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Sun M, Liu W, Jiang H, Wu X, Zhang S, Liu H. Large-scale, comprehensive plasma metabolomic analyses reveal potential biomarkers for the diagnosis of early-stage coronary atherosclerosis. Clin Chim Acta 2024; 562:119832. [PMID: 38936535 DOI: 10.1016/j.cca.2024.119832] [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: 01/09/2024] [Revised: 06/11/2024] [Accepted: 06/23/2024] [Indexed: 06/29/2024]
Abstract
BACKGROUND Coronary atherosclerosis (CAS) is a prevalent and chronic life-threatening disease. However, the detection of CAS at an early stage is difficult because of the lack of effective noninvasive diagnostic methods. The present study aimed to characterize the plasma metabolome of early-stage CAS patients to discover metabolomic biomarkers, develop a novel metabolite-based model for accurate noninvasive diagnosis of early-stage CAS, and explore the underlying metabolic mechanisms involved. METHODS A total of 100 patients with early-stage CAS and 120 age- and sex-matched control subjects were recruited from the Chinese Han population and further randomly divided into training (n = 120) and test sets (n = 100). The metabolomic profiles of the plasma samples were analyzed by an integrated untargeted liquid chromatography-mass spectrometry approach, including two separation modes and two ionization modes. Univariate and multivariate statistical analyses were employed to identify potential biomarkers and construct an early-stage CAS diagnostic model. RESULTS The integrated analytical method established herein improved metabolite coverage compared with single chromatographic separation and MS ionization mode. A total of 80 metabolites were identified as potential biomarkers of early-stage CAS, and these metabolites were mainly involved in glycerophospholipid, fatty acid, sphingolipid, and amino acid metabolism. An effective diagnostic model for early-stage CAS was established, incorporating 11 metabolites and achieving areas under the receiver operating characteristic curve (AUCs) of 0.984 and 0.908 in the training and test sets, respectively. CONCLUSIONS Our study not only successfully developed an effective noninvasive diagnostic model for identifying early-stage CAS but also provided novel insights into the pathogenesis of CAS.
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Affiliation(s)
- Meng Sun
- Department of Cardiology, The Second Affiliated Hospital of Harbin Medical University, and The Key Laboratory of Myocardial Ischemia, Chinese Ministry of Education, Harbin 150086, PR China
| | - Wei Liu
- Department of Cardiology, The Second Affiliated Hospital of Harbin Medical University, and The Key Laboratory of Myocardial Ischemia, Chinese Ministry of Education, Harbin 150086, PR China
| | - Hao Jiang
- Department of Cardiology, The Second Affiliated Hospital of Harbin Medical University, and The Key Laboratory of Myocardial Ischemia, Chinese Ministry of Education, Harbin 150086, PR China
| | - Xiaoyan Wu
- Department of Epidemiology and Biostatistics, Guilin Medical University, Guilin 514499, PR China
| | - Shuo Zhang
- Department of Cardiology, The Second Affiliated Hospital of Harbin Medical University, and The Key Laboratory of Myocardial Ischemia, Chinese Ministry of Education, Harbin 150086, PR China.
| | - Haixia Liu
- Department of Cardiology, The Second Affiliated Hospital of Harbin Medical University, and The Key Laboratory of Myocardial Ischemia, Chinese Ministry of Education, Harbin 150086, PR China.
