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Dong QJ, Xu XY, Fan CX, Xiao JP. Transcriptome and metabolome analyses reveal chlorogenic acid accumulation in pigmented potatoes at different altitudes. Genomics 2024; 116:110883. [PMID: 38857813 DOI: 10.1016/j.ygeno.2024.110883] [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: 02/13/2024] [Revised: 06/06/2024] [Accepted: 06/08/2024] [Indexed: 06/12/2024]
Abstract
Pigmented potato tubers are abundant in chlorogenic acids (CGAs), a metabolite with pharmacological activity. This article comprehensively analyzed the transcriptome and metabolome of pigmented potato Huaxingyangyu and Jianchuanhong at four altitudes of 1800 m, 2300 m, 2800 m, and 3300 m. A total of 20 CGAs and intermediate CGA compounds were identified, including 3-o-caffeoylquinic acid, 4-o-caffeoylquinic acid, and 5-o-caffeoylquinic acid. CGA contents in Huaxinyangyu and Jianchuanhong reached its maximum at an altitude of 2800 m and slightly decreased at 3300 m. 48 candidate genes related to the biosynthesis pathway of CGAs were screened through transcriptome analysis. Weighted gene co-expression network analysis (WGCNA) identified that the structural genes of phenylalanine deaminase (PAL), coumarate-3 hydroxylase (C3H), cinnamic acid 4-hydroxylase (C4H) and the transcription factors of MYB and bHLH co-regulate CGA biosynthesis. The results of this study provide valuable information to reveal the changes in CGA components in pigmented potato at different altitudes.
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Affiliation(s)
- Qiu-Ju Dong
- College of Agronomy and Biotechnology, Yunnan Agricultural University, No.95 Jinhei Road, Panlong District, Kunming City, Yunnan 650201, China
| | - Xiao-Yu Xu
- College of Agronomy and Biotechnology, Yunnan Agricultural University, No.95 Jinhei Road, Panlong District, Kunming City, Yunnan 650201, China
| | - Cai-Xia Fan
- College of Agronomy and Biotechnology, Yunnan Agricultural University, No.95 Jinhei Road, Panlong District, Kunming City, Yunnan 650201, China
| | - Ji-Ping Xiao
- College of Agronomy and Biotechnology, Yunnan Agricultural University, No.95 Jinhei Road, Panlong District, Kunming City, Yunnan 650201, China.
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Böhmová A, Mikoška M, Syslová K, Šindelářová D, Hříbek P, Urbánek P, Setnička V. Untargeted metabolomics of blood plasma samples of patients with hepatocellular carcinoma. J Pharm Biomed Anal 2024; 248:116263. [PMID: 38852296 DOI: 10.1016/j.jpba.2024.116263] [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: 01/19/2024] [Revised: 05/21/2024] [Accepted: 05/26/2024] [Indexed: 06/11/2024]
Abstract
Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related deaths in the world. HCC is often diagnosed late because patients with early-stage cancer have no apparent symptoms. Therefore, it is desirable to find a reliable method for an early diagnosis based on the detection of metabolites - biomarkers, that can be detected in the early stages of the disease. Untargeted metabolomics is often used as a tool to find a suitable biomarker for several diseases. In this work, untargeted metabolomics was performed on blood plasma samples of HCC patients and compared with healthy individuals and patients with liver cirrhosis. A combination of liquid chromatography and high-resolution mass spectrometry was used as an analytical method. More than a thousand peaks were detected in the blood plasma samples, from which mainly amino acids, carboxylic acids, lipids, and their derivatives were evaluated as potential biomarkers. The data obtained were statistically processed using the analysis of variance, correlation analysis, and principal component analysis.
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Affiliation(s)
- Adéla Böhmová
- Department of Organic Technology, University of Chemistry and Technology Prague, Technická 5, Prague 6 166 28, Czech Republic
| | - Miloš Mikoška
- Department of Organic Technology, University of Chemistry and Technology Prague, Technická 5, Prague 6 166 28, Czech Republic
| | - Kamila Syslová
- Department of Organic Technology, University of Chemistry and Technology Prague, Technická 5, Prague 6 166 28, Czech Republic.
