Abstract
Clear cell renal cell carcinoma (ccRCC) is one of the most common and lethal types of cancer within the urinary system. Great efforts have been made to elucidate the pathogeny. However, the molecular mechanism of ccRCC is still not well understood. The aim of this study is to identify key genes in the carcinogenesis and progression of ccRCC. The mRNA microarray dataset GSE53757 was downloaded from the Gene Expression Omnibus database. The GSE53757 dataset contains tumor and matched paracancerous specimens from 72 ccRCC patients with clinical stage I to IV. The linear model of microarray data (limma) package in R language was used to identify differentially expressed genes (DEGs). The protein–protein interaction (PPI) network of the DEGs was constructed using the search tool for the retrieval of interacting genes (STRING). Subsequently, we visualized molecular interaction networks by Cytoscape software and analyzed modules with MCODE. A total of 1,284, 1,416, 1,610 and 1,185 up-regulated genes, and 932, 1,236, 1,006 and 929 down-regulated genes were identified from clinical stage I to IV ccRCC patients, respectively. The overlapping DEGs among the four clinical stages contain 870 up-regulated and 645 down-regulated genes. The enrichment analysis of DEGs in the top module was carried out with DAVID. The results showed the DEGs of the top module were mainly enriched in microtubule-based movement, mitotic cytokinesis and mitotic chromosome condensation. Eleven up-regulated genes and one down-regulated gene were identified as hub genes. Survival analysis showed the high expression of CENPE, KIF20A, KIF4A, MELK, NCAPG, NDC80, NUF2, TOP2A, TPX2 and UBE2C, and low expression of ACADM gene could be involved in the carcinogenesis, invasion or recurrence of ccRCC. Literature retrieval results showed the hub gene NDC80, CENPE and ACADM might be novel targets for the diagnosis, clinical treatment and prognosis of ccRCC. In conclusion, the findings of present study may help us understand the molecular mechanisms underlying the carcinogenesis and progression of ccRCC, and provide potential diagnostic, therapeutic and prognostic biomarkers.
Highlights
Renal cell carcinoma (RCC) is a heterogeneous group of cancers, and is one of the 10 most common cancers in the world
Gene ontology (GO) enrichment analysis showed that up-regulated differentially expressed genes (DEGs) were mainly involved in biological processes (BP), including immune response, inflammatory response, and interferon-gamma-mediated signaling pathway, while down-regulated DEGs were significantly enriched in oxidation—reduction process, sodium ion transport and excretion process
kyoto encyclopedia of genes and genomes (KEGG) pathway analysis showed the up-regulated DEGs were enriched in phagosome and allograft rejection, while the downregulated DEGs were enriched in metabolic pathways and biosynthesis of antibiotics
Summary
Renal cell carcinoma (RCC) is a heterogeneous group of cancers, and is one of the 10 most common cancers in the world. Based on the histopathological and molecular characterization of RCC (Hes, 2014; Moch et al, 2016), clear cell RCC (ccRCC), papillary RCC and chromophobe RCC are the major subtypes with ≥5% incidence (Cancer Genome Atlas Research Network, 2013; Cancer Genome Atlas Research Network et al, 2016; Chen et al, 2016). It is important to explore the molecular mechanisms of RCC and find effective biomarkers for early diagnosis. Bioinformatics is a study field that uses computation to extract knowledge from biological data. It includes the collection, retrieval, manipulation and modeling of data for analysis, visualization or prediction through the algorithms and software. Bioinformatics analysis can help us identify differentially expressed genes (DEGs) and functional pathways related to the carcinogenesis and progression of cancer
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