PDF(Pubmed)
Abstract：
UNASSIGNED: Increased glycolytic activity and lactate production are characteristic features of triple-negative breast cancer (TNBC). The aim of this study was to determine whether a subset of lactate-responsive genes (LRGs) could be used to classify TNBC subtypes and predict patient outcomes.
UNASSIGNED: Lactate levels were initially measured in different breast cancer (BC) cell types. Subsequently, MDA-MB-231 cells treated with 2-Deoxy-d-glucose or l-lactate were subjected to RNA sequencing (RNA-seq). The gene set variation analysis algorithm was utilized to calculate the lactate-responsive score, conduct a differential analysis, and establish an association with the extent of immune infiltration. Consensus clustering was then employed to classify TNBC patients. Tumor immune dysfunction and exclusion, cibersort, single-sample gene set enrichment analysis, and EPIC, were used to compare the tumor-infiltrating immune cells between TNBC subtypes and predict the response to immunotherapy. Furthermore, a prognostic model was developed by combining 98 machine learning algorithms, to assess the predictive significance of the LRG signature. The predictive value of immune infiltration and the immunotherapy response was also assessed. Finally, the association between lactate and various anticancer drugs was examined based on expression profile similarity principles.
UNASSIGNED: We found that the lactate levels of TNBC cells were significantly higher than those of other BC cell lines. Through RNA-seq, we identified 14 differentially expressed LRGs in TNBC cells under varying lactate levels. Notably, this LRG signature was associated with interleukin-17 signaling pathway dysregulation, suggesting a link between lactate metabolism and immune impairment. Furthermore, the LRG signature was used to categorize TNBC into two distinct subtypes, whereby Subtype A was characterized by immunosuppression, whereas Subtype B was characterized by immune activation.
UNASSIGNED: We identified an LRG signature in TNBC, which could be used to predict the prognosis of patients with TNBC and gauge their response to immunotherapy. Our findings may help guide the precision treatment of patients with TNBC.
UNASSIGNED: Lactate levels were initially measured in different breast cancer (BC) cell types. Subsequently, MDA-MB-231 cells treated with 2-Deoxy-d-glucose or l-lactate were subjected to RNA sequencing (RNA-seq). The gene set variation analysis algorithm was utilized to calculate the lactate-responsive score, conduct a differential analysis, and establish an association with the extent of immune infiltration. Consensus clustering was then employed to classify TNBC patients. Tumor immune dysfunction and exclusion, cibersort, single-sample gene set enrichment analysis, and EPIC, were used to compare the tumor-infiltrating immune cells between TNBC subtypes and predict the response to immunotherapy. Furthermore, a prognostic model was developed by combining 98 machine learning algorithms, to assess the predictive significance of the LRG signature. The predictive value of immune infiltration and the immunotherapy response was also assessed. Finally, the association between lactate and various anticancer drugs was examined based on expression profile similarity principles.
UNASSIGNED: We found that the lactate levels of TNBC cells were significantly higher than those of other BC cell lines. Through RNA-seq, we identified 14 differentially expressed LRGs in TNBC cells under varying lactate levels. Notably, this LRG signature was associated with interleukin-17 signaling pathway dysregulation, suggesting a link between lactate metabolism and immune impairment. Furthermore, the LRG signature was used to categorize TNBC into two distinct subtypes, whereby Subtype A was characterized by immunosuppression, whereas Subtype B was characterized by immune activation.
UNASSIGNED: We identified an LRG signature in TNBC, which could be used to predict the prognosis of patients with TNBC and gauge their response to immunotherapy. Our findings may help guide the precision treatment of patients with TNBC.
摘要：
■糖酵解活性和乳酸产生增加是三阴性乳腺癌（TNBC）的特征性特征。这项研究的目的是确定乳酸反应基因（LRGs）的子集是否可用于对TNBC亚型进行分类并预测患者预后。
■最初在不同的乳腺癌（BC）细胞类型中测量乳酸水平。随后，将用2-脱氧-d-葡萄糖或1-乳酸处理的MDA-MB-231细胞进行RNA测序(RNA-seq)。利用基因集变异分析算法计算乳酸反应性评分,进行差异分析，并建立与免疫浸润程度的关联。然后采用共识聚类对TNBC患者进行分类。肿瘤免疫功能障碍和排斥，cibersort,单样本基因集富集分析，和EPIC,用于比较TNBC亚型之间的肿瘤浸润免疫细胞并预测对免疫疗法的反应。此外，通过结合98种机器学习算法开发了一个预后模型，评估LRG特征的预测意义。还评估了免疫浸润和免疫疗法反应的预测价值。最后,根据表达谱相似性原则，研究了乳酸与各种抗癌药物之间的关联。
■我们发现TNBC细胞的乳酸水平明显高于其他BC细胞系。通过RNA-seq,我们在不同乳酸水平的TNBC细胞中鉴定出14种差异表达的LRGs.值得注意的是,这个LRG特征与白细胞介素-17信号通路失调有关,提示乳酸代谢和免疫损伤之间的联系。此外，LRG签名用于将TNBC分类为两个不同的亚型，其中亚型A以免疫抑制为特征，而B型以免疫激活为特征。
■我们在TNBC中确定了LRG签名，这可用于预测TNBC患者的预后和评估他们对免疫治疗的反应。我们的发现可能有助于指导TNBC患者的精确治疗。
■最初在不同的乳腺癌（BC）细胞类型中测量乳酸水平。随后，将用2-脱氧-d-葡萄糖或1-乳酸处理的MDA-MB-231细胞进行RNA测序(RNA-seq)。利用基因集变异分析算法计算乳酸反应性评分,进行差异分析，并建立与免疫浸润程度的关联。然后采用共识聚类对TNBC患者进行分类。肿瘤免疫功能障碍和排斥，cibersort,单样本基因集富集分析，和EPIC,用于比较TNBC亚型之间的肿瘤浸润免疫细胞并预测对免疫疗法的反应。此外，通过结合98种机器学习算法开发了一个预后模型，评估LRG特征的预测意义。还评估了免疫浸润和免疫疗法反应的预测价值。最后,根据表达谱相似性原则，研究了乳酸与各种抗癌药物之间的关联。
■我们发现TNBC细胞的乳酸水平明显高于其他BC细胞系。通过RNA-seq,我们在不同乳酸水平的TNBC细胞中鉴定出14种差异表达的LRGs.值得注意的是,这个LRG特征与白细胞介素-17信号通路失调有关,提示乳酸代谢和免疫损伤之间的联系。此外，LRG签名用于将TNBC分类为两个不同的亚型，其中亚型A以免疫抑制为特征，而B型以免疫激活为特征。
■我们在TNBC中确定了LRG签名，这可用于预测TNBC患者的预后和评估他们对免疫治疗的反应。我们的发现可能有助于指导TNBC患者的精确治疗。
