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Identification of potential key genes and pathways predicting pathogenesis and prognosis for triple-negative breast cancer.

ABSTRACT: Background:Triple negative breast cancer (TNBC) is a specific subtype of breast cancer with a poor prognosis due to its aggressive biological behaviour and lack of therapeutic targets. We aimed to explore some novel genes and pathways related to TNBC prognosis through bioinformatics methods as well as potential initiation and progression mechanisms. Methods:Breast cancer mRNA data were obtained from The Cancer Genome Atlas database (TCGA). Differential expression analysis of cancer and adjacent cancer, as well as, triple negative breast cancer and non-triple negative breast cancer were performed using R software. The key genes related to the pathogenesis were identified by functional and pathway enrichment analysis and protein-protein interaction network analysis. Based on univariate and multivariate Cox proportional hazards model analyses, a gene signature was established to predict overall survival. Receiver operating characteristic curve was used to evaluate the prognostic performance of our model. Results:Based on mRNA expression profiling of breast cancer patients from the TCGA database, 755 differentially expressed overlapping mRNAs were detected between TNBC/non-TNBC samples and normal tissue. We found eight hub genes associated with the cell cycle pathway highly expressed in TNBC. Additionally, a novel six-gene (TMEM252, PRB2, SMCO1, IVL, SMR3B and COL9A3) signature from the 755 differentially expressed mRNAs was constructed and significantly associated with prognosis as an independent prognostic signature. TNBC patients with high-risk scores based on the expression of the 6-mRNAs had significantly shorter survival times compared to patients with low-risk scores (P?

SUBMITTER: Lv X

PROVIDER: S-EPMC6599314 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Identification of potential key genes and pathways predicting pathogenesis and prognosis for triple-negative breast cancer.

Lv Xuemei X He Miao M Zhao Yanyun Y Zhang Liwen L Zhu Wenjing W Jiang Longyang L Yan Yuanyuan Y Fan Yue Y Zhao Hongliang H Zhou Shuqi S Ma Heyao H Sun Yezhi Y Li Xiang X Xu Hong H Wei Minjie M

Cancer cell international 20190628

<h4>Background</h4>Triple negative breast cancer (TNBC) is a specific subtype of breast cancer with a poor prognosis due to its aggressive biological behaviour and lack of therapeutic targets. We aimed to explore some novel genes and pathways related to TNBC prognosis through bioinformatics methods as well as potential initiation and progression mechanisms.<h4>Methods</h4>Breast cancer mRNA data were obtained from The Cancer Genome Atlas database (TCGA). Differential expression analysis of cance ...[more]

PMID: 31297036

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Project description: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.

Project description:Triple‑negative breast cancer (TNBC) accounts for the worst prognosis of all types of breast cancers due to a high risk of recurrence and a lack of targeted therapeutic options. Extensive effort is required to identify novel targets for TNBC. In the present study, a robust rank aggregation (RRA) analysis based on genome‑wide gene expression datasets involving TNBC patients from the Gene Expression Omnibus (GEO) database was performed to identify key genes associated with TNBC. A total of 194 highly ranked differentially expressed genes (DEGs) were identified in TNBC vs. non‑TNBC. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analysis was utilized to explore the biological functions of the identified genes. These DEGs were mainly involved in the biological processes termed positive regulation of transcription from RNA polymerase II promoter, negative regulation of apoptotic process, response to drug, response to estradiol and negative regulation of cell growth. Genes were mainly involved in the KEGG pathway termed estrogen signaling pathway. The aberrant expression of several randomly selected DEGs were further validated in cell lines, clinical tissues and The Cancer Genome Atlas (TCGA) cohort. Furthermore, all the top‑ranked DEGs underwent survival analysis using TCGA database, of which overexpression of 4 genes (FABP7, ART3, CT83, and TTYH1) were positively correlated to the life expectancy (P<0.05) of TNBC patients. In addition, a model consisting of two genes (FABP7 and CT83) was identified to be significantly associated with the overall survival (OS) of TNBC patients by means of Cox regression, Kaplan‑Meier, and receiver operating characteristic (ROC) analyses. In conclusion, the present study identified a number of key genes as potential biomarkers involved in TNBC, which provide novel insights into the tumorigenesis of TNBC at the gene level and may serve as independent prognostic factors for TNBC prognosis.

Project description:ObjectiveTriple-negative breast cancer (TNBC) is aggressive cancer usually diagnosed in young women with no effective prognosis prediction model to use. The present study was performed to develop a useful prognostic model for predicting overall survival (OS) for TNBC patients.MethodsThe Cancer Genome Atlas (TCGA) and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) databases were used as training and validation data sets, respectively, in which the gene expression levels and clinical prognostic information of TNBC were collected. Differentially expressed genes (DEGs) between TNBC and non-TNBC (NTNBC) were identified with the thresholds of false discovery rate < 0.05 and |log2 Fold Change| > 1. DEGs in AmiGO2 and the Kyoto Encyclopedia of Genes and Genomes (KEGG) databases were retained for further study. Univariate, multivariate Cox, and logistic regression analysis were conducted for detecting DEG signature with the threshold of log-rank P < 0.05. The prognosis models of mRNA signature, clinical factors were constructed and compared.ResultsOne five-DEG signature, including CHST4, COCH, CST9, SOX11, and TDGF1 was identified in DEG prognosis model. Stratified analysis showed that the patients aged over 60, with higher pathologic stage (III-IV) and recurrence induced a significantly lower survival rate than those aged below 60, lower pathologic stage and without recurrence. Compared with patients with low-risk scores, those presented high-risk scores demonstrated significantly lower survival rate in the subgroup aged over 60 [HR = 3.780 (1.801-7.933), P < 0.0001]. For patients who obtained a higher pathologic stage and recurrence, high-risk scores were correlated with a significantly lower survival rate than patients with low-risk scores. The five-mRNA signature combined with clinical model (AUC = 0.950) predicted better than single clinical model (AUC = 0.795) or five-mRNA signature model (AUC = 0.823).ConclusionOur present study identified a prognostic prediction model (combined with five-mRNA signature and clinical factors) for TNBC patients receiving immunotherapy, which will benefit future research and clinical therapies.

Dataset Information

Identification of potential key genes and pathways predicting pathogenesis and prognosis for triple-negative breast cancer.

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Identification of potential key genes and pathways predicting pathogenesis and prognosis for triple-negative breast cancer.

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