EBioMedicine
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2025
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IF 10.8
Wanshan Liu,Xiaoxiao Hu,Zhouzhou Bao,Yanyan Li,Juxiang Zhang,Shouzhi Yang,Yida Huang,Ruimin Wang,Jiao Wu,Xiaoyu Xu,Qi Sang,Wen Di,Huaiwu Lu,Xia Yin,Kun Qian
Summary Background Ovarian cancer (OC) ranks as the most lethal gynaecological malignancy worldwide, with early diagnosis being crucial yet challenging. Current diagnostic methods like transvaginal ultrasound and blood biomarkers show limited sensitivity/specificity. This study aimed to identify and validate serum metabolic biomarkers for OC diagnosis using the largest cohort reported to date. Methods We constructed a large-scale OC-associated cohort of 1432 subjects, including 662 OC, 563 benign ovarian disease, and 207 healthy control subjects, across retrospective (n = 1073) and set-aside validation (n = 359) cohorts. Serum metabolic fingerprints (SMFs) were recorded using nanoparticle-enhanced laser desorption/ionization mass spectrometry (NELDI-MS). A diagnostic panel was developed through machine learning of SMFs in the discovery cohort and validated in independent verification and set-aside validation cohorts. The identified metabolic biomarkers were further validated using liquid chromatography MS and their biological functions were assessed in OC cell lines. Findings We identified a metabolic biomarker panel including glucose, histidine, pyrrole-2-carboxylic acid, and dihydrothymine. This panel achieved consistent areas under the curve (AUCs) of 0.87–0.89 for distinguishing between malignant and benign ovarian masses across all cohorts, and improved to AUCs of 0.95–0.99 when combined with risk of ovarian malignancy algorithm (ROMA). In vitro validation provided initial biological context for the metabolic alterations observed in our diagnostic panel. Interpretation Our study established a reliable serum metabolic biomarker panel for OC diagnosis with potential clinical translations. The NELDI-MS based approach offers advantages of fast analytical speed (∼30 s/sample) and low cost (∼2–3 dollars/sample), making it suitable for large-scale clinical applications. Funding MOST (2021YFA0910100), NSFC (82421001, 823B2050, 824B2059, and 82173077), Medical-Engineering Joint Funds of Shanghai Jiao Tong University (YG2021GD02, YG2024ZD07, and YG2023ZD08), Shanghai Science and Technology Committee Project (23JC1403000), Shanghai Institutions of Higher Learning (2021-01-07-00-02-E00083), Shanghai Jiao Tong University Inner Mongolia Research Institute (2022XYJG0001-01-16), Sichuan Provincial Department of Science and Technology (2024YFHZ0176), Innovation Research Plan by the Shanghai Municipal Education Commission (ZXWF082101), Innovative Research Team of High-Level Local Universities in Shanghai (SHSMU-ZDCX20210700), Basic-Clinical Collaborative Innovation Project from Shanghai Immune Therapy Institute, Guangdong Basic and Applied Basic Research Foundation (2024A1515013255).
研究领域:
肿瘤学
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代谢组学
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医学诊断
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DOI
PubMed
高影响因子
International Journal of Clinical and Experimental Medicine
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2015
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IF 1.277
Meiling Zhang,Shihui Bao,Feiou Lin,Yingying Lin,Lijing Zhang,Mengzhi Xu,Xueli Huang,Congcong Wen,Lufeng Hu,Guanyang Lin
This study aimed to evaluate the effect of Datura stramonium on rats by examining the differences in urine and serum metabolites between Datura stramonium groups and control group. SIMCA-P+12.0.1.0 software was used for partial least-squares discriminant analysis (PLS-DA) to screen for the differential metabolites. Fifteen metabolites in urine including malonic acid, pentanedioic acid, D-xylose, D-ribose, xylulose, azelaic acid, threitol, glycine, butanoic acid, D-mannose, D-gluconic acid, galactonic acid, myo-inositol, octadecanoic acid, pseudouridine and ten metabolites in serum including alanine, butanedioic acid, L-methionine, propanedioic acid, hexadecanoic acid, D-fructose, tetradecanoic acid, D-glucose, D-galactose, oleic acid were selected as the characteristic metabolites. The PLS-DA scores plot indicated that serum and urine metabolites have a variety of changes among low dose group, high dose group and control group. These metabolites were related with amino metabolism, lipid metabolism and energy metabolism. The result reflected the relationship between metabolites in rat fluid and Datura stramonium spectra. Potential differences in metabolites and metabolic pathway analysis showed that the establishment of urine and serum metabolomics methods for further evaluating drug has great significance.
