Literature DB >> 24763884

Identified differently expressed genes in renal cell carcinoma by using multiple microarray datasets running head: differently expressed genes in renal cell carcinoma.

Y Cheng1, M Hong, B Cheng.   

Abstract

OBJECTIVE: The purpose of this study was to identify differentially expressed genes and analysis biological processes related to renal cell carcinoma.
METHODS: A meta-analysis was performed using the Rank Product package of Gene Expression Omnibus datasets of renal cell carcinoma. Then Gene Ontology enrichment analyses and pathway analysis were performed based on Gene Ontology website and Kyoto Encyclopedia of Genes and Genomes. Protein-protein interaction network was constructed used Cytoscape software.
RESULTS: We identified a total of 1992 differentially expressed genes Rank Product package of renal cell carcinoma, 840 of them were not involved in individual DEGs. Gene Ontology enrichment analyses showed that those 840 genes enriched in terms such as response to hormone stimulus, endogenous stimulus, biological adhesion, and cell proliferation. Pathway analysis showed that significant pathways included pyruvate metabolism, glycerolipid metabolism, complement and coagulation cascades and so on. Protein-protein interaction network indicated that MT2A, MYC, CENPF and NEK2 has high degree which participated many interactions.
CONCLUSIONS: Our study displayed genes that were consistently differentially expressed in renal cell carcinoma, and the biological pathways, protein-protein interaction network associated with those genes.

Entities:  

Mesh:

Year:  2014        PMID: 24763884

Source DB:  PubMed          Journal:  Eur Rev Med Pharmacol Sci        ISSN: 1128-3602            Impact factor:   3.507


  2 in total

Review 1.  Role of NEK2A in human cancer and its therapeutic potentials.

Authors:  Jiliang Xia; Reinaldo Franqui Machin; Zhimin Gu; Fenghuang Zhan
Journal:  Biomed Res Int       Date:  2015-02-01       Impact factor: 3.411

2.  Three-gene risk model in papillary renal cell carcinoma: a robust likelihood-based survival analysis.

Authors:  Yutao Wang; Kexin Yan; Jiaxing Lin; Jianfeng Wang; Zhenhua Zheng; Xinxin Li; Zhixiong Hua; Yuepeng Bu; Jianxiu Shi; Siqing Sun; Xuejie Li; Yang Liu; Jianbin Bi
Journal:  Aging (Albany NY)       Date:  2020-11-05       Impact factor: 5.682

  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.