Diagnosis and prognosis prediction of gastric cancer by high-performance serum lipidome fingerprints

Early detection is warranted to improve prognosis of gastric cancer (GC) but remains challenging. Liquid biopsy combined with machine learning will provide new insights into diagnostic strategies of GC. Lipid metabolism reprogramming plays a crucial role in the initiation and development of tumors. Here, we integrated the lipidomics data of three cohorts ( n = 944) to develop the lipid metabolic landscape of GC. We further constructed the serum lipid metabolic signature (SLMS) by machine learning, which showed great performance in distinguishing GC patients from healthy donors. Notably, the SLMS also held high efficacy in the diagnosis of early-stage GC. Besides, by performing unsupervised consensus clustering analysis on the lipid metabolic matrix of patients with GC, we generated the gastric cancer prognostic subtypes (GCPSs) with significantly different overall survival. Furthermore, the lipid metabolic disturbance in GC tissues was demonstrated by multi-omics analysis, which showed partially consistent with that in GC serums. Collectively, this study revealed an innovative strategy of liquid biopsy for the diagnosis of GC on the basis of the serum lipid metabolic fingerprints. Synopsis Novel liquid biopsy tools allowing for diagnosis and prognostic prediction for gastric cancer (GC) patients are urgently needed. In this study, the serum lipid metabolic signature and gastric cancer prognostic subtypes were identified for early detection and risk stratification of GC, respectively. The serum lipid profile of GC patients was different from that of healthy donors. The proposed serum lipid metabolic signature (SLMS) could act as a screening tool to recognize potential patients with gastric cancer. The gastric cancer prognostic subtypes (GCPSs) could be applied in evaluating the survival of patients with gastric cancer and making postoperative treatment regimen as an assistant to pTNM staging. Multi-omics analysis revealed the lipid disturbance in gastric cancer tissues and demonstrated the reliability of the SLMS and GCPS. Novel liquid biopsy tools allowing for diagnosis and prognostic prediction for gastric cancer (GC) patients are urgently needed. In this study, the serum lipid metabolic signature and gastric cancer prognostic subtypes were identified for early detection and risk stratification of GC, respectively.