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.
- GASTRIC CANCER
Data availability statement
Data are available on reasonable request. Additional data (beyond those included in the main text and Supplementary Information) are available from the corresponding author upon request.
This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.
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ZX, YH and CH are joint first authors.
ZX, YH and CH contributed equally.
Contributors XC, KQ and LY conceived the study and acquired the funding. ZX, YH and CH carried out clinical research, collected clinical samples, analysed clinical data, and wrote articles. LD, YD, JQ, GC, HL, PZ, WH, XW, MX, PW, CH, LY, YZ, JX, JC and QW participated in clinical samples collection. WL, RW, SY, JW, JC and JZ contributed to the data analysis and material characterisation. All authors have read and approved the final manuscript.
Funding This study was supported by National Key R&D Program of China (2021YFA0910100), National Natural Science Foundation of China (82074245, 81973634, 82204828, and 81971771), Medical-Engineering Joint Funds of Shanghai Jiao Tong University (YG2021ZD09, YG2022QN107, YG2023ZD08), Zhejiang Provincial Research Center for Upper Gastrointestinal Tract Cancer (JBZX-202006), Natural Science Foundation of Zhejiang Province (HDMY22H160008), Chinese Postdoctoral Science Foundation (2022M713203), Shanghai Institutions of Higher Learning (2021-01-07-00-02-E00083), Innovative Research Team of High-Level Local Universities in Shanghai (SHSMU-ZDCX20210700), Innovation Research Plan by the Shanghai Municipal Education Commission (ZXWF082101) and National Research Center for Translational Medicine Shanghai (TMSK-2021-124, NRCTM(SH)-2021-06).
Competing interests The authors declare competing financial interests. The authors have filed patents for both the technology and the use of the technology to detect biosamples.
Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.
Provenance and peer review Not commissioned; externally peer reviewed.
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