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2
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Liu M, Tian H, Wang M, Guo C, Xu R, Li F, Liu A, Yang H, Duan L, Shen L, Wu Q, Liu Z, Liu Y, Liu F, Pan Y, Hu Z, Chen H, Cai H, He Z, Ke Y. Construction and validation of serum Metabolic Risk Score for early warning of malignancy in esophagus. iScience 2024; 27:109965. [PMID: 38832013 PMCID: PMC11144720 DOI: 10.1016/j.isci.2024.109965] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2023] [Revised: 03/20/2024] [Accepted: 05/09/2024] [Indexed: 06/05/2024] Open
Abstract
Using noninvasive biomarkers to identify high-risk individuals prior to endoscopic examination is crucial for optimization of screening strategies for esophageal squamous cell carcinoma (ESCC). We conducted a nested case-control study based on two community-based screening cohorts to evaluate the warning value of serum metabolites for esophageal malignancy. The serum samples were collected at enrollment when the cases had not been diagnosed. We identified 74 differential metabolites and two prominent perturbed metabolic pathways, and constructed Metabolic Risk Score (MRS) based on 22 selected metabolic predictors. The MRS generated an area under the receiver operating characteristics curve (AUC) of 0.815. The model performed well for the within-1-year interval (AUC: 0.868) and 1-to-5-year interval (AUC: 0.845) from blood draw to diagnosis, but showed limited ability in predicting long-term cases (>5 years). In summary, the MRS could serve as a potential early warning and risk stratification tool for establishing a precision strategy of ESCC screening.
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Affiliation(s)
- Mengfei Liu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Genetics, Peking University Cancer Hospital & Institute, Beijing 100142, China
| | - Hongrui Tian
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Genetics, Peking University Cancer Hospital & Institute, Beijing 100142, China
| | - Minmin Wang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Genetics, Peking University Cancer Hospital & Institute, Beijing 100142, China
- Department of Global Health, School of Public Health, Peking University, Beijing 100191, China
| | - Chuanhai Guo
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Genetics, Peking University Cancer Hospital & Institute, Beijing 100142, China
| | - Ruiping Xu
- Anyang Cancer Hospital, Anyang 455000, China
| | - Fenglei Li
- Hua County People’s Hospital, Anyang 456400, China
| | - Anxiang Liu
- Endoscopy Center, Anyang Cancer Hospital, Anyang 455000, China
| | - Haijun Yang
- Department of Pathology, Anyang Cancer Hospital, Anyang 455000, China
| | - Liping Duan
- Department of Gastroenterology, Peking University Third Hospital, Beijing 100191, China
| | - Lin Shen
- State Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers, Beijing Key Laboratory of Carcinogenesis and Translational Research, Department of Gastrointestinal Oncology, Peking University Cancer Hospital & Institute, Beijing 100142, China
| | - Qi Wu
- State Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers, Beijing Key Laboratory of Carcinogenesis and Translational Research, Endoscopy Center, Peking University Cancer Hospital & Institute, Beijing 100142, China
| | - Zhen Liu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Genetics, Peking University Cancer Hospital & Institute, Beijing 100142, China
| | - Ying Liu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Genetics, Peking University Cancer Hospital & Institute, Beijing 100142, China
| | - Fangfang Liu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Genetics, Peking University Cancer Hospital & Institute, Beijing 100142, China
| | - Yaqi Pan
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Genetics, Peking University Cancer Hospital & Institute, Beijing 100142, China
| | - Zhe Hu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Genetics, Peking University Cancer Hospital & Institute, Beijing 100142, China
| | - Huanyu Chen
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Genetics, Peking University Cancer Hospital & Institute, Beijing 100142, China
| | - Hong Cai
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Genetics, Peking University Cancer Hospital & Institute, Beijing 100142, China
| | - Zhonghu He
- State Key Laboratory of Molecular Oncology, Beijing Key Laboratory of Carcinogenesis and Translational Research, Department of Genetics, Peking University Cancer Hospital & Institute, Beijing 100142, China
| | - Yang Ke
- State Key Laboratory of Molecular Oncology, Beijing Key Laboratory of Carcinogenesis and Translational Research, Department of Genetics, Peking University Cancer Hospital & Institute, Beijing 100142, China
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3
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Mukherjee A, Abraham S, Singh A, Balaji S, Mukunthan KS. From Data to Cure: A Comprehensive Exploration of Multi-omics Data Analysis for Targeted Therapies. Mol Biotechnol 2024:10.1007/s12033-024-01133-6. [PMID: 38565775 DOI: 10.1007/s12033-024-01133-6] [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: 12/27/2023] [Accepted: 02/27/2024] [Indexed: 04/04/2024]
Abstract
In the dynamic landscape of targeted therapeutics, drug discovery has pivoted towards understanding underlying disease mechanisms, placing a strong emphasis on molecular perturbations and target identification. This paradigm shift, crucial for drug discovery, is underpinned by big data, a transformative force in the current era. Omics data, characterized by its heterogeneity and enormity, has ushered biological and biomedical research into the big data domain. Acknowledging the significance of integrating diverse omics data strata, known as multi-omics studies, researchers delve into the intricate interrelationships among various omics layers. This review navigates the expansive omics landscape, showcasing tailored assays for each molecular layer through genomes to metabolomes. The sheer volume of data generated necessitates sophisticated informatics techniques, with machine-learning (ML) algorithms emerging as robust tools. These datasets not only refine disease classification but also enhance diagnostics and foster the development of targeted therapeutic strategies. Through the integration of high-throughput data, the review focuses on targeting and modeling multiple disease-regulated networks, validating interactions with multiple targets, and enhancing therapeutic potential using network pharmacology approaches. Ultimately, this exploration aims to illuminate the transformative impact of multi-omics in the big data era, shaping the future of biological research.
