肝癌相关基因的生物信息学分析及功能预测

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Abstract Objective: To explore the mechanism of liver cancer based on bioinformatics. Methods: Microarray data of Liver cancer related genes were obtained from Gene Expression Omnibus (GEO) database and DEGs were identified using R software. Functional annotations of DEGs were conducted by gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG). Then, a protein-protein interaction (PPI) network was constructed to screen hub genes. Finally, the hub genes were verified by GEPIA. Results: A total of 154 differentially expressed genes were identified in liver cancer. GO enrichment analysis revealed that DEGs were mainly involved in telomere organization, DNA replication and regulation of gene expression. KEGG pathway analysis was mainly related to mineral absorption, systemic lupus erythematosus and carbon metabolism in cancer. Ten hub genes including TOP2A, CENPF, ASPM, NEK2, CCNA2, PRC1, MELK, CCNB2, RACGAP1 and NUSAP1 were screened out by constructing PPI network. Subsequent validation in GEPIA database showed that all these 10 genes were up-regulated in liver cancer and associated with the prognosis of liver cancer patients. Conclusion: Bioinformatics can effectively screen and analyze liver cancer related DEGs, which may provide theoretical reference for further exploration of the liver cancer mechanism.

Key words： liver neoplasms hub gene bioinformatics

Received: 27 April 2018

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	CHEN Linbo
	LI Xianpeng
	JIANG Hao
	ZENG Lili
	ZHENG Jinglei
	XU Feng.

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https://xb.wmu.edu.cn/EN/ OR https://xb.wmu.edu.cn/EN/Y2018/V48/I11/828

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