| | - Dominika Šindelářová
- Department of Organic Technology, University of Chemistry and Technology Prague, Technická 5, Prague 6 166 28, Czech Republic
| | - Petr Hříbek
- Military University Hospital Prague, Department of Medicine 1st Faculty of Medicine Charles University and Military University Hospital Prague, U Vojenské nemocnice 1200, Prague 6 169 02, Czech Republic; Department of Internal Medicine, Faculty of Military Health Sciences in Hradec Králové, University of Defence, Třebešská 1575, Hradec Králové 500 01, Czech Republic
| | - Petr Urbánek
- Military University Hospital Prague, Department of Medicine 1st Faculty of Medicine Charles University and Military University Hospital Prague, U Vojenské nemocnice 1200, Prague 6 169 02, Czech Republic
| | - Vladimír Setnička
- Department of Analytical Chemistry, University of Chemistry and Technology Prague, Technická 5, Prague 6 166 28, Czech Republic
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Kaźmierczak-Siedlecka K, Muszyński D, Styburski D, Makarewicz J, Sobocki BK, Ulasiński P, Połom K, Stachowska E, Skonieczna-Żydecka K, Kalinowski L. Untargeted metabolomics in gastric and colorectal cancer patients - preliminary results. Front Cell Infect Microbiol 2024; 14:1394038. [PMID: 38774628 PMCID: PMC11106370 DOI: 10.3389/fcimb.2024.1394038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Accepted: 04/08/2024] [Indexed: 05/24/2024] Open
Abstract
Introduction Recent years, microbiota-associated aspects have been analysed in multiple disorders regarding cancers. Existing evidence pints that gut microorganisms might take part in tumour origin and therapy efficacy. Nevertheless, to date, data on faecal metabolomics in cancer patients is still strongly limited. Therefore, we aimed to analyse gut untargeted metabolome in gastrointestinal cancer patients (i.e., gastric and colorectal cancer). Patients and methods There were 12 patients with either gastric (n=4) or colorectal cancer (n=8) enrolled and 8 analysed (n=4 each). Stool samples were collected prior to anti-cancer treatments. Untargeted metabolomics analyses were conducted by means of mass spectrometry. Results A plethora of metabolites in cancer patients we analysed were noted, with higher homogenity in case of gastric cancer patients. We found that the level of Deoxyguanosine,m/z 266.091,[M-H]-, Uridine,m/z 245.075,[M+H]+, Deoxyguanosine,m/z 268.104,[M]+, 3-Indoleacetic acid,m/z 176.07,[M+H]+, Indoxyl,m/z 132.031,[M-H]-, L-Phenylalanine,m/z 164.073,[M-H]-, L-Methionine,m/z 150.058,[M+NH4]+, was significantly higher in colorectal cancer patients and Ethyl hydrogen malonate,m/z 133.031,[M+H]+ in gastric cancer. Conclusion The overall insights into untargeted metabolomics showed that most often higher levels of analysed metabolites were detected in colorectal cancer patients compared to gastric cancer patients. The link between gut metabolome and both local and distal metastasis might exist, however it requires confirmation in further multi-centre studies regarding larger sample size.
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Affiliation(s)
- Karolina Kaźmierczak-Siedlecka
- Department of Medical Laboratory Diagnostics – Fahrenheit Biobank BBMRI.pl, Medical University of Gdansk, Gdansk, Poland
| | - Damian Muszyński
- Scientific Circle of Studies Regarding Personalized Medicine Associated with Department of Medical Laboratory Diagnostics, Medical University of Gdansk, Gdansk, Poland
| | | | - Jakub Makarewicz
- Scientific Circle of Studies Regarding Personalized Medicine Associated with Department of Medical Laboratory Diagnostics, Medical University of Gdansk, Gdansk, Poland
| | - Bartosz Kamil Sobocki
- Department of Oncology and Radiotherapy, Medical University of Gdansk, Gdansk, Poland
| | - Paweł Ulasiński
- Unit of Surgery with Unit of Surgery with Unit of Oncological Surgery, Specialist Hospital in Koscierzyna, Koscierzyna, Poland
| | - Karol Połom
- Academy of Medical and Social Applied Sciences, Elbląg, Poland
- Department of Surgical Oncology, Medical University of Gdansk, Gdansk, Poland
- Department of Gastrointestinal Surgical Oncology, Greater Poland Cancer Centre, Poznan, Poland
| | - Ewa Stachowska
- Department of Human Nutrition and Metabolomics, Pomeranian Medical University in Szczecin, Szczecin, Poland
| | | | - Leszek Kalinowski
- Department of Medical Laboratory Diagnostics – Fahrenheit Biobank BBMRI.pl, Medical University of Gdansk, Gdansk, Poland
- BioTechMed Centre/Department of Mechanics of Materials and Structures, Gdansk University of Technology, Gdansk, Poland
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Liu B, Shi J, Su R, Zheng R, Xing F, Zhang Y, Wang N, Chen H, Feng S. Predicting effect of anti-PD-1/PD-L1 inhibitors therapy for hepatocellular carcinoma by detecting plasma metabolite based on UHPLC-MS. Front Immunol 2024; 15:1370771. [PMID: 38707906 PMCID: PMC11067499 DOI: 10.3389/fimmu.2024.1370771] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Accepted: 04/03/2024] [Indexed: 05/07/2024] Open
Abstract
Introduction Anti-PD-1/PD-L1 inhibitors therapy has become a promising treatment for hepatocellular carcinoma (HCC), while the therapeutic efficacy varies significantly among effects for individual patients are significant difference. Unfortunately, specific predictive biomarkers indicating the degree of benefit for patients and thus guiding the selection of suitable candidates for immune therapy remain elusive.no specific predictive biomarkers are available indicating the degree of benefit for patients and thus screening the preferred population suitable for the immune therapy. Methods Ultra-high-pressure liquid chromatography-mass spectrometry (UHPLC-MS) considered is an important method for analyzing biological samples, since it has the advantages of high rapid, high sensitivity, and high specificity. Ultra-high-pressure liquid chromatography-mass spectrometry (UHPLC-MS) has emerged as a pivotal method for analyzing biological samples due to its inherent advantages of rapidity, sensitivity, and specificity. In this study, potential metabolite biomarkers that can predict the therapeutic effect of HCC patients receiving immune therapy were identified by UHPLC-MS. Results A partial least-squares discriminant analysis (PLS-DA) model was established using 14 glycerophospholipid metabolites mentioned above, and good prediction parameters (R2 = 0.823, Q2 = 0.615, prediction accuracy = 0.880 and p < 0.001) were obtained. The relative abundance of glycerophospholipid metabolite ions is closely related to the survival benefit of HCC patients who received immune therapy. Discussion This study reveals that glycerophospholipid metabolites play a crucial role in predicting the efficacy of immune therapy for HCC.