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Affiliation(s)
- Arnab Mukherjee
- Department of Biotechnology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India
| | - Suzanna Abraham
- Department of Biotechnology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India
| | - Akshita Singh
- Department of Biotechnology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India
| | - S Balaji
- Department of Biotechnology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India
| | - K S Mukunthan
- Department of Biotechnology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India.
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4
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Wang X, Zhang J, Zheng K, Du Q, Wang G, Huang J, Zhou Y, Li Y, Jin H, He J. Discovering metabolic vulnerability using spatially resolved metabolomics for antitumor small molecule-drug conjugates development as a precise cancer therapy strategy. J Pharm Anal 2023; 13:776-787. [PMID: 37577390 PMCID: PMC10422108 DOI: 10.1016/j.jpha.2023.02.010] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Revised: 02/19/2023] [Accepted: 02/23/2023] [Indexed: 03/04/2023] Open
Abstract
Against tumor-dependent metabolic vulnerability is an attractive strategy for tumor-targeted therapy. However, metabolic inhibitors are limited by the drug resistance of cancerous cells due to their metabolic plasticity and heterogeneity. Herein, choline metabolism was discovered by spatially resolved metabolomics analysis as metabolic vulnerability which is highly active in different cancer types, and a choline-modified strategy for small molecule-drug conjugates (SMDCs) design was developed to fool tumor cells into indiscriminately taking in choline-modified chemotherapy drugs for targeted cancer therapy, instead of directly inhibiting choline metabolism. As a proof-of-concept, choline-modified SMDCs were designed, screened, and investigated for their druggability in vitro and in vivo. This strategy improved tumor targeting, preserved tumor inhibition and reduced toxicity of paclitaxel, through targeted drug delivery to tumor by highly expressed choline transporters, and site-specific release by carboxylesterase. This study expands the strategy of targeting metabolic vulnerability and provides new ideas of developing SMDCs for precise cancer therapy.
