expression levels, and the results are presented in a heatmap (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). The infiltration levels of immune cells that included B-cell plasma, CD8\u0026thinsp;+\u0026thinsp;T-cells, CD4\u0026thinsp;+\u0026thinsp;memory-activated T-cells, follicular helper T-cells, resting NK cells, NK cells activation, M0 macrophages, M1 macrophages, M2 macrophages, mast cells activation, and neutrophils, significantly differed between high and low SLC7A11 expression in breast cancer (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). Additionally, we analyzed the TIMER database to determine the correlation between SLC7A11 expression and the infiltration of six immune infiltrating cell types. The results revealed that B cells and CD4\u0026thinsp;+\u0026thinsp;T cells were negatively correlated, whereas the other four were relatively positively correlated with SLC7A11 expression in breast cancer (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.7. Associations between SLC7A11 and immune checkpoints in breast cancer\u003c/h2\u003e \u003cp\u003eTo understand the immune landscape of breast cancer with different SLC7A11 expression level, we examined ten commonly immune checkpoints, namely, CD274, CTLA4, HAVCR2, IGSF8, ITPRIPL1, LAG3, PDCD1, PDCD1LG2, SIGLEC15 and TIGIT, to investigate the potential role of SLC7A11 in tumor immunity. The outcomes displayed that CD274, CTLA4, HAVCR2, LAG3, PDCD1LG2 and TIGIT expression levels were accumulated with high SLC7A11 group, whereas the expression of IGSF8 and SIGLEC15 decreased (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). We further conducted a correlation analysis between SLC7A11 and the eight immune checkpoints with statistically significant differences. When SLC7A11 expression was increased, the expression of six immune checkpoints (CD274, CTLA4, HAVCR2, LAG3, PDCD1LG2 and TIGIT) was positively correlated with SLC7A11 expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB-\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eG). In contrast, as SLC7A11 level increased, IGSF8 and SIGLEC15 were negatively correlated with SLC7A11 expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eH-\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eI). The correlation and differential expression results were consistent, suggesting a meaningful and potentially biological relationship between SLC7A11 and immune checkpoints in breast cancer.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.8. IC50 analysis of SLC7A11 in breast cancer\u003c/h2\u003e \u003cp\u003eFinally, the relationship between SLC7A11 and six commonly used drugs for the treatment of breast cancer were explored using chemotherapy sensitivity data from the GDSC database. The IC50 results demonstrated that higher SLC7A11 expression was associated with lower IC50 values, indicating higher target affinity and better therapeutic efficacy (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA-\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eF). To assess drug resistance in breast cancer, the IC50 of six drugs was validated in triple-negative breast cancer (TNBC) with differential expression of SLC7A11. The results revealed that the IC50 values of tamoxifen, docetaxel and paclitaxel were significantly lower in the high SLC7A11 expression group than in the low SLC7A11 expression group, while 5-fluorouracil and doxorubicin had relatively lower IC50 values in the high SLC7A11 expression group (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eG-\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eK). The IC50 of one chemotherapeutic drug, rapamycin, was not significantly different between the two cohorts (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eI). These findings suggested that commonly used drugs are comparably effective in treating TNBC patients with high SLC7A11 expression. SLC7A11 might be a promising target for reducing drug resistance in breast cancer patients.