Identification of Potential Biomarkers Associated with Prognosis in Gastric Cancer via Bioinformatics Analysis

Abstract

Background Gastric cancer (GC) is one of the leading causes of cancer-related mortality worldwide. We aimed to identify differentially expressed genes (DEGs) and their potential mechanisms associated with the prognosis of GC patients. Material/Methods This study was based on gene profiling information for 37 paired samples of GC and adjacent normal tissues from the GSE118916, GSE79973, and GSE19826 datasets in the Gene Expression Omnibus database. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were used to investigate the biological role of the DEGs. The protein–protein interaction (PPI) network was constructed by Cytoscape, and the Kaplan-Meier plotter was used for prognostic analysis. Results We identified 119 DEGs, including 21 upregulated and 98 downregulated genes, in GC. The 21 upregulated genes were mainly enriched in extracellular matrix-receptor interaction, focal adhesion, and transforming growth factor-β signaling, while the 98 downregulated genes were significantly associated with gastric acid secretion, retinol metabolism, and metabolism of xenobiotics by cytochrome P450. Thirty hub DEGs were obtained for further analysis. Twenty-five of the 30 hub DEGs were significantly associated with the prognosis of GC, and 21 of the 25 hub DEGs showed consistent expression trends within the 3 profile datasets. KEGG reanalysis of these 21 hub DEGs showed that COL1A1 , COL1A2 , COL2A1 , COL11A1 , THBS2 , and SPP1 were mainly enriched in the extracellular matrix-receptor interaction pathways. Conclusions We identified 6 genes that were significantly related to the prognosis of GC patients. These genes and pathways could serve as potential prognostic markers and be used to develop treatments for GC patients.

Quantitative Analysis on Molecular Characteristics Evolution of Gastric Cancer Progression and Prognosis

“…Using the expression levels of key genes in the model and the corresponding clinical information (including age, sex, stage) as features, the accuracy of the 3‐gene model was evaluated and compared with the 6‐ and 7‐gene models. [ 49,50 ] Our model had the highest C‐index of the three compared methods in the GSE62254 training cohort ( Table 1 ). In the TCGA‐STAD test cohort, our model achieved a C‐index of 0.68, slightly higher than the 6‐gene model and lower than the 7‐gene model.…”

“…Moreover, the calibration curve and C‐index showed good agreement. The C‐index of the 3‐gene model is higher than that of the 6‐gene [ 49 ] and 7‐gene models [ 50 ] in the training cohort and it also performed well in the test cohort, indicating that the 3‐gene model offers excellent prognostic guidance to GC patients. Since six of 81 EGC‐related DEmRNAs (KRT17, FCGR3A, CTAG1B, CCN6, AGMO, and CBLIF) were not included in the transcriptional expression experiments of GSE62254, they were excluded in the construction of GC prognostic model.…”

Immune Checkpoint Gene Expression Profiling Identifies Programmed Cell Death Ligand-1 Centered Immunologic Subtypes of Oral and Squamous Cell Carcinoma With Favorable Survival

“…Differentially expressed ICGs in the OSCC-TCGA data were identified by performing a differential expression analysis and using the edgeR package (version 3.14) ( 34 ) in the R software (version 3.6.3). Genes with log FC > 0 and the value of p < 0.05 were regarded as upregulated differentially expressed genes (DEGs); while genes with logFC < 0 and the value of p < 0.05 were regarded as downregulated DEGs ( 35 ). The relationship between these 88 ICGs and the OS of patients with OSCC was analyzed using a univariate Cox regression analysis (log-rank p < 0.05).…”

TWIST1-EP300 Expedites Gastric Cancer Cell Resistance to Apatinib by Activating the Expression of COL1A2

“…Interestingly, COL1A2 was found as a gene with greater than a 50-fold increase in expression in cisplatin-, paclitaxel-, doxorubicin-, topotecan-, vincristine-, and methotrexate-resistant ovarian cancer cells [9]. Consistently, COL1A2 was identified as one of the 21 upregulated genes in 3 datasets between GC tissues and adjacent normal tissues [10]. However, there is little information regarding the regulatory role of COL1A2 in apatinib resistance.…”