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Affiliation(s)
- Xiangyi Wang
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
- NMPA Key Laboratory of Safety Research and Evaluation of Innovative Drug, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
| | - Jin Zhang
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
- NMPA Key Laboratory of Safety Research and Evaluation of Innovative Drug, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
| | - Kailu Zheng
- Beijing Key Laboratory of New Drug Mechanisms and Pharmacological Evaluation Study, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
| | - Qianqian Du
- Beijing Key Laboratory of New Drug Mechanisms and Pharmacological Evaluation Study, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
| | - Guocai Wang
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
- NMPA Key Laboratory of Safety Research and Evaluation of Innovative Drug, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
| | - Jianpeng Huang
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
- NMPA Key Laboratory of Safety Research and Evaluation of Innovative Drug, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
| | - Yanhe Zhou
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
- NMPA Key Laboratory of Safety Research and Evaluation of Innovative Drug, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
| | - Yan Li
- Beijing Key Laboratory of New Drug Mechanisms and Pharmacological Evaluation Study, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
| | - Hongtao Jin
- NMPA Key Laboratory of Safety Research and Evaluation of Innovative Drug, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
- New Drug Safety Evaluation Center, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
| | - Jiuming He
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
- NMPA Key Laboratory of Safety Research and Evaluation of Innovative Drug, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
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5
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Di Paola R, De A, Izhar R, Abate M, Zappavigna S, Capasso A, Perna AF, La Russa A, Capasso G, Caraglia M, Simeoni M. Possible Effects of Uremic Toxins p-Cresol, Indoxyl Sulfate, p-Cresyl Sulfate on the Development and Progression of Colon Cancer in Patients with Chronic Renal Failure. Genes (Basel) 2023; 14:1257. [PMID: 37372437 DOI: 10.3390/genes14061257] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2023] [Revised: 06/09/2023] [Accepted: 06/10/2023] [Indexed: 06/29/2023] Open
Abstract
Chronic kidney disease (CKD) induces several systemic effects, including the accumulation and production of uremic toxins responsible for the activation of various harmful processes. Gut dysbiosis has been widely described in CKD patients, even in the early stages of the disease. The abundant discharge of urea and other waste substances into the gut favors the selection of an altered intestinal microbiota in CKD patients. The prevalence of bacteria with fermentative activity leads to the release and accumulation in the gut and in the blood of several substances, such as p-Cresol (p-C), Indoxyl Sulfate (IS) and p-Cresyl Sulfate (p-CS). Since these metabolites are normally eliminated in the urine, they tend to accumulate in the blood of CKD patients proportionally to renal impairment. P-CS, IS and p-C play a fundamental role in the activation of various pro-tumorigenic processes, such as chronic systemic inflammation, the increase in the production of free radicals and immune dysfunction. An up to two-fold increase in the incidence of colon cancer development in CKD has been reported in several studies, although the pathogenic mechanisms explaining this compelling association have not yet been described. Based on our literature review, it appears likely the hypothesis of a role of p-C, IS and p-CS in colon cancer development and progression in CKD patients.
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Affiliation(s)
- Rossella Di Paola
- Department of Mental and Physical Health and Preventive Medicine, University of Campania "Luigi Vanvitelli", 80138 Naples, Italy
| | - Ananya De
- Department of Mental and Physical Health and Preventive Medicine, University of Campania "Luigi Vanvitelli", 80138 Naples, Italy
| | - Raafiah Izhar
- Department of Mental and Physical Health and Preventive Medicine, University of Campania "Luigi Vanvitelli", 80138 Naples, Italy
| | - Marianna Abate
- Department of Precision Medicine, University of Campania "Luigi Vanvitelli", 80138 Naples, Italy
| | - Silvia Zappavigna
- Department of Precision Medicine, University of Campania "Luigi Vanvitelli", 80138 Naples, Italy
| | - Anna Capasso
- Department of Oncology, Livestrong Cancer Institutes, Dell Medical School, The University of Texas, Austin, TX 75063, USA
| | - Alessandra F Perna
- Nephrology and Dialysis Unit, Department of Translational Medical Sciences, University of Campania "Luigi Vanvitelli", 80131 Naples, Italy
| | - Antonella La Russa
- Department of Sperimental Medical and Surgical Sciences, Magna Graecia University, 88100 Catanzaro, Italy
| | | | - Michele Caraglia
- Department of Precision Medicine, University of Campania "Luigi Vanvitelli", 80138 Naples, Italy
- Biogem S.c.a.r.l. Research Institute, 83031 Ariano Irpino, Italy
| | - Mariadelina Simeoni
- Nephrology and Dialysis Unit, Department of Translational Medical Sciences, University of Campania "Luigi Vanvitelli", 80131 Naples, Italy
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6