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Affiliation(s)
- Botong Liu
- State Key Laboratory of Inorganic Synthesis and Preparative Chemistry, College of Chemistry, Jilin University, Changchun, China
| | - Jinyu Shi
- The Cancer Center, The First Hospital of Jilin University, Changchun, China
| | - Rui Su
- State Key Laboratory of Inorganic Synthesis and Preparative Chemistry, College of Chemistry, Jilin University, Changchun, China
| | - Ran Zheng
- State Key Laboratory of Inorganic Synthesis and Preparative Chemistry, College of Chemistry, Jilin University, Changchun, China
| | - Fan Xing
- State Key Laboratory of Inorganic Synthesis and Preparative Chemistry, College of Chemistry, Jilin University, Changchun, China
| | - Yuan Zhang
- State Key Laboratory of Inorganic Synthesis and Preparative Chemistry, College of Chemistry, Jilin University, Changchun, China
| | - Nanya Wang
- The Cancer Center, The First Hospital of Jilin University, Changchun, China
| | - Huanwen Chen
- School of Pharmacy, Jiangxi University of Chinese Medicine, Nanchang, China
| | - Shouhua Feng
- State Key Laboratory of Inorganic Synthesis and Preparative Chemistry, College of Chemistry, Jilin University, Changchun, China
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Sun C, Cheng X, Xu J, Chen H, Tao J, Dong Y, Wei S, Chen R, Meng X, Ma Y, Tian H, Guo X, Bi S, Zhang C, Kang J, Zhang M, Lv H, Shang Z, Lv W, Zhang R, Jiang Y. A review of disease risk prediction methods and applications in the omics era. Proteomics 2024:e2300359. [PMID: 38522029 DOI: 10.1002/pmic.202300359] [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: 09/15/2023] [Revised: 03/08/2024] [Accepted: 03/12/2024] [Indexed: 03/25/2024]
Abstract
Risk prediction and disease prevention are the innovative care challenges of the 21st century. Apart from freeing the individual from the pain of disease, it will lead to low medical costs for society. Until very recently, risk assessments have ushered in a new era with the emergence of omics technologies, including genomics, transcriptomics, epigenomics, proteomics, and so on, which potentially advance the ability of biomarkers to aid prediction models. While risk prediction has achieved great success, there are still some challenges and limitations. We reviewed the general process of omics-based disease risk model construction and the applications in four typical diseases. Meanwhile, we highlighted the problems in current studies and explored the potential opportunities and challenges for future clinical practice.
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Affiliation(s)
- Chen Sun
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
| | - Xiangshu Cheng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
| | - Jing Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
| | - Haiyan Chen
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Junxian Tao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
| | - Yu Dong
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
| | - Siyu Wei
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
| | - Rui Chen
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Xin Meng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Yingnan Ma
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
| | - Hongsheng Tian
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Xuying Guo
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Shuo Bi
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Chen Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Jingxuan Kang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Mingming Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Hongchao Lv
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Zhenwei Shang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Wenhua Lv
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Ruijie Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Yongshuai Jiang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
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Li W, Chen X, Yao M, Sun B, Zhu K, Wang W, Zhang A. LC-MS based untargeted metabolomics studies of the metabolic response of Ginkgo biloba extract on arsenism patients. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2024; 274:116183. [PMID: 38471343 DOI: 10.1016/j.ecoenv.2024.116183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Revised: 02/14/2024] [Accepted: 03/05/2024] [Indexed: 03/14/2024]
Abstract
Arsenic is an environmentally ubiquitous toxic metalloid. Chronic exposure to arsenic may lead to arsenicosis, while no specific therapeutic strategies are available for the arsenism patients. And Ginkgo biloba extract (GBE) exhibited protective effect in our previous study. However, the mechanisms by which GBE protects the arsenism patients remain poorly understood. A liquid chromatography-mass spectrometry (LC-MS) based untargeted metabolomics analysis was used to study metabolic response in arsenism patients upon GBE intervention. In total, 39 coal-burning type of arsenism patients and 50 healthy residents were enrolled from Guizhou province of China. The intervention group (n = 39) were arsenism patients orally administered with GBE (three times per day) for continuous 90 days. Plasma samples from 50 healthy controls (HC) and 39 arsenism patients before and after GBE intervention were collected and analyzed by established LC-MS method. Statistical analysis was performed by MetaboAnalyst 5.0 to identify differential metabolites. Multivariate analysis revealed a separation in arsenism patients between before (BG) and after GBE intervention (AG) group. It was observed that 35 differential metabolites were identified between BG and AG group, and 30 of them were completely or partially reversed by GBE intervention, with 14 differential metabolites significantly up-regulated and 16 differential metabolites considerably down-regulated. These metabolites were involved in promoting immune response and anti-inflammatory functions, and alleviating oxidative stress. Taken together, these findings indicate that the GBE intervention could probably exert its protective effects by reversing disordered metabolites modulating these functions in arsenism patients, and provide insights into further exploration of mechanistic studies.
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Affiliation(s)
- Weiwei Li
- Key Laboratory of Environmental Pollution, Monitoring and Disease Control, Ministry of Education, Department of Toxicology, School of Public Health, Guizhou Medical University, Guiyang 550025, PR China
| | - Xiong Chen
- Key Laboratory of Environmental Pollution, Monitoring and Disease Control, Ministry of Education, Department of Toxicology, School of Public Health, Guizhou Medical University, Guiyang 550025, PR China
| | - Maolin Yao
- Key Laboratory of Environmental Pollution, Monitoring and Disease Control, Ministry of Education, Department of Toxicology, School of Public Health, Guizhou Medical University, Guiyang 550025, PR China
| | - Baofei Sun
- Key Laboratory of Environmental Pollution, Monitoring and Disease Control, Ministry of Education, Department of Toxicology, School of Public Health, Guizhou Medical University, Guiyang 550025, PR China
| | - Kai Zhu
- Key Laboratory of Environmental Pollution, Monitoring and Disease Control, Ministry of Education, Department of Toxicology, School of Public Health, Guizhou Medical University, Guiyang 550025, PR China
| | - Wenjuan Wang
- Key Laboratory of Environmental Pollution, Monitoring and Disease Control, Ministry of Education, Department of Toxicology, School of Public Health, Guizhou Medical University, Guiyang 550025, PR China
| | - Aihua Zhang
- Key Laboratory of Environmental Pollution, Monitoring and Disease Control, Ministry of Education, Department of Toxicology, School of Public Health, Guizhou Medical University, Guiyang 550025, PR China.
