Dengzhong Sun1, Mulin Liu1, Fuxin Huang2, Fuxin Huang2. 1. Department of Gastrointestinal Surgery, Bengbu Medical College, Bengbu 233003, China. 2. First Affiliated Hospital, Department of Biological Sciences, Bengbu Medical College, Bengbu 233003, China.
Abstract
OBJECTIVE: To investigate the differentially expressed genes between gastric cancer and normal gastric mucosa by bioinformatics analysis, identify the important gene participating in the occurrence and progression of gastric cancer, and predict the functions of these genes. METHODS: The gene expression microarray data GSE100935 (including 18 gastric cancer samples and normal gastric mucosal tissues) downloaded from the GEO expression profile database were analyzed using Morpheus to obtain the differentially expressed genes in gastric cancer, and a cluster analysis heat map was constructed. The online database UALCAN was used to obtain the expression levels of these differentially expressed genes in gastric cancer and normal gastric mucosa. The prognostic value of the differentially expressed genes in gastric cancer was evaluated with Kaplan-Meier survival analysis. GO functional enrichment analysis was performed using Fun-Rich software, and the STRING database was exploited to establish a PPI network for the differentially expressed genes. RESULTS: A total of 45119 differentially expressed genes were identified from GSE100935 microarray data. Analysis with UALCAN showed an obvious high expression of EXD3 gene in gastric cancer, and survival analysis suggested that a high expression level of EXD3 was associated with a poorer prognosis of the patients with gastric cancer. GO functional enrichment analysis found that the differentially expressed genes in gastric cancer were involved mainly in the regulation of nucleotide metabolism and the activity of transcription factors in the cancer cells. CONCLUSIONS: EXD3 may be a potential oncogene in gastric cancer possibly in relation to DNA damage repair. The up-regulation of EXD3 plays an important role in the development and prognosis of gastric cancer, and may serve as an important indicator for prognostic evaluation of the patients.
OBJECTIVE: To investigate the differentially expressed genes between gastric cancer and normal gastric mucosa by bioinformatics analysis, identify the important gene participating in the occurrence and progression of gastric cancer, and predict the functions of these genes. METHODS: The gene expression microarray data GSE100935 (including 18 gastric cancer samples and normal gastric mucosal tissues) downloaded from the GEO expression profile database were analyzed using Morpheus to obtain the differentially expressed genes in gastric cancer, and a cluster analysis heat map was constructed. The online database UALCAN was used to obtain the expression levels of these differentially expressed genes in gastric cancer and normal gastric mucosa. The prognostic value of the differentially expressed genes in gastric cancer was evaluated with Kaplan-Meier survival analysis. GO functional enrichment analysis was performed using Fun-Rich software, and the STRING database was exploited to establish a PPI network for the differentially expressed genes. RESULTS: A total of 45119 differentially expressed genes were identified from GSE100935 microarray data. Analysis with UALCAN showed an obvious high expression of EXD3 gene in gastric cancer, and survival analysis suggested that a high expression level of EXD3 was associated with a poorer prognosis of the patients with gastric cancer. GO functional enrichment analysis found that the differentially expressed genes in gastric cancer were involved mainly in the regulation of nucleotide metabolism and the activity of transcription factors in the cancer cells. CONCLUSIONS:EXD3 may be a potential oncogene in gastric cancer possibly in relation to DNA damage repair. The up-regulation of EXD3 plays an important role in the development and prognosis of gastric cancer, and may serve as an important indicator for prognostic evaluation of the patients.
Authors: Christian von Mering; Martijn Huynen; Daniel Jaeggi; Steffen Schmidt; Peer Bork; Berend Snel Journal: Nucleic Acids Res Date: 2003-01-01 Impact factor: 16.971
Authors: Lilli Arndt; Jan Castonguay; Elisabeth Arlt; Dorke Meyer; Sami Hassan; Heike Borth; Susanna Zierler; Gunther Wennemuth; Andreas Breit; Martin Biel; Christian Wahl-Schott; Thomas Gudermann; Norbert Klugbauer; Ingrid Boekhoff Journal: Mol Biol Cell Date: 2014-01-22 Impact factor: 4.138