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.

BACKGROUND: Breast cancer is one of the most common cancer in women and a proportion of patients experiences brain metastases with poor prognosis. The study aimed to construct a novel predictive clinical model to evaluate the overall survival (OS) of patients with postoperative brain metastasis of breast cancer (BCBM) and validate its effectiveness. METHODS: From 2010 to 2020, a total of 310 female patients with BCBM were diagnosed in The Affiliated Cancer Hospital of Xinjiang Medical University, and they were randomly assigned to the training cohort and the validation cohort. Data of another 173 BCBM patients were collected from the Surveillance, Epidemiology, and End Results Program (SEER) database as an external validation cohort. In the training cohort, the least absolute shrinkage and selection operator (LASSO) Cox regression model was used to determine the fundamental clinical predictive indicators and the nomogram was constructed to predict OS. The model capability was assessed using receiver operating characteristic, C-index, and calibration curves. Kaplan-Meier survival analysis was performed to evaluate clinical effectiveness of the risk stratification system in the model. The accuracy and prediction capability of the model were verified using the validation and SEER cohorts. RESULTS: LASSO Cox regression analysis revealed that lymph node metastasis, molecular subtype, tumor size, chemotherapy, radiotherapy, and lung metastasis were statistically significantly correlated with BCBM. The C-indexes of the survival nomogram in the training, validation, and SEER cohorts were 0.714, 0.710, and 0.670, respectively, which showed good prediction capability. The calibration curves demonstrated that the nomogram had great forecast precision, and a dynamic diagram was drawn to increase the maneuverability of the results. The Risk Stratification System showed that the OS of low-risk patients was considerably better than that of high-risk patients ( P < 0.001). CONCLUSION The nomogram prediction model constructed in this study has a good predictive value, which can effectively evaluate the survival rate of patients with postoperative BCBM.
Female ; Humans ; Breast Neoplasms/surgery* ; Retrospective Studies ; Prognosis ; Brain Neoplasms/surgery* ; Nomograms