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| Content Provider | frontiers |
|---|---|
| Author | Wang, Xiaolong Li, Chen Chen, Tong Li, Wenhao Zhang, Hanwen Zhang, Dong Liu, Ying Han, Dianwen Li, Yaming Li, Zheng Luo, Dan Zhang, Ning Yang, Qifeng |
| Abstract | Background: Recent years, the global prevalence of breast cancer (BC) was still high and the underlying molecular mechanisms were remained largely unknown. The investigation of prognosis-related biomarkers had become an urgent demand. Results: In this study, gene expression profiles and clinical information of breast cancer patients were downloaded from TCGA database. The differentially expressed genes (DEGs) were estimated by Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. A risk score formula involving five novel prognostic associated biomarkers (EDN2, CLEC3B, SV2C, WT1 and MUC2) were then constructed by LASSO. The prognostic value of the risk model was further confirmed in the TCGA entire cohort and an independent external validation cohort. To explore the biological functions of the selected genes, in vitro assays were performed, indicating that these novel biomarkers could markedly influence breast cancer progression. Conclusions: We established a predictive five-gene signature, which could be helpful for personalized management in breast cancer patients. |
| ISSN | 2234943X |
| DOI | 10.3389/fonc.2021.660242 |
| Volume Number | 11 |
| Journal | Frontiers in Oncology |
| Language | English |
| Publisher Date | 2021-08-26 |
| Access Restriction | Open |
| Subject Keyword | Breast cancer Prognostic biomarkers Bioinformatics Individualized therapy RiskScore LASSO COX |
| Content Type | Text |
| Resource Type | Article |
| Subject | Cancer Research Oncology |
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