研究领域:
药理学
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代谢组学
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DOI
PubMed
高影响因子
JOURNAL OF CHROMATOGRAPHY B-ANALYTICAL TECHNOLOGIES IN THE BIOMEDICAL AND LIFE SCIENCES
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2024
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IF 2.8
Dengli Wu,Hongmei Wu,Piao Yu,Hongyun Liu,Mei Liu,Junyi Wang,Xiangpei Wang,Feng Xu
Symptoms of ulcerative colitis (UC) are like “intestinal carbuncle” in Chinese medicine. The aqueous extract of Sargentodoxa cuneata (AESc) has good therapeutic effects on UC, but the underlying mechanism needs to be further elucidated. The mechanism of AESc against UC was studied based on metabolomics and bioinformatics in mice with UC. Dextran sodium sulfate was applied to induce a mouse model of UC. After the intervention of AESc, the general condition of the animals was recorded, and efficacy-related indicators were measured. Information on serum metabolites was determined. Multivariate analysis combined with bioinformatics methods were used to identify the differential metabolites. Furthermore, “metabolite-target-disease” network was obtained, and differential metabolites of UC were screened, and further analysis of the metabolites were performed. Molecular docking validation was also carried out. AESc improved general conditions such as blood in stool, hair of animals, and weight loss, reduced disease activity index scores and shortening of colon length in mice with UC. A total of 3445 serum metabolites were obtained, and 64 differentiated metabolites of AESc against UC were screened. Enrichment analysis showed that arachidonic acid metabolism, bile secretion, drug metabolism-other enzymes, and tyrosine metabolism were associated with AESc in the treatment of UC. In addition, based on “metabolite-target-disease” network, the serum metabolites cholylleucine, 9,10,13-TriHOME, birabresib, anthramycin methyl ether, trans -hexadec-2-enoyl carnitine, and lucidumol A were found to have the therapeutic potential for UC. Further, 14 core targets were obtained, and lipids and atherosclerosis, rheumatoid arthritis and multiple immune-inflammatory pathways were associated with AESc for the treatment of UC. AESc corrects serum metabolic disturbances in UC mice, and multiple serum metabolites have therapeutic potential for UC. AESc may treat UC by regulating biological processes such as lipid metabolism, amino acid metabolism, thereby restoring normal physiological function of the intestine.
