Identification of CCNB2 expression in triple-negative breast cancer based on bioinformatics results

2021 ◽  
Author(s):  
jintao cao ◽  
SHUAI SUN ◽  
RAN LI ◽  
RUI MIN ◽  
XINGYU FAN ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Triple Negative Breast Cancer ◽  
Protein Complex ◽  
Triple Negative ◽  
Expression Profiles ◽  
Pathway Enrichment Analysis ◽  
Analysis Tool ◽  
The Core ◽  
Core Genes

Abstract Background The current epidemiology shows that the incidence of breast cancer is increasing year by year and tends to be younger. Triple-negative breast cancer is the most malignant of breast cancer subtypes. The application of bioinformatics in tumor research is becoming more and more extensive. This study provided research ideas and basis for exploring the potential targets of gene therapy for triple-negative breast cancer (TNBC). Methods We analyzed three gene expression profiles (GSE64790、GSE62931、GSE38959) selected from the Gene Expression Omnibus (GEO) database. The GEO2R online analysis tool was used to screen for differentially expressed genes (DEGs) between TNBC and normal tissues. Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were applied to identify the pathways and functional annotation of DEGs. Protein–protein interaction network of these DEGs were visualized by the Metascape gene-list analysis tool so that we could find the protein complex containing the core genes. Subsequently, we investigated the transcriptional data of the core genes in patients with breast cancer from the Oncomine database. Moreover, the online Kaplan–Meier plotter survival analysis tool was used to evaluate the prognostic value of core genes expression in TNBC patients. Finally, immunohistochemistry (IHC) was used to evaluated the expression level and subcellular localization of CCNB2 on TNBC tissues. Results A total of 66 DEGs were identified, including 33 up-regulated genes and 33 down-regulated genes. Among them, a potential protein complex containing five core genes was screened out. The high expression of these core genes was correlated to the poor prognosis of patients suffering breast cancer, especially the overexpression of CCNB2. CCNB2 protein positively expressed in the cytoplasm, and its expression in triple-negative breast cancer tissues was significantly higher than that in adjacent tissues. Conclusions CCNB2 may play a crucial role in the development of TNBC and has the potential as a prognostic biomarker of TNBC.

scholarly journals Identification of Key Genes and Pathways in Triple-Negative Breast Cancer by Integrated Bioinformatics Analysis

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Pengzhi Dong ◽  
Bing Yu ◽  
Lanlan Pan ◽  
Xiaoxuan Tian ◽  
Fangfang Liu
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
Expression Profiles ◽  
Prostate Gland ◽  
Neural Crest Cell ◽  
Nervous System Development ◽  
Chromosome 9 ◽  
Key Genes

Purpose. Triple-negative breast cancer refers to breast cancer that does not express estrogen receptor (ER), progesterone receptor (PR), or human epidermal growth factor receptor 2 (Her2). This study aimed to identify the key pathways and genes and find the potential initiation and progression mechanism of triple-negative breast cancer (TNBC). Methods. We downloaded the gene expression profiles of GSE76275 from Gene Expression Omnibus (GEO) datasets. This microarray Super-Series sets are composed of gene expression data from 265 samples which included 67 non-TNBC and 198 TNBC. Next, all the differentially expressed genes (DEGs) with p<0.01 and fold change ≥1.5 or ≤-1.5 were identified. Result. 56 upregulated and 151 downregulated genes were listed and the gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analysis was performed. These significantly changed genes were mainly involved in the biological process termed prostate gland morphogenesis, inner ear morphogenesis, cell maturation, digestive tract morphogenesis, autonomic nervous system development, monovalent inorganic anion homeostasis, neural crest cell development, regulation of dendrite extension and glial cell proliferation, immune system process termed T cell differentiation, regulation of immune response, and macrophage activation. Genes are mainly involved in the KEGG pathway termed Oocyte meiosis. All DEGs underwent survival analysis using datasets from The Cancer Genome Atlas (TCGA) integrated by cBioPortal, of which amplification of SRY-related HMG-box 8 (SOX8), androgen receptor (AR), and Chromosome 9 Open Reading Frame 152 (C9orf152) were significantly negative while Nik Related Kinase (NRK) and RAS oncogene family 30 (RAB30) were positively correlated to the life expectancy (p<0.05). Conclusions. In conclusion, these pathways and genes identified could help understanding the mechanism of development of TNBC. Besides, SOX8, AR, C9orf152, NRK and RAB30, and other key genes and pathways might be promising targets for the TNBC treatment.

