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Jiang H, Li XS, Yang Y, Qi RX. Plasma lipidomics profiling in predicting the chemo-immunotherapy response in advanced non-small cell lung cancer. Front Oncol 2024; 14:1348164. [PMID: 39040440 PMCID: PMC11260645 DOI: 10.3389/fonc.2024.1348164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Accepted: 06/24/2024] [Indexed: 07/24/2024] Open
Abstract
Background Advanced non-small cell lung cancer (NSCLC) presents significant treatment challenges, with chemo-immunotherapy emerging as a promising approach. This study explores the potential of lipidomic biomarkers to predict responses to chemo-immunotherapy in advanced non-small cell lung cancer (NSCLC) patients. Methods A prospective analysis was conducted on 68 NSCLC patients undergoing chemo-immunotherapy, divided into disease control (DC) and progressive disease (PD) groups based on treatment response. Pre-treatment serum samples were subjected to lipidomic profiling using liquid chromatography-mass spectrometry (LC-MS). Key predictive lipids (biomarkers) were identified through projection to latent structures discriminant analysis. A biomarker combined model and a clinical combined model were developed to enhance the prediction accuracy. The predictive performances of the clinical combined model in different histological subtypes were also performed. Results Six lipids were identified as the key lipids. The expression levels of PC(16:0/18:2), PC(16:0/18:1), PC(16:0/18:0), CE(20:1), and PC(14:0/18:1) were significantly up-regulated. While the expression level of TAG56:7-FA18:2 was significantly down-regulated. The biomarker combined model demonstrated a receiver operating characteristic (ROC) curve of 0.85 (95% CI: 0.75-0.95) in differentiating the PD from the DC. The clinical combined model exhibited an AUC of 0.87 (95% CI: 0.79-0.96) in differentiating the PD from the DC. The clinical combined model demonstrated good discriminability in DC and PD patients in different histological subtypes with the AUC of 0.78 (95% CI: 0.62-0.96), 0.79 (95% CI: 0.64-0.94), and 0.86 (95% CI: 0.52-1.00) in squamous cell carcinoma, large cell carcinoma, and adenocarcinoma subtype, respectively. Pathway analysis revealed the metabolisms of linoleic acid, alpha-linolenic acid, glycerolipid, arachidonic acid, glycerophospholipid, and steroid were implicated in the chemo-immunotherapy response in advanced NSCLC. Conclusion Lipidomic profiling presents a highly accurate method for predicting responses to chemo-immunotherapy in patients with advanced NSCLC, offering a potential avenue for personalized treatment strategies.
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Affiliation(s)
- Hui Jiang
- Department of Ultrasound, Jinshan Hospital, Fudan University, Shanghai, China
| | - Xu-Shuo Li
- Department of Center for Tumor Diagnosis & Therapy, Jinshan Hospital, Fudan University, Shanghai, China
| | - Ying Yang
- Department of Center for Tumor Diagnosis & Therapy, Jinshan Hospital, Fudan University, Shanghai, China
| | - Rui-Xue Qi
- Department of Center for Tumor Diagnosis & Therapy, Jinshan Hospital, Fudan University, Shanghai, China
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Lv M, Shao S, Du Y, Zhuang X, Wang X, Qiao T. Plasma Lipidomics Profiling to Identify the Biomarkers of Diagnosis and Radiotherapy Response for Advanced Non-Small-Cell Lung Cancer Patients. J Lipids 2024; 2024:6730504. [PMID: 38312939 PMCID: PMC10838201 DOI: 10.1155/2024/6730504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 12/21/2023] [Accepted: 01/10/2024] [Indexed: 02/06/2024] Open
Abstract
Background Advanced lung cancer that contributes to a heavy burden on medical institutions is the leading cause of cancer-related death and is often accompanied by metabolic disorders. In this study, we aimed to explore the biomarkers of diagnosis and radiotherapy response in non-small-cell lung cancer (NSCLC) patients by plasma lipidomics analysis. Method Using triple-quadrupole mass spectrometer analysis, our research characterized the plasma lipid metabolomics profile of 25 healthy controls and 31 advanced NSCLC patients in each of three different radiotherapy phases. Results The results showed altered lipid elements and concentrations among NSCLC patients with different radiotherapy phases, NSCLC subtypes, and different radiotherapeutic responses. We found that compared to the healthy controls, myelin-associated glycoprotein (MAG), phosphatidylinositol (PI), and phosphatidylserine (PS) were mainly and significantly altered lipid elements (> twofold, and p < 0.05) among NSCLC patients with different radiotherapy phases. Through comparison of lipid elements between bad and good responses of NSCLC patients with radiotherapy, the obviously declined phosphatidylglycerol (PG 18 : 0/14 : 0, 18 : 1/18 : 3, and 18 : 0/20 : 1) or markedly elevated PI (20 : 0/22 : 5 and 18 : 2/22 : 4) and phosphatidic acid (PA 14 : 0/20 : 4, 14 : 0/20 : 3, and 18 : 2/22 : 4) could indicate poor therapeutic response for NSCLC patients. The results of ROC curve analysis suggested that PG (18 : 0/20 : 1 and 18 : 0/14 : 0) could clearly predict the radiotherapeutic response for NSCLC patients, and PS (18 : 0/20 : 0) and cholesterol were the first two lipid components with the most potential for the diagnosis of advanced NSCLC. Conclusion Our results indicated that plasma lipidomics profiling might have a vital value to uncover the heterogeneity of lipid metabolism in healthy people and advanced NSCLC patients with different radiotherapy phase, and further to screen out radiotherapeutic response-specific biomarkers.
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Affiliation(s)
- Minghe Lv
- Center for Tumor Diagnosis and Therapy, Jinshan Hospital, Fudan University, Jinshan District, Shanghai 201508, China
- Department of Radiotherapy, Shuguang Hospital Affiliated to Shanghai University of Chinese Traditional Medicine, Pudong New Area, Shanghai 201203, China
| | - Shali Shao
- Center for Tumor Diagnosis and Therapy, Jinshan Hospital, Fudan University, Jinshan District, Shanghai 201508, China
| | - Yajing Du
- Center for Tumor Diagnosis and Therapy, Jinshan Hospital, Fudan University, Jinshan District, Shanghai 201508, China
| | - Xibing Zhuang
- Center for Tumor Diagnosis and Therapy, Jinshan Hospital, Fudan University, Jinshan District, Shanghai 201508, China
| | - Xiangdong Wang
- Center for Tumor Diagnosis and Therapy, Jinshan Hospital, Fudan University, Jinshan District, Shanghai 201508, China
| | - Tiankui Qiao
- Center for Tumor Diagnosis and Therapy, Jinshan Hospital, Fudan University, Jinshan District, Shanghai 201508, China
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Bel’skaya LV, Gundyrev IA, Solomatin DV. The Role of Amino Acids in the Diagnosis, Risk Assessment, and Treatment of Breast Cancer: A Review. Curr Issues Mol Biol 2023; 45:7513-7537. [PMID: 37754258 PMCID: PMC10527988 DOI: 10.3390/cimb45090474] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 09/05/2023] [Accepted: 09/12/2023] [Indexed: 09/28/2023] Open
Abstract
This review summarizes the role of amino acids in the diagnosis, risk assessment, imaging, and treatment of breast cancer. It was shown that the content of individual amino acids changes in breast cancer by an average of 10-15% compared with healthy controls. For some amino acids (Thr, Arg, Met, and Ser), an increase in concentration is more often observed in breast cancer, and for others, a decrease is observed (Asp, Pro, Trp, and His). The accuracy of diagnostics using individual amino acids is low and increases when a number of amino acids are combined with each other or with other metabolites. Gln/Glu, Asp, Arg, Leu/Ile, Lys, and Orn have the greatest significance in assessing the risk of breast cancer. The variability in the amino acid composition of biological fluids was shown to depend on the breast cancer phenotype, as well as the age, race, and menopausal status of patients. In general, the analysis of changes in the amino acid metabolism in breast cancer is a promising strategy not only for diagnosis, but also for developing new therapeutic agents, monitoring the treatment process, correcting complications after treatment, and evaluating survival rates.
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Affiliation(s)
- Lyudmila V. Bel’skaya
- Biochemistry Research Laboratory, Omsk State Pedagogical University, 644099 Omsk, Russia;
| | - Ivan A. Gundyrev
- Biochemistry Research Laboratory, Omsk State Pedagogical University, 644099 Omsk, Russia;
| | - Denis V. Solomatin
- Department of Mathematics and Mathematics Teaching Methods, Omsk State Pedagogical University, 644043 Omsk, Russia;
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Anand ST, Ryckman KK, Baer RJ, Charlton ME, Breheny PJ, Terry WW, Kober K, Oltman S, Rogers EE, Jelliffe-Pawlowski LL, Chrischilles EA. Metabolic differences among newborns born to mothers with a history of leukemia or lymphoma. J Matern Fetal Neonatal Med 2022; 35:6751-6758. [PMID: 33980115 PMCID: PMC8586052 DOI: 10.1080/14767058.2021.1922378] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2020] [Revised: 04/18/2021] [Accepted: 04/22/2021] [Indexed: 10/21/2022]
Abstract
BACKGROUND Leukemia and lymphoma are cancers affecting children, adolescents, and young adults and may affect reproductive outcomes and maternal metabolism. We evaluated for metabolic changes in newborns of mothers with a history of these cancers. METHODS A cross-sectional study was conducted on California births from 2007 to 2011 with linked maternal hospital discharge records, birth certificate, and newborn screening metabolites. History of leukemia or lymphoma was determined using ICD-9-CM codes from hospital discharge data and newborn metabolite data from the newborn screening program. RESULTS A total of 2,068,038 women without cancer history and 906 with history of leukemia or lymphoma were included. After adjusting for differences in maternal age, infant sex, age at metabolite collection, gestational age, and birthweight, among newborns born to women with history of leukemia/lymphoma, several acylcarnitines were significantly (p < .001 - based on Bonferroni correction for multiple testing) higher compared to newborns of mothers without cancer history: C3-DC (mean difference (MD) = 0.006), C5-DC (MD = 0.009), C8:1 (MD = 0.008), C14 (MD = 0.010), and C16:1 (MD = 0.011), whereas citrulline levels were significantly lower (MD = -0.581) among newborns born to mothers with history of leukemia or lymphoma compared to newborns of mothers without a history of cancer. CONCLUSION The varied metabolite levels suggest history of leukemia or lymphoma has metabolic impact on newborn offspring, which may have implications for future metabolic consequences such as necrotizing enterocolitis and urea cycle enzyme disorders in children born to mothers with a history of leukemia or lymphoma.
