Beijing Shijitan Hospital, Capital Medical University, Beijing, China.
Shanghai Yunxiang Medical Technology Co., Ltd., Shanghai, China.
BMC Cancer. 2023 Sep 8;23(1):844. doi: 10.1186/s12885-023-11192-8.
Ovarian cancer (OC) is a highly lethal gynecological malignancy. Extensive research has shown that OC cells undergo significant metabolic alterations during tumorigenesis. In this study, we aim to leverage these metabolic changes as potential biomarkers for assessing ovarian cancer.
A functional module-based approach was utilized to identify key gene expression pathways that distinguish different stages of ovarian cancer (OC) within a tissue biopsy cohort. This cohort consisted of control samples (n = 79), stage I/II samples (n = 280), and stage III/IV samples (n = 1016). To further explore these altered molecular pathways, minimal spanning tree (MST) analysis was applied, leading to the formulation of metabolic biomarker hypotheses for OC liquid biopsy. To validate, a multiple reaction monitoring (MRM) based quantitative LCMS/MS method was developed. This method allowed for the precise quantification of targeted metabolite biomarkers using an OC blood cohort comprising control samples (n = 464), benign samples (n = 3), and OC samples (n = 13).
Eleven functional modules were identified as significant differentiators (false discovery rate, FDR < 0.05) between normal and early-stage, or early-stage and late-stage ovarian cancer (OC) tumor tissues. MST analysis revealed that the metabolic L-arginine/nitric oxide (L-ARG/NO) pathway was reprogrammed, and the modules related to "DNA replication" and "DNA repair and recombination" served as anchor modules connecting the other nine modules. Based on this analysis, symmetric dimethylarginine (SDMA) and arginine were proposed as potential liquid biopsy biomarkers for OC assessment. Our quantitative LCMS/MS analysis on our OC blood cohort provided direct evidence supporting the use of the SDMA-to-arginine ratio as a liquid biopsy panel to distinguish between normal and OC samples, with an area under the ROC curve (AUC) of 98.3%.
Our comprehensive analysis of tissue genomics and blood quantitative LC/MSMS metabolic data shed light on the metabolic reprogramming underlying OC pathophysiology. These findings offer new insights into the potential diagnostic utility of the SDMA-to-arginine ratio for OC assessment. Further validation studies using adequately powered OC cohorts are warranted to fully establish the clinical effectiveness of this diagnostic test.
卵巢癌（OC）是一种高度致命的妇科恶性肿瘤。大量研究表明，OC 细胞在肿瘤发生过程中经历了显著的代谢改变。在这项研究中，我们旨在利用这些代谢变化作为评估卵巢癌的潜在生物标志物。
利用基于功能模块的方法来识别区分组织活检队列中不同阶段卵巢癌（OC）的关键基因表达途径。该队列包括对照样本（n=79）、I/II 期样本（n=280）和 III/IV 期样本（n=1016）。为了进一步探索这些改变的分子途径，应用最小生成树（MST）分析，提出了用于 OC 液体活检的代谢生物标志物假设。为了验证，开发了一种基于多反应监测（MRM）的定量 LCMS/MS 方法。该方法允许使用包含对照样本（n=464）、良性样本（n=3）和 OC 样本（n=13）的 OC 血液队列精确量化靶向代谢生物标志物。
鉴定出 11 个功能模块在正常和早期或早期和晚期 OC 肿瘤组织之间具有显著差异（错误发现率，FDR<0.05）。MST 分析表明，代谢 L-精氨酸/一氧化氮（L-ARG/NO）途径被重新编程，与“DNA 复制”和“DNA 修复和重组”相关的模块作为连接其他九个模块的锚模块。基于此分析，对称二甲基精氨酸（SDMA）和精氨酸被提出作为 OC 评估的潜在液体活检生物标志物。我们对 OC 血液队列的定量 LCMS/MS 分析提供了直接证据，支持使用 SDMA-精氨酸比作为区分正常和 OC 样本的液体活检面板，ROC 曲线下面积（AUC）为 98.3%。
我们对组织基因组学和血液定量 LC/MSMS 代谢数据的综合分析揭示了 OC 病理生理学基础的代谢重编程。这些发现为 SDMA-精氨酸比用于 OC 评估的潜在诊断效用提供了新的见解。需要使用足够大的 OC 队列进行进一步的验证研究，以充分建立该诊断测试的临床有效性。
