A study exploring critical pathways in clear cell renal cell carcinoma

Zeng,Zisan; Que,Tengcheng; Zhang,Jiange; Hu,Yanling

doi:10.3892/etm.2013.1392

A study exploring critical pathways in clear cell renal cell carcinoma

Authors:
- Zisan Zeng
- Tengcheng Que
- Jiange Zhang
- Yanling Hu
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Affiliations: Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi 530021, P.R. China, Wild Animal Rescue Center, Forestry Bureau of Guangxi, Nanning, Guangxi 530021, P.R. China, Department of Urology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi 530021, P.R. China, Medical Scientific Research Center, Guangxi Medical University, Nanning, Guangxi 530021, P.R. China
Published online on: November 7, 2013 https://doi.org/10.3892/etm.2013.1392
Pages: 121-130

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Abstract

Renal cell carcinoma (RCC) is the most lethal type of cancer in the urinary system and often presents as a metastatic disease. Furthermore, there are no effective treatments for the disease. Several studies based on gene expression profiling have been performed with the aim of gaining insights into the pathogenesis of RCC; however, few studies have investigated RCC at the pathway level to search for the possible pathways involved in clear cell RCC (CCRCC). In this study, gene set enrichment analysis (GSEA) was conducted on microarray datasets from CCRCC tissue. DAVID functional enrichment analysis was performed based on the dysregulated genes that were identified in a meta‑analysis performed on the microarray datasets from CCRCC tissue. In GSEA, 17 down- and 12 upregulated pathways coexisted in six datasets. The majority of the upregulated pathways were associated with the immune system. In addition, 32 dysregulated pathways were obtained from DAVID functional enrichment analysis, based on the abnormal genes identified by meta‑analysis. This study demonstrated that cross‑GSEA is a useful method for exploring the critical pathways involved CCRCC; however, an individual dataset with a small sample may introduce bias. A cross‑GSEA based on certain well‑designed datasets may be required to further the progress made in this study, following the analysis of its results.

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