1
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Sah S, Bifarin OO, Moore SG, Gaul DA, Chung H, Kwon SY, Cho H, Cho CH, Kim JH, Kim J, Fernández FM. Serum Lipidome Profiling Reveals a Distinct Signature of Ovarian Cancer in Korean Women. Cancer Epidemiol Biomarkers Prev 2024; 33:681-693. [PMID: 38412029 PMCID: PMC11061607 DOI: 10.1158/1055-9965.epi-23-1293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 01/11/2024] [Accepted: 02/23/2024] [Indexed: 02/28/2024] Open
Abstract
BACKGROUND 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. METHODS 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. RESULTS 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. CONCLUSIONS This study provides a systemic analysis of lipidome alterations in human ovarian cancer, specifically in Korean women. IMPACT Here, we show the potential of circulating lipids in distinguishing ovarian cancer from nonovarian cancer conditions.
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Affiliation(s)
- Samyukta Sah
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia
- Petit Institute of Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, Georgia
| | - Olatomiwa O. Bifarin
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia
| | - Samuel G. Moore
- Petit Institute of Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, Georgia
| | - David A. Gaul
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia
- Petit Institute of Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, Georgia
| | - Hyewon Chung
- Department of Obstetrics and Gynecology, School of Medicine, Keimyung University, Daegu Republic of Korea
| | - Sun Young Kwon
- Department of Pathology, School of Medicine, Keimyung University, Daegu, Republic of Korea
| | - Hanbyoul Cho
- Department of Obstetrics and Gynecology, Institute of Women's Life Medical Science, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Chi-Heum Cho
- Department of Obstetrics and Gynecology, School of Medicine, Keimyung University, Daegu Republic of Korea
| | - Jae-Hoon Kim
- Department of Obstetrics and Gynecology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Jaeyeon Kim
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indiana University Melvin and Bren Simon Comprehensive Cancer Center, Indianapolis, Indiana
| | - Facundo M. Fernández
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia
- Petit Institute of Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, Georgia
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2
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Feng Y. An integrated machine learning-based model for joint diagnosis of ovarian cancer with multiple test indicators. J Ovarian Res 2024; 17:45. [PMID: 38378582 PMCID: PMC10877874 DOI: 10.1186/s13048-024-01365-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 02/01/2024] [Indexed: 02/22/2024] Open
Abstract
OBJECTIVE To construct a machine learning diagnostic model integrating feature dimensionality reduction techniques and artificial neural network classifiers to develop the value of clinical routine blood indexes for the auxiliary diagnosis of ovarian cancer. METHODS Patients with ovarian cancer clearly diagnosed in our hospital were collected as a case group (n = 185), and three groups of patients with other malignant otolaryngology tumors (n = 138), patients with benign otolaryngology diseases (n = 339) and those with normal physical examination (n = 92) were used as an overall control group. In this paper, a fully automated segmentation network for magnetic resonance images of ovarian cancer is proposed to improve the reproducibility of tumor segmentation results while effectively reducing the burden on radiologists. A pre-trained Res Net50 is used to the three edge output modules are fused to obtain the final segmentation results. The segmentation results of the proposed network architecture are compared with the segmentation results of the U-net based network architecture and the effect of different loss functions and region of interest sizes on the segmentation performance of the proposed network is analyzed. RESULTS The average Dice similarity coefficient, average sensitivity, average specificity (specificity) and average hausdorff distance of the proposed network segmentation results reached 83.62%, 89.11%, 96.37% and 8.50, respectively, which were better than the U-net based segmentation method. For ROIs containing tumor tissue, the smaller the size, the better the segmentation effect. Several loss functions do not differ much. The area under the ROC curve of the machine learning diagnostic model reached 0.948, with a sensitivity of 91.9% and a specificity of 86.9%, and its diagnostic efficacy was significantly better than that of the traditional way of detecting CA125 alone. The model was able to accurately diagnose ovarian cancer of different disease stages and showed certain discriminative ability for ovarian cancer in all three control subgroups. CONCLUSION Using machine learning to integrate multiple conventional test indicators can effectively improve the diagnostic efficacy of ovarian cancer, which provides a new idea for the intelligent auxiliary diagnosis of ovarian cancer.
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Affiliation(s)
- Yiwen Feng
- Departments of Obstetrics and Gynecology, Shanghai General Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, 200080, P.R. China.
- Jiuquan Hospital, Shanghai General Hospital, 200003, Shanghai, China.
