Oncology Department, The First Affiliated Hospital of Jiamusi University, Jiamusi, China.
Hematology-Oncology Department, Long Nan Hospital, Daqing, China.
Front Public Health. 2022 Jul 6;10:902378. doi: 10.3389/fpubh.2022.902378. eCollection 2022.
Triple negative breast cancer (TNBC) has negative expression of ER, PR and HER-2. TNBC shows high histological grade and positive rate of lymph node metastasis, easy recurrence and distant metastasis. Molecular typing based on metabolic genes can reflect deeper characteristics of breast cancer and provide support for prognostic evaluation and individualized treatment. Metabolic subtypes of TNBC samples based on metabolic genes were determined by consensus clustering. CIBERSORT method was applied to evaluate the score distribution and differential expression of 22 immune cells in the TNBC samples. Linear discriminant analysis (LDA) established a subtype classification feature index. Kaplan-Meier (KM) and receiver operating characteristic (ROC) curves were generated to validate the performance of prognostic metabolic subtypes in different datasets. Finally, we used weighted correlation network analysis (WGCNA) to cluster the TCGA expression profile dataset and screen the co-expression modules of metabolic genes. Consensus clustering of the TCGA cohort/dataset obtained three metabolic subtypes (MC1, MC2, and MC3). The ROC analysis showed a high prognostic performance of the three clusters in different datasets. Specifically, MC1 had the optimal prognosis, MC3 had a poor prognosis, and the three metabolic subtypes had different prognosis. Consistently, the immune characteristic index established based on metabolic subtypes demonstrated that compared with the other two subtypes, MC1 had a higher IFNγ score, T cell lytic activity and lower angiogenesis score, T cell dysfunction and rejection score. TIDE analysis showed that MC1 patients were more likely to benefit from immunotherapy. MC1 patients were more sensitive to immune checkpoint inhibitors and traditional chemotherapy drugs Cisplatin, Paclitaxel, Embelin, and Sorafenib. Multiclass AUC based on RNASeq and GSE datasets were 0.85 and 0.85, respectively. Finally, based on co-expression network analysis, we screened 7 potential gene markers related to metabolic characteristic index, of which CLCA2, REEP6, SPDEF, and CRAT can be used to indicate breast cancer prognosis. Molecular classification related to TNBC metabolism was of great significance for comprehensive understanding of the molecular pathological characteristics of TNBC, contributing to the exploration of reliable markers for early diagnosis of TNBC and predicting metastasis and recurrence, improvement of the TNBC staging system, guiding individualized treatment.
三阴性乳腺癌（TNBC）的 ER、PR 和 HER-2 表达均为阴性。TNBC 组织学分级高，淋巴结转移率高，易复发和远处转移。基于代谢基因的分子分型可以反映乳腺癌更深层次的特征，为预后评估和个体化治疗提供支持。通过共识聚类确定基于代谢基因的 TNBC 样本的代谢亚型。应用 CIBERSORT 方法评估 TNBC 样本中 22 种免疫细胞的评分分布和差异表达。线性判别分析（LDA）建立了亚分类特征指数。生成 Kaplan-Meier（KM）和受试者工作特征（ROC）曲线，以验证不同数据集预后代谢亚型的性能。最后，我们使用加权相关网络分析（WGCNA）对 TCGA 表达谱数据集进行聚类，并筛选代谢基因的共表达模块。对 TCGA 队列/数据集进行共识聚类，得到三个代谢亚型（MC1、MC2 和 MC3）。ROC 分析表明，在不同数据集，三个聚类具有较高的预后性能。具体来说，MC1 具有最佳的预后，MC3 具有较差的预后，三个代谢亚型具有不同的预后。一致的是，基于代谢亚型建立的免疫特征指数表明，与其他两个亚型相比，MC1 具有更高的 IFNγ评分、T 细胞裂解活性和较低的血管生成评分、T 细胞功能障碍和排斥评分。TIDE 分析表明，MC1 患者更有可能受益于免疫治疗。MC1 患者对免疫检查点抑制剂和传统化疗药物顺铂、紫杉醇、Embelin 和 Sorafenib 更敏感。基于 RNASeq 和 GSE 数据集的多类 AUC 分别为 0.85 和 0.85。最后，基于共表达网络分析，筛选出 7 个与代谢特征指数相关的潜在基因标志物，其中 CLCA2、REEP6、SPDEF 和 CRAT 可用于提示乳腺癌预后。与 TNBC 代谢相关的分子分类对于全面了解 TNBC 的分子病理特征具有重要意义，有助于探索 TNBC 早期诊断的可靠标志物，预测转移和复发，改善 TNBC 分期系统，指导个体化治疗。
