Immune Cells In Breast Cancer Research Articles

Prognostic value of PLEKHA4 and its correlation with tumor-infiltrating immune cells in breast cancer: a comprehensive study based on bioinformatics and clinical analysis validation.

Pleckstrin homology containing family A, number 4 (PLEKHA4) plays a role in a number of biological processes in human cells, including cell polarization, growth, and proliferation. However, the relationship between PLEKHA4 expression and survival in breast cancer (BC) remains unclear. The aim of this study is to investigate the potential of PLEKHA4 as a prognostic indicator in BC. We obtained gene expression profiles of BC and normal tissues from the Tumor Immune Estimation Resource (TIMER), UALCAN web. Immunohistochemistry (IHC) staining was performed to investigate the protein expression and prognostic value of PLEKHA4 in BC patients. The prognostic value was analyzed using Kaplan-Meier curve analysis and Cox regression analysis in R software after downloading The Cancer Genome Atlas (TCGA) databases. The correlations between PLEKHA4 and tumor immune infiltrates were investigated via gene set variation analysis (GSVA). Signaling pathways related to PLEKHA4 expression were identified by the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Both bioinformatics and IHC results showed that PLEKHA4 was expressed at low levels in BC tissues compared with the adjacent tissues. Furthermore, the expression of PLEKHA4 was negatively correlated with ages (χ2=6.394, P=0.01), molecular subtype (χ2=15.606, P=0.001), lymph node metastasis (χ2=13.753, P=0.004), tumor-node-metastasis (TNM) stage (χ2=22.616, P<0.001). Kaplan-Meier curves implicated low expression of PLEKHA4 was associated with worse survival of BC patients [hazard ratio (HR) =0.46, P=0.01]. Cox regression models showed that low PLEKHA4 expression could be an independent risk factor for BC (HR =0.911, P=0.006). The results of gene set enrichment analysis (GSEA) showed that cell cycle, Notch signaling pathway, nuclear factor erythroid 2-related factor 2 (NRF2) signaling pathway, and Rho GTPases were highly enriched in the low PLEKHA4 expression group, as identified by GO and KEGG. Additionally, in BC, PLEKHA4 expression displayed a positive correlation with the infiltration of natural killer (NK) cells (P<0.001), CD8+ T cells (P<0.001), B cells (P<0.001), neutrophils (P<0.001), and dendritic cells (DCs) (P<0.001). The findings indicate that PLEKHA4 is an independent prognostic biomarker associated with key signaling pathways and immune infiltration in BC. Targeting PLEKHA4 may contribute to improving immunotherapy for BC.

Abstract 6191: Simultaneous prediction of tumor microenvironment biomarkers from pathology slides using multi-task deep regression

Abstract Understanding the Tumor Microenvironment (TME) is crucial for tailoring personalized cancer treatments and improving patient outcomes. Using deep learning, we can predict properties of the TME from standard Hematoxylin and Eosin (H&amp;E) stained slides, including specific gene expression signatures. While past models were limited to single signature predictions, we explore the potential of multi-task deep learning to identify multiple biomarkers at once, allowing more complex relationships and interactions to be modeled simultaneously. Our study focuses on the prediction of expression patterns of tumor, stromal, and immune cells in breast cancer (BC). We developed a multi-task transformer-based deep regression model, trained in a weakly-supervised setting, to predict biomarkers in the TME from routine H&amp;E-stained pathology slides of a BC cohort (n=876). The performance was evaluated using Pearson’s correlation coefficient (r) and spatial heatmaps generated via gradient-weighted class activation mapping. Our model achieved a significant correlation coefficient above 0.40 (p&amp;lt;0.0001) for all predicted TME biomarkers in the holdout test set (n=176). This includes the biomarkers lymphocyte infiltrating signature score (r=0.46, p&amp;lt;0.0001), tumor infiltrating lymphocyte regional fraction (r=0.48, p&amp;lt;0.0001), leukocyte fraction (r=0.43, p&amp;lt;0.0001), stromal fraction (r=0.42, p&amp;lt;0.0001), and tumor cell proliferation (r=0.47, p&amp;lt;0.0001). Notably, the model's heatmaps highlighted the pertinent cells and histological characteristics for its predictions. These data show that our deep learning model accurately predicts multiple TME biomarkers concurrently from routine pathology slides. This approach offers a promising avenue for cost-effective and efficient biomarker quantification in the TME, with potential applicability across various cancer types. Citation Format: Omar S. El Nahhas, Marta Ligero, Jakob N. Kather. Simultaneous prediction of tumor microenvironment biomarkers from pathology slides using multi-task deep regression [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6191.