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3
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Tzelepi V, Gika H, Begou O, Timotheadou E. The Contribution of Lipidomics in Ovarian Cancer Management: A Systematic Review. Int J Mol Sci 2023; 24:13961. [PMID: 37762264 PMCID: PMC10531399 DOI: 10.3390/ijms241813961] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2023] [Revised: 08/30/2023] [Accepted: 09/09/2023] [Indexed: 09/29/2023] Open
Abstract
Lipidomics is a comprehensive study of all lipid components in living cells, serum, plasma, or tissues, with the aim of discovering diagnostic, prognostic, and predictive biomarkers for diseases such as malignant tumors. This systematic review evaluates studies, applying lipidomics to the diagnosis, prognosis, prediction, and differentiation of malignant and benign ovarian tumors. A literature search was performed in PubMed, Science Direct, and SciFinder. Only publications written in English after 2012 were included. Relevant citations were identified from the reference lists of primary included studies and were also included in our list. All studies included referred to the application of lipidomics in serum/plasma samples from human cases of OC, some of which also included tumor tissue samples. In some of the included studies, metabolome analysis was also performed, in which other metabolites were identified in addition to lipids. Qualitative data were assessed, and the risk of bias was determined using the ROBINS-I tool. A total of twenty-nine studies were included, fifteen of which applied non-targeted lipidomics, seven applied targeted lipidomics, and seven were reviews relevant to our objectives. Most studies focused on the potential application of lipidomics in the diagnosis of OC and showed that phospholipids and sphingolipids change most significantly during disease development. In conclusion, this systematic review highlights the potential contribution of lipids as biomarkers in OC management.
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Affiliation(s)
- Vasiliki Tzelepi
- Department of Oncology, “Papageorgiou” General Hospital, 56429 Thessaloniki, Greece;
| | - Helen Gika
- Biomic_Auth, Bioanalysis and Omics Lab, Centre for Interdisciplinary Research of Aristotle University of Thessaloniki, Innovation Area of Thessaloniki, 57001 Thermi, Greece; (H.G.); (O.B.)
- Laboratory of Forensic Medicine and Toxicology, School of Medicine, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
| | - Olga Begou
- Biomic_Auth, Bioanalysis and Omics Lab, Centre for Interdisciplinary Research of Aristotle University of Thessaloniki, Innovation Area of Thessaloniki, 57001 Thermi, Greece; (H.G.); (O.B.)
- Department of Chemistry, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
| | - Eleni Timotheadou
- Department of Oncology, “Papageorgiou” General Hospital, 56429 Thessaloniki, Greece;
- Department of Medical Oncology, Aristotle University of Thessaloniki School of Medicine, 54124 Thessaloniki, Greece
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4
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Masnikosa R, Pirić D, Post JM, Cvetković Z, Petrović S, Paunović M, Vučić V, Bindila L. Disturbed Plasma Lipidomic Profiles in Females with Diffuse Large B-Cell Lymphoma: A Pilot Study. Cancers (Basel) 2023; 15:3653. [PMID: 37509314 PMCID: PMC10377844 DOI: 10.3390/cancers15143653] [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: 04/18/2023] [Revised: 07/01/2023] [Accepted: 07/07/2023] [Indexed: 07/30/2023] Open
Abstract
Lipidome dysregulation is a hallmark of cancer and inflammation. The global plasma lipidome and sub-lipidome of inflammatory pathways have not been reported in diffuse large B-cell lymphoma (DLBCL). In a pilot study of plasma lipid variation in female DLBCL patients and BMI-matched disease-free controls, we performed targeted lipidomics using LC-MRM to quantify lipid mediators of inflammation and immunity, and those known or hypothesised to be involved in cancer progression: sphingolipids, resolvin D1, arachidonic acid (AA)-derived oxylipins, such as hydroxyeicosatetraenoic acids (HETEs) and dihydroxyeicosatrienoic acids, along with their membrane structural precursors. We report on the role of the eicosanoids in the separation of DLBCL from controls, along with lysophosphatidylinositol LPI 20:4, implying notable changes in lipid metabolic and/or signalling pathways, particularly pertaining to AA lipoxygenase pathway and glycerophospholipid remodelling in the cell membrane. We suggest here the set of S1P, SM 36:1, SM 34:1 and PI 34:1 as DLBCL lipid signatures which could serve as a basis for the prospective validation in larger DLBCL cohorts. Additionally, untargeted lipidomics indicates a substantial change in the overall lipid metabolism in DLBCL. The plasma lipid profiling of DLBCL patients helps to better understand the specific lipid dysregulations and pathways in this cancer.