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Li ZY, Shen QM, Wang J, Tuo JY, Tan YT, Li HL, Xiang YB. Prediagnostic plasma metabolite concentrations and liver cancer risk: a population-based study of Chinese men. EBioMedicine 2024; 100:104990. [PMID: 38306896 PMCID: PMC10847612 DOI: 10.1016/j.ebiom.2024.104990] [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: 09/25/2023] [Revised: 01/15/2024] [Accepted: 01/15/2024] [Indexed: 02/04/2024] Open
Abstract
BACKGROUND Previous metabolic profiling of liver cancer has mostly used untargeted metabolomic approaches and was unable to quantitate the absolute concentrations of metabolites. In this study, we examined the association between the concentrations of 186 targeted metabolites and liver cancer risk using prediagnostic plasma samples collected up to 14 years prior to the clinical diagnosis of liver cancer. METHODS We conducted a nested case-control study (n = 322 liver cancer cases, n = 322 matched controls) within the Shanghai Men's Health Study. Conditional logistic regression models adjusted for demographics, lifestyle factors, dietary habits, and related medical histories were used to estimate the odds ratios. Restricted cubic spline functions were used to characterise the dose-response relationships between metabolite concentrations and liver cancer risk. FINDINGS After adjusting for potential confounders and correcting for multiple testing, 28 metabolites were associated with liver cancer risk. Significant non-linear relationships were observed for 22 metabolites. The primary bile acid biosynthesis and phenylalanine, tyrosine and tryptophan biosynthesis were found to be important pathways involved in the aetiology of liver cancer. A metabolic score consisting of 10 metabolites significantly improved the predictive ability of traditional epidemiological risk factors for liver cancer, with an optimism-corrected AUC increased from 0.84 (95% CI: 0.81-0.87) to 0.89 (95% CI: 0.86-0.91). INTERPRETATION This study characterised the dose-response relationships between metabolites and liver cancer risk, providing insights into the complex metabolic perturbations prior to the clinical diagnosis of liver cancer. The metabolic score may serve as a candidate risk predictor for liver cancer. FUNDING National Key Project of Research and Development Program of China [2021YFC2500404, 2021YFC2500405]; US National Institutes of Health [subcontract of UM1 CA173640].
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Affiliation(s)
- Zhuo-Ying Li
- School of Public Health, Fudan University, Shanghai, 200032, China; State Key Laboratory of System Medicine for Cancer & Department of Epidemiology, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200032, China
| | - Qiu-Ming Shen
- State Key Laboratory of System Medicine for Cancer & Department of Epidemiology, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200032, China
| | - Jing Wang
- State Key Laboratory of System Medicine for Cancer & Department of Epidemiology, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200032, China
| | - Jia-Yi Tuo
- State Key Laboratory of System Medicine for Cancer & Department of Epidemiology, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200032, China; School of Public Health, Shanghai Jiaotong University School of Medicine, Shanghai, 200025, China
| | - Yu-Ting Tan
- State Key Laboratory of System Medicine for Cancer & Department of Epidemiology, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200032, China
| | - Hong-Lan Li
- State Key Laboratory of System Medicine for Cancer & Department of Epidemiology, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200032, China
| | - Yong-Bing Xiang
- School of Public Health, Fudan University, Shanghai, 200032, China; State Key Laboratory of System Medicine for Cancer & Department of Epidemiology, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200032, China; School of Public Health, Shanghai Jiaotong University School of Medicine, Shanghai, 200025, China.
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Barupal DK, Ramos ML, Florio AA, Wheeler WA, Weinstein SJ, Albanes D, Fiehn O, Graubard BI, Petrick JL, McGlynn KA. Identification of pre-diagnostic lipid sets associated with liver cancer risk using untargeted lipidomics and chemical set analysis: A nested case-control study within the ATBC cohort. Int J Cancer 2024; 154:454-464. [PMID: 37694774 PMCID: PMC10845132 DOI: 10.1002/ijc.34726] [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: 05/04/2023] [Revised: 08/17/2023] [Accepted: 08/23/2023] [Indexed: 09/12/2023]
Abstract
In pre-disposed individuals, a reprogramming of the hepatic lipid metabolism may support liver cancer initiation. We conducted a high-resolution mass spectrometry based untargeted lipidomics analysis of pre-diagnostic serum samples from a nested case-control study (219 liver cancer cases and 219 controls) within the Alpha-Tocopherol, Beta-Carotene Cancer Prevention (ATBC) Study. Out of 462 annotated lipids, 158 (34.2%) were associated with liver cancer risk in a conditional logistic regression analysis at a false discovery rate (FDR) <0.05. A chemical set enrichment analysis (ChemRICH) and co-regulatory set analysis suggested that 22/28 lipid classes and 47/83 correlation modules were significantly associated with liver cancer risk (FDR <0.05). Strong positive associations were observed for monounsaturated fatty acids (MUFA), triacylglycerols (TAGs) and phosphatidylcholines (PCs) having MUFA acyl chains. Negative associations were observed for sphingolipids (ceramides and sphingomyelins), lysophosphatidylcholines, cholesterol esters and polyunsaturated fatty acids (PUFA) containing TAGs and PCs. Stearoyl-CoA desaturase enzyme 1 (SCD1), a rate limiting enzyme in fatty acid metabolism and ceramidases seems to be critical in this reprogramming. In conclusion, our study reports pre-diagnostic lipid changes that provide novel insights into hepatic lipid metabolism reprogramming may contribute to a pro-cell growth and anti-apoptotic tissue environment and, in turn, support liver cancer initiation.