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn this study, we explored SLC7A11 expression, prognosis, potential co-expressed genes, enrichment analysis, pathway relevance, immune cell infiltration and preliminary IC50 analysis of traditional drugs for the treatment of breast cancer. Current studies have demonstrated that SLC7A11 varies greatly across various cancer types and was associated with poor prognosis, indicating that SLC7A11 has significant prognostic value. At present, the results of the pan-cancer analysis of SLC7A11 aligned with those of previous reports in the literature (Asleh et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The KM results for breast cancer prognosis also remained consistent with the expression levels, indicating poor OS, PFS, DFS, and DSS with high levels of SLC7A11. Whether in pancreatic carcinoma, hepatocellular carcinoma, colon cancer or lung adenocarcinoma, patients with high SLC7A11 expression have similar poor clinical prognoses (Zhu et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Liang et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Huang et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Qian et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Han et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Koppula et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). On the basis of the results of this in-depth study, SLC7A11 might serve as a prognostic biomarker for breast cancer, particularly patients without distant metastasis (M0).\u003c/p\u003e \u003cp\u003eFerroptosis, a form of programmed cell death that depends on iron and is distinct from apoptosis, is characterized by lipid peroxidation and the accumulation of reactive oxygen species (ROS) due to the inactivation of GPX4 (Jiang et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Compared with normal tissues, cancer cells present distinct metabolic processes, with metabolic alterations being a hallmark of these cells, exemplified by the Warburg effect (Brunner and Finley \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Glutamine metabolism is particularly important in breast cancer, as changes in glutamine synthesis and degradation significantly impact amino acid transporters (Yang et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Inhibiting glutamine metabolism selectively can enhance antitumor T-cell activity, particularly in triple-negative breast cancer, thereby increasing the immune system's antitumor response (Edwards et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). When the expression of glutamine synthetase (GS) is silenced, the metabolism of glutamine is reprogrammed, which leads to drug resistance in the ovarian cancer cell line A2780 (Guo et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Furthermore, the results revealed that SLC7A11 was positively correlated with the biosynthesis of glutamine and glutamate (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). Glutathione, which is synthesized from glycine, glutamate, and cysteine, relies on cysteine as the rate-limiting precursor. Glutathione converts lipid hydroperoxides into lipid alcohols, thereby reducing lipid peroxidation and preventing ferroptosis (Koppula et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Luo et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Our research consistently revealed that SLC7A11 is associated with ferroptosis and related amino acid metabolism pathways.