Identification of differentially expressed mRNAs as novel predictive biomarkers for gastric cancer diagnosis and prognosis

ARTICLE HIGHLIGHTS

Research background

Being one of the most prevalent and lethal cancers globally, gastric cancer (GC) contributes significantly to the overall cancer burden. Due to chemoresistance and metastasis, only 5%–20% of patients with advanced GC survive for five years after diagnosis, and most patients are not able to receive an early diagnosis. The findings of this study shed light on the fundamental mechanisms of GC pathogenesis and has identified new mRNA biomarkers and targets for GC.

Research motivation

The current study was designed to explore the molecular mechanisms involved in GC progression using the bioinformatics methods. We have identified five distinct hub genes that were associated with the development of GC. This study has provided several potential targets regarding GC, which could form the basis of novel strategy to diagnose and treat GC.

Research objectives

The main objectives of this study were to identify the key candidate genes linked with development of GC and to determine the potential pathogenic mechanisms by using integrated bioinformatics analysis. Five distinct hub genes (RUNX2, SPI1, LOX, FBN1 and GPT) were identified as novel biomarkers and targets for GC diagnosis and treatment. The possible effect of GPT on the malignant phenotypes of GC cells and the possible correlation between GPT expression as well as the clinical and pathological features of GC patients were also analyzed. Our results provide a sound theoretical basis for the pathogenesis, clinical diagnosis and therapeutic targets of GC.

Research methods

The GSE183136 dataset that contains 135 GC samples was downloaded from the Gene Expression Omnibus database, and differentially expressed genes (DEGs) were then identified using the limma package in R software. Thereafter, gene ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes analyses were performed for enrichment analyses using the clusterProfile package in R software. Subsequently, the protein-protein interaction networks of DEGs were established by STRING and visualized by Cytoscape software. The key hub genes were identified as the overlapping genes that appeared in the cohort of above mentioned DEGs as well the cohort of the DEGs obtained from the GEPIA database Following that, the expression levels of these hub genes and their association with prognosis in GC patients were obtained from the GEPIA database and Kaplan-Meier curves. In addition, cell counting kit-8 assays, flow cytometry as well as transwell assays and a retrospective analysis on 70 GC patients were performed to detect the potential effect of GPT on cell viability, cell cycle, migration and invasion. The association between the expression level of GPT and the clinical and pathological features of GC patients was also examined.

Research results

We have identified 250 downregulated and 401 upregulated DEGs. After a comprehensive analysis, five different hub genes (RUNX2, SPI1, LOX, FBN1 and GPT) were selected. In this study, we have mainly focused on GPT, and observed that GPT expression was significantly associated with age, lymph node metastasis, pathological staging and distant metastasis in GC patients. Moreover, based on in vitro analysis, GPT upregulation was able to suppress the proliferative, migrative and invasive capabilities of GC cells. Our results might provide potential targets for GC diagnosis and treatment. The problems that remain to be solved include: (1) Additional studies are required to examine the potential effect of DEGs and hub genes on GC development; (2) in vivo experiments were not performed in this study, and the impact of GPT overexpression in GC xenograft models needs further investigation; and (3) the expression of the hub genes in larger clinical samples to establish the clinical relevance and prognosis are required.

Research conclusions

This study has identified several hub genes related to GC development. The methods used in this study have been previously described.

Research perspectives

Further investigation is required to detect the effect of GPT on GC development.