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Qiu S, Cai Y, Yao H, Lin C, Xie Y, Tang S, Zhang A. Small molecule metabolites: discovery of biomarkers and therapeutic targets. Signal Transduct Target Ther 2023; 8:132. [PMID: 36941259 PMCID: PMC10026263 DOI: 10.1038/s41392-023-01399-3] [Citation(s) in RCA: 99] [Impact Index Per Article: 99.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 03/01/2023] [Accepted: 03/03/2023] [Indexed: 03/22/2023] Open
Abstract
Metabolic abnormalities lead to the dysfunction of metabolic pathways and metabolite accumulation or deficiency which is well-recognized hallmarks of diseases. Metabolite signatures that have close proximity to subject's phenotypic informative dimension, are useful for predicting diagnosis and prognosis of diseases as well as monitoring treatments. The lack of early biomarkers could lead to poor diagnosis and serious outcomes. Therefore, noninvasive diagnosis and monitoring methods with high specificity and selectivity are desperately needed. Small molecule metabolites-based metabolomics has become a specialized tool for metabolic biomarker and pathway analysis, for revealing possible mechanisms of human various diseases and deciphering therapeutic potentials. It could help identify functional biomarkers related to phenotypic variation and delineate biochemical pathways changes as early indicators of pathological dysfunction and damage prior to disease development. Recently, scientists have established a large number of metabolic profiles to reveal the underlying mechanisms and metabolic networks for therapeutic target exploration in biomedicine. This review summarized the metabolic analysis on the potential value of small-molecule candidate metabolites as biomarkers with clinical events, which may lead to better diagnosis, prognosis, drug screening and treatment. We also discuss challenges that need to be addressed to fuel the next wave of breakthroughs.
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Affiliation(s)
- Shi Qiu
- International Advanced Functional Omics Platform, Scientific Experiment Center, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), College of Chinese Medicine, Hainan Medical University, Xueyuan Road 3, Haikou, 571199, China
| | - Ying Cai
- Graduate School, Heilongjiang University of Chinese Medicine, Harbin, 150040, China
| | - Hong Yao
- First Affiliated Hospital, Harbin Medical University, Harbin, 150081, China
| | - Chunsheng Lin
- Second Affiliated Hospital, Heilongjiang University of Chinese Medicine, Harbin, 150001, China
| | - Yiqiang Xie
- International Advanced Functional Omics Platform, Scientific Experiment Center, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), College of Chinese Medicine, Hainan Medical University, Xueyuan Road 3, Haikou, 571199, China.
| | - Songqi Tang
- International Advanced Functional Omics Platform, Scientific Experiment Center, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), College of Chinese Medicine, Hainan Medical University, Xueyuan Road 3, Haikou, 571199, China.
| | - Aihua Zhang
- International Advanced Functional Omics Platform, Scientific Experiment Center, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), College of Chinese Medicine, Hainan Medical University, Xueyuan Road 3, Haikou, 571199, China.
- Graduate School, Heilongjiang University of Chinese Medicine, Harbin, 150040, China.
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7
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Shang QX, Kong WL, Huang WH, Xiao X, Hu WP, Yang YS, Zhang H, Yang L, Yuan Y, Chen LQ. Identification of m6a-related signature genes in esophageal squamous cell carcinoma by machine learning method. Front Genet 2023; 14:1079795. [PMID: 36733344 PMCID: PMC9886874 DOI: 10.3389/fgene.2023.1079795] [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: 10/25/2022] [Accepted: 01/03/2023] [Indexed: 01/19/2023] Open
Abstract
Background: We aimed to construct and validate the esophageal squamous cell carcinoma (ESCC)-related m6A regulators by means of machine leaning. Methods: We used ESCC RNA-seq data of 66 pairs of ESCC from West China Hospital of Sichuan University and the transcriptome data extracted from The Cancer Genome Atlas (TCGA)-ESCA database to find out the ESCC-related m6A regulators, during which, two machine learning approaches: RF (Random Forest) and SVM (Support Vector Machine) were employed to construct the model of ESCC-related m6A regulators. Calibration curves, clinical decision curves, and clinical impact curves (CIC) were used to evaluate the predictive ability and best-effort ability of the model. Finally, western blot and immunohistochemistry staining were used to assess the expression of prognostic ESCC-related m6A regulators. Results: 2 m6A regulators (YTHDF1 and HNRNPC) were found to be significantly increased in ESCC tissues after screening out through RF machine learning methods from our RNA-seq data and TCGA-ESCA database, respectively, and overlapping the results of the two clusters. A prognostic signature, consisting of YTHDF1 and HNRNPC, was constructed based on our RNA-seq data and validated on TCGA-ESCA database, which can serve as an independent prognostic predictor. Experimental validation including the western and immunohistochemistry staining were further successfully confirmed the results of bioinformatics analysis. Conclusion: We constructed prognostic ESCC-related m6A regulators and validated the model in clinical ESCC cohort as well as in ESCC tissues, which provides reasonable evidence and valuable resources for prognostic stratification and the study of potential targets for ESCC.
