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

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.

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

10.21203/rs.3.rs-63478/v1 ◽

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.

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

Frontiers in Molecular Biosciences ◽

10.3389/fmolb.2021.604261 ◽

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.

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

The International Journal of Biological Markers ◽

10.5301/ijbm.5000283 ◽

2017 ◽

Vol 33 (1) ◽

pp. 102-108 ◽

Cited By ~ 7

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.

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DNA Methylation Based Molecular Subtypes Predict Prognosis in Breast Cancer Patients

Cancer Control ◽

10.1177/1073274820988519 ◽

2021 ◽

Vol 28 ◽

pp. 107327482098851

Author(s):

Zeng-Hong Wu ◽

Yun Tang ◽

Yan Zhou

Keyword(s):

Breast Cancer ◽

Dna Methylation ◽

Molecular Subtypes ◽

Methylation Status ◽

The Cancer Genome Atlas ◽

Training Dataset ◽

Consensus Clustering ◽

Breast Cancer Patients ◽

Cancer Subtypes ◽

Novel Biomarkers

Background: Epigenetic changes are tightly linked to tumorigenesis development and malignant transformation’ However, DNA methylation occurs earlier and is constant during tumorigenesis. It plays an important role in controlling gene expression in cancer cells. Methods: In this study, we determining the prognostic value of molecular subtypes based on DNA methylation status in breast cancer samples obtained from The Cancer Genome Atlas database (TCGA). Results: Seven clusters and 204 corresponding promoter genes were identified based on consensus clustering using 166 CpG sites that significantly influenced survival outcomes. The overall survival (OS) analysis showed a significant prognostic difference among the 7 groups (p<0.05). Finally, a prognostic model was used to estimate the results of patients on the testing set based on the classification findings of a training dataset DNA methylation subgroups. Conclusions: The model was found to be important in the identification of novel biomarkers and could be of help to patients with different breast cancer subtypes when predicting prognosis, clinical diagnosis and management.

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

10.21203/rs.3.rs-182567/v1 ◽

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.

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

Pathology & Oncology Research ◽

10.3389/pore.2021.600727 ◽

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) < 1), and HLA-F was risky (HR > 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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High RPS27A Expression Predicts Poor Prognosis in Patients With HPV Type 16 Cervical Cancer

Frontiers in Oncology ◽

10.3389/fonc.2021.752974 ◽

2021 ◽

Vol 11 ◽

Author(s):

Qiming Wang ◽

Yan Cai ◽

Xuewen Fu ◽

Liang Chen

Keyword(s):

Cervical Cancer ◽

Poor Prognosis ◽

Biological Significance ◽

Enrichment Analysis ◽

Hpv Infection ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Protein Protein Interaction ◽

Cervical Cancer Cells ◽

Cancer Genome Atlas

In recent years, the incidence and the mortality rate of cervical cancer have been gradually increasing, becoming one of the major causes of cancer-related death in women. In particular, patients with advanced and recurrent cervical cancers present a very poor prognosis. In addition, the vast majority of cervical cancer cases are caused by human papillomavirus (HPV) infection, of which HPV16 infection is the main cause and squamous cell carcinoma is the main presenting type. In this study, we performed screening of differentially expressed genes (DEGs) based on The Cancer Genome Atlas (TCGA) database and GSE6791, constructed a protein–protein interaction (PPI) network to screen 34 hub genes, filtered to the remaining 10 genes using the CytoHubba plug-in, and used survival analysis to determine that RPS27A was most associated with the prognosis of cervical cancer patients and has prognostic and predictive value for cervical cancer. The most significant biological functions and pathways of RPS27A enrichment were subsequently investigated with gene set enrichment analysis (GSEA), and integration of TCGA and GTEx database analyses revealed that RPS27A was significantly expressed in most cancer types. In this study, our analysis revealed that RPS27A can be used as a prognostic biomarker for HPV16 cervical cancer and has biological significance for the growth of cervical cancer cells.

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Prognostic Significance of SQSTM1 in Breast Cancer: A Comprehensive Analysis

10.21203/rs.3.rs-64416/v1 ◽

2020 ◽

Author(s):

Yang Liu ◽

Qian Du ◽

Dan Sun ◽

Ruiying Han ◽

Mengmeng Teng ◽

...

Keyword(s):

Breast Cancer ◽

Cancer Patients ◽

Somatic Mutation ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Functional Enrichment ◽