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Affiliation(s)
- Dinesh K Barupal
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Mark L Ramos
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland, USA
| | - Andrea A Florio
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | | | - Stephanie J Weinstein
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland, USA
| | - Demetrius Albanes
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland, USA
| | - Oliver Fiehn
- West Coast Metabolomics Center, University of California Davis, Davis, California, USA
| | - Barry I Graubard
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland, USA
| | - Jessica L Petrick
- Slone Epidemiology Center at Boston University, Boston, Massachusetts, USA
| | - Katherine A McGlynn
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland, USA
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9
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Hu J, Dai J, Sheng N. Kynurenic Acid Plays a Protective Role in Hepatotoxicity Induced by HFPO-DA in Male Mice. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:1842-1853. [PMID: 38228288 DOI: 10.1021/acs.est.3c08033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/18/2024]
Abstract
Following its introduction as an alternative to perfluorooctanoic acid, hexafluoropropylene oxide dimer acid (HFPO-DA) has been extensively detected in various environmental matrices. Despite this prevalence, limited information is available regarding its hepatotoxicity biomarkers. In this study, toxicokinetic simulations indicated that under repeated treatment, HFPO-DA in mice serum reached a steady state by the 4th day. To assess its subacute hepatic effects and identify potential biomarkers, mice were administered HFPO-DA orally at doses of 0, 0.1, 0.5, 2.5, 12.5, or 62.5 mg/kg/d for 7 d. Results revealed that the lowest observed adverse effect levels were 0.5 mg/kg/d for hepatomegaly and 2.5 mg/kg/d for hepatic injury. Serum metabolomics analysis identified 34, 58, and 118 differential metabolites in the 0.1, 0.5, and 2.5 mg/kg/d groups, respectively, compared to the control group. Based on weighted gene coexpression network analysis, eight potential hepatotoxicity-related metabolites were identified; among them, kynurenic acid (KA) in mouse serum exhibited the highest correlation with liver injury. Furthermore, liver-targeted metabolomics analysis demonstrated that HFPO-DA exposure induced metabolic migration of the kynurenine pathway from KA to nicotinamide adenine dinucleotide, resulting in the activation of endoplasmic reticulum stress and the nuclear factor kappa-B signaling pathway. Notably, pretreatment with KA significantly attenuated liver injury induced by HFPO-DA exposure in mice, highlighting the pivotal roles of KA in the hepatotoxicity of HFPO-DA.
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Affiliation(s)
- Jianglin Hu
- State Environmental Protection Key Laboratory of Environmental Health Impact Assessment of Emerging Contaminants, School of Environmental Science and Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Minhang District, Shanghai 200240, China
| | - Jiayin Dai
- State Environmental Protection Key Laboratory of Environmental Health Impact Assessment of Emerging Contaminants, School of Environmental Science and Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Minhang District, Shanghai 200240, China
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Nan Sheng
- State Environmental Protection Key Laboratory of Environmental Health Impact Assessment of Emerging Contaminants, School of Environmental Science and Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Minhang District, Shanghai 200240, China
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Bonhomme MM, Patarin F, Kruse CJ, François AC, Renaud B, Couroucé A, Leleu C, Boemer F, Toquet MP, Richard EA, Seignot J, Wouters CP, Votion DM. Untargeted Metabolomics Profiling Reveals Exercise Intensity-Dependent Alterations in Thoroughbred Racehorses' Plasma after Routine Conditioning Sessions. ACS OMEGA 2023; 8:48557-48571. [PMID: 38144146 PMCID: PMC10733985 DOI: 10.1021/acsomega.3c08583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 11/23/2023] [Accepted: 11/24/2023] [Indexed: 12/26/2023]
Abstract
Thoroughbred (TB) racehorses undergo rigorous conditioning programs to optimize their physical and mental capabilities through varied exercise sessions. While conventional investigations focus on limited hematological and biochemical parameters, this field study employed untargeted metabolomics to comprehensively assess metabolic responses triggered by exercise sessions routinely used in TB conditioning. Blood samples were collected pre- and post-exercise from ten racehorses, divided into two groups based on exercise intensity: high intensity (n = 6, gallop at ± 13.38 m/s, 1400 m) and moderate intensity (n = 4, soft canter at ± 7.63 m/s, 2500 m). Intensity was evaluated through monitoring of the speed, heart rate, and lactatemia. Resting and 30 min post-exercise plasma samples were analyzed using ultraperformance liquid chromatography coupled with high-resolution mass spectrometry. Unsupervised principal component analysis revealed exercise-induced metabolome changes, with high-intensity exercise inducing greater alterations. Following high-intensity exercise, 54 metabolites related to amino acid, fatty acid, nucleic acid, and vitamin metabolism were altered versus 23 metabolites, primarily linked to fatty acid and amino acid metabolism, following moderate-intensity exercise. Metabolomics confirmed energy metabolism changes reported by traditional biochemistry studies and highlighted the involvement of lipid and amino acid metabolism during routine exercise and recovery, aspects that had previously been overlooked in TB racehorses.
