Efficient plasma metabolic fingerprinting as a novel tool for diagnosis and prognosis of gastric cancer: a large-scale, multicentre study

Abstract

ObjectiveMetabolic 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.DesignWe 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).ResultsWe 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.ConclusionWe developed and validated diagnostic and prognostic models for GC, which also contribute to advanced metabolic analysis towards diseases, including but not limited to GC.

Plasmonic Alloys Enhanced Metabolic Fingerprints for the Diagnosis of COPD and Exacerbations

“…A probability of close to 1 implied a high level of certainty in the model that the sample belonged to class 1 (patient). In contrast, a probability close to 0 indicated a model inclination toward classifying the sample as class 0 (healthy control). , ROC curves differentiate (i) HC from SCOPD and (j) HC from AECOPD for the discovery (blue) and validation (red) cohorts.…”

A large gastric splenosis mimicking gastrointestinal stromal tumor: A case report and literature review

“…On a contrast-enhanced CT scan, the lesion exhibited uniform enhancement in the three stages: Prominent enhancement in the arterial stage, sustained enhancement in the portal vein stage and a slight decrease in the delayed stage. Splenosis can manifest in various organs, such as the lung, pancreas, liver, kidney and gastrointestinal tract, necessitating differentiation from the primary tumor organ ( 40 , 41 ). On contrast-enhanced CT scans, splenosis often lacks the speck enhancement characteristic of normal splenic tissue in the arterial stage.…”

Current status of early gastric cancer screening research

“…Guo et al have found that the lncRNA signature from circulating exosomes can be used as a biomarker for early detection of GC 18 . In our center, we have constructed a model based on plasma metabolic fingerprints for GC diagnosis, which has shown excellent diagnostic performance in both retrospective and prospective cohorts 19 . However, although liquid biopsies in the field of GC screening continue to advance, the challenges associated with their application should not be underestimated.…”