\u003c/p\u003e \u003cp\u003eThe tumor microenvironment (TME) consists of various cell types recruited to tumor sites and influenced by the tumor, forming a peculiar environment distinct from that of normal tissues (Elhanani et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Immune cells within the TME, especially lymphocytes, also known as tumor-infiltrating lymphocytes (TILs), have a significant impact on tumor biological behaviors, including proliferation, migration, epithelial‒mesenchymal transition (EMT), apoptosis, and distant metastasis (Zheng et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Further analysis revealed that SLC7A11 was linked to immune responses and IL-10 expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eJ-\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eK). T cells, especially CD8\u0026thinsp;+\u0026thinsp;T cells, are the fighters in the immune system (St Paul and Ohashi \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Targeting ferroptosis-related genes in gastric cancer patients could deactivate CD4\u0026thinsp;+\u0026thinsp;T cells and enhance their immunotherapeutic effects (Yao et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Upregulation of SLC7A11 in breast cancer was associated with not only a decrease in CD8\u0026thinsp;+\u0026thinsp;T cells but also in activated NK cells and B-cell plasma, whereas activated CD4\u0026thinsp;+\u0026thinsp;T memory cells clearly increased, which suggested that SLC7A11 acted as a crucial player at the crossroads of tumor metabolic reprogramming and immunity. Its influence on immune cell populations, particularly the reduction in CD8\u0026thinsp;+\u0026thinsp;T cells and NK cells, highlighted its role in fostering immune evasion.\u003c/p\u003e \u003cp\u003eMacrophages, the most prevalent immune cells in the TME, could differentiate into either M1 (proinflammatory and antitumor) or M2 (anti-inflammatory and protumor) phenotypes, depending on various stimuli (Ma et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Hu et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In the immunological field of macrophage polarization, many advanced studies have confirmed that the repolarization of M2 macrophages can be accomplished, especially in the TME (Locati et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). A recent study revealed that certain microtubule-targeting agents, including vinblastine, colchicine, and paclitaxel, can convert the M2 phenotype into a phenotype resembling M1 macrophages (Wang et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e). Sun Hwa Kim et al. also demonstrated that M1 macrophage-derived exosomes could reprogram M2 macrophages into functional M1-like macrophages, resulting in dynamic changes among macrophages in the TME (Kim et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). An in vitro study indicated that Moringa oleifera leaf polysaccharide (MOLP) could induce the conversion of M2 macrophages to M1 macrophages by targeting TLR4 (Wang et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e). Our CIBERSORT and TIMER results revealed that with elevated SLC7A11 levels, macrophages exhibit characteristics of both M1 and M2 types rather than predominantly presenting as M2 in breast cancer. This dynamic polarization and repolarization indicated that SLC7A11 might play a regulatory role in macrophage behavior, influencing their function and contributing to the complexity of immune responses within the TME. This unexpected observation revealed the potential role of SLC7A11 in modulating macrophage polarization in breast cancer, leading to diverse immune responses and tumor interactions.\u003c/p\u003e \u003cp\u003eCD274 encoded programmed death-ligand 1 (PD-L1), which was often highly expressed in tumors, allowing them to evade immune detection through the PD-1/PD-L1 signaling pathway. Blocking this pivotal pathway has been shown to significantly reduce tumor growth and enhance the natural antitumor immune response, particularly through the activation of CD8\u0026thinsp;+\u0026thinsp;T cells (Yamaguchi et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; de Vries et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Similarly, CTLA4, which interacted with B7, played a role in preventing the activation of naive T cells, while the PD-1/PD-L1 (B7-H1) interaction could deplete effector T cells within the TME. Recent in vitro experiments have demonstrated that blocking CTLA-4 could increase immune cell infiltration and improve metabolic activity, especially in tumors with low glycolysis (Zappasodi et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Our study revealed that CD274 and CTLA4 levels were significantly elevated in tumors with high SLC7A11 expression, along with varying expression levels of other immune checkpoints. Furthermore, our correlation analysis between tumor mutational burden (TMB) and SLC7A11 highlighted the critical role of SLC7A11 in the TME and its potential impact on immunotherapy outcomes (Supplementary Fig.