Related Experiment Video
Updated: May 19, 2026
The Use of Reverse Phase Protein Arrays (RPPA) to Explore Protein Expression Variation within Individual Renal Cell Cancers
Published on: January 22, 2013
Expression profiling based graph-clustering approach to determine renal carcinoma related pathway in response to
1Department of Urology, Zhejiang Provincial People's Hospital, China. loushuixinff@gmail.com
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.
Insights
Researchers identified key molecular pathways involved in renal cell carcinoma (RCC) progression. Metabolism, adhesion, and MAPK signaling pathways were significantly associated with RCC, offering potential targets for new therapies.
Area of Science:
- Oncology
- Bioinformatics
- Molecular Biology
Background:
- Renal cell carcinoma (RCC) is the most common kidney cancer.
- Metastatic RCC has a poor 5-year survival rate (<10%), necessitating novel therapeutic strategies.
Purpose of the Study:
- Identify discriminating genes in RCC versus normal kidney tissue.
- Predict molecular pathways implicated in RCC progression using bioinformatics approaches.
Main Methods:
- Utilized the GSE6344 gene expression dataset.
- Applied statistical analysis, network analysis, graph clustering, and pathway enrichment analysis.
Main Results:
- Metabolism pathways (PPAR signaling, Glycolysis) were identified in clusters 1 and 6.
- Adhesion pathways (Focal adhesion, Cell adhesion molecules, Gap junction) were linked to RCC progression in clusters 2, 3, 5, and 7.
- MAPK signaling pathway was associated with RCC in cluster 4.
Conclusions:
- Identified key metabolic, adhesion, and signaling pathways crucial for RCC progression.
- Suggests these pathways as potential targets for novel RCC chemotherapeutics.
- Recommends further investigation of unidentified genes for prognostic and therapeutic potential.
