Dataset Information

Characterization of ceRNA network to reveal potential prognostic biomarkers in triple-negative breast cancer.

ABSTRACT: Triple-negative breast cancer (TNBC) is a particular subtype of breast malignant tumor with poorer prognosis than other molecular subtypes. Previous studies have demonstrated that some abnormal expression of non-coding RNAs including microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) were closely related to tumor cell proliferation, apoptosis, invasion, migration and drug sensitivity. However, the role of non-coding RNAs in the pathogenesis of TNBC is still unclear. In order to characterize the molecular mechanism of non-coding RNAs in TNBC, we downloaded RNA data and miRNA data from the cancer genome atlas database. We successfully identified 686 message RNAs (mRNAs), 26 miRNAs and 50 lncRNAs as key molecules for high risk of TNBC. Then, we hypothesized that the lncRNA-miRNA-mRNA regulatory axis positively correlates with TNBC and constructed a competitive endogenous RNA (ceRNA) network of TNBC. Our series of analyses has shown that five molecules (TERT, TRIML2, PHBP4, mir-1-3p, mir-133a-3p) were significantly associated with the prognosis of TNBC, and there is a prognostic ceRNA sub-network between those molecules. We mapped the Kaplan-Meier curve of RNA on the sub-network and also suggested that the expression level of the selected RNA is related to the survival rate of breast cancer. Reverse transcription-quantitative polymerase chain reaction showed that the expression level of TRIML2 in TNBC cells was higher than normal. In general, our findings have implications for predicting metastasis, predicting prognosis and discovering new therapeutic targets for TNBC.

SUBMITTER: Song X

PROVIDER: S-EPMC6741283 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Characterization of ceRNA network to reveal potential prognostic biomarkers in triple-negative breast cancer.

Song Xiang X Zhang Chao C Liu Zhaoyun Z Liu Qi Q He Kewen K Yu Zhiyong Z

PeerJ 20190909

Triple-negative breast cancer (TNBC) is a particular subtype of breast malignant tumor with poorer prognosis than other molecular subtypes. Previous studies have demonstrated that some abnormal expression of non-coding RNAs including microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) were closely related to tumor cell proliferation, apoptosis, invasion, migration and drug sensitivity. However, the role of non-coding RNAs in the pathogenesis of TNBC is still unclear. In order to characterize t ...[more]

PMID: 31565554

Similar Datasets

Project description:BackgroundMore and more studies have proven that circular RNAs (circRNAs) play vital roles in cancer development via sponging miRNAs. However, the expression pattern of competing endogenous RNA (ceRNA) in lung adenocarcinoma (LUAD) remains largely unclear. The current study explored functional roles and the regulatory mechanisms of circRNA as ceRNAs in LUAD and their potential impact on LUAD patient prognosis.MethodsIn this study, we systematically screened differential expression circRNAs (DEcircRNAs), miRNAs (DEmiRNAs) and mRNAs (DEGs) associated with LUAD. Then, DEcircRNAs, DEmiRNAs and DEGs were selected to construct a circRNA-miRNA-mRNA prognosis-related regulatory network based on interaction information from the ENCORI database. Subsequently, the gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed on the genes in the network to predict the potential underlying mechanisms and functions of circRNAs in LUAD. In addition, Kaplan-Meier survival analysis was performed to evaluate clinical outcomes of LUAD patients, and drug sensitivity analysis was used to screen potential biomarkers for drug treatment of patients with LUAD.ResultsAs a result, 10 circRNAs were aberrantly expressed in LUAD tissues. The ceRNA network was built, which included 3 DEcircRNAs, 6 DEmiRNAs and 157 DEGs. The DEGs in the ceRNA network of hsa_circ_0049271 enriched in biological processes of cell proliferation and the Jak-STAT signaling pathway. We also detected 7 mRNAs in the ceRNA network of hsa_circ_0049271 that were significantly associated with the overall survival of LUAD patients (P < 0.05). Importantly, four genes (PDGFB, CCND2, CTF1, IL7R) identified were strongly associated with STAT3 activation and drugs sensitivity in GDSC.ConclusionsIn summary, a ceRNA network of hsa_circ_0049271 was successfully constructed, which including one circRNA, two miRNAs, and seven mRNAs. Seven mRNAs (PDGFB, TNFRSF19, CCND2, CTF1, IL11RA, IL7R and MAOA) were remarkably associated with the prognosis of LUAD patients. Among seven mRNA species, four genes (PDGFB, CCND2, CTF1, and IL7R) could be considered as drug targets in LUAD. Our research will provide new insights into the prognosis-related ceRNA network in LUAD.

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

Characterization of ceRNA network to reveal potential prognostic biomarkers in triple-negative breast cancer.

Publications

Characterization of ceRNA network to reveal potential prognostic biomarkers in triple-negative breast cancer.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets