Identification of three metabolic subtypes in gastric cancer and the construction of a metabolic pathway-based risk model that predicts the overall survival of GC patients

Frontiers in Genetics

Genetics

Junshuo Chen¹  Tongzuan Chen²  Xiangjian Chen²  Ming Zhu²  Liqian zhao³  Liang Wu⁴  Le Qin⁵  Tongke Chen⁶  Weiming Ai⁶  Qianying Huang⁷  Junbo Chen⁷  Zhengxi Yuan⁷  Mosheng Tang⁸  Gaowei Jin⁹  Ruixia Dai⁹  Xiaokun Lin^1,10 

[1] College of International Education, Henan University, Kaifeng, Henan, China;Department of Gastrointestinal Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China;Department of Neurosurgery, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China;Department of Pathology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China;Department of Pediatric Surgery, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China;Laboratory Animal Centre, Wenzhou Medical University, Wenzhou, Zhejiang, China;School of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China;Scientific Research Laboratory, Lishui City People’s Hospital, Lishui, Zhejiang, China;Second School of Clinical Medicine, Wenzhou Medical University, Wenzhou, Zhejiang, China;The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China;

关键词: gastric cancer;    prognosis;    metabolism;    molecular subtypes;    multi-omics;   

DOI  :  10.3389/fgene.2023.1094838

 received in 2022-11-10, accepted in 2023-01-31,  发布年份 2023

来源: Frontiers

【 摘 要 】

Gastric cancer (GC) is highly heterogeneous and GC patients have low overall survival rates. It is also challenging to predict the prognosis of GC patients. This is partly because little is known about the prognosis-related metabolic pathways in this disease. Hence, our objective was to identify GC subtypes and genes related to prognosis, based on changes in the activity of core metabolic pathways in GC tumor samples. Differences in the activity of metabolic pathways in GC patients were analyzed using Gene Set Variation Analysis (GSVA), leading to the identification of three clinical subtypes by non-negative matrix factorization (NMF). Based on our analysis, subtype 1 showed the best prognosis while subtype 3 exhibited the worst prognosis. Interestingly, we observed marked differences in gene expression between the three subtypes, through which we identified a new evolutionary driver gene, CNBD1. Furthermore, we used 11 metabolism-associated genes identified by LASSO and random forest algorithms to construct a prognostic model and verified our results using qRT-PCR (five matched clinical tissues of GC patients). This model was found to be both effective and robust in the GSE84437 and GSE26253 cohorts, and the results from multivariate Cox regression analyses confirmed that the 11-gene signature was an independent prognostic predictor (p < 0.0001, HR = 2.8, 95% CI 2.1–3.7). The signature was found to be relevant to the infiltration of tumor-associated immune cells. In conclusion, our work identified significant GC prognosis-related metabolic pathways in different GC subtypes and provided new insights into GC-subtype prognostic assessment.

【 授权许可】

Unknown   
Copyright © 2023 Chen, zhao, Chen, Jin, Huang, Zhu, Dai, Yuan, Chen, Tang, Chen, Lin, Ai, Wu, Chen and Qin.