Identification of FPR3 as a unique biomarker for targeted therapy in the immune microenvironment of breast cancer

2020 ◽  
Author(s):  
Jian Qi ◽  
Yu Liu ◽  
Jiliang Hu ◽  
Li Lu ◽  
Zhen Dou ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Network Analysis ◽  
Poor Prognosis ◽  
Functional Module ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Ppi Network ◽  
Immune Microenvironment ◽  
Tumor Stages ◽  
Immune Score

Abstract Background Immunotherapy is in the ascendant, but its use in the treatment of breast cancer remains limited. Thus, identification and evaluation of prognostic biomarkers of tissue microenvironment will reveal new immune-based therapeutic strategies for breast cancer. Methods Using in silico bioinformatic approach, we investigated the tumor microenvironmental and genetic factors related to breast cancer. We calculated the Immune score, Stromal score, Estimate score, Tumor purity, Tumor mutation burden (TMB), Mutant-allele tumor heterogeneity (MATH) of breast cancer patients from the Cancer Genome Atlas (TCGA) using the ESTIMATE algorithm and Maftools. Weighted correlation network analysis (WGCNA) was used to identify gene patterns association with the Immune score. Then we use the MCODE plugin of Cytoscape to analyze the protein-protein interaction (PPI) network for mining the functional gene modules. Survival and Cox analysis was further performed to identify the key prognostic targets in immune microenvironment. Gene set enrichment analysis (GSEA) was utilized to explore the carcinogenic pathways associated with the target genes. Results Significant correlations between Immune/Stromal scores with breast cancer subtypes and tumor stages were established. Importantly, we found that the Immune score, but not the Stromal score, was significantly related to the patient's prognosis. WGCNA identified a pattern of gene function associated with Immune score, and that almost all of these genes (388 genes) are significantly upregulated in the higher Immune score group. PPI network analysis revealed the enrichment of immune checkpoint genes in the functional module but predicting a good prognosis by survival analysis. Among all the upregulated genes, FPR3, a G protein-coupled receptor essential for neutrophil activation, is the sole factor that predicts poor prognosis. GSEA analysis showed FRP3 upregulation synergizes with the activation of many pathways involved in carcinogenesis. Conclusions This study identified FPR3 as a key immune-related biomarker predicting a poor prognosis for breast cancer, revealing it as a promising targetable gene for immunotherapy.

scholarly journals Identification of FPR3 as a Unique Biomarker for Targeted Therapy in the Immune Microenvironment of Breast Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Jian Qi ◽  
Yu Liu ◽  
Jiliang Hu ◽  
Li Lu ◽  
Zhen Dou ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Network Analysis ◽  
Poor Prognosis ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Breast Cancer Patients ◽  
Cancer Subtypes ◽  
Tumor Stages ◽  
Immune Score

Although research into immunotherapy is growing, its use in the treatment of breast cancer remains limited. Thus, identification and evaluation of prognostic biomarkers of tissue microenvironments will reveal new immune-based therapeutic strategies for breast cancer. Using an in silico bioinformatic approach, we investigated the tumor microenvironmental and genetic factors related to breast cancer. We calculated the Immune score, Stromal score, Estimate score, Tumor purity, TMB (Tumor mutation burden), and MATH (Mutant-allele tumor heterogeneity) of Breast cancer patients from the Cancer Genome Atlas (TCGA) using the ESTIMATE algorithm and Maftools. Significant correlations between Immune/Stromal scores with breast cancer subtypes and tumor stages were established. Importantly, we found that the Immune score, but not the Stromal score, was significantly related to the patient's prognosis. Weighted correlation network analysis (WGCNA) identified a pattern of gene function associated with Immune score, and that almost all of these genes (388 genes) are significantly upregulated in the higher Immune score group. Protein-protein interaction (PPI) network analysis revealed the enrichment of immune checkpoint genes, predicting a good prognosis for breast cancer. Among all the upregulated genes, FPR3, a G protein-coupled receptor essential for neutrophil activation, is the sole factor that predicts poor prognosis. Gene set enrichment analysis analysis showed FRP3 upregulation synergizes with the activation of many pathways involved in carcinogenesis. In summary, this study identified FPR3 as a key immune-related biomarker predicting a poor prognosis for breast cancer, revealing it as a promising intervention target for immunotherapy.

scholarly journals RNA N6-Methyladenosine Regulators Contribute to Tumor Immune Microenvironment and Have Clinical Prognostic Impact in Breast Cancer