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Affiliation(s)
- Romana Masnikosa
- Department of Physical Chemistry, Vinca Institute of Nuclear Sciences-National Institute of the Republic of Serbia, University of Belgrade, Mike Petrovica Alasa 12-14, 11000 Belgrade, Serbia
| | - David Pirić
- Department of Physical Chemistry, Vinca Institute of Nuclear Sciences-National Institute of the Republic of Serbia, University of Belgrade, Mike Petrovica Alasa 12-14, 11000 Belgrade, Serbia
| | - Julia Maria Post
- Clinical Lipidomics Unit, Institute of Physiological Chemistry, University Medical Centre of the J.G.U Mainz, Duesbergweg 6, 55128 Mainz, Germany
| | - Zorica Cvetković
- Department of Haematology, Clinical Hospital Centre Zemun, Vukova 9, 11000 Belgrade, Serbia
- Faculty of Medicine, University of Belgrade, Dr. Subotića 8, 11000 Belgrade, Serbia
| | - Snježana Petrović
- Group for Nutritional Biochemistry and Dietology, Centre of Research Excellence in Nutrition and Metabolism, Institute for Medical Research, National Institute of the Republic of Serbia, University of Belgrade, Tadeusa Koscuska 1, 11000 Belgrade, Serbia
| | - Marija Paunović
- Group for Nutritional Biochemistry and Dietology, Centre of Research Excellence in Nutrition and Metabolism, Institute for Medical Research, National Institute of the Republic of Serbia, University of Belgrade, Tadeusa Koscuska 1, 11000 Belgrade, Serbia
| | - Vesna Vučić
- Group for Nutritional Biochemistry and Dietology, Centre of Research Excellence in Nutrition and Metabolism, Institute for Medical Research, National Institute of the Republic of Serbia, University of Belgrade, Tadeusa Koscuska 1, 11000 Belgrade, Serbia
| | - Laura Bindila
- Clinical Lipidomics Unit, Institute of Physiological Chemistry, University Medical Centre of the J.G.U Mainz, Duesbergweg 6, 55128 Mainz, Germany
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5
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Ryan MJ, Grant-St James A, Lawler NG, Fear MW, Raby E, Wood FM, Maker GL, Wist J, Holmes E, Nicholson JK, Whiley L, Gray N. Comprehensive Lipidomic Workflow for Multicohort Population Phenotyping Using Stable Isotope Dilution Targeted Liquid Chromatography-Mass Spectrometry. J Proteome Res 2023; 22:1419-1433. [PMID: 36828482 PMCID: PMC10167688 DOI: 10.1021/acs.jproteome.2c00682] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/26/2023]
Abstract
Dysregulated lipid metabolism underpins many chronic diseases including cardiometabolic diseases. Mass spectrometry-based lipidomics is an important tool for understanding mechanisms of lipid dysfunction and is widely applied in epidemiology and clinical studies. With ever-increasing sample numbers, single batch acquisition is often unfeasible, requiring advanced methods that are accurate and robust to batch-to-batch and interday analytical variation. Herein, an optimized comprehensive targeted workflow for plasma and serum lipid quantification is presented, combining stable isotope internal standard dilution, automated sample preparation, and ultrahigh performance liquid chromatography-tandem mass spectrometry with rapid polarity switching to target 1163 lipid species spanning 20 subclasses. The resultant method is robust to common sources of analytical variation including blood collection tubes, hemolysis, freeze-thaw cycles, storage stability, analyte extraction technique, interinstrument variation, and batch-to-batch variation with 820 lipids reporting a relative standard deviation of <30% in 1048 replicate quality control plasma samples acquired across 16 independent batches (total injection count = 6142). However, sample hemolysis of ≥0.4% impacted lipid concentrations, specifically for phosphatidylethanolamines (PEs). Low interinstrument variability across two identical LC-MS systems indicated feasibility for intra/inter-lab parallelization of the assay. In summary, we have optimized a comprehensive lipidomic protocol to support rigorous analysis for large-scale, multibatch applications in precision medicine. The mass spectrometry lipidomics data have been deposited to massIVE: data set identifiers MSV000090952 and 10.25345/C5NP1WQ4S.
