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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

Authors :
Tongzuan Chen
Liqian zhao
Junbo Chen
Gaowei Jin
Qianying Huang
Ming Zhu
Ruixia Dai
Zhengxi Yuan
Junshuo Chen
Mosheng Tang
Tongke Chen
Xiaokun Lin
Weiming Ai
Liang Wu
Xiangjian Chen
Le Qin
Source :
Frontiers in Genetics, Vol 14 (2023)
Publication Year :
2023
Publisher :
Frontiers Media S.A., 2023.

Abstract

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.

Details

Language :
English
ISSN :
16648021
Volume :
14
Database :
Directory of Open Access Journals
Journal :
Frontiers in Genetics
Publication Type :
Academic Journal
Accession number :
edsdoj.ff39e6f03fbc44b28b4cfe87aea2a5a8
Document Type :
article
Full Text :
https://doi.org/10.3389/fgene.2023.1094838