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Affiliation(s)
- Sonia T. Anand
- Department of Epidemiology, University of Iowa, Iowa City, Iowa, United States of America
| | - Kelli K. Ryckman
- Department of Epidemiology, University of Iowa, Iowa City, Iowa, United States of America
- Department of Pediatrics, University of Iowa, Iowa City, Iowa, United States of America
| | - Rebecca J. Baer
- Department of Pediatrics, University of California San Diego, La Jolla, California, United States of America
- California Preterm Birth Initiative, University of California San Francisco, San Francisco, California, United States of America
| | - Mary E. Charlton
- Department of Epidemiology, University of Iowa, Iowa City, Iowa, United States of America
| | - Patrick J. Breheny
- Department of Biostatistics, University of Iowa, Iowa City, Iowa, United States of America
| | - William W. Terry
- Department of Pediatrics, University of Iowa, Iowa City, Iowa, United States of America
| | - Kord Kober
- Department of Physiological Nursing, University of California San Francisco, San Francisco, California, United States of America
| | - Scott Oltman
- California Preterm Birth Initiative, University of California San Francisco, San Francisco, California, United States of America
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California, United States of America
| | - Elizabeth E. Rogers
- California Preterm Birth Initiative, University of California San Francisco, San Francisco, California, United States of America
- Department of Pediatrics, University of California San Francisco School of Medicine, San Francisco, California, United States of America
| | - Laura L. Jelliffe-Pawlowski
- California Preterm Birth Initiative, University of California San Francisco, San Francisco, California, United States of America
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California, United States of America
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Liu J, Zhou Y, Liu H, Ma M, Wang F, Liu C, Yuan Q, Wang H, Hou X, Yin P. Metabolic reprogramming enables the auxiliary diagnosis of breast cancer by automated breast volume scanner. Front Oncol 2022; 12:939606. [PMCID: PMC9597368 DOI: 10.3389/fonc.2022.939606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Accepted: 09/15/2022] [Indexed: 12/24/2022] Open
Abstract
Breast cancer is the leading cause of female cancer-related deaths worldwide. New technologies with enhanced sensitivity and specificity for early diagnosis and monitoring of postoperative recurrence are in critical demand. Automatic breast full volume scanning system (ABVS) is an emerging technology used as an alternative imaging method for breast cancer screening. Despite its improved detection rate of malignant tumors, ABVS cannot accurately stage breast cancer preoperatively in 30–40% of cases. As a major hallmark of breast cancer, the characteristic metabolic reprogramming may provide potential biomarkers as an auxiliary method for ABVS.ObjectiveThe objective of this study was to identify differential metabolomic signatures between benign and malignant breast tumors and among different subtypes of breast cancer patients based on untargeted metabolomics and improve breast cancer detection rate by combining key metabolites and ABVS.MethodsUntargeted metabolomics approach was used to profile serum samples from 70 patients with different subtypes of breast cancer and benign breast tumor to determine specific metabolomic profiles through univariate and multivariate statistical data analysis.ResultsMetabolic profiles correctly distinguished benign and malignant breast tumors patients, and a total of 791 metabolites were identified. There were 54 different metabolites between benign and malignant breast tumors and 17 different metabolites between invasive and non-invasive breast cancer. Notably, the missed diagnosis rate of ABVS could be reduced by differential metabolite analysis. Moreover, the diagnostic performance analyses of combined metabolites (pelargonic acid, N-acetylasparagine, and cysteine-S-sulfate) with ABVS performance gave a ROC area under the curve of 0.967 (95% CI: 0.926, 0.993).ConclusionsOur study identified metabolic features both in benign and malignant breast tumors and in invasive and non-invasive breast cancer. Combined ultrasound ABVS and a panel of differential serum metabolites could further improve the accuracy of preoperative diagnosis of breast cancer and guide surgical therapy.
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Affiliation(s)
- Jianjun Liu
- Clinical Laboratory of Integrative Medicine, The First Affiliated Hospital of Dalian Medical University, Dalian, China
- College of Integrative Medicine, Dalian Medical University, Dalian, China
| | - Yang Zhou
- Department of Ultrasound, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Huiying Liu
- Clinical Laboratory of Integrative Medicine, The First Affiliated Hospital of Dalian Medical University, Dalian, China
- College of Integrative Medicine, Dalian Medical University, Dalian, China
| | - Mengyan Ma
- Department of Ultrasound, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Fei Wang
- Breast Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Chang Liu
- Clinical Laboratory of Integrative Medicine, The First Affiliated Hospital of Dalian Medical University, Dalian, China
- College of Integrative Medicine, Dalian Medical University, Dalian, China
| | - Qihang Yuan
- Department of General Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Hongjiang Wang
- Breast Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Xiukun Hou
- Department of Ultrasound, The First Affiliated Hospital of Dalian Medical University, Dalian, China
- *Correspondence: Peiyuan Yin, ; Xiukun Hou,
| | - Peiyuan Yin
- Clinical Laboratory of Integrative Medicine, The First Affiliated Hospital of Dalian Medical University, Dalian, China
- College of Integrative Medicine, Dalian Medical University, Dalian, China
- *Correspondence: Peiyuan Yin, ; Xiukun Hou,
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How previous treatment changes the metabolomic profile in patients with metastatic breast cancer. Arch Gynecol Obstet 2022; 306:2115-2122. [PMID: 35467121 PMCID: PMC9633507 DOI: 10.1007/s00404-022-06558-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Accepted: 04/01/2022] [Indexed: 01/29/2023]
Abstract
Purpose Metabolites are in the spotlight of attention as promising novel breast cancer biomarkers. However, no study has been conducted concerning changes in the metabolomics profile of metastatic breast cancer patients according to previous therapy. Methods We performed a retrospective, single-center, nonrandomized, partially blinded, treatment-based study. Metastatic breast cancer (MBC) patients were enrolled between 03/2010 and 09/2016 at the beginning of a new systemic therapy. The endogenous metabolites in the plasma samples were analyzed using the AbsoluteIDQ® p180 Kit (Biocrates Life Sciences AG, Innsbruck) a targeted, quality and quantitative-controlled metabolomics approach. The statistical analysis was performed using R package, version 3.3.1. ANOVA was used to statistically assess age differences within groups. Furthermore, we analyzed the CTC status of the patients using the CellSearch™ assay. Results We included 178 patients in our study. Upon dividing the study population according to therapy before study inclusion, we found the following: 4 patients had received no therapy, 165 chemotherapy, and 135 anti-hormonal therapy, 30 with anti-Her2 therapy and 38 had received treatment with bevacizumab. Two metabolites were found to be significantly different, depending on the further therapy of the patients: methionine and serine. Whereas methionine levels were higher in the blood of patients who received an anti-Her2-therapy, serine was lower in patients with endocrine therapy only. Conclusion We identified two metabolites for which concentrations differed significantly depending on previous therapies, which could help to choose the next therapy in patients who have already received numerous different treatments.
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Blood and urine biomarkers in invasive ductal breast cancer: Mass spectrometry applied to identify metabolic alterations. J Mol Struct 2022. [DOI: 10.1016/j.molstruc.2021.131369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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Wolrab D, Jirásko R, Peterka O, Idkowiak J, Chocholoušková M, Vaňková Z, Hořejší K, Brabcová I, Vrána D, Študentová H, Melichar B, Holčapek M. Plasma lipidomic profiles of kidney, breast and prostate cancer patients differ from healthy controls. Sci Rep 2021; 11:20322. [PMID: 34645896 PMCID: PMC8514434 DOI: 10.1038/s41598-021-99586-1] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Accepted: 09/28/2021] [Indexed: 01/10/2023] Open
Abstract
Early detection of cancer is one of the unmet needs in clinical medicine. Peripheral blood analysis is a preferred method for efficient population screening, because blood collection is well embedded in clinical practice and minimally invasive for patients. Lipids are important biomolecules, and variations in lipid concentrations can reflect pathological disorders. Lipidomic profiling of human plasma by the coupling of ultrahigh-performance supercritical fluid chromatography and mass spectrometry is investigated with the aim to distinguish patients with breast, kidney, and prostate cancers from healthy controls. The mean sensitivity, specificity, and accuracy of the lipid profiling approach were 85%, 95%, and 92% for kidney cancer; 91%, 97%, and 94% for breast cancer; and 87%, 95%, and 92% for prostate cancer. No association of statistical models with tumor stage is observed. The statistically most significant lipid species for the differentiation of cancer types studied are CE 16:0, Cer 42:1, LPC 18:2, PC 36:2, PC 36:3, SM 32:1, and SM 41:1 These seven lipids represent a potential biomarker panel for kidney, breast, and prostate cancer screening, but a further verification step in a prospective study has to be performed to verify clinical utility.
