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The identification of key biomarkers in patients with lung adenocarcinoma based on bioinformatics

1 Department of Cardio-Thoracic Surgery, Zhejiang Provincial People's Hospital
2 People's Hospital of Hangzhou Medical College

Special Issues: Advanced Big Data Analysis for Precision Medicine

Lung adenocarcinoma (LUAD) is one of the leading causes of cancer death globally. This study aims to investigate the underlying mechanisms implicated with LUAD and identify the key biomarkers. LUAD-associated gene expression dataset (GSE10072) was obtained from GEO database. Based on the GEO2R tool, we screened the differentially expressed genes (DEGs) between the patients with LUAD and normal individuals. Subsequently, Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were employed to find out the enriched pathways of these DEGs. Meanwhile, a protein-protein interaction (PPI) network was also employed to construct to visualize the interactions of these DEGs. Finally, the survival analysis of the top5 up-regulated and top5 down-regulated genes were conducted via GEPIA, aiming to figure out their potential effects on LUAD. In our study, a total of 856 DEGs were captured, including 559 up-regulated genes and 297 down-regulated genes. Among these DEGs, the top5 up-regulated genes were AGER, SFTPC, FABP4, CYP4B1 and WIF1 while the top5 down-regulated genes were GREM1, SPINK1, MMP1, COL11A1 and SPP1. GO analysis disclosed that these DEGs were mainly enriched in DNA synthesis, cell adhesion, signal transduction and cell apoptosis. KEGG analysis unveiled that the enriched pathway included pathways in cancer, PI3K/Akt signaling pathway, MAPK signaling pathway and cell cycle. Survival analysis showed that the expression level of ZG16 may correlate with the prognosis of LUAD patients. Furthermore, according to the connectivity degree of these DEGs, we selected the top15 hub genes, namely IL6, MMP9, EDN1, FOS, CDK1, CDH1, BIRC5, VWF, UBE2C, CDKN3, CDKN2A, CD34, AURKA, CCNB2 and EGR1, which were expected to be promising therapeutic target in LUAD. In conclusion, our study disclosed potential biomarkers and candidate targets in LUAD, which could be helpful to the diagnosis and treatment of LUAD.
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Keywords Lung adenocarcinoma, biomarkers, bioinformatics analysis

Citation: Kewei Ni, Gaozhong Sun. The identification of key biomarkers in patients with lung adenocarcinoma based on bioinformatics. Mathematical Biosciences and Engineering, 2019, 16(6): 7671-7687. doi: 10.3934/mbe.2019384


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