scholarly journals TNBCtype: A Subtyping Tool for Triple-Negative Breast Cancer

2012 ◽  
Vol 11 ◽  
pp. CIN.S9983 ◽  
Author(s):  
Xi Chen ◽  
Jiang Li ◽  
William H. Gray ◽  
Brian D. Lehmann ◽  
Joshua A. Bauer ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Genomic Research ◽  
Data Matrix ◽  
Cancer Data ◽  
Subtyping Tool

Motivation Triple-negative breast cancer (TNBC) is a heterogeneous breast cancer group, and identification of molecular subtypes is essential for understanding the biological characteristics and clinical behaviors of TNBC as well as for developing personalized treatments. Based on 3,247 gene expression profiles from 21 breast cancer data sets, we discovered six TNBC subtypes from 587 TNBC samples with unique gene expression patterns and ontologies. Cell line models representing each of the TNBC subtypes also displayed different sensitivities to targeted therapeutic agents. Classification of TNBC into subtypes will advance further genomic research and clinical applications. Result We developed a web-based subtyping tool TNBCtype for candidate TNBC samples using our gene expression meta data and classification methods. Given a gene expression data matrix, this tool will display for each candidate sample the predicted subtype, the corresponding correlation coefficient, and the permutation P-value. We offer a user-friendly web interface to predict the subtypes for new TNBC samples that may facilitate diagnostics, biomarker selection, drug discovery, and the more tailored treatment of breast cancer.

scholarly journals Analysis of gene expression patterns in triple negative breast cancer: MERTK and WHSC1L1.

2021 ◽  
Author(s):  
Shahan Mamoor
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Black Women ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
Molecular Subtype ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Primary Tumors

Triple negative breast cancer (TNBC) shares overlap with the basal molecular subtype of breast cancer and is more frequently diagnosed in African-American (black) women for reasons not understood (1,2). To understand genes whose expression may be of pertinence to the development or progression of triple negative breast cancer, we mined published microarray data (3,4) comparing global gene expression profiles of TNBC histology groups, identifying genes whose expression changed the least between among TNBCs, suggesting that these genes may be important for TNBC biology. We identified the MER proto-oncogene tyrosine kinase MERTK and the Wolf-Hirschhorn syndrome candidate 1-like 1 WHSC1L1 among the genes whose expression differed the least when comparing TNBC cases and subtypes. In another dataset, MERTK and WHSCL1 were found to be differentially expressed in TNBC when comparing primary tumors of the breast to normal breast tissue. Kaplan-Meier survival analysis revealed that expression levels of MERTK and WHSCL1 correlated with survival outcomes in human breast cancer, and that this correlation differed based on race of the patient. MERTK and WHSCL1 may be of relevance in understanding the etiology or progression of triple negative breast cancer.

scholarly journals Gene Expression Profiles Induced by High-dose Ionizing Radiation in MDA-MB-231 Triple-negative Breast Cancer Cell Line

2019 ◽  
Vol 16 (4) ◽  
pp. 257-266 ◽  
Author(s):  
VALENTINA BRAVATÀ ◽  
FRANCESCO PAOLO CAMMARATA ◽  
LUIGI MINAFRA ◽  
ROSA MUSSO ◽  
GAIA PUCCI ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Ionizing Radiation ◽  
Triple Negative Breast Cancer ◽  
Breast Cancer Cell Line ◽  
Triple Negative ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cancer Cell Line ◽  
High Dose

scholarly journals A triple-negative breast cancer surrogate subtype classification that correlates with gene expression subtypes

2021 ◽  
Author(s):  
Tae-Kyung Yoo ◽  
Jun Kang ◽  
Awon Lee ◽  
Byung Joo Chae
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Clinical Practice ◽  
Androgen Receptor ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
Expression Profiles ◽  
Breast Cancer Surgery ◽  
Study Cohort ◽  
Subtype Classification

