Research Article

Network spatio-temporal analysis predicts disease stage-related genes and pathways in renal cell carcinoma

Published: May 06, 2016
Genet. Mol. Res. 15(2): gmr8061 DOI: https://doi.org/10.4238/gmr.15028061
Cite this Article:
(2016). Network spatio-temporal analysis predicts disease stage-related genes and pathways in renal cell carcinoma. Genet. Mol. Res. 15(2): gmr8061. https://doi.org/10.4238/gmr.15028061
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Abstract

The purpose of this study was to screen the key genes and pathways of renal cell carcinoma (RCC) and lay the foundation for its diagnosis and therapy. Microarray data of normal subjects and RCC patients at different stages of disease were used to screen differentially expressed genes (DEGs). Based on the DEGs in the four disease stages, four co-expression networks were constructed using the Empirical Bayes method and hub genes were obtained by centrality analysis. The enriched pathways of the DEGs and the mutual hub genes in the cluster of each disease stage were investigated. The mutual hub genes of the four disease stages in RCC tissue were validated using reverse transcription-polymerase chain reaction (RT-PCR) and western blot analysis. A total of 432 DEGs were screened, including 233 upregulated and 199 downregulated genes, by statistical analysis. Centrality analysis of co-expression networks in different disease stages suggested that PLXDC1, IKZF1, RUNX2, and RNF125 were mutual hub genes. Pathway analysis showed that the DEGs were significantly enriched in seven terms. The hub modules in stage I disease were significantly enriched in the complement coagulation cascade pathway and the hub modules of the other three disease stages were enriched in natural killer cell-mediated cytotoxicity. The expression levels of PLXDC1, IKZF1, RUNX2, and RNF125 were significantly different between normal subjects and RCC patients by RT-PCR and western blot. Our study revealed four hub genes (PLXDC1, IKZF1, RUNX2, and RNF125) and two biological pathways that might be underlying biomarkers involved in RCC.

The purpose of this study was to screen the key genes and pathways of renal cell carcinoma (RCC) and lay the foundation for its diagnosis and therapy. Microarray data of normal subjects and RCC patients at different stages of disease were used to screen differentially expressed genes (DEGs). Based on the DEGs in the four disease stages, four co-expression networks were constructed using the Empirical Bayes method and hub genes were obtained by centrality analysis. The enriched pathways of the DEGs and the mutual hub genes in the cluster of each disease stage were investigated. The mutual hub genes of the four disease stages in RCC tissue were validated using reverse transcription-polymerase chain reaction (RT-PCR) and western blot analysis. A total of 432 DEGs were screened, including 233 upregulated and 199 downregulated genes, by statistical analysis. Centrality analysis of co-expression networks in different disease stages suggested that PLXDC1, IKZF1, RUNX2, and RNF125 were mutual hub genes. Pathway analysis showed that the DEGs were significantly enriched in seven terms. The hub modules in stage I disease were significantly enriched in the complement coagulation cascade pathway and the hub modules of the other three disease stages were enriched in natural killer cell-mediated cytotoxicity. The expression levels of PLXDC1, IKZF1, RUNX2, and RNF125 were significantly different between normal subjects and RCC patients by RT-PCR and western blot. Our study revealed four hub genes (PLXDC1, IKZF1, RUNX2, and RNF125) and two biological pathways that might be underlying biomarkers involved in RCC.

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