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Gao Y, Liu X, Pan M, Zeng D, Zhou X, Tsunoda M, Zhang Y, Xie X, Wang R, Hu W, Li L, Yang H, Song Y. Integrated untargeted fecal metabolomics and gut microbiota strategy for screening potential biomarkers associated with schizophrenia. J Psychiatr Res 2022; 156:628-638. [PMID: 36375230 DOI: 10.1016/j.jpsychires.2022.10.072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/02/2022] [Revised: 10/04/2022] [Accepted: 10/31/2022] [Indexed: 11/09/2022]
Abstract
Schizophrenia (SZ) is a serious neurodevelopmental disorder. As the etiology of SZ is complex and the pathogenesis is not thoroughly understood, the diagnosis of different subtypes still depends on the subjective judgment of doctors. Therefore, there is an urgent need to develop early objective laboratory diagnostic biomarkers to screen different subtypes of patients as early as possible, and to implement targeted prevention and precision medicine to reduce the risk of SZ and improve patients' quality of life. In this study, untargeted metabolomics and 16S rDNA sequencing were used to analyze the differences in metabolites and gut microflora among 28 patients with two types of schizophrenia and 11 healthy subjects. The results showed that the metabolome and sequencing data could effectively discriminate among paranoid schizophrenia patients, undifferentiated schizophrenia patients and healthy controls. We obtained 65 metabolites and 76 microorganisms with significant changes, and fecal metabolite composition was significantly correlated with the differential genera (|r|>0.5), indicating that there was a regulatory relationship between the gut microbiota and the host metabolites. The gut microbiome, as an objective and measurable index, showed good diagnostic value for distinguishing schizophrenia patients from healthy people, especially with a combination of several differential microorganisms, which had the best diagnostic effect (AUC>0.9). Our results are conducive to understanding the complicated metabolic changes in SZ patients and providing valuable information for the clinical diagnosis of SZ.
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Affiliation(s)
- Yuhang Gao
- Key Laboratory of Tropical Biological Resources of Ministry of Education, School of Pharmaceutical Sciences, Hainan University, Haikou, 570228, China
| | - Xianglai Liu
- Hainan Provincial Anning Hospital, Haikou, 571100, China
| | - Mingyu Pan
- Key Laboratory of Tropical Biological Resources of Ministry of Education, School of Pharmaceutical Sciences, Hainan University, Haikou, 570228, China
| | - Debin Zeng
- Hainan Provincial Anning Hospital, Haikou, 571100, China
| | - Xiying Zhou
- Key Laboratory of Tropical Biological Resources of Ministry of Education, School of Pharmaceutical Sciences, Hainan University, Haikou, 570228, China
| | - Makoto Tsunoda
- Graduate School of Pharmaceutical Sciences, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan
| | - Yingxia Zhang
- Key Laboratory of Tropical Biological Resources of Ministry of Education, School of Pharmaceutical Sciences, Hainan University, Haikou, 570228, China
| | - Xi Xie
- Key Laboratory of Tropical Biological Resources of Ministry of Education, School of Pharmaceutical Sciences, Hainan University, Haikou, 570228, China
| | - Rong Wang
- Key Laboratory of Tropical Biological Resources of Ministry of Education, School of Pharmaceutical Sciences, Hainan University, Haikou, 570228, China
| | - Wenting Hu
- Key Laboratory of Tropical Biological Resources of Ministry of Education, School of Pharmaceutical Sciences, Hainan University, Haikou, 570228, China
| | - Lushuang Li
- Key Laboratory of Tropical Biological Resources of Ministry of Education, School of Pharmaceutical Sciences, Hainan University, Haikou, 570228, China.