研究领域:
医学
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生物信息学
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药理学
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DOI
PubMed
高影响因子
Frontiers in Endocrinology
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2021
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IF 5.555
Jing Ziwei,Liu Liwei,Shi Yingying,Du Qiuzheng,Zhang Dingding,Zuo Lihua,Du Shuzhang,Sun Zhi,Zhang Xiaojian
Introduction: Individuals with metabolic syndrome (MetS) are at increasing risk of coronary artery disease (CAD). We investigated the common metabolic perturbations of CAD and MetS via serum metabolomics to provide insight into potential associations.Methods: Non-targeted serum metabolomics analyses were performed using ultra high-performance liquid chromatography coupled with Q Exactive hybrid quadrupole-orbitrap high-resolution accurate mass spectrometry (UHPLC-Q-Orbitrap HRMS) in samples from 492 participants (272 CAD vs. 121 healthy controls (HCs) as cohort 1, 55 MetS vs. 44 HCs as cohort 2). Cross-sectional data were obtained when the participants were recruited from the First Affiliated Hospital of Zhengzhou University. Multivariate statistics and Student’s t test were applied to obtain the significant metabolites [with variable importance in the projection (VIP) values >1.0 and p values <0.05] for CAD and MetS. Logistic regression was performed to investigate the association of identified metabolites with clinical cardiac risk factors, and the association of significant metabolic perturbations between CAD and MetS was visualized by Cytoscape software 3.6.1. Finally, the receiver operating characteristic (ROC) analysis was evaluated for the risk prediction values of common changed metabolites.Results: Thirty metabolites were identified for CAD, mainly including amino acids, lipid, fatty acids, pseudouridine, niacinamide; 26 metabolites were identified for MetS, mainly including amino acids, lipid, fatty acids, steroid hormone, and paraxanthine. The logistic regression results showed that all of the 30 metabolites for CAD, and 15 metabolites for MetS remained significant after adjustments of clinical risk factors. In the common metabolic signature association analysis between CAD and MetS, 11 serum metabolites were significant and common to CAD and MetS outcomes. Out of this, nine followed similar trends while two had differing directionalities. The nine common metabolites exhibiting same change trend improved risk prediction for CAD (86.4%) and MetS (90.9%) using the ROC analysis.Conclusion: Serum metabolomics analysis might provide a new insight into the potential mechanisms underlying the common metabolic perturbations of CAD and MetS.
研究领域:
医学
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代谢组学
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心血管疾病
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DOI
PubMed
高影响因子
Reviews in Cardiovascular Medicine
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2025
·Vol. 26
·期 4
·27001
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IF 1.3
Yipin Yu,Qiong Yi,Chenglong Yang,Xudong Song,Duoting Tan,Qinghua Peng,Xiang Sun,Hao Liang
Background:Diuretic resistance (DR) is characterized by insufficient fluid and sodium excretion enhancement despite maximum loop diuretic doses, indicating a phenotype of refractory heart failure (HF). Recently, metabolomics has emerged as a crucial tool for diagnosing and understanding the pathogenesis of various diseases. This study aimed to differentiate diuretic-resistant patients from non-resistant HF to identify biomarkers linked to the emergence of DR.Methods:Serum samples from HF patients, both with and without DR, were subjected to non-targeted metabolomic analysis using liquid chromatography-tandem mass spectrometry. Metabolite variations between groups were identified using principal component analysis and orthogonal partial least-square discriminant analysis. Metabolic pathways were assessed through the Kyoto Encyclopedia of Genes and Genomes database enrichment analysis, and potential biomarkers were determined using receiver operating characteristic curves (ROCs).Results:In total, 192 metabolites exhibited significant differences across the two sample groups. Among these, up-regulation was observed in 164 metabolites, while 28 metabolites were down-regulated. A total of 28 pathways involving neuroactive ligand-receptor interaction and amino acid biosynthesis were affected. The top five metabolites identified by ROC analysis as potential DR biomarkers were hydroxykynurenine, perillic acid, adrenic acid, 5-acetamidovalerate, and adipic acid.Conclusions:Significant differences in metabolite profiles were observed between the diuretic-resistant and non-diuretic-resistant groups among patients with HF. The top five differentially expressed endogenous metabolites were hydroxykynurenine, perillic acid, adrenic acid, 5-acetamidovalerate, and adipic acid. The metabolic primary pathways implicated in DR were noted as amino acid, energy, and nucleotide metabolism.Clinical Trial Registration:This study was registered with the China Clinical Trials Registry (https://www.chictr.org.cn/hvshowproject.html?id=197183&v=1.7, ChiCTR2100053587).
