Department of Neonatology, Obstetrics and Gynecology Hospital of Fudan University, No. 419, Fangxie Road, Shanghai, 200011, China.
Department of Rehabilitation Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China.
Sci Rep. 2021 Feb 17;11(1):3986. doi: 10.1038/s41598-021-83628-9.
One of the most frequently identified tumors and a contributing cause of death in women is breast cancer (BC). Many biomarkers associated with survival and prognosis were identified in previous studies through database mining. Nevertheless, the predictive capabilities of single-gene biomarkers are not accurate enough. Genetic signatures can be an enhanced prediction method. This research analyzed data from The Cancer Genome Atlas (TCGA) for the detection of a new genetic signature to predict BC prognosis. Profiling of mRNA expression was carried out in samples of patients with TCGA BC (n = 1222). Gene set enrichment research has been undertaken to classify gene sets that vary greatly between BC tissues and normal tissues. Cox models for additive hazards regression were used to classify genes that were strongly linked to overall survival. A subsequent Cox regression multivariate analysis was used to construct a predictive risk parameter model. Kaplan-Meier survival predictions and log-rank validation have been used to verify the value of risk prediction parameters. Seven genes (PGK1, CACNA1H, IL13RA1, SDC1, AK3, NUP43, SDC3) correlated with glycolysis were shown to be strongly linked to overall survival. Depending on the 7-gene-signature, 1222 BC patients were classified into subgroups of high/low-risk. Certain variables have not impaired the prognostic potential of the seven-gene signature. A seven-gene signature correlated with cellular glycolysis was developed to predict the survival of BC patients. The results include insight into cellular glycolysis mechanisms and the detection of patients with poor BC prognosis.
乳腺癌 (BC) 是女性最常见的肿瘤之一，也是导致死亡的一个重要原因。许多与生存和预后相关的生物标志物在以前的研究中通过数据库挖掘被鉴定出来。然而，单个基因生物标志物的预测能力还不够准确。遗传特征可以作为一种增强的预测方法。本研究通过分析癌症基因组图谱 (TCGA) 中的数据，发现了一种新的遗传特征，用于预测 BC 的预后。对 TCGA BC 患者的样本进行了 mRNA 表达谱分析（n=1222）。进行了基因集富集研究，以分类在 BC 组织和正常组织之间差异很大的基因集。采用 Cox 模型进行加性风险回归分析，以分类与总生存期密切相关的基因。随后进行 Cox 回归多变量分析，构建预测风险参数模型。采用 Kaplan-Meier 生存预测和对数秩验证来验证风险预测参数的价值。与糖酵解相关的 7 个基因（PGK1、CACNA1H、IL13RA1、SDC1、AK3、NUP43、SDC3）与总生存期密切相关。根据 7 基因特征，将 1222 例 BC 患者分为高/低风险亚组。某些变量并没有损害七个基因特征的预后潜力。已经开发出与细胞糖酵解相关的七个基因特征来预测 BC 患者的生存情况。研究结果包括对细胞糖酵解机制的深入了解和检测预后不良的 BC 患者。