Integrated analysis reveals FLI1 regulates the tumor immune microenvironment via its cell-type-specific expression and transcriptional regulation of distinct target genes of immune cells in breast cancer

BackgroundImmunotherapy is a practical therapeutic approach in breast cancer (BRCA), and the role of FLI1 in immune regulation has gradually been unveiled. However, the specific role of FLI1 in BRCA was conflicted; thus, additional convincing evidence is needed.MethodsWe explored the upstream regulation of FLI1 expression via summary data-based Mendelian randomization (SMR) analysis and ncRNA network construction centering on FLI1 using BRCA genome-wide association study (GWAS) summary data with expression quantitative trait loci (eQTLs) and DNA methylation quantitative trait loci (mQTLs) from the blood and a series of in silico analyses, respectively. We illuminated the downstream function of FLI1 in immune regulation by integrating a series of analyses of single-cell RNA sequence data (scRNA-seq).ResultsWe verified a causal pathway from FLI1 methylation to FLI1 gene expression to BRCA onset and demonstrated that FLI1 was downregulated in BRCA. FLI1, a transcription factor, served as myeloid and T cells’ communication regulator by targeting immune-related ligands and receptor transcription in BRCA tissues. We constructed a ceRNA network centering on FLI1 that consisted of three LncRNAs (CKMT2-AS1, PSMA3-AS1, and DIO3OS) and a miRNA (hsa-miR-324-5p), and the expression of FLI1 was positively related to a series of immune-related markers, including immune cell infiltration, biomarkers of immune cells, and immune checkpoints.ConclusionLow-methylation-induced or ncRNA-mediated downregulation of FLI1 is associated with poor prognosis, and FLI1 might regulate the tumor immune microenvironment via a cell-type-specific target genes manner in BRCA.

The NDV-MLS as an Immunotherapeutic Strategy for Breast Cancer: Proof of Concept in Female Companion Dogs with Spontaneous Mammary Cancer.

The absence of tumor-infiltrating lymphocytes negatively impacts the response to chemotherapy and prognosis in all subtypes of breast cancer. Therapies that stimulate a proinflammatory environment may help improve the response to standard treatments and also to immunotherapies such as checkpoint inhibitors. Newcastle disease virus (NDV) shows oncolytic activity, as well as immune modulating potential, in the treatment of breast cancer in vitro and in vivo; however, its potential to enhance tumor-infiltrating immune cells in breast cancer has yet to be evaluated. Since spontaneous canine mammary tumors represent a translational model of human breast cancer, we conducted this proof-of-concept study, which could provide a rationale for further investigating NDV-MLS as immunotherapy for mammary cancer. Six female companion dogs with spontaneous mammary cancer received a single intravenous and intratumoral injection of oncolytic NDV-MLS. Immune cell infiltrates were evaluated by histology and immunohistochemistry in the stromal, intratumoral, and peritumoral compartments on day 6 after viral administration. Increasing numbers of immune cells were documented post-viral treatment, mainly in the peritumoral compartment, where plasma cells and CD3+ and CD3-/CD79- lymphocytes predominated. Viral administration was well tolerated, with no significant adverse events. These findings support additional research on the use of NDV-MLS immunotherapy for mammary cancer.

YTHDF1 shapes “cold” tumor and inhibits CD8+ T cells infiltration and function in breast cancer

While YTH N6-methyladenosine RNA binding protein 1 (YTHDF1) was recognized as a crucial contributor in the development and immune-related regulation of various types of tumors, its function in the immune response of breast cancer has largely remained uninvestigated. Through analysis of public databases, we found YTHDF1 as a highly expressed gene in breast cancers and confirmed this finding in breast cancer cells and clinical specimens from our center. Subsequently, we examined the link between YTHDF1 expression and immune cells and molecules by utilizing immune-related public databases and algorithm. We further validated our findings through cellular and animal experiments, as well as RNA sequencing. YTHDF1 was found highly expressed in tumor tissues of breast cancer, which negatively correlated with patient survival. The downregulation of YTHDF1 promoted the expression of pro-inflammatory markers and improved the anti-cancer ability of immune cells in breast cancer. RNA sequencing analysis revealed that YTHDF1 knockdown resulted in enrichment of differential genes in signal transduction pathways. Additionally, in vitro experiments showed that immune cells had higher cytotoxicity against breast cancer cells with decreased YTHDF1 expression. Moreover, in vivo studies indicated that YTHDF1 promoted breast cancer growth while inhibiting CD8+ T cell infiltration and function. Our study demonstrates that YTHDF1 plays a crucial role in establishing a “cold” tumor microenvironment in breast cancer by inhibiting the release of pro-inflammatory cytokines from cancer cells. As a result, the infiltration and functional differentiation of anti-tumor CD8+ T cells are hindered, ultimately resulting in the immune evasion of breast cancer.

CCL20 is a potential therapeutic target associated with immune infiltration in breast cancer.