2022 ◽  
Vol 12 ◽  
Author(s):  
Lan-Xin Mu ◽  
You-Cheng Shao ◽  
Lei Wei ◽  
Fang-Fang Chen ◽  
Jing-Wei Zhang
Keyword(s):  
Breast Cancer ◽  
Immune Cell ◽  
Risk Model ◽  
Cox Regression ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Prognostic Impact ◽  
Immune Microenvironment ◽  
Model Based ◽  
Tumor Immune Microenvironment

Purpose: This study aims to reveal the relationship between RNA N6-methyladenosine (m6A) regulators and tumor immune microenvironment (TME) in breast cancer, and to establish a risk model for predicting the occurrence and development of tumors.Patients and methods: In the present study, we respectively downloaded the transcriptome dataset of breast cancer from Gene Expression Omnibus (GEO) database and The Cancer Genome Atlas (TCGA) database to analyze the mutation characteristics of m6A regulators and their expression profile in different clinicopathological groups. Then we used the weighted correlation network analysis (WGCNA), the least absolute shrinkage and selection operator (LASSO), and cox regression to construct a risk prediction model based on m6A-associated hub genes. In addition, Immune infiltration analysis and gene set enrichment analysis (GSEA) was used to evaluate the immune cell context and the enriched gene sets among the subgroups.Results: Compared with adjacent normal tissue, differentially expressed 24 m6A regulators were identified in breast cancer. According to the expression features of m6A regulators above, we established two subgroups of breast cancer, which were also surprisingly distinguished by the feature of the immune microenvironment. The Model based on modification patterns of m6A regulators could predict the patient’s T stage and evaluate their prognosis. Besides, the low m6aRiskscore group presents an immune-activated phenotype as well as a lower tumor mutation load, and its 5-years survival rate was 90.5%, while that of the high m6ariskscore group was only 74.1%. Finally, the cohort confirmed that age (p < 0.001) and m6aRiskscore (p < 0.001) are both risk factors for breast cancer in the multivariate regression.Conclusion: The m6A regulators play an important role in the regulation of breast tumor immune microenvironment and is helpful to provide guidance for clinical immunotherapy.

Association of Th2 high tumors with aggressive features of breast cancer.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e12584-e12584
Author(s):  
Yoshihisa Tokumaru ◽  
Lan Le ◽  
Masanori Oshi ◽  
Eriko Katsuta ◽  
Nobuhisa Matsuhashi ◽  
...  
Keyword(s):  
Breast Cancer ◽  
T Cells ◽  
Cancer Progression ◽  
Immune Cells ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Th2 Cells ◽  
Immune Microenvironment ◽  
Adaptive Immune Cells ◽  
Tumor Immune Microenvironment

e12584 Background: Recent studies have shown that infiltrating T-lymphocytes have been implicated in the promotion of breast cancer progression. Upon activation, these antigen-presenting cells then recruit adaptive immune cells. It has been proposed that polarization of CD4+ effector T-cells towards the immunosuppressive Th2 cells induce cytokine release and T-cell anergy, which lead to polarization of M2 tumor-associated macrophages (TAM’s), providing a protumorigenic microenvironment. We hypothesized that there is a correlation between high levels of Th2 cells and aggressive features of breast cancer and unfavorable tumor immune environment. Methods: Clinicopathological data and overall survival information was obtained on 1069 breast cancer patients from The Cancer Genome Atlas (TCGA) database. We defined Th2 high and low levels with the median cutoff. Results: Analysis of cell composition of the immune cells within tumor immune microenvironment demonstrated that Th2 high tumors did not consistently associated with unfavorable tumor immune microenvironment. Pro-cancer immune cells, such as macrophage M2 cells were increased with Th2 high tumors whereas, regulatory T cells were decreased with Th2 high tumors (p < 0.01 and p < 0.001 respectively). On the contrary, infiltration of anti-cancer cells, such as macrophage M1 was increased whereas CD8 T cells were decreased with Th2 high tumors (p < 0.05 and p < 0.001 respectively). Th2 was not shown to have correlation with IL-4, IL-6, IL-10 and IL-13, all of which has been reported to associate with Th2 cells. Th2 levels were associated with advanced grades. Also, correlation analysis demonstrated that there was a strong correlation between Th2 levels and Ki-67. These results were further validated with gene set enrichment analysis (GSEA). GSEA revealed that in Th2 high tumors enriched the gene sets associated with cell proliferation and cell cycle. Conclusions: High expression of immunosuppressive Th2 cells was associated with highly proliferative features of breast cancer, but not with unfavorable tumor immune microenvironment.

