Identification of significant genes as prognostic markers and potential tumor suppressors in lung adenocarcinoma via bioinformatical analysis.

BACKGROUND: Lung adenocarcinoma (LAC) is the predominant histologic subtype of lung cancer and has a complicated pathogenesis with high mortality. The purpose of this study was to identify differentially expressed genes (DEGs) with prognostic value and determine their underlying mechanisms. METHODS: Gene expression data of GSE27262 and GSE118370 were acquired from the Gene Expression Omnibus database, enrolling 31 LAC and 31 normal tissues. Common DEGs between LAC and normal tissues were identified using the GEO2R tool and Venn diagram software. Next, the Database for Annotation, Visualization, and Integrated Discovery (DAVID) was used to analyze the Gene Ontology and Kyoto Encyclopedia of Gene and Genome (KEGG) pathways. Then, protein-protein interaction (PPI) network of DEGs was visualized by Cytoscape with Search Tool for the Retrieval of Interacting Genes and central genes were identified via Molecular Complex Detection. Furthermore, the expression and prognostic information of central genes were validated via Gene Expression Profiling Interactive Analysis (GEPIA) and Kaplan-Meier analysis, respectively. Finally, DAVID, real-time PCR and immunohistochemistry were applied to re-analyze the identified genes, which were also further validated in two additional datasets from ArrayExpress database. RESULTS: First, 189 common DEGs were identified among the two datasets, including 162 downregulated and 27 upregulated genes. Next, Gene Ontology and KEGG pathway analysis of the DEGs were conducted through DAVID. Then, PPI network of DEGs was constructed and 17 downregulated central genes were identified. Furthermore, the 17 downregulated central genes were validated via GEPIA and datasets from ArrayExpress, and 12 of them showed a significantly better prognosis. Finally, six genes were identified significantly enriched in neuroactive ligand-receptor interactions (EDNRB, RXFP1, P2RY1, CALCRL) and Rap1 signaling pathway (TEK, P2RY1, ANGPT1) via DAVID, which were further validated to be weakly expressed in LAC tissues via RNA quantification and immunohistochemistry analysis. CONCLUSIONS: The low expression pattern and relation to prognosis indicated that the six genes were potential tumor suppressor genes in LAC. In conclusion, we identified six significantly downregulated DEGs as prognostic markers and potential tumor suppressor genes in LAC based on integrated bioinformatics methods, which could act as potential molecular markers and therapeutic targets for LAC patients.

Integrated Bioinformatics Analysis for Identifying the Significant Genes as Poor Prognostic Markers in Gastric Adenocarcinoma.

Gastric adenocarcinoma (GAC) is the most common histological type of gastric cancer and imposes a considerable health burden globally. The purpose of this study was to identify significant genes and key pathways participated in the initiation and progression of GAC. Four datasets (GSE13911, GSE19826, GSE54129, and GSE79973) including 171 GAC and 77 normal tissues from Gene Expression Omnibus (GEO) database were collected and analyzed. Through integrated bioinformatics analysis, we obtained 69 commonly differentially expressed genes (DEGs) among the four datasets, including 20 upregulated and 49 downregulated genes. The prime module in protein-protein interaction network of DEGs, including ADAMTS2, COL10A1, COL1A1, COL1A2, COL8A1, BGN, and SPP1, was enriched in protein digestion and absorption, ECM-receptor interaction, focal adhesion, PI3K-Akt signaling pathway, and amoebiasis. Furthermore, expression and survival analysis found that all seven hub genes were highly expressed in GAC tissues and 6 of them (except for SPP1) were able to predict poor prognosis of GAC. Finally, we verified the 6 high-expressed hub genes in GAC tissues via immunohistochemistry, Western blot, and RNA quantification analysis. Altogether, we identified six significantly upregulated DEGs as poor prognostic markers in GAC based on integrated bioinformatical methods, which could be potential molecular markers and therapeutic targets for GAC patients.