Identification of new hub genes associated with bladder carcinoma via bioinformatics analysis


Aims and Background

Bladder carcinoma (BC) is one of the most common malignant cancers worldwide. Several genes related to the mechanism of BC have been studied in recent years, but the current understanding of BC is still rather limited. This study aimed to find new differentially expressed genes (DEGs) associated with the occurrence and development of BC.


In this work, we downloaded gene expression data from Gene Expression Omnibus under accession number GSE27448, which included 10 GeneChips from urinary BC tissues and 5 from normal tissues. DEGs were identified by the LIMMA package in R. Then the protein–protein interactions (PPIs) networks were analyzed with the database of Search Tool for the Retrieval of Interacting Genes, and gene ontology (GO) was applied to explore the underlying function of the DEGs using the Database for Annotation, Visualization and Integrated Discovery.


A total of 2,068 DEGs were found between BC and normal tissues. These genes were involved in 49 functional clusters. The top 10 highest degree nodes, such as POLR2F/2H (DNA directed RNA polymerase II polypeptide F/polypeptide H) and RPS14/15 (ribosomal protein S14/S15), were proven to be hub nodes in the PPIs network. ITGA7 (integrin, alpha 7), GRB14 (growth factor receptor-bound protein 14), CDC20 (cell division cycle 20) and PSMB1 (proteasome subunit, beta type, 1) were significant DEGs identified in the functional clusters.


Genes such as POLR2F/2H, RPS14/15, ITGA7, GRB14, CDC20 and PSMB1 were forecast to play important roles in the occurrence and progression of BC.

Tumori 2015; 101(1): 117 - 122




Zhuomin Jia, Xing Ai, Fengling Sun, Tong Zang, Yawei Guan, Feng Gao

Article History


Financial support: No financial support was received for this submission.
Conflict of interest: The authors declare that they have no conflict of interest.

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  • Department of Urology, Military General Hospital of Beijing PLA, Beijing - PR China
  • Z. Jia and X. Ai contributed equally to this work and should be considered co–first authors

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