CCL20 is a chemotactic factor that is involved in immune cell recruitment and cancer progression. However, the role of CCL20 in the prognosis of breast cancer remains unclear. This study analyzed correlations between CCL20 expression and immune infiltration, clinicopathological parameters, and prognosis in breast cancer patients. Correlations between CCL20 expression and clinicopathological parameters, prognosis, and immune infiltration in breast cancer were determined using the TIMER, UALCAN, and PrognoScan databases. Furthermore, gene-gene and protein-protein interactions were determined using GeneMANIA and STING network construction, respectively. CCL20 expression was significantly upregulated in breast cancer and had significant associations with clinicopathological features, including race, sex, age, menopause status, cancer stage, cancer subclass, and nodal metastasis; moreover, patients with higher CCL20 expression exhibited poor prognosis. Meanwhile, CCL20 expression was significantly correlated with the infiltration of immune cells in breast cancer, including monocytes, neutrophils, tumor-associated macrophages, Th1 cells, regulatory T cells, and exhausted T cells. Moreover, the network of CCL20 expression showed the majority genes and proteins were associated with immune reactions. CCL20 is a prognosis-related biomarker in breast cancer on the basis of its correlation with immune infiltration levels and has potential to also be a therapeutic target.

The prognostic significance of KLRB1 and its further association with immune cells in breast cancer.

Killer cell lectin-like receptor B1 (KLRB1) is an important member of the natural killer cell gene family. This study explored the potential value of KLRB1 as a breast cancer (BC) biomarker and its close association with the tumor immune microenvironment during the development of BC. We examined the differential expression of KLRB1 in pan-cancer. Clinical and RNA-Seq data from BC samples were evaluated in The Cancer Genome Atlas (TCGA) and validated in Gene Expression Omnibus (GEO) datasets and by immunohistochemistry (IHC) staining. The relationship between KLRB1 and clinical parameters was explored through Chi-square tests. The diagnostic value of KLRB1 was evaluated using a receiver operating characteristic (ROC) curve. Survival analysis was tested by Kaplan-Meier curves to demonstrate the relationship between KLRB1 and survival. Univariable and multivariate cox regression analyses were carried out as well. The analysis of immune infiltration level and gene set enrichment analysis (GSEA) were conducted to examine KLRB1's mechanism during the progression of BC. We used the Tumor Immune Estimation Resource (TIMER), the Cancer Single-cell Expression Map (CancerSCEM) database, the Tumor Immune Single-cell Hub (TISCH) database, and the Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) method to explore KLRB1's association with immune infiltration level and different quantitative distribution of immune cells. The relevant signaling pathways in BC associated with KLRB1 were identified using GSEA. The expression of KLRB1 was downregulated across the majority of cancers including BC. The lower KLRB1 expression group exhibited shorter relapse free survival (RFS) and overall survival (OS). IHC staining showed that KLRB1 staining was weaker in breast tumor tissues than in paratumors. Additionally, GSEA identified several pathway items distinctly enriched in BC. KLRB1 expression level was also positively related to the infiltrating number of immune cells in BC. Moreover, the CancerSCEM and TISCH databases as well as the CIBERSORT method demonstrated the close relationship between KLRB1 and immune cells, particularly macrophages. Low KLRB1 expression was considered an independent prognostic biomarker and played an important role in the tumor immune microenvironment of BC patients.

Comprehensive analysis of exosomal circRNA-miRNA-mRNA network in breast cancer.

Exosomal circRNAs played critical roles in tumor development and progression and might be novel biomarkers in the diagnosis and treatment of various cancers. However, the biological functions and clinical implications of exosomal circRNAs in breast cancer are unclear. Expression profiles of exosomal circRNAs in exoRBase 2.0 database were used to identify differentially expressed exosomal circRNAs in breast cancer. The LASSO and SVM-RFE algorithm followed by multivariate logistic regression analysis were performed to construct the diagnostic model. The target genes of circRNAs were selected by combing differential expression analysis and CSCD, TargetScan and ENCORI database. Univariate and multivariate survival analysis were conducted to construct a survival-associated exosomal circRNA-miRNA-mRNA network. GSVA and CIBERSORT algorithm were used to evaluate the cancer hallmarks and immune cells in breast cancer and spearman correlation analysis was used to investigate their correlations with the circRNA-miRNA-mRNA network. A total of 347 upregulated and 3 downregulated exosomal circRNAs were identified in breast cancer patients. The diagnostic model basing on 14 exosomal circRNAs showed a high area under the curve (AUC) value in both the training (AUC = 0.98) and validation dataset (AUC = 0.94). A total of 70 miRNAs and 1147 mRNAs were selected as the downstream targets of circRNAs and were revealed to participate into tumor-associated pathways including PI3K-AKT, MAPK, RAS and RAP1 pathways, calcium signaling pathways and transcriptional misregulations. The constructed survival-associated exosomal circRNA-miRNA-mRNA network contained 9 exosomal circRNAs, 12 miRNAs and 10 mRNAs and showed complicated correlations and interactions within networks. Cancer hallmark pathways including TGF-β, KRAS and MYC signaling pathways, tumor angiogenesis, epithelial-mesenchymal transition, DNA repair and G2M checkpoint and immune cells including CD4+ and CD8+ T cells, dendritic cells, mast cells, macrophage cells, memory B cells and natural killer cells were closely correlated with the circRNA-miRNA-mRNA network. Our study was the first to systematically analyze the exosomal circRNAs in breast cancer. We established an exosomal circRNA diagnostic model and constructed a survival-associated exosomal circRNA-miRNA-mRNA network. Our results revealed the complicated functions and potential mechanisms of exosomal circRNA-miRNA-mRNA network in breast cancer which need to be further validated in the future.

