Liu Liu, Chen Zhilin, Shi Wenjie, Liu Hui, Pang Weiyi
Department of Pharmacy, Pharmacy School of Guilin Medical University, Guilin, Guangxi 541000, P.R. China.
Department of Breast and Thoracic Oncological Surgery, The First Affiliated Hospital of Hainan Medical College, Haikou, Hainan 570102, P.R. China.
Oncol Lett. 2019 Sep;18(3):2907-2916. doi: 10.3892/ol.2019.10635. Epub 2019 Jul 18.
Breast cancer (BC) is one of the most prevalent forms of cancer globally. However, the practical relevance of the RNA expression-based prediction of BC is not clearly understood and requires further study. Using gene expression data downloaded from The Cancer Genome Atlas (TCGA), a risk score staging classification was created using Cox's multiple regression and was used to predict the clinical outcomes of patients with BC. In total, 7 genes, including AC123595.1, leukocyte immunoglobulin-like receptor B5, CD209 molecule, AL049749.1, lymphatic vessel endothelial hyaluronan receptor 1, transmembrane protein 190 and tubulin α 3D chain were identified in association with patient survival. The patients with lower risk scores had considerably improved survival rates than those with higher risk scores. Compared with other clinical factors, the risk score more accurately predicted the clinical outcome of patients with BC. In summary, 7 genes were identified using the Cox regression model, and subsequently used to develop a risk staging model for BC, which may be of use for the medical management of patients.
乳腺癌（BC）是全球最常见的癌症形式之一。然而，基于RNA表达的BC预测的实际相关性尚不清楚，需要进一步研究。利用从癌症基因组图谱（TCGA）下载的基因表达数据，使用Cox多元回归创建了风险评分分期分类，并用于预测BC患者的临床结局。总共鉴定出7个基因，包括AC123595.1、白细胞免疫球蛋白样受体B5、CD209分子、AL049749.1、淋巴管内皮透明质酸受体1、跨膜蛋白190和微管蛋白α 3D链，它们与患者生存相关。风险评分较低的患者的生存率明显高于风险评分较高的患者。与其他临床因素相比，风险评分能更准确地预测BC患者的临床结局。总之，使用Cox回归模型鉴定出7个基因，随后用于开发BC风险分期模型，这可能对患者的医疗管理有用。
