Abstract
Gastric cancer is a common cancer afflicting people worldwide. Although incremental progress has been achieved in gastric cancer research, the molecular mechanisms underlying remain unclear. In this study, we conducted bioinformatics methods to identify prognostic marker genes associated with gastric cancer progression. Three hundred and twenty-seven overlapping DEGs were identified from three GEO microarray datasets. Functional enrichment analysis revealed that these DEGs are involved in extracellular matrix organization, tissue development, extracellular matrix–receptor interaction, ECM-receptor interaction, PI3K-Akt signaling pathway, focal adhesion, and protein digestion and absorption. A protein–protein interaction network (PPI) was constructed for the DEGs in which 25 hub genes were obtained. Furthermore, the turquoise module was identified to be significantly positively coexpressed with macrophage M2 infiltration by weighted gene coexpression network analysis (WGCNA). Hub genes of COL1A1, COL4A1, COL12A1, and PDGFRB were overlapped in both PPI hub gene list and the turquoise module with significant association with the prognosis in gastric cancer. Moreover, functional analysis demonstrated that these hub genes play pivotal roles in cancer cell proliferation and invasion. The investigation of the gene markers can help deepen our understanding of the molecular mechanisms of gastric cancer. In addition, these genes may serve as potential prognostic biomarkers for gastric cancer diagnosis.
Highlights
Gastric cancer (GC) is a malignant tumor originating from the epithelium of gastric mucosa and has the highest incidence rate among all types of malignant tumors in China (Kang et al, 2015)
GO function enrichment analysis revealed that the overlapping differentially expressed genes (DEGs) were engaged in biological processes, such as extracellular matrix organization, collagen catabolic process, and tissue development (Figure 2C); molecular functions, such as extracellular region, extracellular matrix, and collagen trimer (Figure 2C); and cellular components, such as extracellular matrix structural constituent and growth factor binding (Figure 2C)
KEGG pathway analysis revealed that the DEGs were engaged in pathways of extracellular matrix (ECM)–receptor interaction, protein digestion and absorption, focal adhesion, xenobiotic metabolism by cytochrome P450, and chemical carcinogenesis (Figure 2C)
Summary
Gastric cancer (GC) is a malignant tumor originating from the epithelium of gastric mucosa and has the highest incidence rate among all types of malignant tumors in China (Kang et al, 2015). GC is a complex disease involving in multiple genes and pathways (Shiozaki et al, 2001; Carneiro et al, 2012; Ma et al, 2013), the exact molecular mechanisms of its development and prognosis need more investigations. Given the development of highthroughput technologys, such as microarray and generation sequencing, which can detect a whole genome simultaneously, numerous mRNA expression datasets have been produced for various biological purposes, facilitating the analysis of multiple genes (He et al, 2016; Li et al, 2019a). Microarray analysis for cancers has been widely used to identify cancer-related genes and pathways, allowing the mechanisms of Biomarkers Indentification in Gastric Cancer GEO. We integrated differentially expressed genes (DEGs) from three different datasets to reduce the false discovery rate as much as possible. A series of bioinformatics analyses was performed on overlapping DEGs to explore a reliable basis for the molecular mechanisms of GC pathogenesis and identify the molecular markers for GC diagnosis
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