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Chen L, Tang Q, Zhang K, Huang Q, Ding Y, Jin B, Liu S, Hwa K, Chou CJ, Zhang Y, Thyparambil S, Liao W, Han Z, Mortensen R, Schilling J, Li Z, Heaton R, Tian L, Cohen HJ, Sylvester KG, Arent RC, Zhao X, McElhinney DB, Wu Y, Bai W, Ling XB. Altered expression of the L-arginine/nitric oxide pathway in ovarian cancer: metabolic biomarkers and biological implications. BMC Cancer 2023; 23:844. [PMID: 37684587 PMCID: PMC10492322 DOI: 10.1186/s12885-023-11192-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Accepted: 07/19/2023] [Indexed: 09/10/2023] Open
Abstract
MOTIVATION 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. METHODS 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). RESULTS 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%. CONCLUSION 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.
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Affiliation(s)
- Linfeng Chen
- Beijing Shijitan Hospital, Capital Medical University, Beijing, China
| | - Qiming Tang
- Shanghai Yunxiang Medical Technology Co., Ltd., Shanghai, China
- Binhai Industrial Technology Research Institute, Zhejiang University, Tianjin, China
| | - Keying Zhang
- Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care Hospital, Beijing, China
| | | | | | - Bo Jin
- Tianjin Yunjian Medical Laboratory Institute Co., Ltd, Tianjin, China
| | - Szumam Liu
- School of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
| | | | - C James Chou
- School of Medicine, Stanford University, Stanford, CA, USA
| | - Yani Zhang
- Tianjin Yunjian Medical Laboratory Institute Co., Ltd, Tianjin, China
| | | | | | - Zhi Han
- School of Medicine, Stanford University, Stanford, CA, USA
| | | | | | - Zhen Li
- Shanghai Yunxiang Medical Technology Co., Ltd., Shanghai, China
- Binhai Industrial Technology Research Institute, Zhejiang University, Tianjin, China
| | | | - Lu Tian
- School of Medicine, Stanford University, Stanford, CA, USA
| | - Harvey J Cohen
- School of Medicine, Stanford University, Stanford, CA, USA
| | | | - Rebecca C Arent
- School of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Xinyang Zhao
- School of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
| | | | - Yumei Wu
- Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care Hospital, Beijing, China.
| | - Wenpei Bai
- Beijing Shijitan Hospital, Capital Medical University, Beijing, China.
| | - Xuefeng B Ling
- School of Medicine, Stanford University, Stanford, CA, USA.
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