Pathway-enrichment analysis

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

X. H. Li, Yang, C. Z., Wang, J., Li, X. H., Yang, C. Z., and Wang, J., “Network spatio-temporal analysis predicts disease stage-related genes and pathways in renal cell carcinoma”, vol. 15, p. -, 2016.

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.

Selecting key genes associated with osteosarcoma based on a differential expression network

Y. B. Wang, Jia, N., Xu, C. M., Zhao, L., Zhao, Y., Wang, X., and Jia, T. H., “Selecting key genes associated with osteosarcoma based on a differential expression network”, vol. 14, pp. 17708-17717, 2015.

Despite recent advances in osteosarcoma diagnosis and therapy, much remains unclear about the molecular mechanisms involved in the disorder, and the discovery of novel drug-targeted genes is essential. We explored the potential molecular mechanisms and target genes involved in the development and progression of osteosarcoma. First, we identified the differentially expressed genes in osteosarcoma patients and matching normal controls. We then constructed a differential expression network based on differential and non-differential interactions.

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