Abstract Background Triple-negative breast cancer (TNBC) is a heterogeneous tumor lacking specific therapeutic targets. Several TNBC classifications have been identified using gene expression profiles, but they are difficult to use in clinical practice. In this study, we have developed a TNBC surrogate subtype classification that represents TNBC subtypes based on the Vanderbilt subtype classification. Methods This study included patients who underwent primary curative breast cancer surgery for TNBC between January 2009 and October 2017 at Seoul St. Mary’s Hospital. Representative formalin-fixed paraffin embedded blocks were used for gene expression analysis and tissue microarray construction for immunohistochemical (IHC) staining. The Vanderbilt subtypes were re-classified into 4 groups: basal-like (BL), mesenchymal-like (M), immunomodulatory (IM) and luminal androgen receptor (LAR) subtype. After IHC staining, classification and regression tree (CART) modeling was applied to develop a surrogate subtype classification. Results A total of 145 patients were included in this study. The study cohort was allocated to the Vanderbilt 4 subtypes as LAR (n = 22, 15.2%), IM (n = 32, 22.1%), M (n = 38, 26.2%), BL (n = 25, 17.2%) and unclassified (n = 28, 19.3%). After excluding nine (6.2%) patients due to poor IHC staining quality, CART modeling was performed. TNBC surrogate subtypes were defined as follows: LAR subtype, androgen receptor Allred score 8; IM subtype, LAR-negative with a tumor-infiltrating lymphocyte (TIL) score > 70%; M subtype, LAR-negative with a TIL score < 20%; BL subtype, LAR-negative with a TIL score 20–70% and diffuse, strong p16 staining. The study cohort was classified by the surrogate subtypes as LAR (n = 26, 17.9%), IM (n = 21, 14.5%), M (n = 44, 30.3%), BL1 (n = 27, 18.6%) and unclassified (n = 18, 12.4%). The performance of the surrogate subtypes to predict TNBC Vanderbilt 4 subtypes was good with an accuracy of 0.708. Each subtype exhibited distinct clinicopathologic features, and the M subtype showed a significantly worse survival rate than the other subtypes. Conclusions In this study, we have developed a TNBC surrogate subtype classification that correlates with the Vanderbilt subtype adopting AR, TIL and p16. The surrogate subtype classification is a practical and accessible diagnostic test, that can be easily applied in clinical practice.

scholarly journals Analysis of gene expression patterns in triple negative breast cancer II: TNBC molecular subtype.

2021 ◽  
Author(s):  
Shahan Mamoor
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Black Women ◽  
Triple Negative Breast Cancer ◽  
Human Breast Cancer ◽  
Triple Negative ◽  
Molecular Subtype ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Human Breast

Triple negative breast cancer (TNBC) shares overlap with the basal molecular subtype of breast cancer and is more frequently diagnosed in African-American (black) women for reasons not understood (1,2). To understand genes whose expression may be of pertinence to the development or progression of triple negative breast cancer, we mined published microarray data (3) comparing global gene expression profiles of TNBC molecular subtypes, identifying genes whose expression changed the least between among TNBCs, suggesting that these genes may be important for TNBC biology. We identified PVT1, PHC3, WNT8B, MLLT6, MSH3, IHH, and WNT2B among the genes whose expression differed the least when comparing TNBC cases and subtypes. Kaplan-Meier survival analysis revealed that expression levels of each of these genes correlated with survival outcomes in human breast cancer; in some cases, this correlation differed based on race of the patient, and in other cases, this correlation was found in the basal subtype of human breast cancer, which shares significant overlap with triple negative breast cancer at the level of gene expression (2). PVT1, PHC3, WNT8B, MLLT6, MSH3, IHH, and WNT2B may be of relevance in understanding the etiology or progression of triple negative breast cancer.

scholarly journals An open-label, pilot study of veliparib and lapatinib in patients with metastatic, triple-negative breast cancer

2021 ◽  
Vol 23 (1) ◽  
Author(s):  
Erica M. Stringer-Reasor ◽  
Jori E. May ◽  
Eva Olariu ◽  
Valerie Caterinicchia ◽  
Yufeng Li ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Dna Repair ◽  
Pilot Study ◽  
Expression Analysis ◽  
Triple Negative Breast Cancer ◽  
Gene Expression Analysis ◽  
Triple Negative ◽  
Egfr Inhibition ◽  
Link Type