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Affiliation(s)
- Denise Wolrab
- Department of Analytical Chemistry, Faculty of Chemical Technology, University of Pardubice, Studentská 573, 532 10, Pardubice, Czech Republic
| | - Robert Jirásko
- Department of Analytical Chemistry, Faculty of Chemical Technology, University of Pardubice, Studentská 573, 532 10, Pardubice, Czech Republic
| | - Ondřej Peterka
- Department of Analytical Chemistry, Faculty of Chemical Technology, University of Pardubice, Studentská 573, 532 10, Pardubice, Czech Republic
| | - Jakub Idkowiak
- Department of Analytical Chemistry, Faculty of Chemical Technology, University of Pardubice, Studentská 573, 532 10, Pardubice, Czech Republic
| | - Michaela Chocholoušková
- Department of Analytical Chemistry, Faculty of Chemical Technology, University of Pardubice, Studentská 573, 532 10, Pardubice, Czech Republic
| | - Zuzana Vaňková
- Department of Analytical Chemistry, Faculty of Chemical Technology, University of Pardubice, Studentská 573, 532 10, Pardubice, Czech Republic
| | - Karel Hořejší
- Department of Analytical Chemistry, Faculty of Chemical Technology, University of Pardubice, Studentská 573, 532 10, Pardubice, Czech Republic
| | - Ivana Brabcová
- Department of Analytical Chemistry, Faculty of Chemical Technology, University of Pardubice, Studentská 573, 532 10, Pardubice, Czech Republic
| | - David Vrána
- Department of Oncology, Faculty of Medicine and Dentistry, Palacký University and University Hospital, I.P. Pavlova 6, 775 20, Olomouc, Czech Republic
- Comprehensive Cancer Center Nový Jičín, Hospital Nový Jičín, Nový Jičín, Czech Republic
| | - Hana Študentová
- Department of Oncology, Faculty of Medicine and Dentistry, Palacký University and University Hospital, I.P. Pavlova 6, 775 20, Olomouc, Czech Republic
| | - Bohuslav Melichar
- Department of Oncology, Faculty of Medicine and Dentistry, Palacký University and University Hospital, I.P. Pavlova 6, 775 20, Olomouc, Czech Republic
| | - Michal Holčapek
- Department of Analytical Chemistry, Faculty of Chemical Technology, University of Pardubice, Studentská 573, 532 10, Pardubice, Czech Republic.
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Zheng C, Wu Y, Liang ZH, Pi JS, Cheng SB, Wei WZ, Liu JB, Lu LZ, Li CF, Zhang H. Plasma metabolites associated with physiological and biochemical indexes indicate the effect of caging stress on mallard ducks (Anas platyrhynchos). Anim Biosci 2021; 35:224-235. [PMID: 34474531 PMCID: PMC8738941 DOI: 10.5713/ab.21.0241] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Accepted: 07/23/2021] [Indexed: 11/27/2022] Open
Abstract
Objective Cage rearing has critical implications for the laying duck industry because it is convenient for feeding and management. However, caging stress is a type of chronic stress that induces maladaptation. Environmental stress responses have been extensively studied, but no detailed information is available about the comprehensive changes in plasma metabolites at different stages of caging stress in ducks. We designed this experiment to analyze the effects of caging stress on performance parameters and oxidative stress indexes in ducks. Methods Liquid chromatography tandem mass spectrometry (LC/MS-MS) was used to determine the changes in metabolites in duck plasma at 5 (CR5), 10 (CR10), and 15 (CR15) days after cage rearing and traditional breeding (TB). The associated pathways of differentially altered metabolites were analyzed using Kyoto encyclopedia of genes and genomes (KEGG) database. Results The results of this study indicate that caging stress decreased performance parameters, and the plasma total superoxide dismutase levels were increased in the CR10 group compared with the other groups. In addition, 1,431 metabolites were detected. Compared with the TB group, 134, 381, and 190 differentially produced metabolites were identified in the CR5, CR10, and CR15 groups, respectively. The results of principal component analysis (PCA) show that the selected components sufficiently distinguish the TB group and CR10 group. KEGG analysis results revealed that the differentially altered metabolites in duck plasma from the CR5 and TB groups were mainly associated with ovarian steroidogenesis, biosynthesis of unsaturated fatty acids, and phenylalanine metabolism. Conclusion In this study, the production performance, blood indexes, number of metabolites and PCA were compared to determine effect of the caging stress stage on ducks. We inferred from the experimental results that caging-stressed ducks were in the sensitive phase in the first 5 days after caging, caging for approximately 10 days was an important transition phase, and then the duck continually adapted.
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Affiliation(s)
- Chao Zheng
- Institute of Animal Husbandry and Veterinary Science, Hubei Academy of Agricultural Sciences/Hubei Key Laboratory of Animal Embryo Engineering and Molecular Breeding, Wuhan, 430064, China.,School of Life Science and Engineering, Southwest University of Science and Technology, Mianyang, 621010, China
| | - Yan Wu
- Institute of Animal Husbandry and Veterinary Science, Hubei Academy of Agricultural Sciences/Hubei Key Laboratory of Animal Embryo Engineering and Molecular Breeding, Wuhan, 430064, China
| | - Zhen Hua Liang
- Institute of Animal Husbandry and Veterinary Science, Hubei Academy of Agricultural Sciences/Hubei Key Laboratory of Animal Embryo Engineering and Molecular Breeding, Wuhan, 430064, China
| | - Jin Song Pi
- Institute of Animal Husbandry and Veterinary Science, Hubei Academy of Agricultural Sciences/Hubei Key Laboratory of Animal Embryo Engineering and Molecular Breeding, Wuhan, 430064, China
| | - Shi Bin Cheng
- Institute of Animal Husbandry and Veterinary Science, Hubei Academy of Agricultural Sciences/Hubei Key Laboratory of Animal Embryo Engineering and Molecular Breeding, Wuhan, 430064, China
| | | | - Jing Bo Liu
- School of Life Science and Engineering, Southwest University of Science and Technology, Mianyang, 621010, China
| | - Li Zhi Lu
- Institute of Animal Husbandry and Veterinary Medicine, Zhejiang Academy of Agricultural Sciences, Hangzhou, 310021, China
| | - Cheng Feng Li
- Hubei Shendan Healthy Food Co..Ltd, Anlu, 432600, China
| | - Hao Zhang
- Institute of Animal Husbandry and Veterinary Science, Hubei Academy of Agricultural Sciences/Hubei Key Laboratory of Animal Embryo Engineering and Molecular Breeding, Wuhan, 430064, China
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Wan L, Liu Q, Liang D, Guo Y, Liu G, Ren J, He Y, Shan B. Circulating Tumor Cell and Metabolites as Novel Biomarkers for Early-Stage Lung Cancer Diagnosis. Front Oncol 2021; 11:630672. [PMID: 34136379 PMCID: PMC8202280 DOI: 10.3389/fonc.2021.630672] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Accepted: 04/09/2021] [Indexed: 12/12/2022] Open
Abstract
Background Lung cancer is a malignant tumor that has the highest morbidity and mortality rate among all cancers. Early diagnosis of lung cancer is a key factor in reducing mortality and improving prognosis. Methods In this study, we performed CTC next-generation sequencing (NGS) in early-stage lung cancer patients to identify lung cancer-related gene mutations. Meanwhile, a serum liquid chromatography-tandem mass spectrometry (LC-MS) untargeted metabolomics analysis was performed in the CTC-positive patients. To screen potential diagnostic markers for early lung cancer. Results 62.5% (30/48) of lung cancer patients had ≥1 CTC. By CTC NGS, we found that > 50% of patients had 4 commonly mutated genes, namely, NOTCH1, IGF2, EGFR, and PTCH1. 47.37% (9/19) patients had ARIDH1 mutations. Additionally, 30 CTC-positive patients and 30 healthy volunteers were subjected to LC-MS untargeted metabolomics analysis. We found 100 different metabolites, and 10 different metabolites were identified through analysis, which may have potential clinical application value in the diagnosis of CTC-positive early-stage lung cancer (AUC >0.9). Conclusions Our results indicate that NGS of CTC and metabolomics may provide new tumor markers for the early diagnosis of lung cancer.
