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Li Z, Li N, Ndzie Noah ML, Shao Q, Zhan X. Pharmacoproteomics reveals energy metabolism pathways as therapeutic targets of ivermectin in ovarian cancer toward 3P medical approaches. EPMA J 2024; 15:711-737. [PMID: 39635022 PMCID: PMC11612093 DOI: 10.1007/s13167-024-00385-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2024] [Accepted: 11/08/2024] [Indexed: 12/07/2024]
Abstract
Objective Ovarian cancer is the malignant tumor with the highest mortality rate in the female reproductive system, enormous socio-economic burden, and limited effective drug therapy. There is an urgent need to find novel effective drugs for ovarian cancer therapy. Our previous in vitro studies demonstrate that ivermectin effectively inhibits ovarian cancer cells and affects energy metabolism pathways. This study aims to clarify in vivo mechanisms and therapeutic targets of ivermectin in the treatment of ovarian cancer to establish predictive biomarkers, guide personalized treatments, and improve preventive strategies in the framework of 3P medicine. Methods A TOV-21G tumor-bearing mouse model was constructed based on histopathological data and biochemical parameters. TMT-based proteomic analysis was performed on tumor tissues from the different treatment groups. All significantly differentially abundant proteins were characterized by hierarchical clustering, Gene Ontology (GO) enrichment analyses, and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. In addition, the data were integrated and analyzed with the proteomic data of clinical ovarian cancer tissues from our previous study and the proteomic data of ivermectin intervention in ovarian cancer cells to identify key regulators of ivermectin. Results Ivermectin (10 mg/kg) had a significant anti-ovarian cancer effect in mice, with a tumor inhibitory rate of 61.5%. Molecular changes in tumor tissue of ivermectin-treated mice were established, and protein-protein interaction (PPI) analysis showed that the main differential pathway networks included the TCA cycle, propanoate metabolism, 2-0xocarboxyacid metabolism, and other pathways. Integrating our previous clinical ovarian cancer tissue and cell experimental data, this study found that ivermectin significantly interfered with the energy metabolic pathways of ovarian cancer, including glycolysis, TCA cycle, oxidative phosphorylation, and other related pathways. Conclusions This study evaluated the anti-ovarian cancer effect in vitro and in vivo, and its specific regulatory effect on energy metabolism. The expressions of drug target molecules in the energy metabolism pathway of ovarian cancer will be used to guide the diagnosis and prevention of ovarian cancer. The significant efficacy of ivermectin will be applied to the treatment of ovarian cancer and personalized medication. This has guiding significance for the clinical diagnosis, treatment, personalized medication, and prognosis evaluation of ovarian cancer. Supplementary Information The online version contains supplementary material available at 10.1007/s13167-024-00385-1.
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Affiliation(s)
- Zhijun Li
- Shandong Provincial Key Laboratory of Precision Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University & Shandong Academy of Medical Sciences, 440 Jiyan Road, Jinan, Shandong 250117 People’s Republic of China
| | - Na Li
- Shandong Provincial Key Laboratory of Precision Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University & Shandong Academy of Medical Sciences, 440 Jiyan Road, Jinan, Shandong 250117 People’s Republic of China
| | - Marie Louise Ndzie Noah
- Shandong Provincial Key Laboratory of Precision Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University & Shandong Academy of Medical Sciences, 440 Jiyan Road, Jinan, Shandong 250117 People’s Republic of China
| | - Qianwen Shao
- Shandong Provincial Key Laboratory of Precision Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University & Shandong Academy of Medical Sciences, 440 Jiyan Road, Jinan, Shandong 250117 People’s Republic of China
| | - Xianquan Zhan
- Shandong Provincial Key Laboratory of Precision Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University & Shandong Academy of Medical Sciences, 440 Jiyan Road, Jinan, Shandong 250117 People’s Republic of China
- Shandong Provincial Key Medical and Health Laboratory of Ovarian Cancer Multiomics, & Jinan Key Laboratory of Cancer Multiomics, Medical Science and Technology Innovation Center, Shandong First Medical University & Shandong Academy of Medical Sciences, 6699 Qingao Road, Jinan, Shandong 250117 People’s Republic of China
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Kim SS, Shin H, Ahn KG, Park YM, Kwon MC, Lim JM, Oh EK, Kim Y, Han SM, Noh DY. Quantifiable peptide library bridges the gap for proteomics based biomarker discovery and validation on breast cancer. Sci Rep 2023; 13:8991. [PMID: 37268731 DOI: 10.1038/s41598-023-36159-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Accepted: 05/30/2023] [Indexed: 06/04/2023] Open
Abstract
Mass spectrometry (MS) based proteomics is widely used for biomarker discovery. However, often, most biomarker candidates from discovery are discarded during the validation processes. Such discrepancies between biomarker discovery and validation are caused by several factors, mainly due to the differences in analytical methodology and experimental conditions. Here, we generated a peptide library which allows discovery of biomarkers in the equal settings as the validation process, thereby making the transition from discovery to validation more robust and efficient. The peptide library initiated with a list of 3393 proteins detectable in the blood from public databases. For each protein, surrogate peptides favorable for detection in mass spectrometry was selected and synthesized. A total of 4683 synthesized peptides were spiked into neat serum and plasma samples to check their quantifiability in a 10 min liquid chromatography-MS/MS run time. This led to the PepQuant library, which is composed of 852 quantifiable peptides that cover 452 human blood proteins. Using the PepQuant library, we discovered 30 candidate biomarkers for breast cancer. Among the 30 candidates, nine biomarkers, FN1, VWF, PRG4, MMP9, CLU, PRDX6, PPBP, APOC1, and CHL1 were validated. By combining the quantification values of these markers, we generated a machine learning model predicting breast cancer, showing an average area under the curve of 0.9105 for the receiver operating characteristic curve.
