Abstracting and Indexing

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Identification of Hub Genes with Prognostic Values in Lung Cancer by Bioinformatics Analysis

Author(s): Meng Wang, Tian Xie, Jiubo Fan

Lung cancer is the main cause of mortalities among all types of cancer. Lots of efforts have been made to elucidate the pathogenesis of lung cancer, but the molecular mechanisms are still not well understood. To identify the candidate genes in the carcinogenesis and progression of lung cancer, we acquired three datasets (GSE19188, GSE33532 and GSE30219) from Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs), then function enrichment analyses were performed. The protein-protein interaction network (PPI) was constructed and the module analysis was performed using STRING and Cytoscape. A total of 185 DEGs was identified, consisting of 32 upregulated genes and 153 down regulated genes. The enriched functions and pathways of the DEGs include angiogenesis, cell adhesion, G2/M transition of mitotic cell cycle, mitotic nuclear division, ECM-receptor interaction, PPAR signaling pathway, etc. we selected ten hub genes (UBE2C, RRM2, KIF11, CDKN3, KIAA0101, PRC1, ASPM, HMMR, TOP2A, and BIRC5) and further analyzed their biological functions. It was found that these genes play an active role in positive regulation of exit from mitosis, mitotic nuclear division, spindle organization, and cell division. Furthermore, Survival analysis showed that these 10 hub genes positively correlated with survival time of lung cancer patients. In conclusion, the discovery of the functions of DEGs and Hub genes can help us to understand the mechanism of the occurrence and development of lung cancer at the molecular level, and provide candidate targets for the diagnosis and treatment of lung cancer in the future.

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