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

作者：Zerong Cai, Wen Wang, Di Chen, Haojie Chen, Yan Hu, Xiao-Jing Luo, Yi-Ting Wang, Yi‐Qian Pan, Hai‐Yu Mo, Shuyu Luo, Kun Lun Liao, Zhao-Lei Zeng, Shan-Shan Li, Xin‐Yuan Guan, Xinjuan Fan, Hai‐long Piao, Rui‐Hua Xu, Huai‐Qiang Ju · 发表于：EMBO Molecular Medicine · 年份：2024 · DOI：10.1038/s44321-024-00169-0 · 被引用次数：15 · 研究领域：Cancer, Lipids, and Metabolism、Metabolomics and Mass Spectrometry Studies、Cancer-related molecular mechanisms research

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.