Construction of a fatty acid metabolism-related gene signature for predicting prognosis and immune response in breast cancer.

Background: Breast cancer has the highest incidence among malignant tumors in women, and its prevalence ranks first in global cancer morbidity. Aim: This study aimed to explore the feasibility of a prognostic model for patients with breast cancer based on the differential expression of genes related to fatty acid metabolism. Methods: The mRNA expression matrix of breast cancer and paracancer tissues was downloaded from The Cancer Genome Atlas database. The differentially expressed genes related to fatty acid metabolism were screened in R language. The TRRUST database was used to predict transcriptional regulators related to hub genes and construct an mRNA-transcription factor interaction network. A consensus clustering approach was used to identify different fatty acid regulatory patterns. In combination with patient survival data, Lasso and multivariate Cox proportional risk regression models were used to establish polygenic prognostic models based on fatty acid metabolism. The median risk score was used to categorize patients into high- and low-risk groups. Kaplan-Meier survival curves were used to analyze the survival differences between both groups. The Cox regression analysis included risk score and clinicopathological factors to determine whether risk score was an independent risk factor. Models based on genes associated with fatty acid metabolism were evaluated using receiver operating characteristic curves. A comparison was made between risk score levels and the fatty acid metabolism-associated genes in different subtypes of breast cancer. The differential gene sets of the Kyoto Encyclopedia of Genes and Genomes for screening high- and low-risk populations were compared using a gene set enrichment analysis. Furthermore, we utilized CIBERSORT to examine the abundance of immune cells in breast cancer in different clustering models. Results: High expression levels of ALDH1A1 and UBE2L6 prevented breast cancer, whereas high RDH16 expression levels increased its risk. Our comprehensive assessment of the association between prognostic risk scoring models and tumor microenvironment characteristics showed significant differences in the abundance of various immune cells between high- and low-risk breast cancer patients. Conclusions: By assessing fatty acid metabolism patterns, we gained a better understanding of the infiltration characteristics of the tumor microenvironment. Our findings are valuable for prognosis prediction and treatment of patients with breast cancer based on their clinicopathological characteristics.

Low expression of HIF1AN accompanied by less immune infiltration is associated with poor prognosis in breast cancer.

Hypoxia-inducible factor 1-alpha (HIF-1α) stability and transcriptional action are reduced by the hypoxia-inducible factor 1-alpha subunit suppressor (HIF1AN). Its inappropriate expression is associated with the development of cancer and immune control. It is yet unknown how HIF1AN, clinical outcomes, and immune involvement in breast cancer (BC) are related. Using the GEPIA, UALCAN, TIMER, Kaplan-Meier plotter, and TISIDB datasets, a thorough analysis of HIF1AN differential expression, medical prognosis, and the relationship between HIF1AN and tumor-infiltrating immune cells in BC was conducted. Quantitative real-time PCR (qRT-PCR) analysis of BC cells were used for external validation. The findings revealed that, as compared to standard specimens, BC cells had significantly lower levels of HIF1AN expression. Good overall survival (OS) for BC was associated with higher HIF1AN expression. Additionally, in BC, the expression of HIF1AN was closely associated with the chemokines and immune cell infiltration, including neutrophils, macrophages, T helper cells, B cells, Tregs, monocytes, dendritic cells, and NK cells. A high correlation between HIF1AN expression and several immunological indicators of T-cell exhaustion was particularly revealed by the bioinformatic study. HIF1AN is a predictive indicator for breast tumors, and it is useful for predicting survival rates.

Low expression of NR1H3 correlates with macrophage infiltration and indicates worse survival in breast cancer.

