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Efficient plasma metabolic fingerprinting as a novel tool for diagnosis and prognosis of gastric cancer: a large-scale, multicentre study.
Xu, Zhiyuan; Huang, Yida; Hu, Can; Du, Lingbin; Du, Yi-An; Zhang, Yanqiang; Qin, Jiangjiang; Liu, Wanshan; Wang, Ruimin; Yang, Shouzhi; Wu, Jiao; Cao, Jing; Zhang, Juxiang; Chen, Gui-Ping; Lv, Hang; Zhao, Ping; He, Weiyang; Wang, Xiaoliang; Xu, Min; Wang, Pingfang; Hong, Chuanshen; Yang, Li-Tao; Xu, Jingli; Chen, Jiahui; Wei, Qing; Zhang, Ruolan; Yuan, Li; Qian, Kun; Cheng, Xiangdong.
Afiliação
  • Xu Z; Department of Gastric Surgery, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, China.
  • Huang Y; Key Laboratory of Prevention, Diagnosis and Therapy of Upper Gastrointestinal Cancer of Zhejiang Province, Hangzhou, China.
  • Hu C; State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Institute of Medical Robotics and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, China.
  • Du L; Department of Obstetrics and Gynecology, Shanghai Key Laboratory of Gynecologic Oncology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
  • Du YA; Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
  • Zhang Y; Department of Gastric Surgery, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, China.
  • Qin J; Key Laboratory of Prevention, Diagnosis and Therapy of Upper Gastrointestinal Cancer of Zhejiang Province, Hangzhou, China.
  • Liu W; Office of Cancer Center, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, China.
  • Wang R; Department of Gastric Surgery, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, China.
  • Yang S; Department of Gastric Surgery, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, China.
  • Wu J; Department of Gastric Surgery, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, China.
  • Cao J; State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Institute of Medical Robotics and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, China.
  • Zhang J; Department of Obstetrics and Gynecology, Shanghai Key Laboratory of Gynecologic Oncology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
  • Chen GP; Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
  • Lv H; State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Institute of Medical Robotics and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, China.
  • Zhao P; Department of Obstetrics and Gynecology, Shanghai Key Laboratory of Gynecologic Oncology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
  • He W; Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
  • Wang X; State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Institute of Medical Robotics and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, China.
  • Xu M; Department of Obstetrics and Gynecology, Shanghai Key Laboratory of Gynecologic Oncology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
  • Wang P; Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
  • Hong C; State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Institute of Medical Robotics and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, China.
  • Yang LT; Department of Obstetrics and Gynecology, Shanghai Key Laboratory of Gynecologic Oncology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
  • Xu J; Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
  • Chen J; State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Institute of Medical Robotics and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, China.
  • Wei Q; Department of Obstetrics and Gynecology, Shanghai Key Laboratory of Gynecologic Oncology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
  • Zhang R; Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
  • Yuan L; State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Institute of Medical Robotics and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, China.
  • Qian K; Department of Obstetrics and Gynecology, Shanghai Key Laboratory of Gynecologic Oncology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
  • Cheng X; Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Gut ; 72(11): 2051-2067, 2023 Nov.
Article em En | MEDLINE | ID: mdl-37460165
ABSTRACT

OBJECTIVE:

Metabolic biomarkers are expected to decode the phenotype of gastric cancer (GC) and lead to high-performance blood tests towards GC diagnosis and prognosis. We attempted to develop diagnostic and prognostic models for GC based on plasma metabolic information.

DESIGN:

We conducted a large-scale, multicentre study comprising 1944 participants from 7 centres in retrospective cohort and 264 participants in prospective cohort. Discovery and verification phases of diagnostic and prognostic models were conducted in retrospective cohort through machine learning and Cox regression of plasma metabolic fingerprints (PMFs) obtained by nanoparticle-enhanced laser desorption/ionisation-mass spectrometry (NPELDI-MS). Furthermore, the developed diagnostic model was validated in prospective cohort by both NPELDI-MS and ultra-performance liquid chromatography-MS (UPLC-MS).

RESULTS:

We demonstrated the high throughput, desirable reproducibility and limited centre-specific effects of PMFs obtained through NPELDI-MS. In retrospective cohort, we achieved diagnostic performance with areas under curves (AUCs) of 0.862-0.988 in the discovery (n=1157 from 5 centres) and independent external verification dataset (n=787 from another 2 centres), through 5 different machine learning of PMFs, including neural network, ridge regression, lasso regression, support vector machine and random forest. Further, a metabolic panel consisting of 21 metabolites was constructed and identified for GC diagnosis with AUCs of 0.921-0.971 and 0.907-0.940 in the discovery and verification dataset, respectively. In the prospective study (n=264 from lead centre), both NPELDI-MS and UPLC-MS were applied to detect and validate the metabolic panel, and the diagnostic AUCs were 0.855-0.918 and 0.856-0.916, respectively. Moreover, we constructed a prognosis scoring system for GC in retrospective cohort, which can effectively predict the survival of GC patients.

CONCLUSION:

We developed and validated diagnostic and prognostic models for GC, which also contribute to advanced metabolic analysis towards diseases, including but not limited to GC.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2023 Tipo de documento: Article
Texto completo
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XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2023 Tipo de documento: Article