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Affiliation(s)
- Lingling Wan
- Cancer Institute, The Fourth Hospital of Hebei Medical University/The Tumor Hospital of Hebei Province, Shijiazhuang, China
| | - Qingyi Liu
- Department of Thoracic Surgery, The Fourth Hospital of Hebei Medical University/The Tumor Hospital of Hebei Province, Shijiazhuang, China
| | - Di Liang
- Cancer Institute, The Fourth Hospital of Hebei Medical University/The Tumor Hospital of Hebei Province, Shijiazhuang, China
| | - Yongdong Guo
- Cancer Institute, The Fourth Hospital of Hebei Medical University/The Tumor Hospital of Hebei Province, Shijiazhuang, China
| | - Guangjie Liu
- Department of Thoracic Surgery, The Fourth Hospital of Hebei Medical University/The Tumor Hospital of Hebei Province, Shijiazhuang, China
| | - Jinxia Ren
- Cancer Institute, The Fourth Hospital of Hebei Medical University/The Tumor Hospital of Hebei Province, Shijiazhuang, China
| | - Yutong He
- Cancer Institute, The Fourth Hospital of Hebei Medical University/The Tumor Hospital of Hebei Province, Shijiazhuang, China
| | - Baoen Shan
- Cancer Institute, The Fourth Hospital of Hebei Medical University/The Tumor Hospital of Hebei Province, Shijiazhuang, China
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Kozar N, Kruusmaa K, Bitenc M, Argamasilla R, Adsuar A, Takač I, Arko D. Identification of Novel Diagnostic Biomarkers in Breast Cancer Using Targeted Metabolomic Profiling. Clin Breast Cancer 2020; 21:e204-e211. [PMID: 33281038 DOI: 10.1016/j.clbc.2020.09.006] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2020] [Revised: 09/02/2020] [Accepted: 09/08/2020] [Indexed: 01/01/2023]
Abstract
INTRODUCTION Breast cancer (BC) is the most common cancer in women, with a high disease burden, especially in the advanced disease stages. Our study investigated the metabolomic profile of breast cancer patients' serum with the aim of identifying novel diagnostic biomarkers that could be used, especially for early disease detection. MATERIALS AND METHODS Using targeted metabolomic serum profiling based on high-performance liquid chromatography mass spectrometry, women with BC (n = 39) and a control group (n = 21) were examined for 232 endogenous metabolites. RESULTS The top performing biomarkers included acylcarnitines (ACs) and 9,12-linoleic acid. A combined panel of the top 4 biomarkers achieved 83% sensitivity and 81% specificity, with an area under the curve (AUC) of 0.839 (95% confidence interval, 0.811-0.867). Individual markers also provided significant predictive values: AC 12:0, sensitivity of 72%, specificity of 67%, and AUC of 0.71; AC 14:2, sensitivity of 74%, specificity of 71%, and AUC of 0.73; AC 14:0: sensitivity of 67%, specificity of 81%, and AUC of 0.73; and 9,12-linoleic acid, sensitivity of 69%, specificity of 67%, and AUC of 0.71. The individual markers, however, did not reach the high sensitivity and specificity of the 4-biomarker combination. CONCLUSION Using mass spectrometry-targeted metabolomic profiling, ACs and 9,12-linoleic acid were identified as potential diagnostic biomarkers for breast cancer. Additionally, these identified metabolites could provide additional insight into cancer cell metabolism.
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Affiliation(s)
- Nejc Kozar
- Division of Gynaecology and Perinatology, University Medical Centre Maribor, Maribor, Slovenia; Faculty of Medicine, University of Maribor, Maribor, Slovenia.
| | - Kristi Kruusmaa
- Faculty of Pharmacy, University of Ljubljana, Ljubljana, Slovenia; Universal Diagnostics, S.L. Centre of Research Technology and Innovation, University of Seville, Seville, Spain
| | - Marko Bitenc
- Universal Diagnostics, S.L. Centre of Research Technology and Innovation, University of Seville, Seville, Spain
| | - Rosa Argamasilla
- Universal Diagnostics, S.L. Centre of Research Technology and Innovation, University of Seville, Seville, Spain
| | - Antonio Adsuar
- Universal Diagnostics, S.L. Centre of Research Technology and Innovation, University of Seville, Seville, Spain
| | - Iztok Takač
- Division of Gynaecology and Perinatology, University Medical Centre Maribor, Maribor, Slovenia; Faculty of Medicine, University of Maribor, Maribor, Slovenia
| | - Darja Arko
- Division of Gynaecology and Perinatology, University Medical Centre Maribor, Maribor, Slovenia; Faculty of Medicine, University of Maribor, Maribor, Slovenia
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12
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Zhao H, Shen J, Ye Y, Wu X, Esteva FJ, Tripathy D, Chow WH. Validation of plasma metabolites associated with breast cancer risk among Mexican Americans. Cancer Epidemiol 2020; 69:101826. [PMID: 33010726 PMCID: PMC7710579 DOI: 10.1016/j.canep.2020.101826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2020] [Revised: 08/26/2020] [Accepted: 09/14/2020] [Indexed: 10/23/2022]
Abstract
In our previous breast cancer case control study in Hispanics, we found 14 metabolites whose levels differed between cases and controls. To validate the results, we carried out a nested case control study of 100 incident breast cancer and 100 matched healthy women identified from the Mano-A-Mano Mexican American Cohort study. With the adjustment of parity, education, birth place, language acculturation, BMI category, smoking, drinking, physical activity, and sitting time, 4 metabolites were associated with breast cancer risk: 3-hydroxyoctanoate (Odds ratio (OR) = 1.51, 95% confidence interval (CI): 1.10, 3.47), 3-hydroxybutyrate (BHBA) (OR = 1.42, 95%CI: 1.01, 3.72), linoleate (18:2n6) (OR = 1.39, 95% CI: 1.07, 4.04), and bilirubin (OR = 0.54, 95%CI: 0.42, 0.95). Then, we used 3 non-redundant metabolites, namely 3-hydroxyoctanoate, linoleate (18:2n6), and bilirubin, to generate a metabolic risk score. Increased metabolites risk score was associated with a 1.67-fold increased risk of breast cancer (OR = 1.67, 95%CI: 1.32, 3.94). And the significant association was more evident among those who were diagnosed with cancer earlier during the follow-up (≤ 5 years) than their counterparts. In conclusion, we identified four significant metabolites which may help elucidate metabolic pathways that contribute to breast carcinogenesis. Our findings warrant further replication efforts.
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Affiliation(s)
- Hua Zhao
- Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, United States; Department of Family Medicine and Population Health, School of Medicine, Virginia Commonwealth University, Richmond, VA, 23284, United States.
| | - Jie Shen
- Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, United States; Department of Family Medicine and Population Health, School of Medicine, Virginia Commonwealth University, Richmond, VA, 23284, United States
| | - Yuanqing Ye
- Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, United States; Department of Precision Health and DataScience, School of Public Health, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310058, PR China
| | - Xifeng Wu
- Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, United States; Department of Precision Health and DataScience, School of Public Health, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310058, PR China
| | - Francisco J Esteva
- Perlmutter Cancer Center at New York University Langone Health, New York, NY, 10016, United States
| | - Debasish Tripathy
- Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, United States
| | - Wong-Ho Chow
- Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, United States
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13
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Patel SK, George B, Rai V. Artificial Intelligence to Decode Cancer Mechanism: Beyond Patient Stratification for Precision Oncology. Front Pharmacol 2020; 11:1177. [PMID: 32903628 PMCID: PMC7438594 DOI: 10.3389/fphar.2020.01177] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2020] [Accepted: 07/20/2020] [Indexed: 12/13/2022] Open
Abstract
The multitude of multi-omics data generated cost-effectively using advanced high-throughput technologies has imposed challenging domain for research in Artificial Intelligence (AI). Data curation poses a significant challenge as different parameters, instruments, and sample preparations approaches are employed for generating these big data sets. AI could reduce the fuzziness and randomness in data handling and build a platform for the data ecosystem, and thus serve as the primary choice for data mining and big data analysis to make informed decisions. However, AI implication remains intricate for researchers/clinicians lacking specific training in computational tools and informatics. Cancer is a major cause of death worldwide, accounting for an estimated 9.6 million deaths in 2018. Certain cancers, such as pancreatic and gastric cancers, are detected only after they have reached their advanced stages with frequent relapses. Cancer is one of the most complex diseases affecting a range of organs with diverse disease progression mechanisms and the effectors ranging from gene-epigenetics to a wide array of metabolites. Hence a comprehensive study, including genomics, epi-genomics, transcriptomics, proteomics, and metabolomics, along with the medical/mass-spectrometry imaging, patient clinical history, treatments provided, genetics, and disease endemicity, is essential. Cancer Moonshot℠ Research Initiatives by NIH National Cancer Institute aims to collect as much information as possible from different regions of the world and make a cancer data repository. AI could play an immense role in (a) analysis of complex and heterogeneous data sets (multi-omics and/or inter-omics), (b) data integration to provide a holistic disease molecular mechanism, (c) identification of diagnostic and prognostic markers, and (d) monitor patient's response to drugs/treatments and recovery. AI enables precision disease management well beyond the prevalent disease stratification patterns, such as differential expression and supervised classification. This review highlights critical advances and challenges in omics data analysis, dealing with data variability from lab-to-lab, and data integration. We also describe methods used in data mining and AI methods to obtain robust results for precision medicine from "big" data. In the future, AI could be expanded to achieve ground-breaking progress in disease management.