| | - Haimei Yang
- Key Laboratory of Tropical Biological Resources of Ministry of Education, School of Pharmaceutical Sciences, Hainan University, Haikou, 570228, China.
| | - Yanting Song
- Key Laboratory of Tropical Biological Resources of Ministry of Education, School of Pharmaceutical Sciences, Hainan University, Haikou, 570228, China.
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9
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Yang C, Zhou S, Zhu J, Sheng H, Mao W, Fu Z, Chen Z. Plasma Lipid-based Machine Learning Models Provides a Potential Diagnostic Tool for Colorectal Cancer Patients. Clin Chim Acta 2022; 536:191-199. [PMID: 36191612 DOI: 10.1016/j.cca.2022.09.002] [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: 04/23/2022] [Revised: 08/23/2022] [Accepted: 09/01/2022] [Indexed: 11/29/2022]
Abstract
Colorectal cancer is the second leading cause of cancer-related death across the world. So far, screening methods for colorectal cancer are limited to blood test, imaging test, and digital rectal examination, that are either invasive or ineffective. So, this study aims to explore novel, more convenient and effective diagnostic methods for colorectal cancer. First, the experiment cohort was randomly split to train set and test set, and LC-MS-based plasma lipidomics was applied to identify lipid features in colorectal cancer. Second, univariate and multivariate analyses were performed to screen for significantly differentially expressed lipids. Third, single-lipid-based ROC analysis and multiple-lipid-based machine learning modelling were conducted to assess differential lipids' diagnostic performance. Lastly, survival analyses were used to evaluate lipids' prognostic values. In total, 41 differential lipids were screened out, 10 were upregulated and 31 were downregulated in CRC. Only CerP(d15:0_22:0+O) showed fine predictive accuracy in single-lipid-base ROC analysis. Among the four machine learning models, SVM showed best predictive performance with accuracy (in predicting test set) of 1.0000 (95%CI: 0.8806, 1.0000), that can be reached by modelling with only 14 lipids. Four lipids had significant prognostic values, that were TG(11:0_18:0_18:0) (HR: 0.34), TG(18:0_18:0_18:1) (HR: 0.34), PC(22:1_12:3) (HR: 2.22), LPC(17:0) (HR: 3.16). In conclusion, this study discovered novel lipid features that has potential diagnostic and prognostic values, and showed combination of plasma lipidomics and machine learning modelling could have outstanding diagnostic performance and may serve as a convenient and more accessible way to aid clinical diagnosis of colorectal cancer.
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Affiliation(s)
- Chenxi Yang
- The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, Zhejiang, China; Institute of Cancer and Basic Medicine (IBMC), Chinese Academy of Sciences, Hangzhou, Zhejiang, China
| | - Sicheng Zhou
- The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, Zhejiang, China; Institute of Cancer and Basic Medicine (IBMC), Chinese Academy of Sciences, Hangzhou, Zhejiang, China
| | - Jing Zhu
- The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, Zhejiang, China; Institute of Cancer and Basic Medicine (IBMC), Chinese Academy of Sciences, Hangzhou, Zhejiang, China
| | - Huaying Sheng
- The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, Zhejiang, China; Institute of Cancer and Basic Medicine (IBMC), Chinese Academy of Sciences, Hangzhou, Zhejiang, China
| | - Weimin Mao
- The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, Zhejiang, China; Institute of Cancer and Basic Medicine (IBMC), Chinese Academy of Sciences, Hangzhou, Zhejiang, China; Zhejiang Key Laboratory of Diagnosis & Treatment Technology on Thoracic Oncology (Lung and Esophagus), Hangzhou, Zhejiang, China
| | - Zhixuan Fu
- The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, Zhejiang, China; Institute of Cancer and Basic Medicine (IBMC), Chinese Academy of Sciences, Hangzhou, Zhejiang, China
| | - Zhongjian Chen
- The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, Zhejiang, China; Institute of Cancer and Basic Medicine (IBMC), Chinese Academy of Sciences, Hangzhou, Zhejiang, China; Zhejiang Key Laboratory of Diagnosis & Treatment Technology on Thoracic Oncology (Lung and Esophagus), Hangzhou, Zhejiang, China.