Background: Nuclear receptor NR1H3 is a key regulator of macrophage function and lipid homeostasis. Here, we aimed to visualize the prognostic value and immunological characterization of NR1H3 in breast cancer. Methods: The expression pattern and prognostic value of NR1H3 were analyzed via multiple databases, including TIMER2, GEPIA2 and Kaplan-Meier Plotter. TISIDB, TIMER2 and immunohistochemical analysis were used to investigate the correlation between NR1H3 expression and immune infiltration. GO enrichment analysis, KEGG analysis, Reactome analysis, ConsensusPathDB and GeneMANIA were used to visualize the functional enrichment of NR1H3 and signaling pathways related to NR1H3. Results: We demonstrated that the expression of NR1H3 was significantly lower in breast cancer compared with adjacent normal tissues. Kaplan-Meier survival curves showed shorter overall survival in basal breast cancer patients with low NR1H3 expression, and poorer prognosis of relapse-free survival in breast cancer patients with low NR1H3 expression. NR1H3 was mainly expressed in immune cells, and its expression was closely related with infiltrating levels of tumor-infiltrating immune cells in breast cancer. Additionally, univariate and multivariate analysis indicated that the expression of NR1H3 and the level of macrophage infiltration were independent prognostic factors for breast cancer. Gene interaction network analysis showed the function of NR1H3 involved in regulating of innate immune response and macrophage activation. Moreover, NR1H3 may function as a predictor of chemoresponsiveness in breast cancer. Conclusion: These findings suggest that NR1H3 serves as a prognostic biomarker and contributes to the regulation of macrophage activation in breast cancer.

CD47 expression and tumor-associated immune cells in breast cancer and their correlation with molecular subtypes and prognostic factors.

Breast cancer is the most common malignancy in women and a heterogeneous disease at the molecular level. Since most breast cancer cases are not of a special type, it is suggested that tumor-associated macrophages and tumor-infiltrating lymphocytes, which are involved in tumor growth, invasion, angiogenesis, and metastasis, may be important factors that should be evaluated together with standard criteria to determine the prognosis of cancer and assist in treatment decisions and outcome stratification. In this study, CD47 expression, which is involved in macrophage-mediated immune escape, tumor-infiltrating lymphocytes, and tumor-associated macrophages were evaluated in breast cancer molecular subgroups and correlated with prognostic factors. The immunohistochemistry of CD47, CD163, and CD3 was analyzed on the tissue microarrays of 278 invasive breast cancer cases. The CD47, CD163, and CD3 expressions were found to be correlated with various clinicopathological parameters in breast cancer. High levels of CD47, CD163, and CD3 expressions had a significant correlation with the ER status and PR status, Ki-67 proliferation index, and molecular subtype (P<0.05). The CD47 expression had a significant correlation with the CD3 and CD163 expressions (p=0.021 and p=0.001, respectively). Our results suggest that CD47, CD163, and CD3 may be among the prognostic factors of breast cancer. The combined use of CD47, CD163, and CD3 can be a new prognostic factor for patients with breast cancer, especially as a therapeutic target in hormone receptor-negative breast cancer cases and those with a high proliferation index.

FN1 overexpression is correlated with unfavorable prognosis and immune infiltrates in breast cancer.

Objective: To investigate the correlation of fibronectin 1 (FN1) expression with prognosis and tumor-infiltrating immune cells in breast cancer (BRCA). Methods: FN1 mRNA and protein expressions were analyzed through Tumor Immune Estimation Resource (TIMER), Gene Set Cancer Analysis (GSCA), Human Protein Atlas (HPA) databases, and immunohistochemical analysis. The clinicopathological characteristics and genetic factors affecting the FN1 mRNA expression were assessed by various public databases. Then, we analyzed the prognostic value of FN1 in BRCA by Kaplan-Meier plotter, receiver operating characteristic, and Cox regression analyses. Further, the UCSC Xena database was used to retrieve TCGA-BRCA expression profiles for functional enrichment analysis and immune cell infiltration analysis. The potential drugs for the BRCA patients with high- FN1 expression were identified using the connectivity map analysis. Results: FN1 was upregulated in BRCA tissues compared with normal tissues. High FN1 mRNA expression was correlated with poor clinical outcomes and had good performance in predicting the survival status of BRCA patients. Further, Cox regression analysis showed that FN1 was an independent prognostic factor for predicting the overall survival of patients with BRCA. Moreover, hypermethylation of FN1 contributed to a better prognosis for BRCA patients. Functional enrichment analyses revealed the ECM-receptor interaction pathway and focal adhesion as the common pathways. Moreover, FN1 showed a significant association with tumor-infiltrating immune cells and immune checkpoint inhibitors. Several drugs such as telmisartan, malotilate, and seocalcitol may have therapeutic effects in BRCA patients with high FN1 expression. Conclusion: FN1 might serve as a novel prognostic biomarker and a novel therapeutic target for BRCA. Besides, the association of FN1 with immune cells and immune checkpoint inhibitors may provide assistance for BRCA treatment.