scholarly journals LncRNA ST7-AS1 is a Potential Novel Biomarker and Correlated With Immune Infiltrates for Breast Cancer

2021 ◽  
Vol 8 ◽  
Author(s):  
Ziwen Zhang ◽  
Han Zhang ◽  
Dongbo Li ◽  
Xiaoping Zhou ◽  
Jinlu Wang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cell Cycle ◽  
Dna Repair ◽  
Noncoding Rna ◽  
Poor Prognosis ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Her2 Status

Background: Long noncoding RNA (lncRNA) ST7-AS1 can be observed in various cancers, but its role in breast cancer (BRC) remains unclear. Our aim is to, on the basis of The Cancer Genome Atlas (TCGA) database, prove the correlation between lncRNA ST7-AS1 and BRC.Methods: The lncRNA ST7-AS1 expression and its roles in the prognosis of BRC were explored using data from the TCGA database. The expression level of lncRNA ST7-AS1 in BRC samples was detected using RT-PCR. The 1-, 3-, or 5-year survival rate was predicted using a nomogram established through Cox proportional hazard regression. At last, the biological function was explored through gene ontology (GO) analysis and gene set enrichment analysis (GSEA). The hallmark pathways significantly involved in hub genes were described through functional enrichment analysis. The correlation between lncRNA ST7-AS1 expression and immune infiltration was analyzed through single-sample GSEA (ssGSEA).Results: LncRNA ST7-AS1 expression was downregulated in BRC. Decreased lncRNA ST7-AS1 expression in BRC was correlated with advanced clinical pathologic characteristics (high grade, histological type, age, menopause status, and HER2 status), survival time, and poor prognosis. The nomogram was established for using lncRNA ST7-AS1 to predict 1-, 3-, or 5-year survival in patients with BRC. In addition, GO and pathway analyses suggested the involvement of lncRNA ST7-AS1 in cell cycle, DNA repair, and immune cell infiltration in the BRC immune microenvironment. We found the correlation of lncRNA ST7-AS1 with T helper cells and DC cells.Conclusion: Low expression of lncRNA ST7-AS1 indicates poor prognosis and has an impact on cell cycle, DNA repair, and proportion of infiltrating immune cells in the BRC microenvironment. Therefore, lncRNA ST7-AS1 can be used as a protective prognostic marker and a potential treatment target for BRC.

scholarly journals m6A RNA Methylation Regulator YTHDF1 Correlated With Immune Microenvironment Predicts Clinical Outcomes and Therapeutic Efficacy in Breast Cancer

2021 ◽  
Vol 8 ◽  
Author(s):  
Ying Hu ◽  
Qinwen Pan ◽  
Minghao Wang ◽  
Xiang Ai ◽  
Yuzhao Yan ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Mrna Levels ◽  
Somatic Variation ◽  
Immune Microenvironment ◽  
Normal Tissues ◽  
Monocytes And Macrophages ◽  
M6a Rna Methylation

Objective: Increasing evidence highlights the roles of N6-methyladenosine (m6A) and its regulators in oncogenesis. Herein, this study observed the associations of m6A regulators with breast cancer.Methods: RNA-seq profiles of breast cancer were retrieved from the Cancer Genome Atlas (TCGA) database. The expression of m6A regulators was analyzed in tumor and normal tissues. Their expression correlations were analyzed by Spearson test. Overall survival (OS) analysis of these regulators was then presented. Gene set enrichment analysis (GSEA) was performed in high and low YTHDF1 expression groups. The correlations of YTHDF1 expression with immune cells and tumor mutation burden (TMB) were calculated in breast cancer samples. Somatic variation was assessed in high and low YTHDF1 expression groups.Results: Most of m6A regulators were abnormally expressed in breast cancer compared to normal tissues. At the mRNA levels, there were closely relationships between them. Among them, YTHDF1 up-regulation was significantly related to undesirable prognosis (p = 0.025). GSEA results showed that high YTHDF1 expression was associated with cancer-related pathways. Furthermore, YTHDF1 expression was significantly correlated with T cells CD4 memory activated, NK cells activated, monocytes, and macrophages. There were higher TMB scores in YTHDF1 up-regulation group than its down-regulation group. Missense mutation and non-sense mutation were the most frequent mutation types.Conclusion: Our findings suggested that dysregulated m6A regulator YTHDF1 was predictive of survival outcomes as well as response to immunotherapy of breast cancer, and were closely related to immune microenvironment.