\u0026nbsp;5, S5). These findings suggested that SLC7A11 might serve as a sensitive biomarker for assessing the effectiveness of immunotherapy in breast cancer patients, although the efficacy of targeting SLC7A11 remains to be validated.\u003c/p\u003e \u003cp\u003eThe current treatment options for breast cancer are abundant and include systematic therapy, localized therapy, endocrine therapy, monoclonal therapy, immunotherapy, chemotherapy, and other novel treatments, which are usually used in combination with surgery (Rodrigues-Ferreira and Nahmias \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Despite enormous amendments in overall survival rates for patients with breast cancer, patients with poorly differentiated breast cancer, particularly triple-negative breast cancer (TNBC) patients, still face problems such as poor prognosis and drug resistance (Leon-Ferre and Goetz \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). TNBC patients were highly insensitive to most treatments, but in the TNBC cohort with high SLC7A11 expression, the IC50 values of tamoxifen and paclitaxel were significantly lower than those in the low-expression group (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). These interesting findings suggested that the presence of SLC7A11 might improve the resistance of TNBC to tamoxifen or paclitaxel.\u003c/p\u003e"},{"header":"5. Limitation","content":"\u003cp\u003eThis exploration deepened our understanding of the relationship between SLC7A11 and breast cancer, but its limitations are also distinct. First, all the results were solely based on bioinformatic analysis of public databases, and in vitro or in vivo experiments to support the results are lacking. Second, we did not explore the upstream genes or mRNAs of SLC7A11, leaving the upstream regulatory mechanisms of SLC7A11 unclear. Third, while our bioinformatic analysis suggested dynamic changes in macrophages within the tumor microenvironment (TME), these changes have not been experimentally validated, indicating the need for further exploration of macrophages in breast cancer. Finally, additional immune-related or clinical experiments are necessary to validate the relationship between SLC7A11 and immunotherapy.\u003c/p\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eTaken together, we cautiously demonstrated the overexpression and unfavorable prognostic value of SLC7A11 in multiple ways for breast cancer. Gathering results indicated that SLC7A11 was highly expressed in breast cancer and was associated with poor prognosis, particularly in patients without distant metastasis (M0). Additionally, we proposed that SLC7A11 played a significant role in regulating immune cells in the tumor microenvironment (TME), suggesting its potential as a tumor biomarker for effective immunotherapy. Specifically, combined therapies targeting CD274 and CTLA4 could offer new targeting points and directions for breast cancer immunotherapy.