School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia.
Petit Institute of Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, Georgia.
Cancer Epidemiol Biomarkers Prev. 2024 May 1;33(5):681-693. doi: 10.1158/1055-9965.EPI-23-1293.
Distinguishing ovarian cancer from other gynecological malignancies is crucial for patient survival yet hindered by non-specific symptoms and limited understanding of ovarian cancer pathogenesis. Accumulating evidence suggests a link between ovarian cancer and deregulated lipid metabolism. Most studies have small sample sizes, especially for early-stage cases, and lack racial/ethnic diversity, necessitating more inclusive research for improved ovarian cancer diagnosis and prevention.
Here, we profiled the serum lipidome of 208 ovarian cancer, including 93 early-stage patients with ovarian cancer and 117 nonovarian cancer (other gynecological malignancies) patients of Korean descent. Serum samples were analyzed with a high-coverage liquid chromatography high-resolution mass spectrometry platform, and lipidome alterations were investigated via statistical and machine learning (ML) approaches.
We found that lipidome alterations unique to ovarian cancer were present in Korean women as early as when the cancer is localized, and those changes increase in magnitude as the diseases progresses. Analysis of relative lipid abundances revealed specific patterns for various lipid classes, with most classes showing decreased abundance in ovarian cancer in comparison with other gynecological diseases. ML methods selected a panel of 17 lipids that discriminated ovarian cancer from nonovarian cancer cases with an AUC value of 0.85 for an independent test set.
This study provides a systemic analysis of lipidome alterations in human ovarian cancer, specifically in Korean women.
Here, we show the potential of circulating lipids in distinguishing ovarian cancer from nonovarian cancer conditions.
区分卵巢癌与其他妇科恶性肿瘤对患者的生存至关重要，但由于非特异性症状和对卵巢癌发病机制的了解有限，这一目标难以实现。越来越多的证据表明卵巢癌与脂质代谢失调之间存在关联。大多数研究的样本量较小，尤其是早期病例，且缺乏种族/民族多样性，因此需要开展更具包容性的研究，以改善卵巢癌的诊断和预防。
在此，我们对 208 例卵巢癌患者（包括 93 例早期卵巢癌患者和 117 例非卵巢癌（其他妇科恶性肿瘤）患者）的血清脂质组进行了分析。采用高覆盖液相色谱-高分辨质谱平台对血清样本进行分析，并通过统计和机器学习（ML）方法研究脂质组的改变。
我们发现，在癌症处于局部阶段时，韩国女性中就存在卵巢癌特有的脂质组改变，并且随着疾病的进展，这些改变的幅度会增加。对相对脂质丰度的分析显示出各种脂质类别的特定模式，与其他妇科疾病相比，大多数脂质类别的丰度在卵巢癌中降低。ML 方法选择了一个由 17 个脂质组成的panel，用于区分卵巢癌和非卵巢癌病例，在独立测试集中 AUC 值为 0.85。
本研究对人类卵巢癌，特别是韩国女性的脂质组改变进行了系统分析。
本研究表明循环脂质在区分卵巢癌与非卵巢癌方面具有潜在应用价值。