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Affiliation(s)
- Sandip Kumar Patel
- Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Mumbai, India
- Buck Institute for Research on Aging, Novato, CA, United States
| | - Bhawana George
- Department of Hematopathology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Vineeta Rai
- Department of Entomology & Plant Pathology, North Carolina State University, Raleigh, NC, United States
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Eghlimi R, Shi X, Hrovat J, Xi B, Gu H. Triple Negative Breast Cancer Detection Using LC-MS/MS Lipidomic Profiling. J Proteome Res 2020; 19:2367-2378. [PMID: 32397718 DOI: 10.1021/acs.jproteome.0c00038] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Breast cancer (BC) is a heterogeneous malignancy that is responsible for a great portion of female cancer cases and cancer-related deaths in the United States. In comparison to other major BC subtypes, triple negative breast cancer (TNBC) presents with a relatively low survival rate and a high rate of metastasis. This has led to a strong, though largely unmet, need for more sensitive and specific methods of early-stage TNBC (ES-TNBC) detection to combat its high-grade pathology and relatively low survival rate. The current study employs a liquid chromatography-tandem mass spectrometry assay capable of targeted, highly specific, and sensitive detection of lipids to propose two diagnostic biomarker panels for TNBC/ES-TNBC. Using this approach, 110 lipids were reliably detected in 166 human plasma samples, 45 controls, and 121 BC (96 non-TNBC and 25 TNBC) subjects. Univariate and multivariate analyses allowed the construction and application of a 19-lipid biomarker panel capable of distinguishing TNBC (and ES-TNBC) from controls, as well as a 5-lipid biomarker panel capable of differentiating TNBC from non-TNBC and ES-TNBC from ES-non-TNBC. Receiver operating characteristic curves with notable classification performances were generated from the biomarker panels according to their orthogonal partial least-squares discrimination analysis models. TNBC was distinguished from controls with an area under the receiving operating characteristic curve (AUROC) = 0.93, sensitivity = 0.96, and specificity = 0.76 and ES-TNBC from controls with an AUROC = 0.96, sensitivity = 0.95, and specificity = 0.89. TNBC was differentiated from non-TNBC with an AUROC = 0.88, sensitivity = 0.88, and specificity = 0.79 and ES-TNBC from ES-non-TNBC with an AUROC = 0.95, sensitivity = 0.95, and specificity = 0.87. A pathway enrichment analysis between TNBC and controls also revealed significant disturbances in choline metabolism, sphingolipid signaling, and glycerophospholipid metabolism. To the best of our knowledge, this is the first study to propose a diagnostic lipid biomarker panel for TNBC detection. All raw mass spectrometry data have been deposited to MassIVE (dataset identifier MSV000085324).
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Affiliation(s)
- Ryan Eghlimi
- Arizona Metabolomics Laboratory, College of Health Solutions, Arizona State University, Scottsdale, Arizona 85259, United States
| | - Xiaojian Shi
- Arizona Metabolomics Laboratory, College of Health Solutions, Arizona State University, Scottsdale, Arizona 85259, United States
| | - Jonathan Hrovat
- Arizona Metabolomics Laboratory, College of Health Solutions, Arizona State University, Scottsdale, Arizona 85259, United States
| | - Bowei Xi
- Department of Statistics, Purdue University, West Lafayette, Indiana 47907, United States
| | - Haiwei Gu
- Arizona Metabolomics Laboratory, College of Health Solutions, Arizona State University, Scottsdale, Arizona 85259, United States
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15
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Serum lipidomic biomarkers for non-small cell lung cancer in nonsmoking female patients. J Pharm Biomed Anal 2020; 185:113220. [PMID: 32145537 DOI: 10.1016/j.jpba.2020.113220] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2019] [Revised: 02/26/2020] [Accepted: 02/28/2020] [Indexed: 02/06/2023]
Abstract
Lung cancer (Lca) is one of the malignant tumors with the fastest morbidity and mortality increase and the greatest threat to human health and life. The incidence of non-small cell lung cancer (NSCLC) in the nonsmoking female has increased recently. However, its pathogenesis is still unclear, and there is an urgent need for clinical diagnostic biomarkers, especially for early diagnosis. A nontargeted lipidomic approach based on ultra-high-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF/MS), as well as two machine learning approaches (genetic algorithm and binary logistic regression) was used to screen candidate discriminating lipids and define a combinational lipid biomarker in serum samples to distinguish female patients with NSCLC from healthy controls. Moreover, the candidate biomarkers were verified by using an external validation sample set. Our result revealed that fatty acid (FA) (20:4), FA (22:0) and LPE (20:4) can serve as a combinational biomarker for distinguishing female patients with NSCLC from healthy control with good sensitivity and specificity. Furthermore, this combinational biomarker also showed good performance in distinguishing early-stage NSCLC female patients from a healthy control. We observed that levels of unsaturated fatty acids clearly decreased, while saturated fatty acids and lysophosphatidylethanolamines pronouncedly increased in Lca patients, compared with the healthy controls, which revealed significant disturbance of lipid metabolism in NSCLC females. Our results not only provide hints to the pathological mechanism of NSCLC in nonsmoking female but also supply a combinational lipid biomarker to aid the diagnosis of NSCLC at early-stage.
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16
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Yang L, Wang Y, Cai H, Wang S, Shen Y, Ke C. Application of metabolomics in the diagnosis of breast cancer: a systematic review. J Cancer 2020; 11:2540-2551. [PMID: 32201524 PMCID: PMC7066003 DOI: 10.7150/jca.37604] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2019] [Accepted: 12/31/2019] [Indexed: 12/24/2022] Open
Abstract
Breast cancer (BC) remains the most frequent type of cancer in females worldwide. However, the pathogenesis of BC is still under the cloud, along with the huge challenge of early diagnosis, which is widely acknowledged as the key to a successful therapy. Metabolomics, a newborn innovative technique in recent years, has demonstrated great potential in cancer-related researches. The aim of this review is to look back on clinical and cellular metabolomic studies in the diagnosis of BC over the past decade, and provide a systematic summary of metabolic biomarkers and pathways related to BC diagnosis.
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Affiliation(s)
- Liqing Yang
- Medical College of Soochow University, Suzhou 215123, P. R. China
| | - Ying Wang
- Medical College of Soochow University, Suzhou 215123, P. R. China
| | - Haishan Cai
- Medical College of Soochow University, Suzhou 215123, P. R. China
| | - Shuang Wang
- Medical College of Soochow University, Suzhou 215123, P. R. China
| | - Yueping Shen
- Department of Epidemiology and Biostatistics, School of Public Health, Medical College of Soochow University, 199 Renai Road, Suzhou 215123, P. R. China
| | - Chaofu Ke
- Department of Epidemiology and Biostatistics, School of Public Health, Medical College of Soochow University, 199 Renai Road, Suzhou 215123, P. R. China
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17
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Li L, Zheng X, Zhou Q, Villanueva N, Nian W, Liu X, Huan T. Metabolomics-Based Discovery of Molecular Signatures for Triple Negative Breast Cancer in Asian Female Population. Sci Rep 2020; 10:370. [PMID: 31941951 PMCID: PMC6962155 DOI: 10.1038/s41598-019-57068-5] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2019] [Accepted: 12/16/2019] [Indexed: 11/09/2022] Open
Abstract
Triple negative breast cancer (TNBC) is a devastating cancer disease characterized by its poor prognosis, distinct metastatic patterns, and aggressive biological behavior. Research indicates that the prevalence and presentation of TNBC varies among races, with Asian TNBC patients more commonly presenting with large invasive tumors, high node positivity, and high histologic grade. In this work, we applied ultra-high performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS)-based metabolomics to discover metabolic signatures in Asian female TNBC patients. Serum samples from 31 TNBC patients and 31 healthy controls (CN) were involved in this study. A total of 2860 metabolic features were detected in the serum samples. Among them, 77 metabolites, whose levels were significantly different between TNBC with CN, were confirmed. Using multivariate statistical analysis, literature mining, metabolic network and pathway analysis, we performed an in-depth study of the metabolic alterations in the Asian TNBC population. In addition, we discovered a panel of metabolic signatures that are highly correlated with the 5-year survival rate of the TNBC patients. This metabolomic study provides a better understanding of the metabolic details of TNBC in the Asian population.
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Affiliation(s)
- Lixian Li
- Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment, Chongqing University Cancer Hospital & Chongqing Cancer Institute & Chongqing Cancer Hospital, Chongqing, 400030, P.R. China. .,Department of Chemistry, University of British Columbia, Vancouver, British Columbia, V6T 1Z1, Canada.
| | - Xiaodong Zheng
- Department of Breast Cancer, Chongqing University Cancer Hospital & Chongqing Cancer Institute & Chongqing Cancer Hospital, Chongqing, 400030, P.R. China
| | - Qi Zhou
- Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment, Chongqing University Cancer Hospital & Chongqing Cancer Institute & Chongqing Cancer Hospital, Chongqing, 400030, P.R. China
| | - Nathaniel Villanueva
- Department of Chemistry, University of British Columbia, Vancouver, British Columbia, V6T 1Z1, Canada
| | - Weiqi Nian
- Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment, Chongqing University Cancer Hospital & Chongqing Cancer Institute & Chongqing Cancer Hospital, Chongqing, 400030, P.R. China.
| | - Xingming Liu
- Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment, Chongqing University Cancer Hospital & Chongqing Cancer Institute & Chongqing Cancer Hospital, Chongqing, 400030, P.R. China
| | - Tao Huan
- Department of Chemistry, University of British Columbia, Vancouver, British Columbia, V6T 1Z1, Canada.
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18
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Wolrab D, Jirásko R, Chocholoušková M, Peterka O, Holčapek M. Oncolipidomics: Mass spectrometric quantitation of lipids in cancer research. Trends Analyt Chem 2019. [DOI: 10.1016/j.trac.2019.04.012] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
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19
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Geijsen AJ, Brezina S, Keski‐Rahkonen P, Baierl A, Bachleitner‐Hofmann T, Bergmann MM, Boehm J, Brenner H, Chang‐Claude J, van Duijnhoven FJ, Gigic B, Gumpenberger T, Hofer P, Hoffmeister M, Holowatyj AN, Karner‐Hanusch J, Kok DE, Leeb G, Ulvik A, Robinot N, Ose J, Stift A, Schrotz‐King P, Ulrich AB, Ueland PM, Kampman E, Scalbert A, Habermann N, Gsur A, Ulrich CM. Plasma metabolites associated with colorectal cancer: A discovery-replication strategy. Int J Cancer 2019; 145:1221-1231. [PMID: 30665271 PMCID: PMC6614008 DOI: 10.1002/ijc.32146] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2018] [Accepted: 01/08/2019] [Indexed: 12/24/2022]
Abstract
Colorectal cancer is known to arise from multiple tumorigenic pathways; however, the underlying mechanisms remain not completely understood. Metabolomics is becoming an increasingly popular tool in assessing biological processes. Previous metabolomics research focusing on colorectal cancer is limited by sample size and did not replicate findings in independent study populations to verify robustness of reported findings. Here, we performed a ultrahigh performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UHPLC-QTOF-MS) screening on EDTA plasma from 268 colorectal cancer patients and 353 controls using independent discovery and replication sets from two European cohorts (ColoCare Study: n = 180 patients/n = 153 controls; the Colorectal Cancer Study of Austria (CORSA) n = 88 patients/n = 200 controls), aiming to identify circulating plasma metabolites associated with colorectal cancer and to improve knowledge regarding colorectal cancer etiology. Multiple logistic regression models were used to test the association between disease state and metabolic features. Statistically significant associated features in the discovery set were taken forward and tested in the replication set to assure robustness of our findings. All models were adjusted for sex, age, BMI and smoking status and corrected for multiple testing using False Discovery Rate. Demographic and clinical data were abstracted from questionnaires and medical records.
