BACKGROUND: Renal cell carcinoma (RCC) is the most common cancer of the kidney. Despite advances in treatment, 5-year survival rate for metastatic RCC is estimated to be less than 10%. Thus, new therapeutic options for RCC are urgently needed.
AIM: In this study, our objective here was to identify a set of discriminating genes in RCC and normal kidney tissue, and predict their underlying molecular pathway in response to RCC using graph-clustering approach and Gene Ontology (GO) term analysis.
MATERIALS AND METHODS: The GSE6344 expression profile was used in this study and the tissues used were either de-identified or were archival tissues. Through Statistical analysis, Network analyses, graph clustering and Pathway enrichment analysis to predict underlying molecular pathway.
RESULTS: The results indicated the genes in cluster 1 and cluster 6 were involved in metabolism pathways, such as PPAR (peroxisome proliferator activated receptor) signaling pathway and Glycolysis pathway, etc. The genes in cluster 2, 3, 5, and 7 were associated with RCC progression through adhesion pathways, such as Focal adhesion, Cell adhesion molecules, and Gap junction. Besides, cluster 4 participated in MAPK (mitogen activated protein kinases) signaling pathway.
CONCLUSIONS: These results suggested these pathways play an important role in RCC progression. Further study may pay more attention to confirm the unidentified genes, explore their prognosis for RCC, and novel chemotherapeutic targets.
Corresponding Author: Shuixin Lou, MD; e-mail: firstname.lastname@example.orgFree PDF Download
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To cite this article
S. Lou, L. Ren, J. Xiao, Q. Ding, W. Zhang
Expression profiling based graph-clustering approach to determine renal carcinoma related pathway in response to kidney cancer
Eur Rev Med Pharmacol Sci
Vol. 16 - N. 6