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

Abstract

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.

Keywords: Bioinformatics analysis; Lung adenocarcinoma; Prognostic markers; Tumor suppressors.

Fig. 1

Identification of 189 common DEGs among GSE27262 and GSE118370 datasets by Venn diagram software. Different colors represent different datasets. a, c 162 DEGs were downregulated among the two datasets (log₂FC < −2). b, d 27 DEGs were upregulated among the two datasets (log₂FC > 2)

Fig. 2

Common DEGs PPI network was constructed using the STRING online database and Cytoscape software. a There were 137 nodes and 254 edges in the PPI network. The nodes represent proteins; the edges represent the interaction between proteins; green circles represent downregulated DEGs, and red circles represent upregulated DEGs. b Modular analysis via MCODE (degree cutoff = 2, max. Depth = 100, k-core = 2, and node score cutoff = 0.2). In total, 17 central nodes were screened. Circle size represents node degree, and label font size represents betweenness centrality

Fig. 3

Expression levels of the 17 central genes in lung adenocarcinoma patients compared to healthy people. The GEPIA website was applied to validate the expression level of the 17 central genes between LAC patients and normal people. All 17 genes were lowly expressed in LAC specimens compared to normal specimens (*P < 0.05). Red indicates LAC tissues (n = 483) and gray indicates normal tissues (n = 347)

Fig. 4

Prognostic information of the 17 central genes in lung adenocarcinoma. The Kaplan-Meier plotter online tools were used to analyze the prognostic information of the 17 central genes. a High expression of 12 of the 17 genes had a significantly better survival rate (P < 0.05). b High expression of ADRA1A, TIE1, and LYVE1 showed a significantly worse survival rate (P < 0.05)

Fig. 5

General information of neuroactive ligand-receptor interactions pathway. DAVID was used to re-analyze the 12 core DEGs for KEGG pathway enrichment. Four genes (EDNRB, RXFP1, P2RY1, and CALCRL) were enriched in neuroactive ligand-receptor interactions (P < 0.05)

Fig. 6

General information of Rap1 signaling pathway. DAVID was used to re-analyze the 12 core DEGs for KEGG pathway enrichment. Three genes (TEK, P2RY1, and ANGPT1) were enriched in Rap1 signaling pathway (P < 0.05)

Fig. 7

Validation of expression levels of EDNRB, RXFP1, P2RY1, CALCRL, TEK, and ANGPT1 in LAC patients. To further validate the expression level in LAC patients, six genes were re-analyzed via real-time PCR (a) and immunohistochemistry (b) analysis. Representative images of IHC staining were shown. Scale bar, 50 μm. Real-time PCR data were presented as mean ± SEM and the differences were estimated by Wilcoxon paired signed-rank test (*P < 0.05, **P < 0.01, ***P < 0.001). Data were normalized to GAPDH expression. All six genes were markedly weakly expressed in LAC tissue compared to adjacent normal tissue