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Affiliation(s)
- Anne J.M.R. Geijsen
- Division of Human Nutrition and HealthWageningen University & ResearchWageningenThe Netherlands
| | - Stefanie Brezina
- Institute of Cancer Research, Department of Medicine IMedical University of ViennaAustria
| | | | - Andreas Baierl
- Department of Statistics and Operations ResearchUniversity of ViennaAustria
| | | | | | - Juergen Boehm
- Huntsman Cancer InstituteSalt Lake CityUT
- Department of Population Health SciencesUniversity of UtahSalt Lake CityUT
| | - Hermann Brenner
- Division of Preventive OncologyNational Center for Tumor Diseases and German Cancer Research CenterHeidelbergGermany
- Division of Clinical Epidemiology and Aging ResearchGerman Cancer Research Center (DKFZ)HeidelbergGermany
- German Cancer Consortium (DKTK)German Cancer Research Center (DKFZ)HeidelbergGermany
| | - Jenny Chang‐Claude
- Division of Cancer EpidemiologyGerman Cancer Research CenterHeidelbergGermany
| | | | - Biljana Gigic
- Department of General, Visceral and Transplantation SurgeryUniversity of HeidelbergGermany
| | - Tanja Gumpenberger
- Institute of Cancer Research, Department of Medicine IMedical University of ViennaAustria
| | - Philipp Hofer
- Institute of Cancer Research, Department of Medicine IMedical University of ViennaAustria
| | - Michael Hoffmeister
- Division of Clinical Epidemiology and Aging ResearchGerman Cancer Research Center (DKFZ)HeidelbergGermany
| | - Andreana N. Holowatyj
- Huntsman Cancer InstituteSalt Lake CityUT
- Department of Population Health SciencesUniversity of UtahSalt Lake CityUT
| | | | - Dieuwertje E. Kok
- Division of Human Nutrition and HealthWageningen University & ResearchWageningenThe Netherlands
| | | | | | | | - Jennifer Ose
- Huntsman Cancer InstituteSalt Lake CityUT
- Department of Population Health SciencesUniversity of UtahSalt Lake CityUT
| | - Anton Stift
- Department of SurgeryMedical University ViennaAustria
| | - Petra Schrotz‐King
- Division of Preventive OncologyNational Center for Tumor Diseases and German Cancer Research CenterHeidelbergGermany
| | - Alexis B. Ulrich
- Department of General, Visceral and Transplantation SurgeryUniversity of HeidelbergGermany
| | | | - Ellen Kampman
- Division of Human Nutrition and HealthWageningen University & ResearchWageningenThe Netherlands
| | - Augustin Scalbert
- Biomarkers GroupInternational Agency for Research on CancerLyonFrance
| | - Nina Habermann
- Division of Preventive OncologyNational Center for Tumor Diseases and German Cancer Research CenterHeidelbergGermany
- Genome BiologyEuropean Molecular Biology Laboratory (EMBL)HeidelbergGermany
| | - Andrea Gsur
- Institute of Cancer Research, Department of Medicine IMedical University of ViennaAustria
| | - Cornelia M. Ulrich
- Huntsman Cancer InstituteSalt Lake CityUT
- Department of Population Health SciencesUniversity of UtahSalt Lake CityUT
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20
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Chen Z, Li Z, Li H, Jiang Y. Metabolomics: a promising diagnostic and therapeutic implement for breast cancer. Onco Targets Ther 2019; 12:6797-6811. [PMID: 31686838 PMCID: PMC6709037 DOI: 10.2147/ott.s215628] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2019] [Accepted: 07/22/2019] [Indexed: 12/24/2022] Open
Abstract
Breast cancer (BC) is the most commonly diagnosed cancer among women and the leading cause of cancer death. Despite the advent of numerous diagnosis and treatment methods in recent years, this heterogeneous disease still presents great challenges in early diagnosis, curative treatments and prognosis monitoring. Thus, finding promising early diagnostic biomarkers and therapeutic targets and approaches is meaningful. Metabolomics, which focuses on the analysis of metabolites that change during metabolism, can reveal even a subtle abnormal change in an individual. In recent decades, the exploration of cancer-related metabolomics has increased. Metabolites can be promising biomarkers for the screening, response evaluation and prognosis of BC. In this review, we summarized the workflow of metabolomics, described metabolite signatures based on molecular subtype as well as reclassification and then discussed the application of metabolomics in the early diagnosis, monitoring and prognosis of BC to offer new insights for clinicians in breast cancer diagnosis and treatment.
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Affiliation(s)
- Zhanghan Chen
- Department of General Surgery, Zhongshan Hospital, Fudan University, Shanghai 200032, People's Republic of China
| | - Zehuan Li
- Department of General Surgery, Zhongshan Hospital, Fudan University, Shanghai 200032, People's Republic of China
| | - Haoran Li
- Department of Thoracic Surgery, Zhongshan Hospital, Fudan University, Shanghai 200032, People's Republic of China
| | - Ying Jiang
- Department of General Surgery, Zhongshan Hospital, Fudan University, Shanghai 200032, People's Republic of China
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21
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Zhao H, Shen J, Moore SC, Ye Y, Wu X, Esteva FJ, Tripathy D, Chow WH. Breast cancer risk in relation to plasma metabolites among Hispanic and African American women. Breast Cancer Res Treat 2019; 176:687-696. [PMID: 30771047 PMCID: PMC6588417 DOI: 10.1007/s10549-019-05165-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2018] [Accepted: 02/09/2019] [Indexed: 12/20/2022]
Abstract
PURPOSE The metabolic etiology of breast cancer has been explored in the past several years using metabolomics. However, most of these studies only included non-Hispanic White individuals. METHODS To fill this gap, we performed a two-step (discovery and validation) metabolomics profiling in plasma samples from 358 breast cancer patients and 138 healthy controls. All study subjects were either Hispanics or non-Hispanic African Americans. RESULTS A panel of 14 identified metabolites significantly differed between breast cancer cases and healthy controls in both the discovery and validation sets. Most of these identified metabolites were lipids. In the pathway analysis, citrate cycle (TCA cycle), arginine and proline metabolism, and linoleic acid metabolism pathways were observed, and they significantly differed between breast cancer cases and healthy controls in both sets. From those 14 metabolites, we selected 9 non-correlated metabolites to generate a metabolic risk score. Increased metabolites risk score was associated with a 1.87- and 1.63-fold increased risk of breast cancer in the discovery and validation sets, respectively (Odds ratio (OR) 1.87, 95% Confidence interval (CI) 1.50, 2.32; OR 1.63, 95% CI 1.36, 1.95). CONCLUSIONS In summary, our study identified metabolic profiles and pathways that significantly differed between breast cancer cases and healthy controls in Hispanic or non-Hispanic African American women. The results from our study might provide new insights on the metabolic etiology of breast cancer.
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Affiliation(s)
- Hua Zhao
- Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.
| | - Jie Shen
- Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Steven C Moore
- Divisions of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, 20892, USA
| | - Yuanqing Ye
- Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Xifeng Wu
- Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Francisco J Esteva
- Perlmutter Cancer Center at New York University Langone Health, New York, NY, 10016, USA
| | - Debasish Tripathy
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, 1155 Pressler Street, racial Houston, TX, 77030, USA
| | - Wong-Ho Chow
- Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
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Current Status and Future Prospects of Clinically Exploiting Cancer-specific Metabolism-Why Is Tumor Metabolism Not More Extensively Translated into Clinical Targets and Biomarkers? Int J Mol Sci 2019; 20:ijms20061385. [PMID: 30893889 PMCID: PMC6471292 DOI: 10.3390/ijms20061385] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2019] [Revised: 03/14/2019] [Accepted: 03/15/2019] [Indexed: 02/07/2023] Open
Abstract
Tumor cells exhibit a specialized metabolism supporting their superior ability for rapid proliferation, migration, and apoptotic evasion. It is reasonable to assume that the specific metabolic needs of the tumor cells can offer an array of therapeutic windows as pharmacological disturbance may derail the biochemical mechanisms necessary for maintaining the tumor characteristics, while being less important for normally proliferating cells. In addition, the specialized metabolism may leave a unique metabolic signature which could be used clinically for diagnostic or prognostic purposes. Quantitative global metabolic profiling (metabolomics) has evolved over the last two decades. However, despite the technology’s present ability to measure 1000s of endogenous metabolites in various clinical or biological specimens, there are essentially no examples of metabolomics investigations being translated into actual utility in the cancer clinic. This review investigates the current efforts of using metabolomics as a tool for translation of tumor metabolism into the clinic and further seeks to outline paths for increasing the momentum of using tumor metabolism as a biomarker and drug target opportunity.
