Guo Jiani, Yi Xuesong, Ji Zhuqing, Yao Mengchu, Yang Yu, Song Wei, Huang Mingde
Department of Medical Oncology, The Affiliated Huaian No. 1 People's Hospital of Nanjing Medical University, Huai'an, Jiangsu, China.
Department of Medical Oncology, The Huaian Clinical College of Xuzhou Medical University, Huai'an, Jiangsu, China.
J Oncol. 2021 Nov 27;2021:9219961. doi: 10.1155/2021/9219961. eCollection 2021.
Triple-negative breast cancer (TNBC) remains the most incurable subtype of breast cancer owing to high heterogeneity, aggressive nature, and lack of treatment options. It is generally acknowledged that epithelial-mesenchymal transition (EMT) is the key step in tumor metastasis.
With the application of TCGA and GEO databases, we identified EMT-related lncRNAs by the Cox univariate regression analysis. Optimum risk scores were calculated and used to divide TNBC patients into high-/low-risk subgroups by the median value using the Lasso regression analysis. The Kaplan-Meier and ROC curve analyses were applied for model validation. Then, we assessed the risk model from multi-omic aspects including immune infiltration, drug sensitivity, mutability spectrum, signaling pathways, and clinical indicators. We also analyzed the expression pattern of lncRNAs involved in the model using qRT-PCR in TNBC cell lines and constructed the ceRNA network.
The risk model was composed of EMT-related long noncoding RNAs (lncRNAs), which seemed to be valuable in the prognostic prediction of TNBC patients. The model could act as an independent prognostic factor of TNBC and showed a robust prognostic ability in the stratification analysis. Further investigation demonstrated that the expression of lncRNAs was different between high aggressive and low aggressive TNBC cell lines, as well as TNBC patients.
Together, our study successfully established a risk model with great accuracy and efficacy in the prognostic prediction of TNBC patients.
三阴性乳腺癌（TNBC）由于高度异质性、侵袭性强且缺乏治疗选择，仍然是最难以治愈的乳腺癌亚型。上皮-间质转化（EMT）是肿瘤转移的关键步骤，这一点已得到普遍认可。
通过应用TCGA和GEO数据库，我们通过Cox单变量回归分析鉴定了与EMT相关的长链非编码RNA（lncRNA）。计算最佳风险评分，并使用Lasso回归分析通过中位数将TNBC患者分为高/低风险亚组。应用Kaplan-Meier和ROC曲线分析进行模型验证。然后，我们从免疫浸润、药物敏感性、突变谱、信号通路和临床指标等多组学方面评估了风险模型。我们还使用qRT-PCR分析了TNBC细胞系中模型所涉及的lncRNA的表达模式，并构建了ceRNA网络。
风险模型由与EMT相关的长链非编码RNA（lncRNA）组成，这似乎对TNBC患者的预后预测具有重要价值。该模型可作为TNBC的独立预后因素，并且在分层分析中显示出强大的预后能力。进一步研究表明，lncRNA的表达在高侵袭性和低侵袭性TNBC细胞系以及TNBC患者之间存在差异。
总之，我们的研究成功建立了一个在TNBC患者预后预测中具有高度准确性和有效性的风险模型。
