Dataset Information

Identification of biomarkers associated with diagnosis and prognosis of gastroesophageal junction adenocarcinoma-a study based on integrated bioinformatics analysis in GEO and TCGA database.

ABSTRACT:

Abstract

Gastroesophageal junction adenocarcinoma (GEJAC) is a malignant tumor with high mortality. Its incidence has increased sharply all over the world in recent years. The study aims to search for potential biomarkers for the diagnosis and prognosis of GEJAC based on the Gene Expression Omnibus database (GEO) database and The Cancer Genome Atlas (TCGA) database.Microarray dataset (GSE96668 and GSE74553) of GEJAC was downloaded from the GEO. After screening overlapping differentially expressed genes (DEGs) by GEO2R and Wayne map, functional enrichment analysis of the DEGs was performed by the DAVID database. Then, a protein-protein interaction (PPI) network was constructed, and the hub gene was identified by using STRING and Cytoscape, as well as the diagnostic value of hub genes was evaluated by the receiver operating characteristic (ROC) curves. Finally, the gene transcriptome profiles of gastric cancer named TCGA-STAD were downloaded from TCGA database to screen the potential prognostic genes and construct the prognostic risk model using Cox proportional hazards regression. Meanwhile, the Kaplan-Meier curve and time-dependent ROC curve were adopted to test the prognostic value of the prognostic gene signature.In this study, we identified 10 hub genes that might have high diagnostic value for GEJAC, and inferred that they might be involved in the occurrence and development of GEJAC. Moreover, we conducted a survival prediction model consisting of 6 genes and proved that they have value to some extent in predicting prognosis for GEJAC patients.

SUBMITTER: Song D

PROVIDER: S-EPMC7748358 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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Identification of biomarkers associated with diagnosis and prognosis of gastroesophageal junction adenocarcinoma-a study based on integrated bioinformatics analysis in GEO and TCGA database.

Song Danlei D Tian Jiming J Hu Yuping Y Wei Yongjian Y Lu Hong H Wang Yuping Y Guan Quanlin Q Zhou Yongning Y

Medicine 20201201 51

<h4>Abstract</h4>Gastroesophageal junction adenocarcinoma (GEJAC) is a malignant tumor with high mortality. Its incidence has increased sharply all over the world in recent years. The study aims to search for potential biomarkers for the diagnosis and prognosis of GEJAC based on the Gene Expression Omnibus database (GEO) database and The Cancer Genome Atlas (TCGA) database.Microarray dataset (GSE96668 and GSE74553) of GEJAC was downloaded from the GEO. After screening overlapping differentially ...[more]

PMID: 33371094

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Project description:BackgroundBladder cancer (BLCA) is the ninth most common cancer globally, as well as the fourth most common cancer in men, with an incidence of 7%. However, few effective prognostic biomarkers or models of BLCA are available at present.MethodsThe prognostic genes of BLCA were screened from one cohort of The Cancer Genome Atlas (TCGA) database through univariate Cox regression analysis and functionally annotated by Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. The intersecting genes of the BLCA gene set and focal adhesion-related gene were obtained and subjected to the least absolute shrinkage and selection operator regression (LASSO) to construct a prognostic model. Gene set enrichment analysis (GSEA) of high- and low-risk patients was performed to explore further the biological process related to focal adhesion genes. Univariate and multivariate Cox analysis, receiver operating characteristic (ROC) curve analysis, and Kaplan-Meier survival analysis (KM) were used to evaluate the prognostic model. DNA methylation analysis was presented to explore the relationship between prognosis and gene methylation. Furthermore, immune cell infiltration was assessed by CIBERSORT, ESTIMATE, and TIMER. The model was verified in an external GSE32894 cohort of the Gene Expression Omnibus (GEO) database, and the Prognoscan database presented further validation of genes. The HPA database validated the related protein level, and functional experiments verified significant risk factors in the model.ResultsVCL, COL6A1, RAC3, PDGFD, JUN, LAMA2, and ITGB6 were used to construct a prognostic model in the TCGA-BLCA cohort and validated in the GSE32894 cohort. The 7-gene model successfully stratified the patients into both cohorts' high- and low-risk groups. The higher risk score was associated with a worse prognosis.ConclusionsThe 7-gene prognostic model can classify BLCA patients into high- and low-risk groups based on the risk score and predict the overall survival, which may aid clinical decision-making.

Dataset Information

Identification of biomarkers associated with diagnosis and prognosis of gastroesophageal junction adenocarcinoma-a study based on integrated bioinformatics analysis in GEO and TCGA database.

Abstract

Publications

Identification of biomarkers associated with diagnosis and prognosis of gastroesophageal junction adenocarcinoma-a study based on integrated bioinformatics analysis in GEO and TCGA database.

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