CLEC10A can serve as a potential therapeutic target and its level correlates with immune infiltration in breast cancer.

Breast cancer (BC) is one of the most common malignant cancers in females worldwide and greatly threatens women's health. The C-type lectin domain family 10 member A (CLEC10A) is a member of the C-type lectin receptor family that has been previously reported to promote the antitumor activity of immune cells. In the present study, the potential prognostic value of CLEC10A expression in BC was assessed using data from The Cancer Genome Atlas online database. Differences in the mRNA expression levels of CLEC10A between BC and normal tissues were then analyzed using the Tumor Immune Estimation Resource (TIMER) platform and the University of Alabama at Birmingham Cancer data analysis portal. Reverse transcription-quantitative PCR was performed to validate the results of this analysis. The Kaplan-Meier plotter database was used to evaluate the association between the mRNA expression levels of CLEC10A and clinical prognosis of BC. Based on the association between the mRNA expression levels of CLEC10A and the tumor immune microenvironment, the TIMER platform and the Tumor and Immune System Interaction Database website were utilized to assess the correlation between CLEC10A expression and the degree of tumor immune cell infiltration. The present study revealed that CLEC10A expression was significantly lower in BC tissues compared with that in normal tissues, which was in turn associated with poorer clinical outcomes. This suggested that lower CLEC10A expression levels were associated with unfavorable prognosis in BC. In addition, the expression level of CLEC10A was found to be positively associated with the level of different tumor-infiltrating immune cells in BC, including CD8 T cells, B cells, macrophages and NK cells which, was in turn closely correlated with some gene markers such as CD19, CD8A, KIR2DS4 and PTGS2. These results suggest that the relationship between lower CLEC10A expression level and poor prognosis in BC may be due to the role of CLEC10A in the tumor immune microenvironment. In conclusion, CLEC10A may be a potential biomarker that can be used to efficiently predict prognosis in patients with BC.

Radiation Induced Lymphopenia Is Associated With the Effective Dose to the Circulating Immune Cells in Breast Cancer.

BackgroundLymphopenia is a known significant factor for treatment outcome in cancer patients, with underlying risk factor poorly understood in breast cancer. We hypothesize that the effective dose to the circulating immune cells (EDIC) which was related with lymphopenia in lung cancer will also have significant effect for radiation induced lymphopenia (RIL) in patients with breast cancer.Material and MethodsPatients treated with adjuvant radiotherapy (RT) and with complete blood tests within one week from RT end/start (post/preRT) were eligible in this study. Radiation dosimetric factors were collected retrospectively, and EDIC for each patient was calculated based on the doses to lung, heart and total body according to the model description, as previously reported. RIL was defined by the CTCAE5.0 based on postRT peripheral lymphocyte count (PLC). Linear regression was first used to test the correlation between EDIC with post/preRT PLC ratio and postRT PLC, using all these as continuous variables. Normal tissue complication probability (NTCP) was used to develop models that predict the CTCAE graded RIL from EDIC.ResultsA total of 735 patients were eligible. The mean post/preRT PLC ratio was 0.66 (95% CI: 0.64-0.68) and mean EDIC of breast cancer was 1.70Gy (95% CI: 1.64-1.75). Both post/preRT PLC ratio and postRT PLC were significantly correlated with EDIC (P<0.001), with R2 of 0.246. For patients with normal preRT PLC, the post/preRT PLC ratio was better associated with EDIC, and postRT PLC was expressed as PLCpreRT × (0.89 – 0.16 × EDIC). For patients with preRT lymphopenia, postRT PLC was better associated with EDIC and it was 1.1 – 0.17 × EDIC. Using binned EDIC as the dose variable, the bootstrap validated NTCPs fit the data nicely with R2 of 0.93, 0.96, and 0.94 for grade-1, grade-2, and grade-3 RIL, respectively. The corresponding EDIC to induce 50% of grade-1, grade-2 and grade-3 RIL was 1.2, 2.1 and 3.7 Gy, respectively.ConclusionEDIC is a significant factor for RIL in patients with breast cancer, and may be used to compute the risk of lymphopenia in each individual patient with the use of the conventional NTCP modeling. External validation is needed before the EDIC can be used to guide RT plan.

Screening of Immune-Related Genes and Predicting the Immunotherapeutic Effects of Formononetin in Breast Cancer: A Bioinformatics Analysis.

