Abstract
Gastric adenocarcinoma (GAC) is the most frequent type of stomach cancer, characterized by high heterogeneity and phenotypic diversity. Although many novel strategies have been developed for treating GAC, recurrence and metastasis rates are still high. Therefore, it is necessary to screen new potential biomarkers correlated with prognosis and novel molecular targets. Gene expression profiles were obtained from the from NCBI Gene Expression Omnibus (GEO) database. We conduct an integrated analysis using the online Venny website to explore candidate hub genes between differentially expressed genes (DEGs) of two datasets. Gene ontology (GO) and Kyoto Encyclopedia 18 of Genes and Genomes (KEGG) pathway enrichment analysis found that extracellular matrix plays an important role in GAC. In addition, we applied protein-protein interaction (PPI) network analysis by using the Search Tool for the Retrieval of Interacting Genes (STRING) and visualized with Cytoscape software. Furthermore, we employed Cytoscape software to analyze the interactive relationship of candidate gene for further analysis. We found that ECM related proteins played an important role in GAC, and 15 hub genes were extracted from 123 DEGs genes. There were four hub genes (bgn, vcan, col1a1 and timp1) predicted to be associated with poor prognosis among the 15 hub genes.
Highlights
Stomach cancer, one of the most common malignancies, is the third leading cause of cancer-related death [1]
Our results revealed that four extracellular matrix (ECM) related hub genes predicted poor prognosis in gastric adenocarcinoma (GAC) based on bioinformatics analysis
To further analyze the correlation between the four genes, we found that VCAN, COL1A1 and TIMP1 genes were closely correlated with biglycan (BGN) serving as an important component of ECM protein belonging to the small leucine-rich proteoglycans family [20]
Summary
One of the most common malignancies, is the third leading cause of cancer-related death [1]. As the most frequent type of stomach cancer, gastric adenocarcinoma (GAC) causes a significant public health burden with a 5-year overall survival (OS) rate of less than 30% [2]. There are four subtypes of gastric cancer identified by TCGA Project: tumors positive for Epstein-Barr virus, microsatellite unstable tumors, genomically stable tumours and tumors with chromosomal instability [3]. Various genetic factors have been reported to be associated with the pathogenesis of GAC. There are several treatment options for GAC including anti-her therapy, anti-VEGF therapy, anti-EGFR therapy and anti-FGFR-2 therapy; their efficiency is hampered by toxicities [4]. A number of studies have been conducted to investigate the pathogenesis of GAC. Some potential biomarkers can be found through big database analysis
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