研究领域:
医学
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代谢组学
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心血管病学
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DOI
PubMed
高影响因子
Food Bioscience
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2024
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IF 4.8
Ning Wang,Qianhui Zhang,Li Zhang,WenWei Lu,Yue Xiao,Hongchao Wang,Wei Chen,Feng Zhang,JianXin Zhao
The number of gluten-restricted dieters is growing, yet its impact on healthy diets and serum profiles is unclear. This study investigated the effects of a 2-week low-gluten diet (LGD) on the dietary patterns, serum metabolic profiles, and health status of 61 young individuals from a university in southeastern China. Data on food intake, serum metabolomics, blood biochemistry, and fecal SCFA were collected. This population was divided into four dietary patterns by k-means method:1 Processed food group (PB, n = 17), 2 Rice group (RB, n = 10), 3 Fish/shrimp-based group (SB, n = 4), and 4 Vegetable group (VB, n = 30). Serum metabolites and fecal SCFA were measured using HPLC-MS and GC-MS, respectively. After 2-week of LGD, the SB group had the highest diet diversity. There were remarked differences in nutrient intake among the four dietary patterns, but no differences in health indices or fecal SCFA. WGCNA method revealed the differences in metabolite metabolite modules 4 and 5, and 3-hydroxybutyric acid, hydrolyzed fumonisin（HFB1）, and palmitoleic acid were the different metabolites among clusters. Following a 2-week LGD, different dietary patterns lead to different nutrient intakes and serum metabolite profiles.
研究领域:
营养学
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代谢组学
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公共卫生
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DOI
高影响因子
JOURNAL OF PROTEOME RESEARCH
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2024
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IF 3.8
Yuqing Zhang,Hui Zhao,Jinhui Zhao,Wangjie Lv,Xueni Jia,Xin Lu,Xinjie Zhao,Guowang Xu
The co-occurrence of multiple chronic metabolic diseases is highly prevalent, posing a huge health threat. Clarifying the metabolic associations between them, as well as identifying metabolites which allow discrimination between diseases, will provide new biological insights into their co-occurrence. Herein, we utilized targeted serum metabolomics and lipidomics covering over 700 metabolites to characterize metabolic alterations and associations related to seven chronic metabolic diseases (obesity, hypertension, hyperuricemia, hyperglycemia, hypercholesterolemia, hypertriglyceridemia, fatty liver) from 1626 participants. We identified 454 metabolites were shared among at least two chronic metabolic diseases, accounting for 73.3% of all 619 significant metabolite–disease associations. We found amino acids, lactic acid, 2-hydroxybutyric acid, triacylglycerols (TGs), and diacylglycerols (DGs) showed connectivity across multiple chronic metabolic diseases. Many carnitines were specifically associated with hyperuricemia. The hypercholesterolemia group showed obvious lipid metabolism disorder. Using logistic regression models, we further identified distinguished metabolites of seven chronic metabolic diseases, which exhibited satisfactory area under curve (AUC) values ranging from 0.848 to 1 in discovery and validation sets. Overall, quantitative metabolome and lipidome data sets revealed widespread and interconnected metabolic disorders among seven chronic metabolic diseases. The distinguished metabolites are useful for diagnosing chronic metabolic diseases and provide a reference value for further clinical intervention and management based on metabolomics strategy.
研究领域:
医学
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生物化学
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代谢研究
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DOI
PubMed
高影响因子
Heliyon
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2022
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IF 3.776
Fang Li,Lei Fu,Xiaoxiong Liu,Xin-an Liu,Yong Liang,Yueguang Lv,Zhiyi Yang,Ang Guo,Zhiyu Chen,Wenbo Li,Fan Pan,Qian Luo
Metabolic reprogramming is a distinctive characteristic of SARS-CoV-2 infection, which refers to metabolic changes in hosts triggered by viruses for their survival and spread. It is current urgent to understand the metabolic health status of COVID-19 survivors and its association with long-term health consequences of infection, especially for the predominant non-severe patients. Herein, we show systemic metabolic signatures of survivors of non-severe COVID-19 from Wuhan, China at six months after discharge using metabolomics approaches. The serum amino acids, organic acids, purine, fatty acids and lipid metabolism were still abnormal in the survivors, but the kynurenine pathway and the level of itaconic acid have returned to normal. These metabolic abnormalities are associated with liver injury, mental health, energy production, and inflammatory responses. Our findings identify and highlight the metabolic abnormalities in survivors of non-severe COVID-19, which provide information on biomarkers and therapeutic targets of infection and cues for post-hospital care and intervention strategies centered on metabolism reprogramming.