Objective Immunotherapy is a promising breast cancer treatment. Nonetheless, tumor heterogeneity and the interaction between immune cells in the tumor microenvironment limit its effectiveness. Formononetin—extracted from the Chinese medicinal plant Astragalus membranaceus—can inhibit tumor growth, induce apoptosis and angiogenesis, and reverse multidrug resistance. However, its efficacy and mechanism of action on the immune cells in breast cancer remain unclear. Here, we screened immune-related genes of breast cancer to determine the potential of formononetin as a therapeutic. Methods GSE103512 and GSE139038 breast cancer microarray data and immune-related gene data were obtained from the GEO and ImmPort databases, respectively, to analyze the differentially expressed immune-related genes (IRGs) in breast cancer tissues compared with normal breast tissues. Protein-protein interaction (PPI) analysis was performed using the STRING database to screen differentially expressed IRGs based on the topological parameters. The Kaplan–Meier test was applied to detect differentially expressed IRGs associated with breast cancer survival, and the interaction of formononetin with differentially expressed IRGs was analyzed using molecular docking. Finally, the relationship between differentially expressed IRGs and breast cancer immune cell infiltration was analyzed using the TIMER2.0 database. Results A total of 29 differentially expressed IRGs of breast cancer were screened through GEO and ImmPort databases and 10 key differentially expressed IRGs based on the topological parameters from the PPI network. Among these, CXCL12, ESR1, IGF1, and FOS were associated with breast cancer survival. Furthermore, IGF1, ESR1, and CXCL12 were found to have stable binding sites for formononetin. These genes were associated with substantial immune cell infiltration in breast cancer tissues. Conclusion In conclusion, formononetin may exert antitumor effects by acting on CXCL12, ESR1, and IGF1 and may have a potential synergistic effect with immune checkpoint inhibitors.

Systematic analysis of the expression and prognostic value of ITPR1 and correlation with tumor infiltrating immune cells in breast cancer

BackgroundITPR1 is a key gene for autophagy, but its biological function is still unclear, and there are few studies on the correlation between ITPR1 gene expression and the occurrence and development of breast cancer.MethodsAnalyze the expression of ITPR1 through online databases such as Oncomine and TIMER. Kaplan–Meier plotter and other databases were used to evaluate the impact of ITPR1 on clinical prognosis. The expression of ITPR1 in analysis of 145 cases of breast cancer and 30 cases of adjacent normal tissue was detected by Immunohistochemistry. Statistical analysis was used to evaluate the clinical relevance and prognostic significance of abnormally expressed proteins. And the Western Blot was used to detect the expression of ITPR1 between breast cancer tissues and cells. The TIMER database studied the relationship between ITPR1 and cancer immune infiltration. And used the ROC plotter database to predict the response of ITPR1 to chemotherapy, endocrine therapy and anti-HER2 therapy in patients with breast cancer.ResultsCompared with normal breast samples, ITPR1 was significantly lower in patients with breast cancer. And the increased expression of ITPR1 mRNA was closely related to longer overall survival (OS), distant metastasis free survival (DMFS), disease specific survival (DSS) and relapse free survival (RFS) in breast cancer. And the expression level of ITPR1 was higher in patients treated with chemotherapy than untreated patients. In addition, the expression of ITPR1 was positively correlated with related gene markers of immune cells in different types of breast cancer, especially with BRCA basal tissue breast cancer.ConclusionITPR1 was lower expressed in breast cancer. The higher expression of ITPR1 suggested favorable prognosis for patients. ITPR1 was related to the level of immune infiltration, especially in BRCA-Basal patients. All research results indicated that ITPR1 might affect breast cancer prognosis and participate in immune regulation. In short, ITPR1 might be a potential target for breast cancer therapy.

Increased Expression of QPRT in Breast Cancer Infers a Poor Prognosis and Is Correlated to Immunocytes Infiltration.

Breast cancer (BRCA) is a class of highly heterogeneous tumors. There is a positive correlation between the overall survival of BRCA and immune infiltration of the tumor microenvironment. QPRT is a rarely reported cancer gene, and the underlying mechanism is poorly understood. Based on TCGA data, the role that QPRT plays in BRCA is evaluated in this study. This study used GEPIA to analyze the expression of QPRT in BRCA and, based on the survival module, assessed the impact of QPRT on the survival of patients with BRCA. Furthermore, this study collected the BRCA data set from TCGA and, through utilizing logistic regression, discussed the relationship between QPRT expression and clinical information. Cox regression analysis was used to obtain clinicopathological features relating to the total survival rate of patients with TCGA. Besides, based on the “correlation” and CIBERSORT module, the relationship between cancer immune infiltration and QPRT was analyzed in GEPIA. Tumor status, pathological staging, and lymph nodes have an obvious correlation with the rise of QPRT expression according to the logistic regression univariate analysis. In this analysis, QPRT is expressed as a categorical-dependent variable (median expression value is 2.5). Furthermore, based on multivariate analysis, independent factors for favorable prognosis include negative pathological stage, increased QPRT expression, and remote metastasis. Among them, CIBERSORT analysis found that the increase in QPRT expression will increase with the growth of the level of immune infiltration of neutrophils, B cells, T cells, and mast cells. In addition, the “correlation” module using GEPIA was used to confirm. Taking all factors into consideration, the rise in QPRT expression is related to a good prognosis and a grown proportion of immune cells in BRCA, such as neutrophils, B cells, mast cells, and T cells. These results suggest that QPRT can be used to be a possible biological indicator to evaluate the immune infiltration level of BRCA and its prognosis.

