整合单细胞全息技术和机器学习,开发基于多胺代谢的乳腺癌患者风险评分模型。

IF 2.7 3区 医学 Q3 ONCOLOGY Journal of Cancer Research and Clinical Oncology Pub Date : 2024-10-23 DOI:10.1007/s00432-024-06001-z
Xiliang Zhang, Hanjie Guo, Xiaolong Li, Wei Tao, Xiaoqing Ma, Yuxing Zhang, Weidong Xiao
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引用次数: 0

摘要

背景:乳腺癌仍然是全球妇女中最主要的恶性肿瘤,给治疗和预后评估带来了巨大挑战。多胺的代谢途径对乳腺癌的进展至关重要,与肿瘤细胞增殖、侵袭和转移能力的增强密切相关:方法:我们采用了一种多组学方法,结合大量 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、基因表达总库和基因型-组织表达的数据确定了286个与乳腺癌多胺通路相关的差异表达基因。利用单变量 COX 和机器学习算法对这些基因进行了分析,从而开发出一种预后评分算法。单细胞 RNA 测序通过检查细胞水平的基因表达异质性验证了该模型:结果:我们的单细胞分析揭示了与多胺代谢相关基因表达不同的亚群,凸显了肿瘤微环境的异质性。SuperPC模型(构建的风险评分)在预测患者预后方面表现出很高的准确性。免疫图谱和功能富集分析表明,所发现的基因在细胞周期控制和免疫调节中发挥着关键作用。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:本研究将单细胞全息技术与机器学习相结合,开发出一种基于多胺代谢通路的乳腺癌稳健评分模型。我们的研究结果为了解肿瘤的异质性提供了新的视角,也为个性化预后提供了新的框架。在这种情况下,单细胞技术的应用将增强我们对乳腺癌复杂分子地形的了解,并支持更有效的临床管理。
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Single-cell omics and machine learning integration to develop a polyamine metabolism-based risk score model in breast cancer patients.

Background: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

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来源期刊
CiteScore
4.00
自引率
2.80%
发文量
577
审稿时长
2 months
期刊介绍: The "Journal of Cancer Research and Clinical Oncology" publishes significant and up-to-date articles within the fields of experimental and clinical oncology. The journal, which is chiefly devoted to Original papers, also includes Reviews as well as Editorials and Guest editorials on current, controversial topics. The section Letters to the editors provides a forum for a rapid exchange of comments and information concerning previously published papers and topics of current interest. Meeting reports provide current information on the latest results presented at important congresses. The following fields are covered: carcinogenesis - etiology, mechanisms; molecular biology; recent developments in tumor therapy; general diagnosis; laboratory diagnosis; diagnostic and experimental pathology; oncologic surgery; and epidemiology.
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