Published online Mar 15, 2019. doi: 10.4251/wjgo.v11.i3.181
Peer-review started: November 15, 2018
First decision: December 7, 2018
Revised: December 17, 2018
Accepted: December 23, 2018
Article in press: December 24, 2018
Published online: March 15, 2019
Gastric cancer (GC) leads to worldwide cancer mortality, especially in developing countries. Recently, The Cancer Genome Atlas (TCGA) Research Group proposed an integrative genomic analysis, dividing gastric cancer into four subtypes—Epstein Barr Virus positive, microsatellite unstable, chromosomally instable (CIN), and genomically stable, based on gene expression profiling of the exome sequences, copy-number alterations, gene expression, DNA methylation, and protein activities. However, the CIN status of GC is still vaguely characterized and lacking the valuable easy-to-use CIN markers to diagnosis in molecular and histological detection. Metabolomics, which study the result of the interaction of the biosystem’s genome with its environment and detect the end product of gene expression, offers the opportunity to understand the complex molecular mechanisms and to identify the diagnostic biomarkers of human GC. Although mass spectrometry (MS) and nuclear magnetic resonance system have been used widely to investigate metabolic changes in biological processes, most of those findings were limited to focus on water-soluble compounds, and volatile metabolites. Perturbation of lipid metabolism would also contribute to observing in the cancer progression by detecting the activity of the dysregulated core enzymes in lipid pathways and the global lipid metabolic alterations in cancer metastasis. Global lipidomics provides the most details detection and qualification of the cellular lipids in systems biology. The background, present status, and significance of the study should be described in detail.
In our previous study, metabolomic profiles of GC tumors and the adjacent healthy tissue are distinct, and altered pathways involving amino acid metabolism, glyoxylate and dicarboxylate metabolism. In this study, we hypothesize that lipidomic alternations reflect the CIN or non-CIN status of GC to provide the exploration of the correlation the lipidomic metabolites of GC with its CIN status.
The main objectives aimed to discover the numerous biomarkers from lipidomic studies and explore the associations of CIN with its downstream lipidomics profiles.
Tumor samples were categorized as CIN or non-CIN type by the TCGA system. We extracted the genomic DNA, and quantified them for genomic analysis. In total 409 leading oncogenes and tumor suppressor genes in the GC tumor tissue were sequenced. For lipidomic metabolite research, tissue extraction through Folch method and performed profiling using an LC/MS system. Data processing and statistical analysis for lipidomic analysis to discover the potential metabolites using MarkerLynx XS software, SIMCA-P+ and MetaboAnalyst 4.0.
This study demonstrated the Lipidomic profiling of GC tumors showed distinct profiles in glycerolipid, glycerophospholipid and sphingolipid compared with adjacent non-cancerous tissues. The glycerophospholipid levels (phosphocholine, phosphatidylethanolamine, and phosphatidylinositol) demonstrated a 1.4- to 2.3-fold increase in the CIN group, compared with the non-CIN group (P < 0.05). Alteration of the glycerolipid and glycerophospholipid pathways involved throughout the evolutions of GC formation toward chromosomal instability.
Lipidomics profiles of GC tumors were distinct against the adjacent non-cancerous tissue. The CIN status of GC primarily associated with the downstream lipidomics in glycerophospholipid pathway.
Our study provided the genomic classification method and discovered lipidomic information to correlate with its CIN status. To validate our initial findings, more sample collections with longer follow up times will be considered.