脂质组学和代谢组学作为乳腺癌进展的潜在生物标记物

Alanis Carmona, Samir Mitri, Ted A. James, Jessalyn M. Ubellacker
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摘要

乳腺癌是美国女性中发病率最高的癌症,每年约占女性癌症新增病例总数的 30%。据估计,2024 年将有 310,720 例新的浸润性乳腺癌病例被确诊,乳腺癌将导致 42,000 多名女性死亡。如今,尽管有许多治疗乳腺癌及其症状的方法,但大多数与癌症相关的死亡病例都是死于无法治疗的转移。这就强调了在乳腺癌扩散之前及早发现和治疗的重要性。对于乳腺癌的初步检测和分期,临床医生通常采用乳房 X 线照相术和超声波照相术,这两种方法虽然对广泛筛查有效,但在灵敏度和特异性方面存在局限性。先进的生物标志物可以大大提高早期检测的精确度,更准确地监测疾病的发展,并有助于根据每个肿瘤的具体分子特征制定个性化的治疗方案。这不仅能提高治疗效果,还有助于避免过度治疗和相关副作用,从而提高患者的生活质量。因此,寻找新型生物标记物(可能包括代谢组学和脂质组学特征)对于推进乳腺癌的诊断和治疗至关重要。在这篇简短的综述中,我们将概述目前用于预测乳腺癌预后和治疗反应的代谢组和脂质组生物标志物的转化潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Lipidomics and metabolomics as potential biomarkers for breast cancer progression
Breast cancer is the most prevalent cancer among women in the United States, representing ~30% of all new female cancer cases annually. For the year 2024, it is estimated that 310,720 new instances of invasive breast cancer will be diagnosed, and breast cancer will be responsible for over 42,000 deaths among women. Today, despite the availability of numerous treatments for breast cancer and its symptoms, most cancer-related deaths result from metastasis for which there is no treatment. This emphasizes the importance of early detection and treatment of breast cancer before it spreads. For initial detection and staging of breast cancer, clinicians routinely employ mammography and ultrasonography, which, while effective for broad screening, have limitations in sensitivity and specificity. Advanced biomarkers could significantly enhance the precision of early detection, enable more accurate monitoring of disease evolution, and facilitate the development of personalized treatment plans tailored to the specific molecular profile of each tumor. This would not only improve therapeutic outcomes, but also help in avoiding overtreatment and the associated side effects, thereby improving the quality of life for patients. Thus, the pursuit of novel biomarkers, potentially encompassing metabolomic and lipidomic signatures, is essential for advancing breast cancer diagnosis and treatment. In this brief review, we will provide an overview of the current translational potential of metabolic and lipidomic biomarkers for predicting breast cancer prognosis and response to therapy.
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