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Affiliation(s)
- Monique J Ryan
- Australian National Phenome Centre, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, Western Australia 6150, Australia.,Centre for Computational and Systems Medicine, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, Western Australia 6150, Australia
| | - Alanah Grant-St James
- Australian National Phenome Centre, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, Western Australia 6150, Australia.,Centre for Computational and Systems Medicine, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, Western Australia 6150, Australia
| | - Nathan G Lawler
- Australian National Phenome Centre, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, Western Australia 6150, Australia.,Centre for Computational and Systems Medicine, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, Western Australia 6150, Australia
| | - Mark W Fear
- Burn Injury Research Unit, University of Western Australia, Perth, Western Australia 6009, Australia.,Fiona Wood Foundation, Perth, Western Australia 6150, Australia
| | - Edward Raby
- Department of Microbiology, PathWest Laboratory Medicine, Perth, Western Australia 6009, Australia.,Department of Infectious Diseases, Fiona Stanley Hospital, Perth, Western Australia 6150, Australia
| | - Fiona M Wood
- Burn Injury Research Unit, University of Western Australia, Perth, Western Australia 6009, Australia.,WA Department of Health, Burns Service WA, Perth, Western Australia 6009, Australia.,Fiona Wood Foundation, Perth, Western Australia 6150, Australia
| | - Garth L Maker
- Australian National Phenome Centre, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, Western Australia 6150, Australia
| | - Julien Wist
- Australian National Phenome Centre, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, Western Australia 6150, Australia.,Centre for Computational and Systems Medicine, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, Western Australia 6150, Australia
| | - Elaine Holmes
- Australian National Phenome Centre, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, Western Australia 6150, Australia.,Centre for Computational and Systems Medicine, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, Western Australia 6150, Australia
| | - Jeremy K Nicholson
- Australian National Phenome Centre, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, Western Australia 6150, Australia.,Centre for Computational and Systems Medicine, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, Western Australia 6150, Australia
| | - Luke Whiley
- Australian National Phenome Centre, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, Western Australia 6150, Australia.,Centre for Computational and Systems Medicine, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, Western Australia 6150, Australia.,Perron Institute for Neurological and Translational Science, Nedlands, Western Australia 6009, Australia
| | - Nicola Gray
- Australian National Phenome Centre, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, Western Australia 6150, Australia.,Centre for Computational and Systems Medicine, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, Western Australia 6150, Australia
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6
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Monavarian M, Elhaw AT, Tang PW, Javed Z, Shonibare Z, Scalise CB, Arend R, Jolly MK, Sewell-Loftin MK, Hempel N, Mythreye K. Emerging perspectives on growth factor metabolic relationships in the ovarian cancer ascites environment. Semin Cancer Biol 2022; 86:709-719. [PMID: 35259492 PMCID: PMC9441472 DOI: 10.1016/j.semcancer.2022.03.004] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Revised: 03/02/2022] [Accepted: 03/03/2022] [Indexed: 02/07/2023]
Abstract
The ascites ecosystem in ovarian cancer is inhabited by complex cell types and is bathed in an environment rich in cytokines, chemokines, and growth factors that directly and indirectly impact metabolism of cancer cells and tumor associated cells. This milieu of malignant ascites, provides a 'rich' environment for the disease to thrive, contributing to every aspect of advanced ovarian cancer, a devastating gynecological cancer with a significant gap in targeted therapeutics. In this perspective we focus our discussions on the 'acellular' constituents of this liquid malignant tumor microenvironment, and how they influence metabolic pathways. Growth factors, chemokines and cytokines are known modulators of metabolism and have been shown to impact nutrient uptake and metabolic flexibility of tumors, yet few studies have explored how their enrichment in malignant ascites of ovarian cancer patients contributes to the metabolic requirements of ascites-resident cells. We focus here on TGF-βs, VEGF and ILs, which are frequently elevated in ovarian cancer ascites and have all been described to have direct or indirect effects on metabolism, often through gene regulation of metabolic enzymes. We summarize what is known, describe gaps in knowledge, and provide examples from other tumor types to infer potential unexplored roles and mechanisms for ovarian cancer. The distribution and variation in acellular ascites components between patients poses both a challenge and opportunity to further understand how the ascites may contribute to disease heterogeneity. The review also highlights opportunities for studies on ascites-derived factors in regulating the ascites metabolic environment that could act as a unique signature in aiding clinical decisions in the future.
