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.

中文翻译：

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通过高性能血脂组指纹图谱预测胃癌的诊断和预后。


早期检测对于改善胃癌 （GC） 的预后是必要的，但仍然具有挑战性。液体活检与机器学习相结合将为 GC 的诊断策略提供新的见解。脂质代谢重编程在肿瘤的发生和发展中起着至关重要的作用。在这里，我们整合了三个队列 （n = 944） 的脂质组学数据，以开发 GC 的脂质代谢景观。我们通过机器学习进一步构建了血清脂质代谢特征 （SLMS），在区分 GC 患者和健康供体方面表现出出色的表现。值得注意的是，SLMS 在诊断早期 GC 方面也具有很高的疗效。此外，通过对 GC 患者的脂质代谢基质进行无监督共识聚类分析，我们生成了总生存期差异显著的胃癌预后亚型 （GCPSs）。此外，多组学分析证明了 GC 组织中的脂质代谢紊乱，结果与 GC 血清中的脂质代谢紊乱部分一致。总的来说，本研究揭示了一种基于血脂代谢指纹的液体活检诊断 GC 的创新策略。