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eACC, adrenocortical\u0026nbsp;carcinoma;\u003c/p\u003e\n\u003cp\u003eBLCA, Bladder urothelial carcinoma;\u003c/p\u003e\n\u003cp\u003eBRCA, Breast invasive carcinoma;\u003c/p\u003e\n\u003cp\u003eCESC, cervical squamous cell carcinoma and endocervical adenocarcinoma;\u003c/p\u003e\n\u003cp\u003eCHOL,\u0026nbsp;cholangiocarcinoma;\u003c/p\u003e\n\u003cp\u003eCOAD, Colon adenocarcinoma;\u003c/p\u003e\n\u003cp\u003eDLBC, Diffuse large B-cell lymphoma;\u003c/p\u003e\n\u003cp\u003eESCA,\u0026nbsp;Esophageal carcinoma;\u003c/p\u003e\n\u003cp\u003eGBM,\u0026nbsp;Glioblastoma multiforme;\u003c/p\u003e\n\u003cp\u003eHNSC,\u0026nbsp;head\u0026nbsp;and\u0026nbsp;neck\u0026nbsp;squamous cell carcinoma;\u003c/p\u003e\n\u003cp\u003eKICH, Kidney chromophobe;\u003c/p\u003e\n\u003cp\u003eKIRC, Kidney renal clear cell carcinoma;\u003c/p\u003e\n\u003cp\u003eKIRP,\u0026nbsp;kidney\u0026nbsp;renal papillary cell carcinoma;\u003c/p\u003e\n\u003cp\u003eLAML, acute myeloid leukemia;\u003c/p\u003e\n\u003cp\u003eLGG,\u0026nbsp;Brain\u0026nbsp;lower grade glioma;\u003c/p\u003e\n\u003cp\u003eLIHC, Liver hepatocellular carcinoma;\u003c/p\u003e\n\u003cp\u003eLUAD,\u0026nbsp;Lung adenocarcinoma;\u003c/p\u003e\n\u003cp\u003e\u0026shy;\u0026shy;\u0026shy;\u0026shy;LUSC, Lung squamous cell carcinoma;\u003c/p\u003e\n\u003cp\u003eMESO,\u0026nbsp;Mesothelioma;\u003c/p\u003e\n\u003cp\u003eOV, ovarian serous cystadenocarcinoma;\u003c/p\u003e\n\u003cp\u003ePAAD,\u0026nbsp;Pancreatic adenocarcinoma;\u003c/p\u003e\n\u003cp\u003ePCPGs, pheochromocytomas and paragangliomas;\u003c/p\u003e\n\u003cp\u003ePRAD,\u0026nbsp;prostate\u0026nbsp;adenocarcinoma;\u003c/p\u003e\n\u003cp\u003eREAD, rectum adenocarcinoma;\u003c/p\u003e\n\u003cp\u003eSARC,\u0026nbsp;Sarcoma;\u003c/p\u003e\n\u003cp\u003eSTAD,\u0026nbsp;stomach\u0026nbsp;adenocarcinoma;\u003c/p\u003e\n\u003cp\u003eTGCTs, testicular germ cell tumors;\u003c/p\u003e\n\u003cp\u003eTHCA, Thyroid carcinoma;\u003c/p\u003e\n\u003cp\u003eTHYM,\u0026nbsp;Thymoma;\u003c/p\u003e\n\u003cp\u003eUCEC, uterine corpus endometrial carcinoma;\u003c/p\u003e\n\u003cp\u003eUCS,\u0026nbsp;uterine carcinosarcoma;\u003c/p\u003e\n\u003cp\u003eUVM, uveal melanoma.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by research grants from the National Natural Science Foundation of China (82103102 to C.Y.Y.; 82203602 to J.W.), the Zhejiang Provincial Natural Science Foundation of China under Grant No. LQ22H160020 to J.W. This work was also supported by start-up funding from Zhejiang Provincial People’s Hospital (ZRY2021A001 to J.W.), and the Basic Scientific Research Funds of the Department of Education of Zhejiang Province (KYQN202109 to J.W.).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003econtributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eX.J.X. wrote the original manuscript and prepared the figures. Y.C.Z. and L.W. wrote the methodology. Y.S., J.X.C., and T.L. organized the bioinformatic data. Y.Y.H., P.H.J. and Q.H.G. collected the bioinformatic data. C.Y.Y. and J.W. supervised the study and revised the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have read and approved the article for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data presented in this article are available in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompliance with Ethical Standards\u003c/strong\u003e\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003e\u003cstrong\u003eDisclosure of potential conflicts of interest\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eAll author states that there is no conflict of interest.\u003c/p\u003e\n\u003col start=\"2\"\u003e\n \u003cli\u003e\u003cstrong\u003eResearch involving human participants and/or animals\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAsleh K, Riaz N, Nielsen TO (2022) Heterogeneity of triple negative breast cancer: Current advances in subtyping and treatment implications. J Exp Clin Cancer Res 41 (1):265. doi:10.1186/s13046-022-02476-1\u003c/li\u003e\n\u003cli\u003eBrunner JS, Finley LWS (2023) Metabolic determinants of tumour initiation. Nat Rev Endocrinol 19 (3):134-150. doi:10.1038/s41574-022-00773-5\u003c/li\u003e\n\u003cli\u003ede Vries NL, van de Haar J, Veninga V, Chalabi M, Ijsselsteijn ME, van der Ploeg M, van den Bulk J, Ruano D, van den Berg JG, Haanen JB, Zeverijn LJ, Geurts BS, de Wit GF, Battaglia TW, Gelderblom H, Verheul HMW, Schumacher TN, Wessels