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Tu JX, Lin XT, Ye HQ, Yang SL, Deng LF, Zhu RL, Wu L, Zhang XQ. Global research trends of artificial intelligence applied in esophageal carcinoma: A bibliometric analysis (2000-2022) via CiteSpace and VOSviewer. Front Oncol 2022; 12:972357. [PMID: 36091151 PMCID: PMC9453500 DOI: 10.3389/fonc.2022.972357] [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: 06/18/2022] [Accepted: 07/29/2022] [Indexed: 12/09/2022] Open
Abstract
ObjectiveUsing visual bibliometric analysis, the application and development of artificial intelligence in clinical esophageal cancer are summarized, and the research progress, hotspots, and emerging trends of artificial intelligence are elucidated.MethodsOn April 7th, 2022, articles and reviews regarding the application of AI in esophageal cancer, published between 2000 and 2022 were chosen from the Web of Science Core Collection. To conduct co-authorship, co-citation, and co-occurrence analysis of countries, institutions, authors, references, and keywords in this field, VOSviewer (version 1.6.18), CiteSpace (version 5.8.R3), Microsoft Excel 2019, R 4.2, an online bibliometric platform (http://bibliometric.com/) and an online browser plugin (https://www.altmetric.com/) were used.ResultsA total of 918 papers were included, with 23,490 citations. 5,979 authors, 39,962 co-cited authors, and 42,992 co-cited papers were identified in the study. Most publications were from China (317). In terms of the H-index (45) and citations (9925), the United States topped the list. The journal “New England Journal of Medicine” of Medicine, General & Internal (IF = 91.25) published the most studies on this topic. The University of Amsterdam had the largest number of publications among all institutions. The past 22 years of research can be broadly divided into two periods. The 2000 to 2016 research period focused on the classification, identification and comparison of esophageal cancer. Recently (2017-2022), the application of artificial intelligence lies in endoscopy, diagnosis, and precision therapy, which have become the frontiers of this field. It is expected that closely esophageal cancer clinical measures based on big data analysis and related to precision will become the research hotspot in the future.ConclusionsAn increasing number of scholars are devoted to artificial intelligence-related esophageal cancer research. The research field of artificial intelligence in esophageal cancer has entered a new stage. In the future, there is a need to continue to strengthen cooperation between countries and institutions. Improving the diagnostic accuracy of esophageal imaging, big data-based treatment and prognosis prediction through deep learning technology will be the continuing focus of research. The application of AI in esophageal cancer still has many challenges to overcome before it can be utilized.
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Affiliation(s)
- Jia-xin Tu
- School of Public Health, Nanchang University, Nanchang, China
- Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, Nanchang, China
| | - Xue-ting Lin
- School of Public Health, Nanchang University, Nanchang, China
- Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, Nanchang, China
| | - Hui-qing Ye
- School of Public Health, Nanchang University, Nanchang, China
| | - Shan-lan Yang
- School of Public Health, Nanchang University, Nanchang, China
- Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, Nanchang, China
| | - Li-fang Deng
- School of Public Health, Nanchang University, Nanchang, China
- Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, Nanchang, China
| | - Ruo-ling Zhu
- School of Public Health, Nanchang University, Nanchang, China
- Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, Nanchang, China
| | - Lei Wu
- School of Public Health, Nanchang University, Nanchang, China
- Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, Nanchang, China
- *Correspondence: Lei Wu, ; Xiao-qiang Zhang,
| | - Xiao-qiang Zhang
- Department of Thoracic Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, China
- *Correspondence: Lei Wu, ; Xiao-qiang Zhang,
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