scholarly journals Exploring the Pyroptosis-related Genes of Breast Cancer and their Effects on Tumor Immune Microenvironment

2021 ◽  
Author(s):  
Congli Jia ◽  
Fu Yang ◽  
Ruining Li
Keyword(s):  
Breast Cancer ◽  
High Risk ◽  
Prognostic Model ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Immune Microenvironment ◽  
Tumor Immune Microenvironment

Abstract Background: Breast cancer （BC） is the most common cancer among women, with high rates of metastasis and recurrence. Some studies have confirmed that pyroptosis is an immune-related programmed cell death. However, the correlation between the expression of pyroptosis-related genes in BC and its prognosis remains unclear. Methods: In this study, we identified 38 pyroptosis-related genes that were differentially expressed between BC and normal tissues. The prognostic value of each pyroptosis-related gene was evaluated using patient data from The Cancer Genome Atlas (TCGA). The Cox regression method was performed to establish a prognostic model for 16-gene signature, classifying all BC patients in the TCGA database into a low-or high-risk group. Results: The survival rate of BC patients in the high-risk group was significantly lower than that in the low-risk group (P＜0.01). Prognostic model is independent prognostic factor for BC patients compared to clinical features. Single sample gene set enrichment analysis (ssGSEA) showed a decrease for immune cells and immune function in the high-risk group. Conclusions: Pyroptosis-related genes influence the tumor immune microenvironment and can predict the prognosis of BC.

scholarly journals PSMB5 is associated with proliferation and drug resistance in triple-negative breast cancer

2017 ◽  
Vol 33 (1) ◽  
pp. 102-108 ◽  
Author(s):  
Wensong Wei ◽  
Yufeng Zou ◽  
Qihua Jiang ◽  
Zhibin Zhou ◽  
Haolong Ding ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Triple Negative Breast Cancer ◽  
Poor Prognosis ◽  
Triple Negative ◽  
Functional Module ◽  
Enrichment Analysis ◽  
Chemotherapeutic Agents ◽  
Gene Set Enrichment Analysis ◽  
Disease Stage ◽  
Cancer Subtypes

Background: Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer, characterized by advanced disease stage and poor prognosis. Moreover, due to the lack of therapeutic markers, TNBC patients can’t benefit fully from currently available targeted therapies. Methods: To fully understand the molecular basis of TNBC, we used gene set enrichment analysis (GSEA) to screen out the most altered functional module in TNBC, from publicly available microarray data and studied the association of the candidate gene with TNBC development. Results: We found that the proteasome was significantly activated in TNBC. As compared with other breast cancer subtypes and normal tissue, proteasome subunit beta 5 (PSMB5), the key regulator of proteasome function, was overexpressed in TNBC tissue and predictive of poor prognosis. Moreover, we also found that PSMB5 knockdown induced TNBC apoptosis and significantly enhanced cancer cell sensitivity to the chemotherapeutic agents bortezomib and paclitaxel. Conclusions: Our results suggest a potential role for PSMB5 as a biomarker and therapeutic target for TNBC.

scholarly journals Identification of novel autophagy-related lncRNAs associated with a poor prognosis of colon adenocarcinoma through bioinformatics analysis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Dejun Wu ◽  
Zhenhua Yin ◽  
Yisheng Ji ◽  
Lin Li ◽  
Yunxin Li ◽  
...  
Keyword(s):  
High Risk ◽  
Poor Prognosis ◽  
Notch Pathway ◽  
Colon Adenocarcinoma ◽  
High Risk Group ◽  
The Cancer Genome Atlas ◽  
Future Research ◽  
Risk Patients ◽  
Cancer Genome Atlas ◽  
Immune Score

