In silico development and validation of a novel glucose and lipid metabolism-related gene signature in gastric cancer

作者：Yuan Yang, Zhaofeng Chen, Lingshan Zhou, Guozhi Wu, Xiaomei Ma, Ya Zheng, Min Liu, Yuping Wang, Rui Ji, Qinghong Guo, Yongning Zhou · 发表于：Translational Cancer Research · 年份：2022 · DOI：10.21037/tcr-22-168 · 被引用次数：14 · 研究领域：Ferroptosis and cancer prognosis、Cancer, Lipids, and Metabolism、Immune cells in cancer

Background: Abnormal glucose and lipid metabolism plays a critical role in gastric carcinogenesis and development. Hence, we presented a systematic analysis of glucose and lipid metabolism-related genes to explore their function and prognostic value in gastric cancer (GC). Methods: The consensus clustering algorithm was used to identify the molecular subtypes based on glucose and lipid metabolism-related genes. Subsequently, cox regression analysis and lasso regression analysis were utilized to establish a risk prediction model. A clinical nomogram was constructed to assist prognosis assessment. In addition, ESTIMATE and single-sample gene set enrichment analysis (ssGSEA) algorithms were performed to evaluate the immune infiltration of the metabolic model, and GSEA was used for enrichment analysis of the metabolic signature. Finally, we explored the association between the risk model and anti-cancer therapy for the purpose of clinical application for GC treatment. Results: GC samples were divided into 2 subtypes based on glucose and lipid metabolism-related genes, patients in cluster 2 had a better overall survival (OS) than those in cluster 1. Fifty-two genes were identified by univariable regression analysis. Finally, a 13-gene metabolic signature (CACNA1H, CHST1, IGFBP3, NASP, STC1, VCAN, NUP205, NUP43, PGM2L1, CAV1, ELOVL4, PRKAA2, TNFAIP8L3) was successfully constructed that demonstrated good performance in different datasets, as well as an independent hazardous factor f...