Dataset Information

Identification of NUF2 and FAM83D as potential biomarkers in triple-negative breast cancer.

ABSTRACT: Background:Breast cancer is a heterogeneous disease. Compared with other subtypes of breast cancer, triple-negative breast cancer (TNBC) is easy to metastasize and has a short survival time, less choice of treatment options. Here, we aimed to identify the potential biomarkers to TNBC diagnosis and prognosis. Material/Methods:Three independent data sets (GSE45827, GSE38959, GSE65194) were downloaded from the Gene Expression Omnibus (GEO). The R software packages were used to integrate the gene profiles and identify differentially expressed genes (DEGs). A variety of bioinformatics tools were used to explore the hub genes, including the DAVID database, STRING database and Cytoscape software. Reverse transcription quantitative PCR (RT-qPCR) was used to verify the hub genes in 14 pairs of TNBC paired tissues. Results:In this study, we screened out 161 DEGs between 222 non-TNBC and 126 TNBC samples, of which 105 genes were up-regulated and 56 were down-regulated. These DEGs were enriched for 27 GO terms and two pathways. GO analysis enriched mainly in "cell division", "chromosome, centromeric region" and "microtubule motor activity". KEGG pathway analysis enriched mostly in "Cell cycle" and "Oocyte meiosis". PPI network was constructed and then 10 top hub genes were screened. According to the analysis results of the Kaplan-Meier survival curve, the expression levels of only NUF2, FAM83D and CENPH were associated with the recurrence-free survival in TNBC samples (P < 0.05). RT-qPCR confirmed that the expression levels of NUF2 and FAM83D in TNBC tissues were indeed up-regulated significantly. Conclusions:The comprehensive analysis showed that NUF2 and FAM83D could be used as potential biomarkers for diagnosis and prognosis of TNBC.

SUBMITTER: Zhai X

PROVIDER: S-EPMC7513746 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Identification of NUF2 and FAM83D as potential biomarkers in triple-negative breast cancer.

Zhai Xiuming X Yang Zhaowei Z Liu Xiji X Dong Zihe Z Zhou Dandan D

PeerJ 20200921

<h4>Background</h4>Breast cancer is a heterogeneous disease. Compared with other subtypes of breast cancer, triple-negative breast cancer (TNBC) is easy to metastasize and has a short survival time, less choice of treatment options. Here, we aimed to identify the potential biomarkers to TNBC diagnosis and prognosis.<h4>Material/methods</h4>Three independent data sets (GSE45827, GSE38959, GSE65194) were downloaded from the Gene Expression Omnibus (GEO). The R software packages were used to integr ...[more]

PMID: 33005492

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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:Familial triple-negative breast cancers are often linked to mutations in the BRCA1 tumor suppressor gene. In sporadic triple-negative breast cancers BRCA1 is frequently inactivated at the transcriptional level, and it has been reported that this inactivation may be brought about by promoter methylation. More recently, it was found that BRCA1 may also be regulated at the post-transcriptional level by miRNAs. Here, we explored the expression of putative BRCA1-regulating miRNAs in sporadic human triple-negative breast cancer cells.Nine sporadic human breast cancer-derived cell lines and one benign breast epithelium-derived cell line were assessed for their hormone receptor, growth factor receptor and cytokeratin status by immunocytochemistry. The expression of 5 selected miRNAs predicted to target BRCA1 was assessed using qRT-PCR in the 10 cell lines. In addition, expression profiles of 84 known breast cancer-associated miRNAs were established in these 10 cell lines using PCR Array and qRT-PCR, respectively. The putative role of pre-selected candidate miRNAs in breast cancer development was assessed through exogenous expression of these miRNAs and their anti-miRNAs ('antagomirs') in MDA-MB-231 and MCF7 breast cancer-derived cells.Based on our expression profiling results, four candidate miRNAs (miR-10b, miR-26a, miR-146a and miR-153) were selected as being potentially involved in triple-negative breast cancer development. Exogenous expression assays revealed that miR-10b and miR-26a, but not miR-146a, can down-regulate the expression of BRCA1 in both triple-negative MDA-MB-231 and luminal epithelial MCF7 breast cancer-derived cells, whereas miR-153 could down-regulate BRCA1 expression only in MCF7 cells. In silico analysis of The Cancer Genome Atlas (TCGA) data confirmed that miR-146a is significantly higher expressed in triple-negative breast tumors compared to other (non triple-negative) breast tumors.Our work provides evidence for the involvement of specific miRNAs in triple-negative breast cancer development through regulating BRCA1 expression.