研究领域:
医学
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代谢组学
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DOI
PubMed
高影响因子
BRITISH JOURNAL OF DERMATOLOGY
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2017
·Vol. 176
·期 3
·713-722
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IF 4.706
Kang H.,Li X.,Zhou Q.,Quan C.,Xue F.,Zheng J.,Yu Y.
BackgroundRecent studies have shown that dysregulated metabolic pathways are linked to psoriasis pathogenesis. However, an extensive, unbiased metabolic analysis in patients with psoriasis has not been completely explored. The metabolome represents the end products of proteomics or cellular processes that may be closely associated with the pathogenesis of psoriasis.ObjectivesTo determine the differences in serum metabolomic profiles among patients with psoriasis and healthy controls with the goal of identifying potential biomarkers in patients with psoriasis.Materials and methodsSerum metabolomic profiles from 29 subjects (14 patients with psoriasis and 15 sex‐ and age‐matched healthy controls). The serum metabolites were analysed by gas chromatography‐mass spectrometry based on a combined full scan and selected‐ion monitoring mode.ResultsMultivariate statistical analysis of metabolomics data revealed altered serum metabolites between the patients with psoriasis and healthy individuals. Compared with healthy individuals, patients with psoriasis had higher levels of amino acids including asparagine, aspartic acid, isoleucine, phenylalanine, ornithine and proline; higher levels of lactic acid and urea; and lower levels of crotonic acid, azelaic acid, ethanolamine and cholesterol.ConclusionsIt appears that the glycolysis pathway and amino acid metabolic activity are increased in patients with psoriasis. These metabolic perturbations may stem from increased demand for protein biosynthesis and keratinocyte hyperproliferation. Our findings may help to elucidate the pathogenesis of psoriasis and provide insights into early diagnosis and therapeutic intervention.
研究领域:
皮肤病学
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代谢组学
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医学
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DOI
PubMed
高影响因子
Scientific Reports
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2024
·Vol. 14
·期 1
·1-12
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IF 3.8
Wang Tao,Zheng Qian,Yang Qian,Guo Fang,Cui Haiyan,Hu Meng,Zhang Chao,Chen Zhe,Fu Shanlin,Guo Zhongyuan,Wei Zhiwen,Yun Keming
Ketamine has recently become an anesthetic drug used in human and veterinary clinical medicine for illicit abuse worldwide, but the detection of illicit abuse and inference of time intervals following ketamine abuse are challenging issues in forensic toxicological investigations. Here, we developed methods to estimate time intervals since ketamine use is based on significant metabolite changes in rat serum over time after a single intraperitoneal injection of ketamine, and global metabolomics was quantified by ultra-performance liquid chromatography-quadrupole-time-of-flight mass spectrometry (UPLC-Q-TOF–MS). Thirty-five rats were treated with saline (control) or ketamine at 3 doses (30, 60, and 90 mg/kg), and the serum was collected at 21 time points (0 h to 29 d). Time-dependent rather than dose-dependent features were observed. Thirty-nine potential biomarkers were identified, including ketamine and its metabolites, lipids, serotonin and other molecules, which were used for building a random forest model to estimate time intervals up to 29 days after ketamine treatment. The accuracy of the model was 85.37% in the cross-validation set and 58.33% in the validation set. This study provides further understanding of the time-dependent changes in metabolites induced by ketamine abuse.