LFA, Koning F, de Miranda N, Voest EE (2023) gammadelta T cells are effectors of immunotherapy in cancers with HLA class I defects. Nature 613 (7945):743-750. doi:10.1038/s41586-022-05593-1\u003c/li\u003e\n\u003cli\u003eEdwards DN, Ngwa VM, Raybuck AL, Wang S, Hwang Y, Kim LC, Cho SH, Paik Y, Wang Q, Zhang S, Manning HC, Rathmell JC, Cook RS, Boothby MR, Chen J (2021) Selective glutamine metabolism inhibition in tumor cells improves antitumor T lymphocyte activity in triple-negative breast cancer. J Clin Invest 131 (4). doi:10.1172/JCI140100\u003c/li\u003e\n\u003cli\u003eElhanani O, Ben-Uri R, Keren L (2023) Spatial profiling technologies illuminate the tumor microenvironment. Cancer Cell 41 (3):404-420. doi:10.1016/j.ccell.2023.01.010\u003c/li\u003e\n\u003cli\u003eGuo J, Satoh K, Tabata S, Mori M, Tomita M, Soga T (2021) Reprogramming of glutamine metabolism via glutamine synthetase silencing induces cisplatin resistance in A2780 ovarian cancer cells. BMC Cancer 21 (1):174. doi:10.1186/s12885-021-07879-5\u003c/li\u003e\n\u003cli\u003eHan L, Yan Y, Fan M, Gao S, Zhang L, Xiong X, Li R, Xiao X, Wang X, Ni L, Tong D, Huang C, Cao Y, Yang J (2022) Pt3R5G inhibits colon cancer cell proliferation through inducing ferroptosis by down-regulating SLC7A11. Life Sci 306:120859. doi:10.1016/j.lfs.2022.120859\u003c/li\u003e\n\u003cli\u003eHing JX, Mok CW, Tan PT, Sudhakar SS, Seah CM, Lee WP, Tan SM (2020) Clinical utility of tumour marker velocity of cancer antigen 15-3 (CA 15-3) and carcinoembryonic antigen (CEA) in breast cancer surveillance. Breast 52:95-101. doi:10.1016/j.breast.2020.05.005\u003c/li\u003e\n\u003cli\u003eHu B, Yin G, Sun X (2022) Identification of specific role of SNX family in gastric cancer prognosis evaluation. Sci Rep 12 (1):10231. doi:10.1038/s41598-022-14266-y\u003c/li\u003e\n\u003cli\u003eHu K, Li K, Lv J, Feng J, Chen J, Wu H, Cheng F, Jiang W, Wang J, Pei H, Chiao PJ, Cai Z, Chen Y, Liu M, Pang X (2020) Suppression of the SLC7A11/glutathione axis causes synthetic lethality in KRAS-mutant lung adenocarcinoma. J Clin Invest 130 (4):1752-1766. doi:10.1172/JCI124049\u003c/li\u003e\n\u003cli\u003eHuang H, Liu J, Wu H, Liu F, Zhou X (2021) Ferroptosis-associated gene SLC7A11 is upregulated in NSCLC and correlated with patient\u0026rsquo;s poor prognosis: An integrated bioinformatics analysis. Pteridines 32 (1):106-116. doi:10.1515/pteridines-2020-0034\u003c/li\u003e\n\u003cli\u003eJiang X, Stockwell BR, Conrad M (2021) Ferroptosis: mechanisms, biology and role in disease. Nat Rev Mol Cell Biol 22 (4):266-282. doi:10.1038/s41580-020-00324-8\u003c/li\u003e\n\u003cli\u003eKim H, Park HJ, Chang HW, Back JH, Lee SJ, Park YE, Kim EH, Hong Y, Kwak G, Kwon IC, Lee JE, Lee YS, Kim SY, Yang Y, Kim SH (2023) Exosome-guided direct reprogramming of tumor-associated macrophages from protumorigenic to antitumorigenic to fight cancer. Bioact Mater 25:527-540. doi:10.1016/j.bioactmat.2022.07.021\u003c/li\u003e\n\u003cli\u003eKoppula P, Zhuang L, Gan B (2021) Cystine transporter SLC7A11/xCT in cancer: ferroptosis, nutrient dependency, and cancer therapy. Protein Cell 12 (8):599-620. doi:10.1007/s13238-020-00789-5\u003c/li\u003e\n\u003cli\u003eLeon-Ferre RA, Goetz MP (2023) Advances in systemic therapies for triple negative breast cancer. BMJ 381:e071674. doi:10.1136/bmj-2022-071674\u003c/li\u003e\n\u003cli\u003eLi J, Liu L, Feng Z, Wang X, Huang Y, Dai H, Zhang L, Song F, Wang D, Zhang P, Ma B, Li H, Zheng H, Song F, Chen K (2020) Tumor markers CA15-3, CA125, CEA and breast cancer survival by molecular subtype: a cohort study. Breast Cancer 27 (4):621-630. doi:10.1007/s12282-020-01058-3\u003c/li\u003e\n\u003cli\u003eLiang Y, Su S, Lun Z, Zhong Z, Yu W, He G, Wang Q, Wang J, Huang S (2022) Ferroptosis regulator SLC7A11 is a prognostic marker and correlated with PD-L1 and immune cell infiltration in liver hepatocellular carcinoma. Front Mol Biosci 9:1012505. doi:10.3389/fmolb.2022.1012505\u003c/li\u003e\n\u003cli\u003eLin Y, Dong