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Transcriptomic analyses identify key differentially expressed genes and clinical outcomes between triple-negative and non-triple-negative breast cancer.

ABSTRACT: Purpose:There are significant differences in the biological behavior between triple-negative breast cancer (TNBC) and non-triple-negative breast cancer (non-TNBC). In the present study, we identify key differential genes and clinical outcomes between TNBC and non-TNBC. Materials and methods:Transcriptomic analyses used GEO datasets (GSE76275), gene ontology, KEGG pathway analysis and cBioPortal. Quantitative RT-PCR analysis (qRT-PCR) was used to validate the differentially expressed genes. We used the KM Plotter Online Tool and 240 patients with TNBC tissue microarray to assay the prognostic value of HORMAD1. Results:The upregulated differentially expressed genes were enriched in transcription factor activity, sequence-specific DNA binding and nucleic acid binding transcription factor activity. Only 16 genes were upregulated when further screened for fold change >4-fold change. HORMAD1 and SOX8 exhibited high frequencies of change of greater than 10% (HORMAD1 was close to 20%). qRT-PCR results indicated that HORMAD1 and SOX8 mRNA levels were significantly upregulated in TNBC samples. In KM Plotter Online Tool, high HORMAD1 was associated with worse outcome. In our tissue microarray (including 240 TNBC tissues), IHC analysis revealed that 29.7% (55/240) of the tumor samples exhibited high HORMAD1 expression and 70.3% (185/240) of the tumor samples exhibited low HORMAD1 expression levels. Meanwhile, high HORMAD1 group has a bad prognosis. Conclusion:The status of transcriptional activation is an important difference between TNBC and non-TNBC. HORMAD1 is a key differential gene associated with poor outcome in TNBC. Epigenetic therapy and agents targeting cancer/testis antigens might potentially help to customize therapies of TNBC.

SUBMITTER: Chen B

PROVIDER: S-EPMC6306052 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Transcriptomic analyses identify key differentially expressed genes and clinical outcomes between triple-negative and non-triple-negative breast cancer.

Chen Bo B Tang Hailin H Chen Xi X Zhang Guochun G Wang Yulei Y Xie Xiaoming X Liao Ning N

Cancer management and research 20181221

<h4>Purpose</h4>There are significant differences in the biological behavior between triple-negative breast cancer (TNBC) and non-triple-negative breast cancer (non-TNBC). In the present study, we identify key differential genes and clinical outcomes between TNBC and non-TNBC.<h4>Materials and methods</h4>Transcriptomic analyses used GEO datasets (GSE76275), gene ontology, KEGG pathway analysis and cBioPortal. Quantitative RT-PCR analysis (qRT-PCR) was used to validate the differentially express ...[more]

PMID: 30613165

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Project description:Background:BRCA1 and BRCA2 genes are currently proven to be closely related to high lifetime risks of breast cancer. To date, the closely related genes to BRCA1/2 mutations in breast cancer remains to be fully elucidated. This study aims to identify the gene expression profiles and interaction networks influenced by BRCA1/2 mutations, so as to reflect underlying disease mechanisms and provide new biomarkers for breast cancer diagnosis or prognosis. Methods:Gene expression profiles from The Cancer Genome Atlas (TCGA) database were downloaded and combined with cBioPortal website to identify exact breast cancer patients with BRCA1/2 mutations. Gene set enrichment analysis (GSEA) was used to analyze some enriched pathways and biological processes associated BRCA mutations. For BRCA1/2-mutant breast cancer, wild-type breast cancer and corresponding normal tissues, three independent differentially expressed genes (DEGs) analysis were performed to validate potential hub genes with each other. Protein-protein interaction (PPI) networks, survival analysis and diagnostic value assessment helped identify key genes associated with BRCA1/2 mutations. Results:The regulation process of cell cycle was significantly enriched in mutant group compared with wild-type group. A total of 294 genes were identified after analysis of DEGs between mutant patients and wild-type patients. Interestingly, by the other two comparisons, we identified 43 overlapping genes that not only significantly expressed in wild-type breast cancer patients relative to normal tissues, but more significantly expressed in BRCA1/2-mutant breast patients. Based on the STRING database and cytoscape software, we constructed a PPI network using 294 DEGs. Through topological analysis scores of the PPI network and 43 overlapping genes, we sought to select some genes, thereby using survival analysis and diagnostic value assessment to identify key genes pertaining to BRCA1/2-mutant breast cancer. CCNE1, NPBWR1, A2ML1, EXO1 and TTK displayed good prognostic/diagnostic value for breast cancer and BRCA1/2-mutant breast cancer. Conclusion:Our research provides comprehensive and new insights for the identification of biomarkers connected with BRCA mutations, availing diagnosis and treatment of breast cancer and BRCA1/2-mutant breast cancer patients.

Project description:BackgroundTreatment strategies for various subtypes of breast cancer (BC) are different based on their distinct molecular characteristics. Therefore, it is very important to identify key differentially expressed genes (DEGs) between ER-positive/HER2-negative BC and ER-negative/HER2-negative BC.MethodsGene expression profiles of GSE22093 and GSE23988 were obtained from the Gene Expression Omnibus database. There were 74 ER-positive/HER2-negative BC tissues and 85 ER-negative/HER2-negative BC tissues in the two profile datasets. DEGs between ER-positive/HER2-negative tissues and ER-negative/HER2-negative BC tissues were identified by the GEO2R tool. The common DEGs among the two datasets were detected with Venn software online. Next, we made use of the Database for Annotation, Visualization and Integrated Discovery to analyze enriched Kyoto Encyclopedia of Gene and Genome (KEGG) pathways and gene ontology terms. Then, the protein-protein interactions (PPIs) of these DEGs were visualized by Cytoscape with the Search Tool for the Retrieval of Interacting Genes. Of the proteins in the PPI network, Molecular Complex Detection plug-in analysis identified nine core upregulated genes and one core downregulated gene. UALCAN and Gene Expression Profiling Interactive Analysis were applied to determine the expression of these 10 genes in BC. Furthermore, for the analysis of overall survival among those genes, the Kaplan-Meier method was implemented.ResultsNinety-three common DEGs (63 upregulated and 30 downregulated) were identified. KEGG pathway enrichment analysis showed that upregulated DEGs were particularly enriched in the progesterone-mediated oocyte maturation pathway. In addition, PGR might be a prognostic biomarker for ER-positive/HER2-negative BC. CCND1 is a poor prognostic biomarker for ER-positive/HER2-negative BC and ER-negative/HER2-negative BC. Moreover, TFF1, AGR2 and EGFR might be predictive biomarkers of node metastasis in ER-positive/HER2-negative BC and ER-negative/HER2-negative BC.ConclusionsCCND1, AGR2, PGR, TFF1 and EGFR are the key DEGs between ER-positive/HER2-negative BC and ER-negative/HER2-negative BC. Further studies are required to confirm the functions of the identified genes.

Dataset Information

Transcriptomic analyses identify key differentially expressed genes and clinical outcomes between triple-negative and non-triple-negative breast cancer.

Publications

Transcriptomic analyses identify key differentially expressed genes and clinical outcomes between triple-negative and non-triple-negative breast cancer.

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