Published online Sep 7, 2018. doi: 10.3748/wjg.v24.i33.3760
Peer-review started: May 23, 2018
First decision: June 13, 2018
Revised: June 27, 2018
Accepted: July 16, 2018
Article in press: July 16, 2018
Published online: September 7, 2018
Gastric cancer (GC) is one of the most common malignancies. GC can be histologically classiﬁed using the Lauren classification system, which divides GC into diffuse and intestinal subtypes. The Cancer Genome Atlas Research Group (TCGA) has developed molecular classification systems based on gene expression profiling. Metabolomics may offer practical solutions to the traditional methods for GC detection and treatment. We hypothesize that metabolic alternations reflect the chromosomal instability (CIN) or genomic stability status of GC and aim to study the comprehensive metabolomic profiles of GC and correlate them with its CIN status.
The numerous biomarkers discovered from metabolomic studies may play a noteworthy role in GC with regard to early-stage detection, diagnosis, prognosis, drug development, and chemosensitivity predictions, but the evidence of metabolomics’ association with CIN status remains lacking.
The aim of our study was to explore the correlation of metabolomics profiles of GC and its CIN status.
Based on 409 oncogenes and tumor suppressor genes sequenced, 19 GC patients were classified as CIN and non-CIN type by TCGA. The aqueous metabolites of the GC tumor and its surrounding adjacent healthy tissues were identified through liquid chromatography-mass spectrometry.
GC tumors demonstrated significantly higher aspartic acid, citicoline, glutamic acid, oxidized glutathione, succinyladenosine, and uridine diphosphate-N-acetylglucosamine levels, but significantly lower butyrylcarnitine, glutathione hydroxyhexanoycarnitine, inosinic acid, isovalerylcarnitine, and threonine levels compared to the adjacent healthy tissues. CIN tumors contained significantly higher phosphocholine and uridine 5’-monophosphate levels but significantly lower beta-citryl-L-glutamic acid level than did non-CIN tumors. CIN GC tumors demonstrated additional altered pathways involving alanine, aspartate, and glutamate metabolism, glyoxylate and dicarboxylate metabolism, histidine metabolism, and phenylalanine, tyrosine, and tryptophan biosynthesis.
Metabolomic profiles of GC tumors and the adjacent healthy tissue are distinct, and the CIN status is associated with downstream metabolic alterations in GC.
The combination of classification method of gene molecules and a metabolomics method may reveal that metabolic information can be used to accurately classify tumors. These findings on metabolomics profiling based on CIN status have translational potential for biomarker discovery and novel therapeutic development.