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The identification of new biomarkers for bladder cancer: A study based on TCGA and GEO datasets
Author(s) -
Hu Junyi,
Zhou Lijie,
Song Zhengshuai,
Xiong Ming,
Zhang Youpeng,
Yang Yu,
Chen Ke,
Chen Zhaohui
Publication year - 2019
Publication title -
journal of cellular physiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.529
H-Index - 174
eISSN - 1097-4652
pISSN - 0021-9541
DOI - 10.1002/jcp.28208
Subject(s) - gene ontology , kegg , bladder cancer , computational biology , gene , identification (biology) , biology , survival analysis , proportional hazards model , hazard ratio , cancer , bioinformatics , oncology , medicine , genetics , gene expression , confidence interval , botany
Bladder cancer (BC) is one of the most common neoplastic diseases worldwide. With the highest recurrence rate among all cancers, treatment of BC only improved a little in the last 30 years. Available biomarkers are not sensitive enough for the diagnosis of BC, whereas the standard diagnostic method, cystoscopy, is an invasive test and expensive. Hence, seeking new biomarkers of BC is urgent and challenging. With that order, we screened the overlapped differentially expressed genes (DEGs) of GSE13507 and TCGA BLCA datasets. Subsequent protein–protein interactions network analysis recognized the hub genes among these DEGs. Further functional analysis including Gene Ontology and KEGG pathway analysis and gene set enrichment analysis were processed to investigate the role of these genes and potential underlying mechanisms in BC. Kaplan–Meier analysis and Cox hazard ratio analysis were carried out to clarify the diagnostic and prognostic role of these genes. In conclusion, our present study demonstrated that ACTA2, CDC20, MYH11, TGFB3, TPM1, VIM, and DCN are all potential diagnostic biomarkers for BC. And may also be potential treatment targets for clinical implication in the future.