RESUMO
Objective:To study the effect of long noncoding RNA growth arrest-specifific transcript 5 (lncRNA GAS5) on the occurrence and development of triple-negative breast cancer (TNBC) by analyzing the differential expression of lncrna GAS5 in The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases.Methods:The expression of GAS5 in each subtype and pathological stage of breast cancer was studied by the TCGA data. The correlation of GAS5 was analyzed by using TNBC data GSE76124 and GSE83937 from the GEO database of the United States. The elated genes were collected and take the intersection. The positive correlation genes were used to analyze the GO function and the enrichment of KEGG pathway. GSEA of GAS5 was analyzed with TCGA database and GEO76124 data. GSE40525 and GSE76250 were selected from GEO data set to screen different miRNA and mRNA of TNBC, and construct the ceRNA network of GAS5-mirna-mrna through prediction.Results:The expression of GAS5 in breast cancer was lower than that in the adjacent tissues. GAS5 was mainly involved in various metabolic processes, including organic metabolism, macromolecular metabolism, nitrogen metabolism, etc. In terms of pathway, GAS5 mainly affected the ribosome biogenesis in eukaryotes, Wnt signaling pathway. By constructing the regulatory network of GAS5 in TNBC, we found that GAS5 was most likely to regulate the expression of 25 genes including SLC7A2 and lLONRF2 by adsorbing hsa-mir-650 and has-mir-532-5p.Conclusion:lncrna GAS5 may play a role of tumor suppressor gene in breast cancer and provide a new therapeutic target for gene therapy of breast cancer.
RESUMO
p53, as a transcription factor, is an important tumor suppressor gene and plays the key role in the p53-dependent gene regulatory network. Therefore, it is important to understand its biological function at the level of the whole system. In this paper, based on KEGG database and related literatures in English and Chinese, the interaction mode and quantitative relationship of the related molecules involved in p53 signaling pathway were extracted. By using S-system equations and 'Simulink' toolbox of Matlab7.0, a dynamic model of p53 signaling pathway was developed, and the dynamic regulatory characteristics of p53 signaling pathway were analyzed on model simulation. The results were in accord with the literatures and could reflect quantitatively the complex regulatory relationship between the interacting molecules involved in p53 signaling pathway. In addition, model simulation helped us find and identify the key molecules in this signaling pathway. Thus, this model can be used as a basis for the follow-up study of the relationship by precise and quantitative assessment.