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Affiliation(s)
- Maëlle M. Bonhomme
- Department
of Functional Sciences, Comparative Veterinary Medicine, Fundamental
and Applied Research for Animals & Health (FARAH), Faculty of
Veterinary Medicine, University of Liege, Boulevard de Colonster 20, 4000 Liège, Belgium
| | - Florence Patarin
- Department
of Functional Sciences, Comparative Veterinary Medicine, Fundamental
and Applied Research for Animals & Health (FARAH), Faculty of
Veterinary Medicine, University of Liege, Boulevard de Colonster 20, 4000 Liège, Belgium
| | - Caroline-J. Kruse
- Department
of Functional Sciences, Comparative Veterinary Medicine, Fundamental
and Applied Research for Animals & Health (FARAH), Faculty of
Veterinary Medicine, University of Liege, Boulevard de Colonster 20, 4000 Liège, Belgium
| | - Anne-Christine François
- Department
of Functional Sciences, Comparative Veterinary Medicine, Fundamental
and Applied Research for Animals & Health (FARAH), Faculty of
Veterinary Medicine, University of Liege, Boulevard de Colonster 20, 4000 Liège, Belgium
| | - Benoît Renaud
- Department
of Functional Sciences, Comparative Veterinary Medicine, Fundamental
and Applied Research for Animals & Health (FARAH), Faculty of
Veterinary Medicine, University of Liege, Boulevard de Colonster 20, 4000 Liège, Belgium
| | - Anne Couroucé
- Equine
Department, Oniris, National Vet School
of Nantes, 101 Route
de Gachet, 44300 Nantes, France
- UR 7450
Biotargen, University of Caen Normandie, 3 Rue Nelson Mandela, 14280 Saint-Contest, France
| | - Claire Leleu
- Equi-Test, La Lande, 53290 Grez-en-Bouère, France
| | - François Boemer
- Biochemical
Genetics Laboratory, Human Genetics Department, University Hospital
of Liege, University of Liege, Avenue de l’Hôpital
1, 4000 Liège, Belgium
| | - Marie-Pierre Toquet
- UR 7450
Biotargen, University of Caen Normandie, 3 Rue Nelson Mandela, 14280 Saint-Contest, France
- LABÉO
(Frank Duncombe), 1 Route
de Rosel, 14280 Saint-Contest, France
| | - Eric A. Richard
- UR 7450
Biotargen, University of Caen Normandie, 3 Rue Nelson Mandela, 14280 Saint-Contest, France
- LABÉO
(Frank Duncombe), 1 Route
de Rosel, 14280 Saint-Contest, France
| | - Jérôme Seignot
- Clinique
Vétérinaire du Parc, 1 Avenue Malesherbes, 78600 Maisons-Laffitte, France
| | - Clovis P. Wouters
- Department
of Functional Sciences, Comparative Veterinary Medicine, Fundamental
and Applied Research for Animals & Health (FARAH), Faculty of
Veterinary Medicine, University of Liege, Boulevard de Colonster 20, 4000 Liège, Belgium
| | - Dominique-Marie Votion
- Department
of Functional Sciences, Comparative Veterinary Medicine, Fundamental
and Applied Research for Animals & Health (FARAH), Faculty of
Veterinary Medicine, University of Liege, Boulevard de Colonster 20, 4000 Liège, Belgium
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Li K, Shi W, Song Y, Qin L, Zang C, Mei T, Li A, Song Q, Zhang Y. Reprogramming of lipid metabolism in hepatocellular carcinoma resulting in downregulation of phosphatidylcholines used as potential markers for diagnosis and prediction. Expert Rev Mol Diagn 2023; 23:1015-1026. [PMID: 37672012 DOI: 10.1080/14737159.2023.2254884] [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: 07/07/2023] [Accepted: 08/28/2023] [Indexed: 09/07/2023]
Abstract
BACKGROUND Aberrant methylation and metabolic perturbations may deepen our understanding of hepatocarcinogenesis and help identify novel biomarkers for diagnosing hepatocellular carcinoma (HCC). We aimed to develop an HCC model based on a multi-omics. RESEARCH DESIGN AND METHODS Four hundred patient samples (200 with HCC and 200 with hepatitis B virus-related liver disease (HBVLD)) were subjected to liquid chromatography-mass spectrometry and multiplex bisulfite sequencing. Integrative analysis of clinical data, CpG data, and metabolome for the 20 complete imputation datasets within a for-loopwas used to identify biomarker. RESULTS Totally, 1,140 metabolites were annotated, of which 125 were differentially expressed. Lipid metabolism reprogramming in HCC, resulting in phosphatidylcholines (PC) significantly downregulated, partly due to the altered mitochondrial beta-oxidation of fatty acids with diverse chain lengths. Age, sex, serum-fetoprotein levels, cg05166871,cg14171514, cg18772205, PC (O-16:0/20:3(8Z, 11Z, 14Z)), and PC (16:1(9Z)/P-18:0) were used to develop the HCC model. The model presented a good diagnostic and an acceptable predictive performance. The cumulative incidence of HCC in low- and high-risk groups of HBVLD patients were 1.19% and 21.40%, respectively (p = 0.0039). CONCLUSIONS PCs serve as potential plasma biomarkers and help identify patients with HBVLD at risk of HCC who should be screened for early diagnosis and intervention.