Regulation Mechanism ◽

Breast Cancer Patients ◽

Gene Set ◽

Multiple Datasets

Abstract Background: SQSTM1 (Sequestosome 1, p62) is degraded by activated autophagy and involved in the progression of in various types of cancers. However, the prognostic role and underlying regulation mechanism of SQSTM1 in the progression and development of breast cancer remain unclear.Methods: In this study, 1336 samples with available mRNA data from Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) database and 27 formalin fixation and paraffin embedding (FFPE) tissue samples from the First Affiliated Hospital of Xi’an Jiaotong University were collected to evaluate SQSTM1 expression in mRNA and protein levels. Kaplan–Meier and Cox regression were used for revealing prognostic value in three independent breast cancer independent datasets. Tumor Immune Estimation Resource (TIMER) database and Gene Set Variation Analysis (GSVA) was used to explore the relationship of SQSTM1 mRNA expression and immune infiltration level in breast cancer. Dysregulation mechanisms of SQSTM1 were also explored including copy number variation (CNV), somatic mutation, epigenetic alterations and other transcription and post-transcription level using multiple datasets. Finally, Gene Set Enrichment Analysis (GSEA) was constructed to elucidate functional regulating performance of SQSTM1 in breast cancer.Results: The results showed that mRNA and protein level of SQSTM1 were significantly elevated in breast cancer and receiver operating characteristic (ROC) curve showed that p62 may act as diagnostic biomarker. Lower expression of SQSTM1 predicted better outcome through multiple datasets. It was also found that SQSTM1 correlated with immune infiltrates in breast cancer. Moreover, CNV and methylation of SQSTM1 DNA was correlated with SQSTM1 dysregulation and act as prognostic factors for breast cancer patients. Yet, somatic mutation status of SQSTM1 didn’t show any prognostic relevance. We also identified diverse transcription factors that directly bound to SQSTM1 DNA and the miRNAs which may regulate SQSTM1 mRNA. Finally, functional enrichment analysis revealed that SQSTM1 is related to cell signal transduction, oxidative stress and autophagy in breast cancer.Conclusion: Our findings revealed that SQSTM1 plays a key role in the progression of breast cancer and might be a promising biomarker for the diagnosis and personalized treatment of breast cancer patients.

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Immune-related gene based prognostic signature for the risk stratification analysis of breast cancer

Current Bioinformatics ◽

10.2174/1574893616666211005110732 ◽

2021 ◽

Vol 16 ◽

Author(s):

Dongqing Su ◽

Qianzi Lu ◽

Yi Pan ◽

Yao Yu ◽

Shiyuan Wang ◽

...

Keyword(s):

Breast Cancer ◽

Regression Analysis ◽

Cancer Patients ◽

Risk Score ◽

Cox Regression ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Breast Cancer Patients ◽

Cox Regression Analysis ◽

Score Model

Background: Breast cancer has plagued women for many years and caused many deaths around the world. Method: In this study, based on the weighted correlation network analysis, univariate Cox regression analysis and least absolute shrinkage and selection operator, 12 immune-related genes were selected to construct the risk score for breast cancer patients. The multivariable Cox regression analysis, gene set enrichment analysis and nomogram were also conducted in this study. Results: Good results were obtained in the survival analysis, enrichment analysis, multivariable Cox regression analysis and immune-related feature analysis. When the risk score model was applied in 22 breast cancer cohorts, the univariate Cox regression analysis demonstrated that the risk score model was significantly associated with overall survival in most of the breast cancer cohorts. Conclusion: Based on these results, we could conclude that the proposed risk score model may be a promising method, and may improve the treatment stratification of breast cancer patients in the future work.

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The Major Prognostic Features of Nuclear ReceptorNR5A2in Infiltrating Ductal Breast Carcinomas

International Journal of Genomics ◽

10.1155/2015/403576 ◽

2015 ◽

Vol 2015 ◽

pp. 1-14 ◽

Cited By ~ 2

Author(s):

Li-Yun Chang ◽

Li-Yu D. Liu ◽

Don A. Roth ◽

Wen-Hung Kuo ◽

Hsiao-Lin Hwa ◽

...

Keyword(s):

Breast Cancer ◽

Network Analysis ◽

Poor Prognosis ◽

Transcriptional Regulatory Network ◽

Expression Profiles ◽

Gene Signature ◽

Cancer Prognosis ◽

Transcriptional Networks ◽

Cancer Subtypes ◽

Prognostic Features

Background. Gene expression profiles of 181 breast cancer samples were analyzed to identify prognostic features of nuclear receptorsNR5A1andNR5A2based upon their associated transcriptional networks.Methods. A supervised network analysis approach was used to build the NR5A-mediated transcriptional regulatory network. Other bioinformatic tools and statistical methods were utilized to confirm and extend results from the network analysis methodology.Results.NR5A2expression is a negative factor in breast cancer prognosis in both ER(−) and ER(−)/ER(+) mixed cohorts. The clinical and cohort significance ofNR5A2-mediated transcriptional activities indicates that it may have a significant role in attenuating grade development and cancer related signal transduction pathways.NR5A2signature that conditions poor prognosis was identified based upon results from 15 distinct probes. Alternatively, the expression ofNR5A1predicts favorable prognosis when concurrentNR5A2expression is low. A favorable signature of eight transcription factors mediated byNR5A1was also identified.Conclusions. Correlation of poor prognosis andNR5A2activity is identified byNR5A2-mediated 15-gene signature.NR5A2may be a potential drug target for treating a subset of breast cancer tumors across breast cancer subtypes, especially ER(−) breast tumors. The favorable prognostic feature ofNR5A1is predicted byNR5A1-mediated 8-gene signature.

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