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Research Paper|Volume 12, Issue 17|pp 17647—17661

Identification and prognostic value of metabolism-related genes in gastric cancer

Fang Wen1,2,3, Jiani Huang1,5, Xiaona Lu1,2,3, Wenjie Huang1,2,3, Yulan Wang1,4, Yingfeng Bai1,5, Shuai Ruan1,2,3, Suping Gu1,2,3, Xiaoxue Chen1,2,3, Peng Shu1,2,3
  • 1Nanjing University of Chinese Medicine, Nanjing 210023, Jiangsu Province, China
  • 2Department of Oncology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing 210029, Jiangsu Province, China
  • 3Department of Oncology, Jiangsu Province Hospital of Chinese Medicine, Nanjing 210029, Jiangsu Province, China
  • 4Department of Hematology, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing 210029, Jiangsu Province, China
  • 5College of Traditional Chinese Medicine, College of Integrated Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing 210023, China
Received: May 19, 2020Accepted: July 14, 2020Published: September 11, 2020
https://doi.org/10.18632/aging.103838

Copyright: © 2020 Wen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

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.