Department of General Surgery, Xinqiao Hospital, Army Medical University, No. 83 Xinqiao Main Street, Shapingba District, Chongqing, 400037, China.
Department of General Surgery, School of Medicine, South China University of Technology, Guangzhou, Guangdong, 510006, China.
J Cancer Res Clin Oncol. 2024 Oct 23;150(10):473. doi: 10.1007/s00432-024-06001-z.
Breast cancer remains the leading malignant neoplasm among women globally, posing significant challenges in terms of treatment and prognostic evaluation. The metabolic pathway of polyamines is crucial in breast cancer progression, with a strong association to the increased capabilities of tumor cells for proliferation, invasion, and metastasis.
We used a multi-omics approach combining bulk RNA sequencing and single-cell RNA sequencing (scRNA-seq) to study polyamine metabolism. Data from The Cancer Genome Atlas, Gene Expression Omnibus, and Genotype-Tissue Expression identified 286 differentially expressed genes linked to polyamine pathways in breast cancer. These genes were analyzed using univariate COX and machine learning algorithms to develop a prognostic scoring algorithm. Single-cell RNA sequencing validated the model by examining gene expression heterogeneity at the cellular level.
Our single-cell analyses revealed distinct subpopulations with different expressions of genes related to polyamine metabolism, highlighting the heterogeneity of the tumor microenvironment. The SuperPC model (a constructed risk score) demonstrated high accuracy when predicting patient outcomes. The immune profiling and functional enrichment analyses revealed that the genes identified play a crucial role in cell cycle control and immune modulation. Single-cell validation confirmed that polyamine metabolism genes were present in specific cell clusters. This highlights their potential as therapeutic targets.
This study integrates single cell omics with machine-learning to develop a robust scoring model for breast cancer based on polyamine metabolic pathways. Our findings offer new insights into tumor heterogeneity, and a novel framework to personalize prognosis. Single-cell technologies are being used in this context to enhance our understanding of the complex molecular terrain of breast cancer and support more effective clinical management.
乳腺癌仍然是全球女性中最常见的恶性肿瘤，在治疗和预后评估方面带来了重大挑战。多胺的代谢途径在乳腺癌的进展中至关重要，与肿瘤细胞增殖、侵袭和转移能力的增强密切相关。
我们使用多组学方法，结合 bulk RNA 测序和单细胞 RNA 测序（scRNA-seq）来研究多胺代谢。来自癌症基因组图谱（The Cancer Genome Atlas）、基因表达综合数据库（Gene Expression Omnibus）和基因型组织表达数据库（Genotype-Tissue Expression）的数据确定了 286 个与乳腺癌多胺途径相关的差异表达基因。我们使用单变量 COX 和机器学习算法分析这些基因，以开发预后评分算法。单细胞 RNA 测序通过检查细胞水平的基因表达异质性来验证模型。
我们的单细胞分析揭示了具有不同多胺代谢相关基因表达的不同亚群，突出了肿瘤微环境的异质性。SuperPC 模型（构建的风险评分）在预测患者结局方面表现出很高的准确性。免疫特征分析和功能富集分析表明，鉴定的基因在细胞周期控制和免疫调节中发挥关键作用。单细胞验证证实多胺代谢基因存在于特定的细胞群中。这凸显了它们作为治疗靶点的潜力。
本研究整合单细胞组学和机器学习，基于多胺代谢途径开发了一种用于乳腺癌的稳健评分模型。我们的研究结果提供了对肿瘤异质性的新见解，并为个体化预后提供了新的框架。单细胞技术正在用于增强我们对乳腺癌复杂分子地形的理解，并支持更有效的临床管理。