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Arena R, Ozemek C, Laddu D, Campbell T, Rouleau CR, Standley R, Bond S, Abril EP, Hills AP, Lavie CJ. Applying Precision Medicine to Healthy Living for the Prevention and Treatment of Cardiovascular Disease. Curr Probl Cardiol 2018; 43:448-483. [DOI: 10.1016/j.cpcardiol.2018.06.001] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
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24
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Cala MP, Aldana J, Medina J, Sánchez J, Guio J, Wist J, Meesters RJW. Multiplatform plasma metabolic and lipid fingerprinting of breast cancer: A pilot control-case study in Colombian Hispanic women. PLoS One 2018; 13:e0190958. [PMID: 29438405 PMCID: PMC5810980 DOI: 10.1371/journal.pone.0190958] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2017] [Accepted: 12/22/2017] [Indexed: 01/22/2023] Open
Abstract
Breast cancer (BC) is a highly heterogeneous disease associated with metabolic reprogramming. The shifts in the metabolome caused by BC still lack data from Latin populations of Hispanic origin. In this pilot study, metabolomic and lipidomic approaches were performed to establish a plasma metabolic fingerprint of Colombian Hispanic women with BC. Data from 1H-NMR, GC-MS and LC-MS were combined and compared. Statistics showed discrimination between breast cancer and healthy subjects on all analytical platforms. The differentiating metabolites were involved in glycerolipid, glycerophospholipid, amino acid and fatty acid metabolism. This study demonstrates the usefulness of multiplatform approaches in metabolic/lipid fingerprinting studies to broaden the outlook of possible shifts in metabolism. Our findings propose relevant plasma metabolites that could contribute to a better understanding of underlying metabolic shifts driven by BC in women of Colombian Hispanic origin. Particularly, the understanding of the up-regulation of long chain fatty acyl carnitines and the down-regulation of cyclic phosphatidic acid (cPA). In addition, the mapped metabolic signatures in breast cancer were similar but not identical to those reported for non-Hispanic women, despite racial differences.
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Affiliation(s)
- Mónica P. Cala
- Department of Chemistry, Grupo de Investigación en Química Analítica y Bioanalítica (GABIO), Universidad de los Andes, Bogotá D.C., Colombia
| | - Julian Aldana
- Department of Chemistry, Grupo de Investigación en Química Analítica y Bioanalítica (GABIO), Universidad de los Andes, Bogotá D.C., Colombia
| | - Jessica Medina
- Department of Chemistry, Universidad del Valle, Cali, Colombia
| | - Julián Sánchez
- Liga contra el Cáncer Seccional Bogotá, Bogotá, Colombia
| | - José Guio
- Liga contra el Cáncer Seccional Bogotá, Bogotá, Colombia
| | - Julien Wist
- Department of Chemistry, Universidad del Valle, Cali, Colombia
| | - Roland J. W. Meesters
- Department of Chemistry, Grupo de Investigación en Química Analítica y Bioanalítica (GABIO), Universidad de los Andes, Bogotá D.C., Colombia
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25
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Penkert J, Ripperger T, Schieck M, Schlegelberger B, Steinemann D, Illig T. On metabolic reprogramming and tumor biology: A comprehensive survey of metabolism in breast cancer. Oncotarget 2018; 7:67626-67649. [PMID: 27590516 PMCID: PMC5341901 DOI: 10.18632/oncotarget.11759] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2016] [Accepted: 08/25/2016] [Indexed: 12/20/2022] Open
Abstract
Altered metabolism in tumor cells has been a focus of cancer research for as long as a century but has remained controversial and vague due to an inhomogeneous overall picture. Accumulating genomic, metabolomic, and lastly panomic data as well as bioenergetics studies of the past few years enable a more comprehensive, systems-biologic approach promoting deeper insight into tumor biology and challenging hitherto existing models of cancer bioenergetics. Presenting a compendium on breast cancer-specific metabolome analyses performed thus far, we review and compile currently known aspects of breast cancer biology into a comprehensive network, elucidating previously dissonant issues of cancer metabolism. As such, some of the aspects critically discussed in this review include the dynamic interplay or metabolic coupling between cancer (stem) cells and cancer-associated fibroblasts, the intratumoral and intertumoral heterogeneity and plasticity of cancer cell metabolism, the existence of distinct metabolic tumor compartments in need of separate yet simultaneous therapeutic targeting, the reliance of cancer cells on oxidative metabolism and mitochondrial power, and the role of pro-inflammatory, pro-tumorigenic stromal conditioning. Comprising complex breast cancer signaling networks as well as combined metabolomic and genomic data, we address metabolic consequences of mutations in tumor suppressor genes and evaluate their contribution to breast cancer predisposition in a germline setting, reasoning for distinct personalized preventive and therapeutic measures. The review closes with a discussion on central root mechanisms of tumor cell metabolism and rate-limiting steps thereof, introducing essential strategies for therapeutic targeting.
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Affiliation(s)
- Judith Penkert
- Institute of Human Genetics, Hannover Medical School, Hannover, Germany
| | - Tim Ripperger
- Institute of Human Genetics, Hannover Medical School, Hannover, Germany
| | | | | | - Doris Steinemann
- Institute of Human Genetics, Hannover Medical School, Hannover, Germany
| | - Thomas Illig
- Institute of Human Genetics, Hannover Medical School, Hannover, Germany.,Hannover Unified Biobank, Hannover Medical School, Hannover, Germany
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26
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Yu Z, Chen H, Ai J, Zhu Y, Li Y, Borgia JA, Yang JS, Zhang J, Jiang B, Gu W, Deng Y. Global lipidomics identified plasma lipids as novel biomarkers for early detection of lung cancer. Oncotarget 2017; 8:107899-107906. [PMID: 29296211 PMCID: PMC5746113 DOI: 10.18632/oncotarget.22391] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2017] [Accepted: 08/26/2017] [Indexed: 12/18/2022] Open
Abstract
Purpose Lipids play roles in membrane structure, energy storage, and signal transduction as well as in human cancers. Here we adopt lipidomics to identify plasma lipid markers for early screening and detection of lung cancer. Experimental Design Using mass spectrometry, we profiled 390 individual lipids using training and validation strategy in a total of 346 plasma samples from 199 early NSCLC patients, including 113 adenocacinoma and 86 squamous cell cancers (SqCC), and from 147 healthy controls. Results In the training stage, we found distinct lipid groups that were significantly distributed between NSCLC cases and healthy controls. We further defined a panel of four lipid markers (LPE(18:1), ePE(40:4), C(18:2)CE and SM(22:0)) for prediction of early cancer with a accuracy of 82.3% AUC (Area under ROC curve), sensitivity of 81.9% and specificity of 70.7% at the training stage and yielded the predictive power with accuracy (AUC,80.8%), sensitivity 78.7%, specificity 69.4% and in the validation stage. Conclusions Using lipidomics we identified several lipid markers capable of discerning early stage lung carcinoma from healthy individuals, which might be further developed as a quick, safe blood test for early diagnosis of this disease.
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Affiliation(s)
- Zongtao Yu
- Department of Laboratory Medicine, Shiyan Taihe Hospital, College of Biomedical Engineering, Hubei University of Medicine, Shiyan 442000, Hubei, China.,Department of Internal Medicine, Rush University Medical Center, Chicago, IL 60612, USA
| | - Hankui Chen
- Department of Internal Medicine, Rush University Medical Center, Chicago, IL 60612, USA
| | - Junmei Ai
- Department of Internal Medicine, Rush University Medical Center, Chicago, IL 60612, USA
| | - Yong Zhu
- National Center of Colorectal Disease, Nanjing Municipal Hospital of Chinese Medicine, The Third Affiliated Hospital, Nanjing University of Chinese Medicine, Guangdong, Nanjing 210001, China
| | - Yan Li
- Department of Internal Medicine, Rush University Medical Center, Chicago, IL 60612, USA
| | - Jeffrey A Borgia
- Department of Pathology, Rush University Medical Center, Chicago, IL 60612, USA
| | - Jin-Song Yang
- Department of Oncology, Nanjing First Hospital, Nanjing Medical University, Nanjing, Jiangsu 210006, China
| | - Jicai Zhang
- Department of Laboratory Medicine, Shiyan Taihe Hospital, College of Biomedical Engineering, Hubei University of Medicine, Shiyan 442000, Hubei, China
| | - Bin Jiang
- National Center of Colorectal Disease, Nanjing Municipal Hospital of Chinese Medicine, The Third Affiliated Hospital, Nanjing University of Chinese Medicine, Guangdong, Nanjing 210001, China
| | - Wei Gu
- Department of Respiration, Nanjing First Hospital, Nanjing Medical University, Nanjing, Jiangsu 210006, China
| | - Youping Deng
- National Center of Colorectal Disease, Nanjing Municipal Hospital of Chinese Medicine, The Third Affiliated Hospital, Nanjing University of Chinese Medicine, Guangdong, Nanjing 210001, China.,Department of Laboratory Medicine, Shiyan Taihe Hospital, College of Biomedical Engineering, Hubei University of Medicine, Shiyan 442000, Hubei, China.,Department of Complementary & Integrative Medicine, University of Hawaii John A. Burns School of Medicine, Honolulu, HI 96813, USA
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27
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Jové M, Collado R, Quiles JL, Ramírez-Tortosa MC, Sol J, Ruiz-Sanjuan M, Fernandez M, de la Torre Cabrera C, Ramírez-Tortosa C, Granados-Principal S, Sánchez-Rovira P, Pamplona R. A plasma metabolomic signature discloses human breast cancer. Oncotarget 2017; 8:19522-19533. [PMID: 28076849 PMCID: PMC5386702 DOI: 10.18632/oncotarget.14521] [Citation(s) in RCA: 55] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2016] [Accepted: 12/26/2016] [Indexed: 01/04/2023] Open
Abstract
PURPOSE Metabolomics is the comprehensive global study of metabolites in biological samples. In this retrospective pilot study we explored whether serum metabolomic profile can discriminate the presence of human breast cancer irrespective of the cancer subtype. METHODS Plasma samples were analyzed from healthy women (n = 20) and patients with breast cancer after diagnosis (n = 91) using a liquid chromatography-mass spectrometry platform. Multivariate statistics and a Random Forest (RF) classifier were used to create a metabolomics panel for the diagnosis of human breast cancer. RESULTS Metabolomics correctly distinguished between breast cancer patients and healthy control subjects. In the RF supervised class prediction analysis comparing breast cancer and healthy control groups, RF accurately classified 100% both samples of the breast cancer patients and healthy controls. So, the class error for both group in and the out-of-bag error were 0. We also found 1269 metabolites with different concentration in plasma from healthy controls and cancer patients; and basing on exact mass, retention time and isotopic distribution we identified 35 metabolites. These metabolites mostly support cell growth by providing energy and building stones for the synthesis of essential biomolecules, and function as signal transduction molecules. The collective results of RF, significance testing, and false discovery rate analysis identified several metabolites that were strongly associated with breast cancer. CONCLUSIONS In breast cancer a metabolomics signature of cancer exists and can be detected in patient plasma irrespectively of the breast cancer type.