Project description:Triple-negative breast cancer (TNBC), an intrinsic subtype of breast cancer, is characterized by aggressive pathology and shorter overall survival. Yet there is no effective therapy for these patients. Breast cancer stem cells (BCSCs) in TNBC may account for treatment failure. It is urgent to identify new therapeutic targets for TNBC. tRNA-derived small noncoding RNAs (tDRs) represent a new class of small noncoding RNAs (sncRNA), which have been reported in some human diseases and biological processes. However, there is no detailed information about the relationship between tDRs and BCSCs. In this study, a population of CD44+ /CD24-/low cells was isolated and identified by reliable BCSC surface markers. tDR expression profiles in TNBC and non-TNBC CSCs were performed by RNA sequencing. A total of 1327 differentially expressed tDRs contained 18 upregulated and 54 downregulated in TNBC group. Furthermore, the selected tDRs were validated by quantitative reverse transcription-polymerase chain reaction (qRT-PCR). tDR-000620 expression level was consistently lower in TNBC cell lines CSCs (all P < 0.05) and serum samples (t = 2.597, P = 0.013). tDR-000620 expression was significant association with age (P = 0.018), node status (P = 0.026) and local recurrence (P = 0.019) by chi-square test. Kaplan-Meier method with log-rank test for comparison of recurrence curves. The results showed that the tDR-000620 expression (P = 0.002) and node status (P = 0.001) group were statistically significant with recurrence-free survival. Furthermore, multivariate Cox regression demonstrated that lymphatic metastasis (HR, 3.616; 95% CI, 1.234-10.596; P = 0.019) and low tDR-000620 expression (HR, 0.265; 95% CI, 0.073-0.959; P = 0.043) were two independent adverse predictive factors for recurrence-free survival. Finally, we found that tDR-000620 participated in some important biological processes though GO and KEGG analysis. Taken together, our study reveals the expression profiles of tDRs in TNBC and non-TNBC CSCs. It offers helpful information to understand the tDR-000620 expression is responsible for the aggressive phenotype of BCSCs. It may provide predictive biomarkers and therapeutic targets for TNBC recurrence.

Project description:BackgroundTriple-negative breast cancer (TNBC) is a highly heterogeneous subtype of breast cancer, showing aggressive clinical behaviors and poor outcomes. It urgently needs new therapeutic strategies to improve the prognosis of TNBC. Bioinformatics analyses have been widely used to identify potential biomarkers for facilitating TNBC diagnosis and management.MethodsWe identified potential biomarkers and analyzed their diagnostic and prognostic values using bioinformatics approaches. Including differential expression gene (DEG) analysis, Receiver Operating Characteristic (ROC) curve analysis, functional enrichment analysis, Protein-Protein Interaction (PPI) network construction, survival analysis, multivariate Cox regression analysis, and Non-negative Matrix Factorization (NMF).ResultsA total of 105 DEGs were identified between TNBC and other breast cancer subtypes, which were regarded as heterogeneous-related genes. Subsequently, the KEGG enrichment analysis showed that these genes were significantly enriched in 'cell cycle' and 'oocyte meiosis' related pathways. Four (FAM83B, KITLG, CFD and RBM24) of 105 genes were identified as prognostic signatures in the disease-free interval (DFI) of TNBC patients, as for progression-free interval (PFI), five genes (FAM83B, EXO1, S100B, TYMS and CFD) were obtained. Time-dependent ROC analysis indicated that the multivariate Cox regression models, which were constructed based on these genes, had great predictive performances. Finally, the survival analysis of TNBC subtypes (mesenchymal stem-like [MSL] and mesenchymal [MES]) suggested that FAM83B significantly affected the prognosis of patients.ConclusionsThe multivariate Cox regression models constructed from four heterogeneous-related genes (FAM83B, KITLG, RBM24 and S100B) showed great prediction performance for TNBC patients' prognostic. Moreover, FAM83B was an important prognostic feature in several TNBC subtypes (MSL and MES). Our findings provided new biomarkers to facilitate the targeted therapies of TNBC and TNBC subtypes.

Dataset Information

Identification of NUF2 and FAM83D as potential biomarkers in triple-negative breast cancer.

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Identification of NUF2 and FAM83D as potential biomarkers in triple-negative breast cancer.

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