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Affiliation(s)
- Kang Li
- Biomedical Information Center, Beijing You'An Hospital, Capital Medical University, Beijing, China
| | - Wanting Shi
- Biomedical Information Center, Beijing You'An Hospital, Capital Medical University, Beijing, China
| | - Yi Song
- Institute of Clinical Medicine, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Lin Qin
- Biomedical Information Center, Beijing You'An Hospital, Capital Medical University, Beijing, China
| | - Chaoran Zang
- Biomedical Information Center, Beijing You'An Hospital, Capital Medical University, Beijing, China
- Hepatobiliary Pancreatic Center Department, Beijing Tsinghua Changgung Hospital Affiliated to Tsinghua University, Beijing, China
| | - Tingting Mei
- Biomedical Information Center, Beijing You'An Hospital, Capital Medical University, Beijing, China
| | - Ang Li
- Biomedical Information Center, Beijing You'An Hospital, Capital Medical University, Beijing, China
| | - Qingkun Song
- Biomedical Information Center, Beijing You'An Hospital, Capital Medical University, Beijing, China
| | - Yonghong Zhang
- Biomedical Information Center, Beijing You'An Hospital, Capital Medical University, Beijing, China
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12
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Cao J, Wei X, Liu MF, An GS, Li J, Du QX, Sun JH. Forensic identification of sudden cardiac death: a new approach combining metabolomics and machine learning. Anal Bioanal Chem 2023; 415:2291-2305. [PMID: 36933055 DOI: 10.1007/s00216-023-04651-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Revised: 03/08/2023] [Accepted: 03/10/2023] [Indexed: 03/19/2023]
Abstract
The determination of sudden cardiac death (SCD) is one of the difficult tasks in the forensic practice, especially in the absence of specific morphological changes in the autopsies and histological investigations. In this study, we combined the metabolic characteristics from corpse specimens of cardiac blood and cardiac muscle to predict SCD. Firstly, ultra-high performance liquid chromatography coupled with high-resolution mass spectrometry (UPLC-HRMS)-based untargeted metabolomics was applied to obtain the metabolomic profiles of the specimens, and 18 and 16 differential metabolites were identified in the cardiac blood and cardiac muscle from the corpses of those who died of SCD, respectively. Several possible metabolic pathways were proposed to explain these metabolic alterations, including the metabolism of energy, amino acids, and lipids. Then, we validated the capability of these combinations of differential metabolites to distinguish between SCD and non-SCD through multiple machine learning algorithms. The results showed that stacking model integrated differential metabolites featured from the specimens showed the best performance with 92.31% accuracy, 93.08% precision, 92.31% recall, 91.96% F1 score, and 0.92 AUC. Our results revealed that the SCD metabolic signature identified by metabolomics and ensemble learning in cardiac blood and cardiac muscle has potential in SCD post-mortem diagnosis and metabolic mechanism investigations.
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Affiliation(s)
- Jie Cao
- School of Forensic Medicine, Shanxi Medical University, Yuci District, Jinzhong, Shanxi, 030604, People's Republic of China
| | - Xue Wei
- School of Forensic Medicine, Shanxi Medical University, Yuci District, Jinzhong, Shanxi, 030604, People's Republic of China
| | - Ming-Feng Liu
- School of Forensic Medicine, Shanxi Medical University, Yuci District, Jinzhong, Shanxi, 030604, People's Republic of China
| | - Guo-Shuai An
- School of Forensic Medicine, Shanxi Medical University, Yuci District, Jinzhong, Shanxi, 030604, People's Republic of China
| | - Jian Li
- School of Forensic Medicine, Shanxi Medical University, Yuci District, Jinzhong, Shanxi, 030604, People's Republic of China
| | - Qiu-Xiang Du
- School of Forensic Medicine, Shanxi Medical University, Yuci District, Jinzhong, Shanxi, 030604, People's Republic of China
| | - Jun-Hong Sun
- School of Forensic Medicine, Shanxi Medical University, Yuci District, Jinzhong, Shanxi, 030604, People's Republic of China.
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13
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Yang M, Zhu C, Du L, Huang J, Lu J, Yang J, Tong Y, Zhu M, Song C, Shen C, Dai J, Lu X, Xu Z, Li N, Ma H, Hu Z, Gu D, Jin G, Hang D, Shen H. A Metabolomic Signature of Obesity and Risk of Colorectal Cancer: Two Nested Case-Control Studies. Metabolites 2023; 13:metabo13020234. [PMID: 36837854 PMCID: PMC9965372 DOI: 10.3390/metabo13020234] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 01/31/2023] [Accepted: 02/02/2023] [Indexed: 02/08/2023] Open
Abstract
Obesity is a leading contributor to colorectal cancer (CRC) risk, but the metabolic mechanisms linking obesity to CRC are not fully understood. We leveraged untargeted metabolomics data from two 1:1 matched, nested case-control studies for CRC, including 223 pairs from the US Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial and 190 pairs from a prospective Chinese cohort. We explored serum metabolites related to body mass index (BMI), constructed a metabolomic signature of obesity, and examined the association between the signature and CRC risk. In total, 72 of 278 named metabolites were correlated with BMI after multiple testing corrections (p FDR < 0.05). The metabolomic signature was calculated by including 39 metabolites that were independently associated with BMI. There was a linear positive association between the signature and CRC risk in both cohorts (p for linear < 0.05). Per 1-SD increment of the signature was associated with 38% (95% CI: 9-75%) and 28% (95% CI: 2-62%) higher risks of CRC in the US and Chinese cohorts, respectively. In conclusion, we identified a metabolomic signature for obesity and demonstrated the association between the signature and CRC risk. The findings offer new insights into the underlying mechanisms of CRC, which is critical for improved CRC prevention.
