Table_1_Integrating single-cell RNA-seq and bulk RNA-seq to construct prognostic signatures to explore the role of glutamine metabolism in breast cancer.docx

BackgroundAlthough breast cancer (BC) treatment has entered the era of precision therapy, the prognosis is good in the case of comprehensive multimodal treatment such as neoadjuvant, endocrine, and targeted therapy. However, due to its high heterogeneity, some patients still cannot benefit from conventional treatment and have poor survival prognoses. Amino acids and their metabolites affect tumor development, alter the tumor microenvironment, play an increasingly obvious role in immune response and regulation of immune cell function, and are involved in acquired and innate immune regulation; therefore, amino acid metabolism is receiving increasing attention.
MethodsBased on public datasets, we carried out a comprehensive transcriptome and single-cell sequencing investigation. Then we used 2.5 Weighted Co-Expression Network Analysis (WGCNA) and Cox to evaluate glutamine metabolism-related genes (GRGs) in BC and constructed a prognostic model for BC patients. Finally, the expression and function of the signature key gene SNX3 were examined by in vitro experiments.
ResultsIn this study, we constituted a risk signature to predict overall survival (OS) in BC patients by glutamine-related genes. According to our risk signature, BC patients can obtain a Prognostic Risk Signature (PRS), and the response to immunotherapy can be further stratified according to PRS. Compared with traditional clinicopathological features, PRS demonstrated robust prognostic power and accurate survival prediction. In addition, altered pathways and mutational patterns were analyzed in PRS subgroups. Our study sheds some light on the immune status of BC. In in vitro experiments, the knockdown of SNX3, an essential gene in the signature, resulted in a dramatic reduction in proliferation, invasion, and migration of MDA-MB-231 and MCF-7 cell lines.
ConclusionWe established a brand-new PRS consisting of genes associated with glutamine metabolism. It expands unique ideas for the diagnosis, treatment, and prognosis of BC.

背景：尽管乳腺癌（breast cancer, BC）治疗已迈入精准治疗时代，若接受新辅助治疗、内分泌治疗及靶向治疗等综合多模态治疗，患者通常可获得良好预后。但由于乳腺癌具有高度异质性，部分患者仍无法从常规治疗中获益，生存预后不佳。氨基酸及其代谢产物可影响肿瘤发生发展、重塑肿瘤微环境，在免疫应答与免疫细胞功能调控中的作用日益凸显，且参与适应性免疫与固有免疫调控；因此，氨基酸代谢受到的研究关注度与日俱增。
方法：本研究基于公共数据集开展了全面的转录组与单细胞测序分析。随后采用2.5加权基因共表达网络分析（Weighted Co-Expression Network Analysis, WGCNA）与Cox比例风险回归模型，评估乳腺癌中谷氨酰胺代谢相关基因（glutamine metabolism-related genes, GRGs），并构建乳腺癌患者的预后预测模型。最后通过体外实验验证了特征核心基因SNX3的表达与功能。
结果：本研究通过谷氨酰胺相关基因构建了可预测乳腺癌患者总生存期（overall survival, OS）的风险特征模型。根据该风险特征模型，乳腺癌患者可获得预后风险特征（Prognostic Risk Signature, PRS），并可进一步依据PRS对免疫治疗应答情况进行分层。相较于传统临床病理特征，PRS展现出稳健的预后评估能力与精准的生存预测效能。此外，本研究还分析了PRS亚组中的通路改变与突变模式。本研究为阐明乳腺癌的免疫状态提供了新的研究视角。在体外实验中，敲低特征核心基因SNX3可显著降低MDA-MB-231与MCF-7细胞系的增殖、侵袭与迁移能力。
结论：本研究构建了一套全新的谷氨酰胺代谢相关基因预后风险特征模型，为乳腺癌的诊断、治疗与预后评估提供了独特的研究思路。