Background. It is well known that cancer stem cells can induce cancer metastasis, which causes the majority of cancer-related death, especially in triple-negative breast cancer (TNBC). TNBC features a high metastatic rate and low metastasis-free survival and is regarded as the most malignant subtype of breast cancer. The purpose of this study is to explore prognostic biomarkers that can predict metastasis of triple-negative breast cancer. Methods. The human triple-negative breast cancerSUM149PT cells were used for the study. The cancer stem cell spheres (sum149-Stem) and paired adherent cancer cells (sum149-Tumor) were collected to extract total RNAs. RNA-seq was used to analysis the mRNA expression of cancer stem cells and paired adherent cancer cells. Two different gene expression omnibus datasets (https://www.ncbi.nlm.nih.gov/gds), GSE58812 and GSE33926, were used to explore the mechanism of different expression genes between stem cells and adherent cancer cells. Seven genes showed prognostic function in all datasets. The STITCH database (https://www.stitchdata.com/) was used to explore the possible metastasis-inhibiting drugs that can target the seven genes. Each gene expression was compared by Pearson analysis. The receiver operating characteristic curve (ROC) and Kaplan–Meier survival curve were performed to assess the metastasis prognostic ability of the seven-gene modeling two different GEO datasets. Results. A subset of 7 stemness-related genes (SRGs) containing UCN, ST3GAL5, FDPS, HK2, MALL, LMTK3, and CRHR2 were identified in three independent cohorts. Univariate Cox analysis showed that ST3GAL5 plays an antitumor role in TNBC metastasis, and the other 6 genes promote the metastatic progression of TNBC. The ability of the 7-SRGs gene Cox model to predict TNBC metastasis was constructed with the GSE58812 dataset. Most of the genes showed significant expression in patients with different risk levels. Additionally, the model showed predictive value in another GEO dataset of TNBC patients. ROC curves indicated that the seven-gene model has a significant predictive value of TNBC metastasis. Conclusions. Expression analysis of the 7-SRGs signature model at diagnosis has predictive value for metastasis in TNBC patients.

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三阴性乳腺癌预后干性相关基因特征的开发和验证

背景。众所周知，癌症干细胞可以诱导癌症转移，导致大多数癌症相关死亡，尤其是三阴性乳腺癌 (TNBC)。TNBC具有高转移率和低无转移生存率的特点，被认为是乳腺癌中恶性程度最高的亚型。本研究的目的是探索可以预测三阴性乳腺癌转移的预后生物标志物。方法. 人类三阴性乳腺癌 SUM149PT 细胞用于该研究。收集癌症干细胞球体 (sum149-Stem) 和配对的贴壁癌细胞 (sum149-Tumor) 以提取总 RNA。RNA-seq 用于分析癌症干细胞和成对贴壁癌细胞的 mRNA 表达。两个不同的基因表达综合数据集 (https://www.ncbi.nlm.nih.gov/gds)，GSE58812 和 GSE33926，用于探索干细胞和贴壁癌细胞之间不同表达基因的机制。七个基因在所有数据集中均显示出预后功能。STITCH 数据库 (https://www.stitchdata.com/) 用于探索可以靶向这七个基因的可能的转移抑制药物。通过 Pearson 分析比较每个基因表达。结果。在三个独立队列中鉴定出 7 个干性相关基因 (SRG) 的子集，其中包含 UCN、ST3GAL5、FDPS、HK2、MALL、LMTK3 和 CRHR2。单变量Cox分析显示ST3GAL5在TNBC转移中发挥抗肿瘤作用，其他6个基因促进TNBC转移进展。7-SRGs 基因 Cox 模型预测 TNBC 转移的能力是用 GSE58812 数据集构建的。大多数基因在不同风险水平的患者中表现出显着表达。此外，该模型在 TNBC 患者的另一个 GEO 数据集中显示了预测价值。ROC曲线表明七基因模型对TNBC转移具有显着的预测价值。结论. 诊断时 7-SRGs 特征模型的表达分析对 TNBC 患者的转移具有预测价值。