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Affiliation(s)
- Mingjia Yang
- Department of Epidemiology, School of Public Health, Southeast University, Nanjing 210009, China
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, 101 Longmian Avenue, Nanjing 211166, China
| | - Chen Zhu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, 101 Longmian Avenue, Nanjing 211166, China
- Department of Cancer Prevention, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou 310022, China
- Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou 310022, China
| | - Lingbin Du
- Department of Cancer Prevention, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou 310022, China
- Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou 310022, China
| | - Jianv Huang
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, 101 Longmian Avenue, Nanjing 211166, China
| | - Jiayi Lu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, 101 Longmian Avenue, Nanjing 211166, China
| | - Jing Yang
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, 101 Longmian Avenue, Nanjing 211166, China
| | - Ye Tong
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, 101 Longmian Avenue, Nanjing 211166, China
| | - Meng Zhu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, 101 Longmian Avenue, Nanjing 211166, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine and International Joint Research Center on Environment and Human Health, Nanjing Medical University, Nanjing 211166, China
| | - Ci Song
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, 101 Longmian Avenue, Nanjing 211166, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine and International Joint Research Center on Environment and Human Health, Nanjing Medical University, Nanjing 211166, China
| | - Chong Shen
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, 101 Longmian Avenue, Nanjing 211166, China
| | - Juncheng Dai
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, 101 Longmian Avenue, Nanjing 211166, China
| | - Xiangfeng Lu
- Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China
- Key Laboratory of Cardiovascular Epidemiology, Chinese Academy of Medical Sciences, Beijing 100037, China
- Research Units of Cohort Study on Cardiovascular Diseases and Cancers, Chinese Academy of Medical Sciences, Beijing 100730, China
| | - Zekuan Xu
- Department of General Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China
| | - Ni Li
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Hongxia Ma
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, 101 Longmian Avenue, Nanjing 211166, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine and International Joint Research Center on Environment and Human Health, Nanjing Medical University, Nanjing 211166, China
- Research Units of Cohort Study on Cardiovascular Diseases and Cancers, Chinese Academy of Medical Sciences, Beijing 100730, China
| | - Zhibin Hu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, 101 Longmian Avenue, Nanjing 211166, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine and International Joint Research Center on Environment and Human Health, Nanjing Medical University, Nanjing 211166, China
| | - Dongfeng Gu
- Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China
- Key Laboratory of Cardiovascular Epidemiology, Chinese Academy of Medical Sciences, Beijing 100037, China
- Research Units of Cohort Study on Cardiovascular Diseases and Cancers, Chinese Academy of Medical Sciences, Beijing 100730, China
| | - Guangfu Jin
- Department of Epidemiology, School of Public Health, Southeast University, Nanjing 210009, China
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, 101 Longmian Avenue, Nanjing 211166, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine and International Joint Research Center on Environment and Human Health, Nanjing Medical University, Nanjing 211166, China
- Correspondence: (G.J.); (D.H.)
| | - Dong Hang
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, 101 Longmian Avenue, Nanjing 211166, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine and International Joint Research Center on Environment and Human Health, Nanjing Medical University, Nanjing 211166, China
- Correspondence: (G.J.); (D.H.)
| | - Hongbing Shen
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, 101 Longmian Avenue, Nanjing 211166, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine and International Joint Research Center on Environment and Human Health, Nanjing Medical University, Nanjing 211166, China
- Research Units of Cohort Study on Cardiovascular Diseases and Cancers, Chinese Academy of Medical Sciences, Beijing 100730, China
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14
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Wu X, Wang Z, Luo L, Shu D, Wang K. Metabolomics in hepatocellular carcinoma: From biomarker discovery to precision medicine. FRONTIERS IN MEDICAL TECHNOLOGY 2023; 4:1065506. [PMID: 36688143 PMCID: PMC9845953 DOI: 10.3389/fmedt.2022.1065506] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Accepted: 12/06/2022] [Indexed: 01/05/2023] Open
Abstract
Hepatocellular carcinoma (HCC) remains a global health burden, and is mostly diagnosed at late and advanced stages. Currently, limited and insensitive diagnostic modalities continue to be the bottleneck of effective and tailored therapy for HCC patients. Moreover, the complex reprogramming of metabolic patterns during HCC initiation and progression has been obstructing the precision medicine in clinical practice. As a noninvasive and global screening approach, metabolomics serves as a powerful tool to dynamically monitor metabolic patterns and identify promising metabolite biomarkers, therefore holds a great potential for the development of tailored therapy for HCC patients. In this review, we summarize the recent advances in HCC metabolomics studies, including metabolic alterations associated with HCC progression, as well as novel metabolite biomarkers for HCC diagnosis, monitor, and prognostic evaluation. Moreover, we highlight the application of multi-omics strategies containing metabolomics in biomarker discovery for HCC. Notably, we also discuss the opportunities and challenges of metabolomics in nowadays HCC precision medicine. As technologies improving and metabolite biomarkers discovering, metabolomics has made a major step toward more timely and effective precision medicine for HCC patients.
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Affiliation(s)
- Xingyun Wu
- West China School of Basic Medical Science & Forensic Medicine, Sichuan University, Chengdu, China
| | - Zihao Wang
- Department of Gastrointestinal Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Li Luo
- Center for Reproductive Medicine, Department of Gynecology and Obstetrics, West China Second University Hospital, Sichuan University, Chengdu, China,Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, China
| | - Dan Shu
- School of Bioscience and Technology, Chengdu Medical College, Chengdu, China,Correspondence: Kui Wang Dan Shu
| | - Kui Wang
- West China School of Basic Medical Science & Forensic Medicine, Sichuan University, Chengdu, China,Correspondence: Kui Wang Dan Shu
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