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Affiliation(s)
- Mehri Monavarian
- Division of Molecular Cellular Pathology, Department of Pathology, O'Neal Comprehensive Cancer Center, University of Alabama Heersink School of Medicine, Birmingham, AL, USA
| | - Amal Taher Elhaw
- Division of Hematology Oncology, Department of Medicine, UPMC Hillman Cancer Center, University of Pittsburgh School of Medicine, Pittsburgh PA 15213, USA
| | - Priscilla W Tang
- Division of Hematology Oncology, Department of Medicine, UPMC Hillman Cancer Center, University of Pittsburgh School of Medicine, Pittsburgh PA 15213, USA
| | - Zaineb Javed
- Division of Hematology Oncology, Department of Medicine, UPMC Hillman Cancer Center, University of Pittsburgh School of Medicine, Pittsburgh PA 15213, USA
| | - Zainab Shonibare
- Division of Molecular Cellular Pathology, Department of Pathology, O'Neal Comprehensive Cancer Center, University of Alabama Heersink School of Medicine, Birmingham, AL, USA
| | - Carly Bess Scalise
- Department of Obstetrics and Gynecology, Division of Gynecologic Oncology, University of Alabama School of Medicine, Birmingham, AL, USA
| | - Rebecca Arend
- Department of Obstetrics and Gynecology, Division of Gynecologic Oncology, University of Alabama School of Medicine, Birmingham, AL, USA
| | - Mohit Kumar Jolly
- Center for Biosystems Science and Engineering, Indian Institute of Science, Bangalore 560012, India
| | - Mary Kathryn Sewell-Loftin
- Department of Biomedical Engineering, O'Neal Comprehensive Cancer Center, University of Alabama School of Medicine, Birmingham, AL, USA
| | - Nadine Hempel
- Division of Hematology Oncology, Department of Medicine, UPMC Hillman Cancer Center, University of Pittsburgh School of Medicine, Pittsburgh PA 15213, USA.
| | - Karthikeyan Mythreye
- Division of Molecular Cellular Pathology, Department of Pathology, O'Neal Comprehensive Cancer Center, University of Alabama Heersink School of Medicine, Birmingham, AL, USA.
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7
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Zhong X, Ran R, Gao S, Shi M, Shi X, Long F, Zhou Y, Yang Y, Tang X, Lin A, He W, Yu T, Han TL. Complex metabolic interactions between ovary, plasma, urine, and hair in ovarian cancer. Front Oncol 2022; 12:916375. [PMID: 35982964 PMCID: PMC9379488 DOI: 10.3389/fonc.2022.916375] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2022] [Accepted: 07/06/2022] [Indexed: 11/13/2022] Open
Abstract
Ovarian cancer (OC) is the third most common malignant tumor of women accompanied by alteration of systemic metabolism, yet the underlying interactions between the local OC tissue and other system biofluids remain unclear. In this study, we recruited 17 OC patients, 16 benign ovarian tumor (BOT) patients, and 14 control patients to collect biological samples including ovary plasma, urine, and hair from the same patient. The metabolic features of samples were characterized using a global and targeted metabolic profiling strategy based on Gas chromatography-mass spectrometry (GC-MS). Principal component analysis (PCA) revealed that the metabolites display obvious differences in ovary tissue, plasma, and urine between OC and non-malignant groups but not in hair samples. The metabolic alterations in OC tissue included elevated glycolysis (lactic acid) and TCA cycle intermediates (malic acid, fumaric acid) were related to energy metabolism. Furthermore, the increased levels of glutathione and polyunsaturated fatty acids (linoleic acid) together with decreased levels of saturated fatty acid (palmitic acid) were observed, which might be associated with the anti-oxidative stress capability of cancer. Furthermore, how metabolite profile changes across differential biospecimens were compared in OC patients. Plasma and urine showed a lower concentration of amino acids (alanine, aspartic acid, glutamic acid, proline, leucine, and cysteine) than the malignant ovary. Plasma exhibited the highest concentrations of fatty acids (stearic acid, EPA, and arachidonic acid), while TCA cycle intermediates (succinic acid, citric acid, and malic acid) were most concentrated in the urine. In addition, five plasma metabolites and three urine metabolites showed the best specificity and sensitivity in differentiating the OC group from the control or BOT groups (AUC > 0.90) using machine learning modeling. Overall, this study provided further insight into different specimen metabolic characteristics between OC and non-malignant disease and identified the metabolic fluctuation across ovary and biofluids.