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Affiliation(s)
- Sung-Soo Kim
- Manufacturing and Technology Division, Bertis Inc., Hungdeok 1-Ro, Giheung-Gu, Yongin-Si, Gyeonggi-Do, 16954, Republic of Korea
- Bio Convergence Research Institute, Bertis Inc., Heungdeok 1-Ro, Giheung-Gu, Yongin-Si, Gyeonggi-Do, 16954, Republic of Korea
| | - HyeonSeok Shin
- Bio Convergence Research Institute, Bertis Inc., Heungdeok 1-Ro, Giheung-Gu, Yongin-Si, Gyeonggi-Do, 16954, Republic of Korea
| | - Kyung-Geun Ahn
- Manufacturing and Technology Division, Bertis Inc., Hungdeok 1-Ro, Giheung-Gu, Yongin-Si, Gyeonggi-Do, 16954, Republic of Korea
| | - Young-Min Park
- Manufacturing and Technology Division, Bertis Inc., Hungdeok 1-Ro, Giheung-Gu, Yongin-Si, Gyeonggi-Do, 16954, Republic of Korea
| | - Min-Chul Kwon
- Manufacturing and Technology Division, Bertis Inc., Hungdeok 1-Ro, Giheung-Gu, Yongin-Si, Gyeonggi-Do, 16954, Republic of Korea
| | - Jae-Min Lim
- Manufacturing and Technology Division, Bertis Inc., Hungdeok 1-Ro, Giheung-Gu, Yongin-Si, Gyeonggi-Do, 16954, Republic of Korea
| | - Eun-Kyung Oh
- Manufacturing and Technology Division, Bertis Inc., Hungdeok 1-Ro, Giheung-Gu, Yongin-Si, Gyeonggi-Do, 16954, Republic of Korea
| | - Yumi Kim
- Department of Surgery, CHA Gangnam Medical Center, CHA University School of Medicine, 566, Nonhyeon-ro, Gangnam-gu, Seoul, 06135, Republic of Korea
| | - Seung-Man Han
- Bertis Inc., 172, Dolma-Ro, Bundang-Gu, Seongnam-Si, Gyeonggi-Do, 13605, Republic of Korea
| | - Dong-Young Noh
- Department of Surgery, CHA Gangnam Medical Center, CHA University School of Medicine, 566, Nonhyeon-ro, Gangnam-gu, Seoul, 06135, Republic of Korea.
- Bertis Inc., 172, Dolma-Ro, Bundang-Gu, Seongnam-Si, Gyeonggi-Do, 13605, Republic of Korea.
- Seoul National University College of Medicine, 103 Daehak-Ro, Seoul, 03080, Republic of Korea.
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Gong J, Peng Y, Yu J, Pei W, Zhang Z, Fan D, Liu L, Xiao X, Liu R, Lu Q, Li P, Shang H, Shi Y, Li J, Ge Q, Liu A, Deng X, Fan S, Pan J, Chen Q, Yuan Y, Gong W. Linkage and association analyses reveal that hub genes in energy-flow and lipid biosynthesis pathways form a cluster in upland cotton. Comput Struct Biotechnol J 2022; 20:1841-1859. [PMID: 35521543 PMCID: PMC9046884 DOI: 10.1016/j.csbj.2022.04.012] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 04/11/2022] [Accepted: 04/11/2022] [Indexed: 11/25/2022] Open
Abstract
Upland cotton is an important allotetraploid crop that provides both natural fiber for the textile industry and edible vegetable oil for the food or feed industry. To better understand the genetic mechanism that regulates the biosynthesis of storage oil in cottonseed, we identified the genes harbored in the major quantitative trait loci/nucleotides (QTLs/QTNs) of kernel oil content (KOC) in cottonseed via both multiple linkage analyses and genome-wide association studies (GWAS). In ‘CCRI70′ RILs, six stable QTLs were simultaneously identified by linkage analysis of CHIP and SLAF-seq strategies. In ‘0-153′ RILs, eight stable QTLs were detected by consensus linkage analysis integrating multiple strategies. In the natural panel, thirteen and eight loci were associated across multiple environments with two algorithms of GWAS. Within the confidence interval of a major common QTL on chromosome 3, six genes were identified as participating in the interaction network highly correlated with cottonseed KOC. Further observations of gene differential expression showed that four of the genes, LtnD, PGK, LPLAT1, and PAH2, formed hub genes and two of them, FER and RAV1, formed the key genes in the interaction network. Sequence variations in the coding regions of LtnD, FER, PGK, LPLAT1, and PAH2 genes may support their regulatory effects on oil accumulation in mature cottonseed. Taken together, clustering of the hub genes in the lipid biosynthesis interaction network provides new insights to understanding the mechanism of fatty acid biosynthesis and TAG assembly and to further genetic improvement projects for the KOC in cottonseeds.
