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多组学分析鉴定出用于乳腺癌早期检测和治疗反应预测的代谢生物标志物。

Multi-omic analysis identifies metabolic biomarkers for the early detection of breast cancer and therapeutic response prediction.

作者信息

Song Huajie, Tang Xiaowei, Liu Miao, Wang Guangxi, Yuan Yuyao, Pang Ruifang, Wang Chenyi, Zhou Juntuo, Yang Yang, Zhang Mengmeng, Jin Yan, Jiang Kewei, Wang Shu, Yin Yuxin

机构信息

Department of Pathology, Institute of Systems Biomedicine, School of Basic Medical Sciences, Peking-Tsinghua Center for Life Sciences, Peking University Health Science Center, Beijing 100191, China.

Breast Center, Peking University People's Hospital, Beijing 100044, China.

出版信息

iScience. 2024 Aug 5;27(9):110682. doi: 10.1016/j.isci.2024.110682. eCollection 2024 Sep 20.

DOI:10.1016/j.isci.2024.110682
PMID:39252976
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11381768/
Abstract

Reliable blood-based tests for identifying early-stage breast cancer remain elusive. Employing single-cell transcriptomic sequencing analysis, we illustrate a close correlation between nucleotide metabolism in the breast cancer and activation of regulatory T cells (Tregs) in the tumor microenvironment, which shows distinctions between subtypes of patients with triple-negative breast cancer (TNBC) and non-TNBC, and is likely to impact cancer prognosis through the A2AR-Treg pathway. Combining machine learning with absolute quantitative metabolomics, we have established an effective approach to the early detection of breast cancer, utilizing a four-metabolite panel including inosine and uridine. This metabolomics study, involving 1111 participants, demonstrates high accuracy across the training, test, and independent validation cohorts. Inosine and uridine prove predictive of the response to neoadjuvant chemotherapy (NAC) in patients with TNBC. This study deepens our understanding of nucleotide metabolism in breast cancer development and introduces a promising non-invasive method for early breast cancer detection and predicting NAC response in patients with TNBC.

摘要

用于识别早期乳腺癌的可靠血液检测方法仍然难以捉摸。通过单细胞转录组测序分析，我们阐明了乳腺癌中的核苷酸代谢与肿瘤微环境中调节性T细胞（Tregs）激活之间的密切相关性，这在三阴性乳腺癌（TNBC）和非TNBC患者亚型之间表现出差异，并可能通过A2AR-Treg途径影响癌症预后。将机器学习与绝对定量代谢组学相结合，我们建立了一种利用包括肌苷和尿苷在内的四种代谢物组合进行早期乳腺癌检测的有效方法。这项涉及1111名参与者的代谢组学研究在训练、测试和独立验证队列中均显示出高精度。肌苷和尿苷被证明可预测TNBC患者对新辅助化疗（NAC）的反应。这项研究加深了我们对乳腺癌发展过程中核苷酸代谢的理解，并引入了一种有前景的非侵入性方法用于早期乳腺癌检测和预测TNBC患者的NAC反应。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c204/11381768/7caf6cd0a0cd/gr6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c204/11381768/82d930d00588/fx1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c204/11381768/8889e2c87302/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c204/11381768/a0a7358a2540/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c204/11381768/8fb432c9cde3/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c204/11381768/1e2d71f2d02b/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c204/11381768/022091f3d305/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c204/11381768/7caf6cd0a0cd/gr6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c204/11381768/82d930d00588/fx1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c204/11381768/8889e2c87302/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c204/11381768/a0a7358a2540/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c204/11381768/8fb432c9cde3/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c204/11381768/1e2d71f2d02b/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c204/11381768/022091f3d305/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c204/11381768/7caf6cd0a0cd/gr6.jpg

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本文引用的文献

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Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries.2022 年全球癌症统计数据：全球 185 个国家和地区 36 种癌症的发病率和死亡率全球估计数。
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