Landscape Analysis of Matrix Metalloproteinases Unveils Key Prognostic Markers for Patients With Breast Cancer.

Breast cancer (BRCA) is the most common cancer in the world, of which incidence rate and mortality are the highest in women. Being responsible for the remodeling and degradation of extracellular matrix proteins, matrix metalloproteinases (MMPs) have been regarded as one of the most important protease family related to tumorigenesis. It has been demonstrated that MMPs play crucial roles in some tumor invasion and metastasis. However, the potential roles of MMPs in tumorigenesis and progression of BRCA and its subtype remain elusive. Herein, we conducted a systematic study on MMPs via a series of database-based retrospective analysis, including TCGA, R Studio, GEPIA, Kaplan-Meier Plotter, cBioPortal, STRING, GeneMANIA and TIMER. As a result, many MMP family members were differentially expressed in patients with BRCA, e.g., the expressions of MMP1, MMP9, MMP11 and MMP13 were up-regulated, whereas the expression levels of MMP19 and MMP28 were down-regulated. MMP9, MMP12, MMP15 and MMP27 were significantly correlated with the clinical stages of BRCA, implying their important roles in the occurrence and development of BRCA. In addition, the survival analysis indicated that different expression pattern of MMPs exhibited distinct outcomes in patient with BRCA, e.g., patients with high expression of MMP2, MMP8, MMP16, MMP17, MMP19, MMP20, MMP21, MMP24, MMP25, MMP26 and MMP27 had a prolonged survival time, while the others (MMP1, MMP7, MMP9, MMP12 and MMP15) exhibited poor prognosis. Subsequent functional and network analysis revealed MMPs were mainly correlated with parathyroid hormone synthesis and secretion pathway, collagen metabolism, and their effect on the activities of serine hydrolase, serine peptidase and aminopeptidase. Notably, our analysis showed that the expression of MMPs was significantly correlated with the infiltration of various immune cells in BRCA, including CD8+T cells, CD4+T cells, macrophages, neutrophils, B cells, and dendritic cells, suggesting the close correlations between MMPs and immune functions. In short, our study disclosed MMPs play multiple biological roles in the development of BRCA, MMP1 and MMP9 might be used as independent prognostic markers and potential therapeutic targets for diagnosis and treatment for patients with BRCA.

High expression of eIF4E is associated with tumor macrophage infiltration and leads to poor prognosis in breast cancer

BackgroundThe expression and activation of eukaryotic translation initiation factor 4E (eIF4E) is associated with cell transformation and tumor initiation, but the functional role and the mechanism whereby it drives immune cell infiltration in breast cancer (BRCA) remain uncertain.MethodsOncomine, Timer and UALCAN were used to analyze the expression of eIF4E in various cancers. PrognoScan, Kaplan–Meier plotter, and GEPIA were utilized to analyze the prognostic value of eIF4E in select cancers. In vitro cell experiments were used to verify the role of eIF4E in promoting the progression of BRCA. ImmuCellAI and TIMER database were used to explore the relationship between eIF4E and tumor infiltrating immune cells. The expression of a macrophage marker (CD68+) and an M2-type marker (CD163+) was evaluated using immunohistochemistry in 50 invasive BRCA samples on tissue microarrays. The Human Protein Atlas (HPA) database was used to show the expression of eIF4E and related immune markers. LinkedOmics and NetworkAnalyst were used to build the signaling network.ResultsThrough multiple dataset mining, we found that the expression of eIF4E in BRCA was higher than that in normal tissues, and patients with increased eIF4E expression had poorer survival and a higher cumulative recurrence rate in BRCA. At the cellular level, BRCA cell migration and invasion were significantly inhibited after eIF4E expression was inhibited by siRNA. Immune infiltration analysis showed that the eIF4E expression level was significantly associated with the tumor purity and immune infiltration levels of different immune cells in BRCA. The results from immunohistochemical (IHC) staining further proved that the expression of CD68+ and CD163+ were significantly increased and correlated with poor prognosis in BRCA patients (P < 0.05). Finally, interaction network and functional enrichment analysis revealed that eIF4E was mainly involved in tumor-related pathways, including the cell adhesion molecule pathway and the JAK-STAT signaling pathway.ConclusionsOur study has demonstrated that eIF4E expression has prognostic value for BRCA patients. eIF4E may act as an essential regulator of tumor macrophage infiltration and may participate in macrophage M2 polarization.