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 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 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.

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 A Novel Immune Checkpoint-Related Gene Signature to Predict Overall Survival and Immunotherapy Response in Triple-Negative Breast Cancer

2021 ◽  
Author(s):  
Jingyi Liu ◽  
Siyuan Tian ◽  
Yuwei Ling ◽  
Xinyi Zhang ◽  
Yan Li ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Triple Negative Breast Cancer ◽  
Immune Checkpoint ◽  
Triple Negative ◽  
Cox Regression ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Molecular Taxonomy ◽  
Gene Signature ◽  
Cox Regression Analysis

Abstract Triple-negative breast cancer (TNBC) is a highly aggressive subtype of breast cancer that lacks effective therapeutic targets. Immunotherapy is considered as a novel treatment strategy for TNBC. However, only some patients could benefit from the treatment. Limited studies have comprehensively explored expression patterns and prognostic value of immune checkpoint genes (ICGs) in TNBC. In this study, we downloaded relevant ICGs expression profiles and clinical TNBC data from the Cancer Genome Atlas (TCGA) and the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) database. The least absolute shrinkage and selection operator (LASSO) Cox regression analysis was employed to develop a multi-gene signature for predicting the prognostic outcome. PDCD1, PDCD1LG2 and KIR3DL2 were identified as hub genes and incorporated into the model. This gene signature could stratify patients into two prognostic subgroups, and unfavorable clinical outcomes were observed in high-risk patients. The predictive performance was assessed by the receiver operating characteristic curves. Moreover, we also analyzed differences in immune status and therapeutic response between both groups. This novel gene signature may be served as a robust prognostic marker, but also an indicator reflecting immunotherapy response.

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 Analysis of gene expression patterns in triple negative breast cancer III: the retinoic acid receptor alpha and other TNBC candidate genes.

2021 ◽  
Author(s):  
Shahan Mamoor
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Retinoic Acid ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
Retinoic Acid Receptor ◽  
Expression Patterns ◽  
Retinoic Acid Receptor Alpha ◽  
Acid Receptor ◽  
Receptor Alpha

Triple negative breast cancer (TNBC) shares overlap with the basal or basal-like molecular subtype of breast cancer and is more frequently diagnosed in women of African descent (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 cases, identifying genes whose expression was least different among TNBC cases, indicating conservation of expression patterns suggestive of importance for TNBC biology. We identified the gene encoding the retinoic acid receptor alpha (RARA), a fatty acid elongase (ELOVL1), as well as multiple genes encoding molecules involved in epigenetic functions or with nucleic acid binding or modification properties, including TDRD7, KDM1B, PHF7, TAF5L, as well as the microRNA hsa-miR-605. Kaplan-Meier survival analysis revealed that expression levels of each of these genes correlated with survival outcomes in the basal subtype of human breast cancer, which shares significant overlap with triple negative breast cancer at the level of gene expression (2). RARA, ELOVL1, TDRD7, KDM1B, PHF7, TAF5L and hsa-miR-605 may be of relevance in understanding the etiology or progression of triple negative breast cancer. Together with our previous findings, the data allude to a potential pathogenic mechanism involving transcriptional perturbation of epigenetic machinery in triple negative breast cancer (4, 5).

Gene expression comparison between primary triple-negative breast cancer and matched axillary lymph node metastasis.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. 565-565 ◽  
Author(s):  
Marissa K. Srour ◽  
Bowen Gao ◽  
Farnaz Dadmanesh ◽  
Ying Qu ◽  
Xiaojiang Cui ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
Expression Patterns ◽  
Fold Change ◽  
Axillary Lymph ◽  
Node Metastasis

565 Background: Sentinel lymph node (SLN) biopsy guides breast cancer treatment and prognostication. To date, there have been few studies examining the genetics of SLN metastasis in triple negative breast cancer (TNBC). The aim of this study is to characterize and compare gene expression patterns of primary breast cancers and autologous matched SLN metastases. Methods: Patients with stage 2-3 ER/PR negative, HER2 negative TNBC with macrometastasis to the SLN who did not have neoadjuvant therapy were selected. The tumor-specific area was isolated from breast and SLN paraffin embedded tissue sections. Gene expression of a panel of 2567 cancer-associated genes was analyzed with the HTG EdgeSeq system coupled with the Illumina Next Generation Sequencing (NGS) platform. Results were validated with RNA-scope assays for RNA in situ detection. Results: 18 pairs of TNBC and matched SLN metastasis were analyzed for 2567 genes. Compared with the primary TNBC, SLN metastasis had 34 statistically significant upregulated genes and 31 downregulated genes. SLN metastasis had at least a 5-fold change (FC) in upregulation in 3 genes and downregulation in 3 genes, compared to primary TNBC [Table]. Notably, there was an upregulation of anti-apoptosis and survival signaling genes (i.e. BIRC3) in the SLN metastasis. There was also an upregulation of chemotaxis genes (CCL13, CXCL19, CXCL21, CXCR5, TNFSF11, p<0.001). The most striking feature is the downregulation of genes known to regulate cell microenvironment interaction (MMP2, MMP14, COL1A1, COL1A2, COL5A2, COL6A6, COL11A1, COL17A1). Conclusions: In TNBC, SLN metastasis has a distinct gene expression profile. Genes associated with anti-apoptosis, survival responses, and chemotaxis are upregulated, and genes associated with regulation of extracellular matrix are downregulated. Upregulated and downregulated genes with at least a 5-fold change in gene expression in lymph node metastasis compared with TNBC.[Table: see text]

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