Y, Liu W, Fan X, Sun Y (2022) Pan-Cancer Analyses Confirmed the Ferroptosis-Related Gene SLC7A11 as a Prognostic Biomarker for Cancer. Int J Gen Med 15:2501-2513. doi:10.2147/IJGM.S341502\u003c/li\u003e\n\u003cli\u003eLocati M, Curtale G, Mantovani A (2020) Diversity, Mechanisms, and Significance of Macrophage Plasticity. Annu Rev Pathol 15:123-147. doi:10.1146/annurev-pathmechdis-012418-012718\u003c/li\u003e\n\u003cli\u003eLuo T, Wang Y, Wang J (2022) Ferroptosis assassinates tumor. J Nanobiotechnology 20 (1):467. doi:10.1186/s12951-022-01663-8\u003c/li\u003e\n\u003cli\u003eMa RY, Black A, Qian BZ (2022) Macrophage diversity in cancer revisited in the era of single-cell omics. Trends Immunol 43 (7):546-563. doi:10.1016/j.it.2022.04.008\u003c/li\u003e\n\u003cli\u003eQian L, Wang F, Lu SM, Miao HJ, He X, Feng J, Huang H, Shi RF, Zhang JG (2022) A Comprehensive Prognostic and Immune Analysis of Ferroptosis-Related Genes Identifies SLC7A11 as a Novel Prognostic Biomarker in Lung Adenocarcinoma. J Immunol Res 2022:1951620. doi:10.1155/2022/1951620\u003c/li\u003e\n\u003cli\u003eRodrigues-Ferreira S, Nahmias C (2022) Predictive biomarkers for personalized medicine in breast cancer. Cancer Lett 545:215828. doi:10.1016/j.canlet.2022.215828\u003c/li\u003e\n\u003cli\u003eSher G, Salman NA, Khan AQ, Prabhu KS, Raza A, Kulinski M, Dermime S, Haris M, Junejo K, Uddin S (2022) Epigenetic and breast cancer therapy: Promising diagnostic and therapeutic applications. Semin Cancer Biol 83:152-165. doi:10.1016/j.semcancer.2020.08.009\u003c/li\u003e\n\u003cli\u003eSt Paul M, Ohashi PS (2020) The Roles of CD8(+) T Cell Subsets in Antitumor Immunity. Trends Cell Biol 30 (9):695-704. doi:10.1016/j.tcb.2020.06.003\u003c/li\u003e\n\u003cli\u003eWang S, Hu Q, Chang Z, Liu Y, Gao Y, Luo X, Zhou L, Chen Y, Cui Y, Wang Z, Wang B, Huang Y, Liu Y, Liu R, Zhang L (2023a) Moringa oleifera leaf polysaccharides exert anti-lung cancer effects upon targeting TLR4 to reverse the tumor-associated macrophage phenotype and promote T-cell infiltration. Food Funct 14 (10):4607-4620. doi:10.1039/d2fo03685a\u003c/li\u003e\n\u003cli\u003eWang YN, Wang YY, Wang J, Bai WJ, Miao NJ, Wang J (2023b) Vinblastine resets tumor-associated macrophages toward M1 phenotype and promotes antitumor immune response. J Immunother Cancer 11 (8). doi:10.1136/jitc-2023-007253\u003c/li\u003e\n\u003cli\u003eXu F, Guan Y, Xue L, Zhang P, Li M, Gao M, Chong T (2021) The roles of ferroptosis regulatory gene SLC7A11 in renal cell carcinoma: A multi-omics study. Cancer Med 10 (24):9078-9096. doi:10.1002/cam4.4395\u003c/li\u003e\n\u003cli\u003eYamaguchi H, Hsu JM, Yang WH, Hung MC (2022) Mechanisms regulating PD-L1 expression in cancers and associated opportunities for novel small-molecule therapeutics. Nat Rev Clin Oncol 19 (5):287-305. doi:10.1038/s41571-022-00601-9\u003c/li\u003e\n\u003cli\u003eYang WH, Qiu Y, Stamatatos O, Janowitz T, Lukey MJ (2021) Enhancing the Efficacy of Glutamine Metabolism Inhibitors in Cancer Therapy. Trends Cancer 7 (8):790-804. doi:10.1016/j.trecan.2021.04.003\u003c/li\u003e\n\u003cli\u003eYao F, Zhan Y, Pu Z, Lu Y, Chen J, Deng J, Wu Z, Chen B, Chen J, Tian K, Ni Y, Mou L (2021) LncRNAs Target Ferroptosis-Related Genes and Impair Activation of CD4(+) T Cell in Gastric Cancer. Front Cell Dev Biol 9:797339. doi:10.3389/fcell.2021.797339\u003c/li\u003e\n\u003cli\u003eZappasodi R, Serganova I, Cohen IJ, Maeda M, Shindo M, Senbabaoglu Y, Watson MJ, Leftin A, Maniyar R, Verma S, Lubin M, Ko M, Mane MM, Zhong H, Liu C, Ghosh A, Abu-Akeel M, Ackerstaff E, Koutcher JA, Ho PC, Delgoffe GM, Blasberg R, Wolchok JD, Merghoub T (2021) CTLA-4 blockade drives loss of T(reg) stability in glycolysis-low tumours. Nature 591 (7851):652-658. doi:10.1038/s41586-021-03326-4\u003c/li\u003e\n\u003cli\u003eZhao X, Jin L, Liu Y, Liu Z, Liu Q (2022) Bioinformatic analysis of the role of solute carrier-glutamine transporters in breast cancer. Ann Transl Med 10 (14):777. doi:10.21037/atm-22-2620\u003c/li\u003e\n\u003cli\u003eZheng L, Qin S, Si W, Wang A, Xing B, Gao R, Ren X, Wang L, Wu X, Zhang J, Wu N, Zhang N, Zheng H, Ouyang H, Chen K, Bu Z, Hu X, Ji J, Zhang Z (2021) Pan-cancer single-cell landscape of tumor-infiltrating T cells. Science 374 (6574):abe6474. doi:10.1126/science.abe6474\u003c/li\u003e\n\u003cli\u003eZhu JH, De Mello RA, Yan QL, Wang JW, Chen Y, Ye QH, Wang ZJ, Tang HJ, Huang T (2020) MiR-139-5p/SLC7A11 inhibits the proliferation, invasion and metastasis of pancreatic carcinoma via PI3K/Akt signaling pathway. Biochim Biophys Acta Mol Basis Dis 1866 (6):165747. doi:10.1016/j.bbadis.2020.165747\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":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Breast cancer, SLC7A11, Survival analysis, Immune infiltrating cells (TILs)","lastPublishedDoi":"10.21203/rs.3.rs-5847099/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5847099/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSLC7A11, a key factor protecting cancer cells from oxidative stress, is upregulated and shows prognostic significance in amounts of malignant tumors largely while its role still remains indistinct in breast cancer. We conduct an explicit analysis of the gene SLC7A11 based on The Cancer Genome Atlas (TCGA) for breast cancer patients. Subsequently the co-expressed genes of SLC7A11 are identified. On account of the previous exploration of SLC7A11 thoroughly, we assess the immune infiltrating cell populations and immune checkpoints in breast cancer to unveil the complexity of tumor microenvironment (TME) by Cibersort and Tumor Infiltrating Estimation Resources (TIMER). We continue to convey a further preliminary investigation into the drug resistance of breast cancer. Comparing to the normal tissue, SLC7A11 is significantly expressed in breast cancer, and its differential expression is evidential in patients without distant metastasis (M0). Elevated expression of SLC7A11 is associated with notable changes in the tumor microenvironment (TME) for breast cancer patients, including a decreased presence of CD8\u0026thinsp;+\u0026thinsp;T cells and activated natural killer (NK) cells. Additionally, there is an increase in immune checkpoint such as CD274, CTLA4, HAVCR2, LAG3, PDCD1LG2 and TIGIT. This modulation in the immune landscape corresponds with improved sensitivity to conventional breast cancer treatments. Our comprehensive analysis confirms that SLC7A11 is a dependable tumor biomarker, offering valuable insights for the development of targeted therapies in breast cancer.\u003c/p\u003e","manuscriptTitle":"Research of SLC7A11 to estimate the prognosis and immune infiltration landscape for breast cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-04 09:03:12","doi":"10.21203/rs.3.rs-5847099/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-03-04T10:42:42+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-02T00:38:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"59954282042481885843156741480245263018","date":"2025-02-26T05:32:01+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-02-18T17:39:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"76338053837646741025284057291553967551","date":"2025-02-12T18:41:47+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-02-06T07:06:18+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-02-03T05:51:06+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-01-30T10:53:36+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Oncology","date":"2025-01-17T07:42:29+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2c7a79bf-ccea-4aeb-8c89-0e76b0d9dd3b","owner":[],"postedDate":"February 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-08-26T10:23:51+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-04 09:03:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5847099","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5847099","identity":"rs-5847099","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}