Identification of critical genes in gastric cancer to predict prognosis using bioinformatics analysis methods

Jing Liu; Liang Ma; Zhiming Chen; Yao Song; Tinging Gu; Xianchen Liu; Hongyu Zhao; Ninghua Yao

doi:10.21037/atm-20-4427

Identification of critical genes in gastric cancer to predict prognosis using bioinformatics analysis methods

Ann Transl Med. 2020 Jul;8(14):884. doi: 10.21037/atm-20-4427.

Authors

Jing Liu¹, Liang Ma², Zhiming Chen^{1

3}, Yao Song⁴, Tinging Gu¹, Xianchen Liu^{1

3}, Hongyu Zhao^{1

3}, Ninghua Yao^{1

3}

Affiliations

¹ Department of Oncology, Affiliated Hospital of Nantong University, Nantong, China.
² Department of Chemotherapy, First People's Hospital of Yancheng, Yancheng, China.
³ Department of Radiotherapy, Affiliated Hospital of Nantong University, Nantong, China.
⁴ Department of Radiation oncology, Tenth People's Hospital Affiliated to Tongji University, Shanghai, China.

Abstract

Background: Ranking fourth in the world in tumor incidence and second in cancer-related death worldwide, gastric cancer (GC) is one of the major malignant tumors, and has a very complicated pathogenesis. In the present study, we aimed to identify new biomarkers to predict the survival rate of GC patients.

Methods: The differentially expressed genes (DEGs) between GC tissues and normal stomach tissues were obtained by using GEO2R, and overlapped DEGs were acquired with Venn diagrams. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were conducted with R software. Then, the protein-protein interaction (PPI) of these DEGs was visualized by Cytoscape. Gene Expression Profiling Interactive Analysis (GEPIA) was used to verify the expression differences of hub genes in gastric adenocarcinoma tissues and normal tissues. Overall survival (OS) of hub genes was calculated by Kaplan-Meier plotter.

Results: There were a total of 128 consistently expressed genes in the two datasets: 85 upregulated genes were enriched in extra-cellular matrix (ECM)-receptor interaction, protein digestion and absorption, focal adhesion, gastric acid secretion, mineral absorption, systemic lupus erythematosus, amoebiasis, and PI3K-Akt signaling pathway, and 43 downregulated genes were enriched in palate development, blood coagulation, positive regulation of transcription from RNA polymerase II promoter, axonogenesis, receptor internalization, negative regulation of transcription from RNA polymerase II promoter, and in no significant signaling pathways. From the PPI network analyzed by Molecular Complex Detection (MCODE) plug-in, all 27 upregulated genes were selected. Furthermore, to analyze the OS among these genes, Kaplan-Meier analysis was conducted, and 25 genes were associated with remarkably worse survival. For validation in GEPIA, 11 of 25 genes were discovered to be highly expressed in GC tissues compared to normal OS tissues. Furthermore, in the re-analysis of the Database for Annotation, Visualization and Integrated Discovery (DAVID), three genes [G2/miotic-specific cyclin B1 (CCNB1), polo-like kinases 1 (PLK1), and pituitary tumor-transforming gene-1 (PTTG1)] were markedly enriched in the cell cycle pathway, particulary the G1-G1/S phase.

Conclusions: Three remarkably upregulated DEGs with poor prognosis in GC were identified and may serve as new prognostic biomarkers and targets in GC therapy.

Keywords: Bioinformatical analysis; G2/miotic-specific cyclin B1 (CCNB1); differentially expressed genes (DEGs); gastric cancer (GC); pituitary tumor-transforming gene-1 (PTTG1); polo-like kinases 1 (PLK1); survival.