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Affiliation(s)
- Mariona Jové
- Department of Experimental Medicine, University of Lleida-Institute for Research in Biomedicine of Lleida (UdL-IRBLleida), Lleida, Spain
| | - Ricardo Collado
- Department of Oncology, Medical Oncology Unit, Hospital San Pedro de Alcántara, Cáceres, Official Postgraduate Programme in Nutrition and Food Technology, University of Granada, Spain
| | - José Luís Quiles
- Institute of Nutrition and Food Technology "José Mataix", Biomedical Research Center, Department of Physiology, University of Granada, Granada, Spain
| | - Mari-Carmen Ramírez-Tortosa
- Institute of Nutrition and Food Technology "José Mataix", Biomedical Research Center, Department of Biochemistry and Molecular Biology II, University of Granada, Granada, Spain
| | - Joaquim Sol
- Department of Experimental Medicine, University of Lleida-Institute for Research in Biomedicine of Lleida (UdL-IRBLleida), Lleida, Spain
| | | | | | | | - Cesar Ramírez-Tortosa
- Department of Pathological Anatomy, Hospital of Jaén, Jaén, Spain.,GENYO, Centre for Genomics and Oncological Research (Pfizer / University of Granada / Andalusian Regional Government), PTS Granada, Granada, Spain
| | | | | | - Reinald Pamplona
- Department of Experimental Medicine, University of Lleida-Institute for Research in Biomedicine of Lleida (UdL-IRBLleida), Lleida, Spain
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28
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Steele HE, Horvath R, Lyon JJ, Chinnery PF. Monitoring clinical progression with mitochondrial disease biomarkers. Brain 2017; 140:2530-2540. [PMID: 28969370 PMCID: PMC5841218 DOI: 10.1093/brain/awx168] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2017] [Accepted: 05/14/2017] [Indexed: 12/21/2022] Open
Abstract
Mitochondrial disorders are genetically determined metabolic diseases due to a biochemical deficiency of the respiratory chain. Given that multi-system involvement and disease progression are common features of mitochondrial disorders they carry substantial morbidity and mortality. Despite this, no disease-modifying treatments exist with clear clinical benefits, and the current best management of mitochondrial disease is supportive. Several therapeutic strategies for mitochondrial disorders are now at a mature preclinical stage. Some are making the transition into early-phase patient trials, but the lack of validated biomarkers of disease progression presents a challenge when developing new therapies for patients. This update discusses current biomarkers of mitochondrial disease progression including metabolomics, circulating serum markers, exercise physiology, and both structural and functional imaging. We discuss the advantages and disadvantages of each approach, and consider emerging techniques with a potential role in trials of new therapies.
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Affiliation(s)
- Hannah E Steele
- Wellcome Trust Centre for Mitochondrial Research, Institute of Genetic Medicine, Newcastle University, Newcastle upon Tyne, NE1 3BZ, UK
| | - Rita Horvath
- Wellcome Trust Centre for Mitochondrial Research, Institute of Genetic Medicine, Newcastle University, Newcastle upon Tyne, NE1 3BZ, UK
| | - Jon J Lyon
- GlaxoSmithKline, Molecular Safety and Disposition, Ware, SG12 0DP, UK
| | - Patrick F Chinnery
- Department of Clinical Neurosciences, University of Cambridge, Cambridge Biomedical Campus, Cambridge CB2 0QQ, UK.,MRC Mitochondrial Biology Unit, Cambridge Biomedical Campus, Cambridge CB2 0QQ, UK
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29
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Mass spectrometry as a tool for biomarkers searching in gynecological oncology. Biomed Pharmacother 2017; 92:836-842. [PMID: 28601044 DOI: 10.1016/j.biopha.2017.05.146] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2016] [Revised: 05/21/2017] [Accepted: 05/31/2017] [Indexed: 01/10/2023] Open
Abstract
Tumors of the female reproductive tract are an important target for the development of diagnostic, prognostic and therapeutic strategies. Recent research has turned to proteomics based on mass spectrometry techniques, to achieve more effective diagnostic results. Mass spectrometry (MS) enables identification and quantification of multiple molecules simultaneously in a single experiment according to mass to charge ratio (m/z). Several proteomic strategies may be applied to establish the function of a particular protein/peptide or to identify a novel disease and specific biomarkers related to it. Therefore, MS could facilitate treatment in patients with tumors by helping researchers discover new biomarkers and narrowly targeted drugs. This review presents a comprehensive discussion of mass spectrometry as a tool for biomarkers searching that may lead to the discovery of easily available diagnostic tests in gynecological oncology with emphasis on clinical proteomics over the past decade. The article provides an insight into different MS based proteomic approaches.
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30
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Camarda R, Williams J, Goga A. In vivo Reprogramming of Cancer Metabolism by MYC. Front Cell Dev Biol 2017; 5:35. [PMID: 28443280 PMCID: PMC5386977 DOI: 10.3389/fcell.2017.00035] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2017] [Accepted: 03/23/2017] [Indexed: 12/22/2022] Open
Abstract
The past few decades have welcomed tremendous advancements toward understanding the functional significance of altered metabolism during tumorigenesis. However, many conclusions drawn from studies of cancer cells in a dish (i.e., in vitro) have been put into question as multiple lines of evidence have demonstrated that the metabolism of cells can differ significantly from that of primary tumors (in vivo). This realization, along with the need to identify tissue-specific vulnerabilities of driver oncogenes, has led to an increased focus on oncogene-dependent metabolic programming in vivo. The oncogene c-MYC (MYC) is overexpressed in a wide variety of human cancers, and while its ability to alter cellular metabolism is well-established, translating the metabolic requirements, and vulnerabilities of MYC-driven cancers to the clinic has been hindered by disparate findings from in vitro and in vivo models. This review will provide an overview of the in vivo strategies, mechanisms, and conclusions generated thus far by studying MYC's regulation of metabolism in various cancer models.
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Affiliation(s)
- Roman Camarda
- Department of Cell and Tissue Biology, University of California, San FranciscoSan Francisco, CA, USA
- Biomedical Sciences Graduate Program, University of California, San FranciscoSan Francisco, CA, USA
| | - Jeremy Williams
- Biomedical Sciences Graduate Program, University of California, San FranciscoSan Francisco, CA, USA
| | - Andrei Goga
- Department of Cell and Tissue Biology, University of California, San FranciscoSan Francisco, CA, USA
- Department of Medicine, University of California, San FranciscoSan Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San FranciscoSan Francisco, CA, USA
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31
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Nuclear Magnetic Resonance metabolomics reveals an excretory metabolic signature of renal cell carcinoma. Sci Rep 2016; 6:37275. [PMID: 27857216 PMCID: PMC5114559 DOI: 10.1038/srep37275] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2016] [Accepted: 10/27/2016] [Indexed: 12/21/2022] Open
Abstract
RCC usually develops and progresses asymptomatically and, when detected, it is frequently at advanced stages and metastatic, entailing a dismal prognosis. Therefore, there is an obvious demand for new strategies enabling an earlier diagnosis. The importance of metabolic rearrangements for carcinogenesis unlocked a new approach for cancer research, catalyzing the increased use of metabolomics. The present study aimed the NMR metabolic profiling of RCC in urine samples from a cohort of RCC patients (n = 42) and controls (n = 49). The methodology entailed variable selection of the spectra in tandem with multivariate analysis and validation procedures. The retrieval of a disease signature was preceded by a systematic evaluation of the impacts of subject age, gender, BMI, and smoking habits. The impact of confounders on the urine metabolomics profile of this population is residual compared to that of RCC. A 32-metabolite/resonance signature descriptive of RCC was unveiled, successfully distinguishing RCC patients from controls in principal component analysis. This work demonstrates the value of a systematic metabolomics workflow for the identification of robust urinary metabolic biomarkers of RCC. Future studies should entail the validation of the 32-metabolite/resonance signature found for RCC in independent cohorts, as well as biological validation of the putative hypotheses advanced.
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