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Affiliation(s)
- Xiaocui Zhong
- Department of Obstetrics and Gynaecology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Rui Ran
- Department of Obstetrics and Gynaecology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Shanhu Gao
- State Key Laboratory of Ultrasound Engineering in Medicine Co-Founded by Chongqing and the Ministry of Science and Technology, School of Biomedical Engineering, Chongqing Medical University, Chongqing, China
| | - Manlin Shi
- Department of Obstetrics and Gynaecology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xian Shi
- Department of Obstetrics and Gynaecology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Fei Long
- State Key Laboratory of Ultrasound Engineering in Medicine Co-Founded by Chongqing and the Ministry of Science and Technology, School of Biomedical Engineering, Chongqing Medical University, Chongqing, China
| | - Yanqiu Zhou
- Department of Obstetrics and Gynaecology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yang Yang
- Department of Obstetrics, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xianglan Tang
- Department of Obstetrics and Gynaecology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Anping Lin
- Department of Obstetrics and Gynaecology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Wuyang He
- Department of Oncology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Tinghe Yu
- Department of Obstetrics and Gynaecology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
- *Correspondence: Tinghe Yu, ; ; Ting-Li Han,
| | - Ting-Li Han
- Department of Obstetrics and Gynaecology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
- Liggins Institute, The University of Auckland, Auckland, New Zealand
- *Correspondence: Tinghe Yu, ; ; Ting-Li Han,
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8
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Chen H, Zhang J, Zhou H, Zhu Y, Liang Y, Zhu P, Zhang Q. UHPLC-HRMS–based serum lipisdomics reveals novel biomarkers to assist in the discrimination between colorectal adenoma and cancer. Front Oncol 2022; 12:934145. [PMID: 35965551 PMCID: PMC9366052 DOI: 10.3389/fonc.2022.934145] [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: 05/02/2022] [Accepted: 06/30/2022] [Indexed: 11/13/2022] Open
Abstract
The development of a colorectal adenoma (CA) into carcinoma (CRC) is a long and stealthy process. There remains a lack of reliable biomarkers to distinguish CA from CRC. To effectively explore underlying molecular mechanisms and identify novel lipid biomarkers promising for early diagnosis of CRC, an ultrahigh-performance liquid chromatography tandem high-resolution mass spectrometry (UHPLC-HRMS) method was employed to comprehensively measure lipid species in human serum samples of patients with CA and CRC. Results showed significant differences in serum lipid profiles between CA and CRC groups, and 85 differential lipid species (P < 0.05 and fold change > 1.50 or < 0.67) were discovered. These significantly altered lipid species were mainly involved in fatty acid (FA), phosphatidylcholine (PC), and triacylglycerol (TAG) metabolism with the constituent ratio > 63.50%. After performance evaluation by the receiver operating characteristic (ROC) curve analysis, seven lipid species were ultimately proposed as potential biomarkers with the area under the curve (AUC) > 0.800. Of particular value, a lipid panel containing docosanamide, SM d36:0, PC 36:1e, and triheptanoin was selected as a composite candidate biomarker with excellent performance (AUC = 0.971), and the highest selected frequency to distinguish patients with CA from patients with CRC based on the support vector machine (SVM) classification model. To our knowledge, this study was the first to undertake a lipidomics profile using serum intended to identify screening lipid biomarkers to discriminate between CA and CRC. The lipid panel could potentially serve as a composite biomarker aiding the early diagnosis of CRC. Metabolic dysregulation of FAs, PCs, and TAGs seems likely involved in malignant transformation of CA, which hopefully will provide new clues to understand its underlying mechanism.
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Affiliation(s)
- Hongwei Chen
- Medical College of Guangxi University, Guangxi University, Nanning, China
| | - Jiahao Zhang
- Medical College of Guangxi University, Guangxi University, Nanning, China
| | - Hailin Zhou
- Medical College of Guangxi University, Guangxi University, Nanning, China
| | - Yifan Zhu
- Medical College of Guangxi University, Guangxi University, Nanning, China
| | - Yunxiao Liang
- Department of Gastroenterology, People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
| | - Pingchuan Zhu
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, Guangxi University, Nanning, China
| | - Qisong Zhang
- Medical College of Guangxi University, Guangxi University, Nanning, China
- *Correspondence: Qisong Zhang,
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Chehade H, Tedja R, Ramos H, Bawa TS, Adzibolosu N, Gogoi R, Mor G, Alvero AB. Regulatory Role of the Adipose Microenvironment on Ovarian Cancer Progression. Cancers (Basel) 2022; 14:cancers14092267. [PMID: 35565396 PMCID: PMC9101128 DOI: 10.3390/cancers14092267] [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: 04/08/2022] [Revised: 04/26/2022] [Accepted: 04/27/2022] [Indexed: 12/04/2022] Open
Abstract
Simple Summary Adipocytes or fat cells are integral part of the ovarian tumor microenvironment. Secreted factors from adipocytes, as well as direct cell-to-cell interaction with ovarian cancer cells have been shown to directly support ovarian tumor progression. Elucidating the molecular pathways involved is crucial in the identification of relevant targets. Abstract The tumor microenvironment of ovarian cancer is the peritoneal cavity wherein adipose tissue is a major component. The role of the adipose tissue in support of ovarian cancer progression has been elucidated in several studies from the past decades. The adipocytes, in particular, are a major source of factors, which regulate all facets of ovarian cancer progression such as acquisition of chemoresistance, enhanced metastatic potential, and metabolic reprogramming. In this review, we summarize the relevant studies, which highlight the role of adipocytes in ovarian cancer progression and offer insights into unanswered questions and possible future directions of research.