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Affiliation(s)
- Juwu Gong
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, Henan, China
- Engineering Research Centre of Cotton, Ministry of Education, College of Agriculture, Xinjiang Agricultural University, 311 Nongda East Road, Urumqi 830052, Xinjiang, China
- School of Agricultural Sciences, Zhengzhou University, Zhengzhou 450001, Henan, China
| | - Yan Peng
- Third Division of the Xinjiang Production and Construction Corps Agricultural Research Institute, Tumushuke, Xijiang 843900, China
| | - Jiwen Yu
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, Henan, China
- School of Agricultural Sciences, Zhengzhou University, Zhengzhou 450001, Henan, China
| | - Wenfeng Pei
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, Henan, China
| | - Zhen Zhang
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, Henan, China
| | - Daoran Fan
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, Henan, China
| | - Linjie Liu
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, Henan, China
- Engineering Research Centre of Cotton, Ministry of Education, College of Agriculture, Xinjiang Agricultural University, 311 Nongda East Road, Urumqi 830052, Xinjiang, China
| | - Xianghui Xiao
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, Henan, China
- Engineering Research Centre of Cotton, Ministry of Education, College of Agriculture, Xinjiang Agricultural University, 311 Nongda East Road, Urumqi 830052, Xinjiang, China
| | - Ruixian Liu
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, Henan, China
- Engineering Research Centre of Cotton, Ministry of Education, College of Agriculture, Xinjiang Agricultural University, 311 Nongda East Road, Urumqi 830052, Xinjiang, China
| | - Quanwei Lu
- College of Biotechnology and Food Engineering, Anyang Institute of Technology, Anyang, China
| | - Pengtao Li
- College of Biotechnology and Food Engineering, Anyang Institute of Technology, Anyang, China
| | - Haihong Shang
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, Henan, China
- School of Agricultural Sciences, Zhengzhou University, Zhengzhou 450001, Henan, China
| | - Yuzhen Shi
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, Henan, China
- School of Agricultural Sciences, Zhengzhou University, Zhengzhou 450001, Henan, China
| | - Junwen Li
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, Henan, China
- School of Agricultural Sciences, Zhengzhou University, Zhengzhou 450001, Henan, China
| | - Qun Ge
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, Henan, China
| | - Aiying Liu
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, Henan, China
| | - Xiaoying Deng
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, Henan, China
| | - Senmiao Fan
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, Henan, China
| | - Jingtao Pan
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, Henan, China
| | - Quanjia Chen
- Engineering Research Centre of Cotton, Ministry of Education, College of Agriculture, Xinjiang Agricultural University, 311 Nongda East Road, Urumqi 830052, Xinjiang, China
| | - Youlu Yuan
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, Henan, China
- Engineering Research Centre of Cotton, Ministry of Education, College of Agriculture, Xinjiang Agricultural University, 311 Nongda East Road, Urumqi 830052, Xinjiang, China
- School of Agricultural Sciences, Zhengzhou University, Zhengzhou 450001, Henan, China
| | - Wankui Gong
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, Henan, China
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Mertins SD. Capturing Biomarkers and Molecular Targets in Cellular Landscapes From Dynamic Reaction Network Models and Machine Learning. Front Oncol 2022; 11:805592. [PMID: 35127516 PMCID: PMC8813744 DOI: 10.3389/fonc.2021.805592] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2021] [Accepted: 12/31/2021] [Indexed: 12/02/2022] Open
Abstract
Computational dynamic ODE models of cell function describing biochemical reactions have been created for decades, but on a small scale. Still, they have been highly effective in describing and predicting behaviors. For example, oscillatory phospho-ERK levels were predicted and confirmed in MAPK signaling encompassing both positive and negative feedback loops. These models typically were limited and not adapted to large datasets so commonly found today. But importantly, ODE models describe reaction networks in well-mixed systems representing the cell and can be simulated with ordinary differential equations that are solved deterministically. Stochastic solutions, which can account for noisy reaction networks, in some cases, also improve predictions. Today, dynamic ODE models rarely encompass an entire cell even though it might be expected that an upload of the large genomic, transcriptomic, and proteomic datasets may allow whole cell models. It is proposed here to combine output from simulated dynamic ODE models, completed with omics data, to discover both biomarkers in cancer a priori and molecular targets in the Machine Learning setting.
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Affiliation(s)
- Susan D. Mertins
- Department of Science, Mount St. Mary’s University, Emmitsburg, MD, United States
- Biomedical Informatics and Data Science Directorate, Frederick National Laboratory for Cancer Research, Leidos Biomedical Research, Limited Liability Company (LLC), Frederick, MD, United States
- BioSystems Strategies, Limited Liability Company (LLC), Frederick, MD, United States
- *Correspondence: Susan D. Mertins,
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