Open AccessIdentification and prognostic value of metabolism-related genes in gastric cancer
Author(s) -
Wen Fang,
Jiani Huang,
Xiaona Lu,
Wenjie Huang,
Yulan Wang,
Yingfeng Bai,
Shuai Ruan,
Suping Gu,
Xiaoxue Chen,
Ping Shu
Publication year - 2020
Publication title -
aging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 90
ISSN - 1945-4589
DOI - 10.18632/aging.103838
Subject(s) - proportional hazards model , univariate , lasso (programming language) , gene , transcriptome , cancer , multivariate statistics , biology , computational biology , logistic regression , oncology , medicine , bioinformatics , genetics , gene expression , computer science , machine learning , world wide web
Gastric cancer (GC) is one of the most commonly occurring cancers, and metabolism-related genes (MRGs) are associated with its development. Transcriptome data and the relevant clinical data were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus databases, and we identified 194 MRGs differentially expressed between GC and adjacent nontumor tissues. Through univariate Cox and lasso regression analyses we identified 13 potential prognostic differentially expressed MRGs (PDEMRGs). These PDEMRGs (CKMT2, ME1, GSTA2, ASAH1, GGT5, RDH12, NNMT, POLR1A, ACYP1, GLA, OPLAH, DCK, and POLD3) were used to build a Cox regression risk model to predict the prognosis of GC patients. Further univariate and multivariate Cox regression analyses showed that this model could serve as an independent prognostic parameter. Gene Set Enrichment Analysis showed significant enrichment pathways that could potentially contribute to pathogenesis. This model also revealed the probability of genetic alterations of PDEMRGs. We have thus identified a valuable metabolic model for predicting the prognosis of GC patients. The PDEMRGs in this model reflect the dysregulated metabolic microenvironment of GC and provide useful noninvasive biomarkers.