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Affiliation(s)
- Hussein Chehade
- C.S. Mott Center for Human Growth and Development, Department of Obstetrics and Gynecology, Wayne State University, Detroit, MI 48201, USA; (H.C.); (R.T.); (H.R.); (T.S.B.); (N.A.); (R.G.); (G.M.)
| | - Roslyn Tedja
- C.S. Mott Center for Human Growth and Development, Department of Obstetrics and Gynecology, Wayne State University, Detroit, MI 48201, USA; (H.C.); (R.T.); (H.R.); (T.S.B.); (N.A.); (R.G.); (G.M.)
- Karmanos Cancer Institute, Detroit, MI 48201, USA
| | - Harry Ramos
- C.S. Mott Center for Human Growth and Development, Department of Obstetrics and Gynecology, Wayne State University, Detroit, MI 48201, USA; (H.C.); (R.T.); (H.R.); (T.S.B.); (N.A.); (R.G.); (G.M.)
| | - Tejeshwar Singh Bawa
- C.S. Mott Center for Human Growth and Development, Department of Obstetrics and Gynecology, Wayne State University, Detroit, MI 48201, USA; (H.C.); (R.T.); (H.R.); (T.S.B.); (N.A.); (R.G.); (G.M.)
| | - Nicholas Adzibolosu
- C.S. Mott Center for Human Growth and Development, Department of Obstetrics and Gynecology, Wayne State University, Detroit, MI 48201, USA; (H.C.); (R.T.); (H.R.); (T.S.B.); (N.A.); (R.G.); (G.M.)
| | - Radhika Gogoi
- C.S. Mott Center for Human Growth and Development, Department of Obstetrics and Gynecology, Wayne State University, Detroit, MI 48201, USA; (H.C.); (R.T.); (H.R.); (T.S.B.); (N.A.); (R.G.); (G.M.)
- Karmanos Cancer Institute, Detroit, MI 48201, USA
| | - Gil Mor
- C.S. Mott Center for Human Growth and Development, Department of Obstetrics and Gynecology, Wayne State University, Detroit, MI 48201, USA; (H.C.); (R.T.); (H.R.); (T.S.B.); (N.A.); (R.G.); (G.M.)
- Karmanos Cancer Institute, Detroit, MI 48201, USA
| | - Ayesha B. Alvero
- C.S. Mott Center for Human Growth and Development, Department of Obstetrics and Gynecology, Wayne State University, Detroit, MI 48201, USA; (H.C.); (R.T.); (H.R.); (T.S.B.); (N.A.); (R.G.); (G.M.)
- Karmanos Cancer Institute, Detroit, MI 48201, USA
- Correspondence:
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Tőkés AM, Vári-Kakas S, Kulka J, Törőcsik B. Tumor Glucose and Fatty Acid Metabolism in the Context of Anthracycline and Taxane-Based (Neo)Adjuvant Chemotherapy in Breast Carcinomas. Front Oncol 2022; 12:850401. [PMID: 35433453 PMCID: PMC9008716 DOI: 10.3389/fonc.2022.850401] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Accepted: 03/08/2022] [Indexed: 12/13/2022] Open
Abstract
Breast cancer is characterized by considerable metabolic diversity. A relatively high percentage of patients diagnosed with breast carcinoma do not respond to standard-of-care treatment, and alteration in metabolic pathways nowadays is considered one of the major mechanisms responsible for therapeutic resistance. Consequently, there is an emerging need to understand how metabolism shapes therapy response, therapy resistance and not ultimately to analyze the metabolic changes occurring after different treatment regimens. The most commonly applied neoadjuvant chemotherapy regimens in breast cancer contain an anthracycline (doxorubicin or epirubicin) in combination or sequentially administered with taxanes (paclitaxel or docetaxel). Despite several efforts, drug resistance is still frequent in many types of breast cancer, decreasing patients’ survival. Understanding how tumor cells rapidly rewire their signaling pathways to persist after neoadjuvant cancer treatment have to be analyzed in detail and in a more complex system to enable scientists to design novel treatment strategies that target different aspects of tumor cells and tumor resistance. Tumor heterogeneity, the rapidly changing environmental context, differences in nutrient use among different cell types, the cooperative or competitive relationships between cells pose additional challenges in profound analyzes of metabolic changes in different breast carcinoma subtypes and treatment protocols. Delineating the contribution of metabolic pathways to tumor differentiation, progression, and resistance to different drugs is also the focus of research. The present review discusses the changes in glucose and fatty acid pathways associated with the most frequently applied chemotherapeutic drugs in breast cancer, as well the underlying molecular mechanisms and corresponding novel therapeutic strategies.