Abstract Background Poly (ADP-ribose)-polymerase inhibitors (PARPi) have been approved for cancer patients with germline BRCA1/2 (gBRCA1/2) mutations, and efforts to expand the utility of PARPi beyond BRCA1/2 are ongoing. In preclinical models of triple-negative breast cancer (TNBC) with intact DNA repair, we have previously shown an induced synthetic lethality with combined EGFR inhibition and PARPi. Here, we report the safety and clinical activity of lapatinib and veliparib in patients with metastatic TNBC. Methods A first-in-human, pilot study of lapatinib and veliparib was conducted in metastatic TNBC (NCT02158507). The primary endpoint was safety and tolerability. Secondary endpoints were objective response rates and pharmacokinetic evaluation. Gene expression analysis of pre-treatment tumor biopsies was performed. Key eligibility included TNBC patients with measurable disease and prior anthracycline-based and taxane chemotherapy. Patients with gBRCA1/2 mutations were excluded. Results Twenty patients were enrolled, of which 17 were evaluable for response. The median number of prior therapies in the metastatic setting was 1 (range 0–2). Fifty percent of patients were Caucasian, 45% African–American, and 5% Hispanic. Of evaluable patients, 4 demonstrated a partial response and 2 had stable disease. There were no dose-limiting toxicities. Most AEs were limited to grade 1 or 2 and no drug–drug interactions noted. Exploratory gene expression analysis suggested baseline DNA repair pathway score was lower and baseline immunogenicity was higher in the responders compared to non-responders. Conclusions Lapatinib plus veliparib therapy has a manageable safety profile and promising antitumor activity in advanced TNBC. Further investigation of dual therapy with EGFR inhibition and PARP inhibition is needed. Trial registration ClinicalTrials.gov, NCT02158507. Registered on 12 September 2014

scholarly journals The BIRC Family Genes Expression in Patients with Triple Negative Breast Cancer

2021 ◽  
Vol 22 (4) ◽  
pp. 1820
Author(s):  
Anna Makuch-Kocka ◽  
Janusz Kocki ◽  
Anna Brzozowska ◽  
Jacek Bogucki ◽  
Przemysław Kołodziej ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Clinical Data ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
Lymphatic Vessels ◽  
The Cancer Genome Atlas ◽  
Expression Level ◽  
Cancer Tissue ◽  
Expression Levels

The BIRC (baculoviral IAP repeat-containing; BIRC) family genes encode for Inhibitor of Apoptosis (IAP) proteins. The dysregulation of the expression levels of the genes in question in cancer tissue as compared to normal tissue suggests that the apoptosis process in cancer cells was disturbed, which may be associated with the development and chemoresistance of triple negative breast cancer (TNBC). In our study, we determined the expression level of eight genes from the BIRC family using the Real-Time PCR method in patients with TNBC and compared the obtained results with clinical data. Additionally, using bioinformatics tools (Ualcan and The Breast Cancer Gene-Expression Miner v4.5 (bc-GenExMiner v4.5)), we compared our data with the data in the Cancer Genome Atlas (TCGA) database. We observed diverse expression pattern among the studied genes in breast cancer tissue. Comparing the expression level of the studied genes with the clinical data, we found that in patients diagnosed with breast cancer under the age of 50, the expression levels of all studied genes were higher compared to patients diagnosed after the age of 50. We observed that in patients with invasion of neoplastic cells into lymphatic vessels and fat tissue, the expression levels of BIRC family genes were lower compared to patients in whom these features were not noted. Statistically significant differences in gene expression were also noted in patients classified into three groups depending on the basis of the Scarff-Bloom and Richardson (SBR) Grading System.

scholarly journals Breast Cancer Type Classification Using Machine Learning

2021 ◽  
Vol 11 (2) ◽  
pp. 61
Author(s):  
Jiande Wu ◽  
Chindo Hicks
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Machine Learning ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
Genomic Research ◽  
Support Vector ◽  
Cancer Type ◽  
Classification Models

Background: Breast cancer is a heterogeneous disease defined by molecular types and subtypes. Advances in genomic research have enabled use of precision medicine in clinical management of breast cancer. A critical unmet medical need is distinguishing triple negative breast cancer, the most aggressive and lethal form of breast cancer, from non-triple negative breast cancer. Here we propose use of a machine learning (ML) approach for classification of triple negative breast cancer and non-triple negative breast cancer patients using gene expression data. Methods: We performed analysis of RNA-Sequence data from 110 triple negative and 992 non-triple negative breast cancer tumor samples from The Cancer Genome Atlas to select the features (genes) used in the development and validation of the classification models. We evaluated four different classification models including Support Vector Machines, K-nearest neighbor, Naïve Bayes and Decision tree using features selected at different threshold levels to train the models for classifying the two types of breast cancer. For performance evaluation and validation, the proposed methods were applied to independent gene expression datasets. Results: Among the four ML algorithms evaluated, the Support Vector Machine algorithm was able to classify breast cancer more accurately into triple negative and non-triple negative breast cancer and had less misclassification errors than the other three algorithms evaluated. Conclusions: The prediction results show that ML algorithms are efficient and can be used for classification of breast cancer into triple negative and non-triple negative breast cancer types.

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