研究领域:
毒理学
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分析化学
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机器学习
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DOI
PubMed
高影响因子
EMBO Molecular Medicine
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2024
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IF 9
LiuWanshan,MaJinglan,ZhangJuxiang,CaoJing,HuXiaoxiao,HuangYida,WangRuimin,WuJiao,DiWen,QianKun,YinXia
Endometrial cancer (EC) stands as the most prevalent gynecological tumor in women worldwide. Notably, differentiation diagnosis of abnormity detected by ultrasound findings (e.g., thickened endometrium or mass in the uterine cavity) is essential and remains challenging in clinical practice. Herein, we identified a metabolic biomarker panel for differentiation diagnosis of EC using machine learning of high-performance serum metabolic fingerprints (SMFs) and validated the biological function. We first recorded the high-performance SMFs of 191 EC and 204 Non-EC subjects via particle-enhanced laser desorption/ionization mass spectrometry (PELDI-MS). Then, we achieved an area-under-the-curve (AUC) of 0.957–0.968 for EC diagnosis through machine learning of high-performance SMFs, outperforming the clinical biomarker of cancer antigen 125 (CA-125, AUC of 0.610–0.684, p < 0.05). Finally, we identified a metabolic biomarker panel of glutamine, glucose, and cholesterol linoleate with an AUC of 0.901–0.902 and validated the biological function in vitro. Therefore, our work would facilitate the development of novel diagnostic biomarkers for EC in clinics.
研究领域:
妇科学
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肿瘤学
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代谢组学
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机器学习
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生物标志物发现
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产品关联
DOI
PubMed
高影响因子
JOURNAL OF ETHNOPHARMACOLOGY
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2020
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IF 3.69
Qian Zhou,Ping Meng,Ya Zhang,Peng Chen,Haibo Wang,Guangguo Tan
Ethnopharmacological relevance Sini decoction (SND) is a famous Traditional Chinese Medicine (TCM) formula composed of Acontium carmichaeli , Zingiber officinale and Glycyrrhiza uralensis , which is considered as an efficient formula against doxorubicin (DOX)-induced heart failure. But the compatibility mechanism of SND remains unclear. Aim of the study The present study aimed to investigate the compatibility mechanism of SND against DOX-induced heart failure in rats. Materials and methods Mass spectrometry-based serum metabolomics were performed. The relative distance values (RDVs) of SND, A. carmichaeli -free decoction (ACFD), Z. officinale -free decoction (ZOFD) and G. uralensis -free decoction (GUFD) treated groups from the control/DOX groups in multidimensional space were calculated to provide a measure of compatibility effect of SND. SND, ACFD, ZOFD, GUFD-targeted metabolic pathways were identified and compared to investigate the synergistic mechanism of SND by computational systems analysis. Real-time quantitative PCR was further employed to validate the key metabolic pathways at the level of the gene. Results The RDVs combined with the hemodynamic and biochemical analysis showed that the protection effects were sorted as SND > GUFD > ZOFD > ACFD. It revealed that DOX-induced heart failure perturbed 16 metabolic pathways, and SND, GUFD, ZOFD and ACFD-treated groups could significantly reversed 12, 10, 7 and 6 metabolic pathways of these 16 metabolic pathways, respectively. Metabolic pathway and RT-PCR analysis indicated that both SND and GUFD could protect DOX-induced heart failure mainly by regulating PLA2-COX pathway and PLA2-CYP pathway. Conclusion It can be concluded that A. carmichaeli played an essential role in attenuation of DOX-induced heart failure among the three herb constituents of SND and the constituent herbs mutually reinforced each other. This work demonstrated that metabolomics combined with computational systems analysis was a promising tool for uncovering the compatibility effects of TCM.
研究领域:
药理学
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中医药学
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代谢组学
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系统生物学
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DOI
PubMed
高影响因子
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