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Affiliation(s)
- Anna Mária Tőkés
- 2nd Department of Pathology, Semmelweis University Budapest, Budapest, Hungary
- *Correspondence: Anna Mária Tőkés,
| | - Stefan Vári-Kakas
- Department of Computers and Information Technology, Faculty of Electrical Engineering and Information Technology, University of Oradea, Oradea, Romania
| | - Janina Kulka
- 2nd Department of Pathology, Semmelweis University Budapest, Budapest, Hungary
| | - Beáta Törőcsik
- Department of Biochemistry, Semmelweis University Budapest, Budapest, Hungary
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Kogej K, Božič D, Kobal B, Herzog M, Černe K. Application of Dynamic and Static Light Scattering for Size and Shape Characterization of Small Extracellular Nanoparticles in Plasma and Ascites of Ovarian Cancer Patients. Int J Mol Sci 2021; 22:ijms222312946. [PMID: 34884751 PMCID: PMC8657631 DOI: 10.3390/ijms222312946] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Accepted: 11/23/2021] [Indexed: 12/14/2022] Open
Abstract
In parallel to medical treatment of ovarian cancer, methods for the early detection of cancer tumors are being sought. In this contribution, the use of non-invasive static (SLS) and dynamic light scattering (DLS) for the characterization of extracellular nanoparticles (ENPs) in body fluids of advanced serous ovarian cancer (OC) and benign gynecological pathology (BP) patients is demonstrated and critically evaluated. Samples of plasma and ascites (OC patients) or plasma, peritoneal fluid, and peritoneal washing (BP patients) were analyzed. The hydrodynamic radius (Rh) and the radius of gyration (Rg) of ENPs were calculated from the angular dependency of LS intensity for two ENP subpopulations. Rh and Rg of the predominant ENP population of OC patients were in the range 20–30 nm (diameter 40–60 nm). In thawed samples, larger particles (Rh mostly above 100 nm) were detected as well. The shape parameter ρ of both particle populations was around 1, which is typical for spherical particles with mass concentrated on the rim, as in vesicles. The Rh and Rg of ENPs in BP patients were larger than in OC patients, with ρ ≈ 1.1–2, implying a more elongated/distorted shape. These results show that SLS and DLS are promising methods for the analysis of morphological features of ENPs and have the potential to discriminate between OC and BP patients. However, further development of the methodology is required.
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Affiliation(s)
- Ksenija Kogej
- Faculty of Chemistry and Chemical Technology, Department of Chemistry and Biochemistry, University of Ljubljana, SI-1000 Ljubljana, Slovenia;
- Correspondence:
| | - Darja Božič
- Faculty of Chemistry and Chemical Technology, Department of Chemistry and Biochemistry, University of Ljubljana, SI-1000 Ljubljana, Slovenia;
- Laboratory of Clinical Biophysics, Faculty of Health Sciences, University of Ljubljana, SI-1000 Ljubljana, Slovenia
| | - Borut Kobal
- Division of Gynecology and Obstetrics, Department of Gynecology, University Medical Centre Ljubljana, SI-1000 Ljubljana, Slovenia; (B.K.); (M.H.)
- Faculty of Medicine, Department of Gynecology and Obstetrics, University of Ljubljana, SI-1000 Ljubljana, Slovenia
| | - Maruša Herzog
- Division of Gynecology and Obstetrics, Department of Gynecology, University Medical Centre Ljubljana, SI-1000 Ljubljana, Slovenia; (B.K.); (M.H.)
- Faculty of Medicine, Department of Gynecology and Obstetrics, University of Ljubljana, SI-1000 Ljubljana, Slovenia
| | - Katarina Černe
- Faculty of Medicine, Department of Pharmacology and Experimental Toxicology, University of Ljubljana, SI-1000 Ljubljana, Slovenia;
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