Dataset Information

Identification of CXCR4 and CXCL10 as Potential Predictive Biomarkers in Triple Negative Breast Cancer (TNBC).

ABSTRACT: BACKGROUND Efficacious therapy for triple negative breast cancer (TNBC) continues to be a profound clinical challenge, but the key driven genes and convoluted signaling pathways are still unknown. MATERIAL AND METHODS A total of 223 samples (163 TNBC and 60 healthy breast tissues) were taken and deeply integrated analyzed by R software from 4 expression profiles in the study, including GSE53752, GSE45827, GSE65194, and GSE38959. We examined differentially expressed genes (DEGs) and screen for critical genes and pathways enrichment. The protein?protein interaction (PPI) network of DEGs-associated was built through the STRING Version: 11.0 database and Cytoscape software to filter the hub gene. Then, we verified hug gene expression levels through the Oncomine database. Also, we analyzed the prognostic value of TNBC patient's hub genes using the Kaplan-Meier plotter database. RESULTS In our study, we filter out 365 DEGs, including 212 upregulated genes and 153 downregulated genes. Then, 10 hub genes were picked out by the intersection of 12 algorithms. At the same time, we discovered that CXCR4 and CXCL10 overexpression are favorable prognostic factors for recurrence-free survival of TNBC through the Kaplan-Meier plotter database. CONCLUSIONS Our research found that CXCR4 and CXCL10 overexpressed, and they were a favorable prognostic factor in patients with TNBC. CXCR4 and CXCL10 might be effective targets for TNBC therapy.

SUBMITTER: Chuan T

PROVIDER: S-EPMC6977636 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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Identification of CXCR4 and CXCL10 as Potential Predictive Biomarkers in Triple Negative Breast Cancer (TNBC).

Chuan Tian T Li Tian T Yi Cui C

Medical science monitor : international medical journal of experimental and clinical research 20200111

BACKGROUND Efficacious therapy for triple negative breast cancer (TNBC) continues to be a profound clinical challenge, but the key driven genes and convoluted signaling pathways are still unknown. MATERIAL AND METHODS A total of 223 samples (163 TNBC and 60 healthy breast tissues) were taken and deeply integrated analyzed by R software from 4 expression profiles in the study, including GSE53752, GSE45827, GSE65194, and GSE38959. We examined differentially expressed genes (DEGs) and screen for cr ...[more]

PMID: 31924747

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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:BackgroundTriple Negative Breast Cancers (TNBCs) lack the appropriate targets for currently used breast cancer therapies, conferring an aggressive phenotype, more frequent relapse and poorer survival rates. The biological heterogeneity of TNBC complicates the clinical treatment further. We have explored and compared the biological pathways in TNBC and other subtypes of breast cancers, using an in silico approach and the hypothesis that two opposing effects (Yin and Yang) pathways in cancer cells determine the fate of cancer cells. Identifying breast subgroup specific components of these opposing pathways may aid in selecting potential therapeutic targets as well as further classifying the heterogeneous TNBC subtype.MethodsGene expression and patient clinical data from The Cancer Genome Atlas (TCGA) and the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) were used for this study. Gene Set Enrichment Analysis (GSEA) was used to identify the more active pathways in cancer (Yin) than in normal and the more active pathways in normal (Yang) than in cancer. The clustering analysis was performed to compare pathways of TNBC with other types of breast cancers. The association of pathway classified TNBC sub-groups to clinical outcomes was tested using Cox regression model.ResultsAmong 4729 curated canonical pathways in GSEA database, 133 Yin pathways (FDR < 0.05) and 71 Yang pathways (p-value <0.05) were discovered in TNBC. The FOXM1 is the top Yin pathway while PPARα is the top Yang pathway in TNBC. The TNBC and other types of breast cancers showed different pathways enrichment significance profiles. Using top Yin and Yang pathways as classifier, the TNBC can be further subtyped into six sub-groups each having different clinical outcomes.ConclusionWe first reported that the FOMX1 pathway is the most upregulated and the PPARα pathway is the most downregulated pathway in TNBC. These two pathways could be simultaneously targeted in further studies. Also the pathway classifier we performed in this study provided insight into the TNBC heterogeneity.

Dataset Information

Identification of CXCR4 and CXCL10 as Potential Predictive Biomarkers in Triple Negative Breast Cancer (TNBC).

Publications

Identification of CXCR4 and CXCL10 as Potential Predictive Biomarkers in Triple Negative Breast Cancer (TNBC).

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