AbstractLncRNAs play a pivotal role in tumorigenesis and development. However, the potential involvement of lncRNAs in colon adenocarcinoma (COAD) needs to be further explored. All the data used in this study were obtained from The Cancer Genome Atlas database, and all analyses were conducted using R software. Basing on the seven prognosis-related lncRNAs finally selected, we developed a prognosis-predicting model with powerful effectiveness (training cohort, 1 year: AUC = 0.70, 95% Cl = 0.57–0.78; 3 years: AUC = 0.71, 95% Cl = 0.6–0.8; 5 years: AUC = 0.76, 95% Cl = 0.66–0.87; validation cohort, 1 year: AUC = 0.70, 95% Cl = 0.58–0.8; 3 years: AUC = 0.73, 95% Cl = 0.63–0.82; 5 years: AUC = 0.68, 95% Cl = 0.5–0.85). The VEGF and Notch pathway were analyzed through GSEA analysis, and low immune and stromal scores were found in high-risk patients (immune score, cor =  − 0.15, P < 0.001; stromal score, cor =  − 0.18, P < 0.001) , which may partially explain the poor prognosis of patients in the high-risk group. We screened lncRNAs that are significantly associated with the survival of patients with COAD and possibly participate in autophagy regulation. This study may provide direction for future research.

Co‑expression Network Analysis by WGCNA and Identify Potential Prognostic Markers Associated with Lung Metastasis in Breast Cancer

2021 ◽  
Author(s):  
xixun zhang
Keyword(s):  
Breast Cancer ◽  
Network Analysis ◽  
Lung Metastasis ◽  
Prognostic Markers ◽  
Clinical Information ◽  
Preventive Treatment ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Breast Cancer Patients ◽  
Kaplan Meier

Abstract Backgroud: Breast cancer (BC) is an aggressive cancer with a high percentage recurrence and metastasis. As one of the most common distant metastasis organ in breast cancer, lung metastasis has a worse prognosis than that of liver and bone. Therefore, it’s important to explore some potential prognostic markers associated with the lung metastasis in breast cancer for preventive treatment. Methods: In our study, transcriptomic data and clinical information of breast cancer patients were downloaded from The Cancer Genome Atlas (TCGA) database. Co-expression modules was built by Weighted gene co-expression network analysis (WGCNA) to find out the royalbule modules which is significantly associated with lung metastasis in breast cancer. Then, co-expression genes were analyzed for functional enrichment. Furthermore, the prognostic value of these genes was assessed by GEPIA Database and Kaplan-Meier Plotter. Results: Results showed that the hub genes, LMNB and CDC20, were up-regulated in breast cancer and indicated worse survival. Therefore, we speculate that these two genes play crucial roles in the process of lung metastasis in breast cancer, and can be used as potential prognostic markers in lung metastasis of breast cancer. Conclusion: Collectively, our study identified two potential key genes in the lung metastasis of breast cancer, which might be applied as the prognostic markers of the precise treatment in breast cancer with lung metastasis.

scholarly journals Development of a Novel Prognostic Signature Based on Antigen Processing and Presentation in Patients with Breast Cancer

2021 ◽  
Vol 27 ◽  
Author(s):  
Aoshuang Qi ◽  
Mingyi Ju ◽  
Yinfeng Liu ◽  
Jia Bi ◽  
Qian Wei ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Antigen Processing ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Gene Signature ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Prognostic Indicators ◽  
Gene Set ◽  
Antigen Processing And Presentation

Background: Complex antigen processing and presentation processes are involved in the development and progression of breast cancer (BC). A single biomarker is unlikely to adequately reflect the complex interplay between immune cells and cancer; however, there have been few attempts to find a robust antigen processing and presentation-related signature to predict the survival outcome of BC patients with respect to tumor immunology. Therefore, we aimed to develop an accurate gene signature based on immune-related genes for prognosis prediction of BC.Methods: Information on BC patients was obtained from The Cancer Genome Atlas. Gene set enrichment analysis was used to confirm the gene set related to antigen processing and presentation that contributed to BC. Cox proportional regression, multivariate Cox regression, and stratified analysis were used to identify the prognostic power of the gene signature. Differentially expressed mRNAs between high- and low-risk groups were determined by KEGG analysis.Results: A three-gene signature comprising HSPA5 (heat shock protein family A member 5), PSME2 (proteasome activator subunit 2), and HLA-F (major histocompatibility complex, class I, F) was significantly associated with OS. HSPA5 and PSME2 were protective (hazard ratio (HR) &lt; 1), and HLA-F was risky (HR &gt; 1). Risk score, estrogen receptor (ER), progesterone receptor (PR) and PD-L1 were independent prognostic indicators. KIT and ACACB may have important roles in the mechanism by which the gene signature regulates prognosis of BC.Conclusion: The proposed three-gene signature is a promising biomarker for estimating survival outcomes in BC patients.

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