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Updated: Nov 16, 2025
The Use of Reverse Phase Protein Arrays RPPA to Explore Protein Expression Variation within Individual Renal Cell Cancers
Published on: January 22, 2013
Bioinformatics analysis and verification of gene targets for renal clear cell carcinoma
Feng Li1, Yi Jin2, Xiaolu Pei3
1Department of Urology, The Fourth Hospital of Hebei Medical University, No.12 Jiankang Road Shijiazhuang, 050011, Hebei Province, China.
Background:
It is estimated that there are 338,000 new renal-cell carcinoma releases every year in the world. Renal cell carcinoma (RCC) is a heterogeneous tumor, of which more than 70% is clear cell renal cell carcinoma (ccRCC). It is estimated that about 30% of new renal-cell carcinoma patients have metastases at the time of diagnosis. However, the pathogenesis of renal clear cell carcinoma has not been elucidated. Therefore, it is necessary to further study the pathogenesis of ccRCC.
Methods:
Two expression profiling datasets (GSE68417, GSE71963) were downloaded from the GEO database. Differentially expressed genes (DEGs) between ccRCC and normal tissue samples were identified by GEO2R. Functional enrichment analysis was made by the DAVID tool. Protein-protein interaction (PPI) network was constructed. The hub genes were excavated. The clustering analysis of expression level of hub genes was performed by UCSC (University of California Santa Cruz) Xena database. The hub gene on overall survival rate (OS) in patients with ccRCC was performed by Kaplan-Meier Plotter. Finally, we used the ccRCC renal tissue samples to verify the hub genes.
Results:
1182 common DEGs between the two datasets were identified. The results of GO and KEGG analysis revealed that variations in were predominantly enriched in intracellular signaling cascade, oxidation reduction, intrinsic to membrane, integral to membrane, nucleoside binding, purine nucleoside binding, pathways in cancer, focal adhesion, cell adhesion molecules. 10 hub genes ITGAX, CD86, LY86, TLR2, TYROBP, FCGR2A, FCGR2B, PTPRC, ITGB2, ITGAM were identified. FCGR2B and TYROBP were negatively correlated with the overall survival rate in patients with ccRCC (P < 0.05). RT-qPCR analysis showed that the relative expression levels of CD86, FCGR2A, FCGR2B, TYROBP, LY86, and TLR2 were significantly higher in ccRCC samples, compared with the adjacent renal tissue groups.
Conclusions:
In summary, bioinformatics technology could be a useful tool to predict the progression of ccRCC. In addition, there are DEGs between ccRCC tumor tissue and normal renal tissue, and these DEGs might be considered as biomarkers for ccRCC.
Insights
Bioinformatics analysis identified 10 hub genes in clear cell renal cell carcinoma (ccRCC). FCGR2B and TYROBP correlate with survival, and several genes are upregulated in ccRCC, suggesting potential biomarkers.
Area of Science:
- Oncology
- Bioinformatics
- Genomics
Background:
- Renal cell carcinoma (RCC), particularly clear cell (ccRCC), accounts for over 70% of kidney cancers.
- Approximately 30% of ccRCC patients present with metastases at diagnosis.
- The pathogenesis of ccRCC remains incompletely understood, necessitating further research.
Purpose of the Study:
- To identify differentially expressed genes (DEGs) and hub genes in ccRCC.
- To investigate the correlation of hub genes with overall survival in ccRCC patients.
- To explore potential biomarkers for ccRCC progression using bioinformatics.
Main Methods:
- Downloaded and analyzed two gene expression datasets (GSE68417, GSE71963) from the GEO database.
- Identified DEGs using GEO2R, performed functional enrichment analysis with DAVID, and constructed a protein-protein interaction (PPI) network.
- Identified hub genes, analyzed their expression and clustering using UCSC Xena, assessed overall survival correlation with Kaplan-Meier Plotter, and validated findings via RT-qPCR on ccRCC tissue samples.
Main Results:
- Identified 1182 common DEGs between ccRCC and normal tissues.
- Functional enrichment analysis revealed significant pathways including intracellular signaling, oxidation-reduction, and cancer-related pathways.
- Ten hub genes were identified: ITGAX, CD86, LY86, TLR2, TYROBP, FCGR2A, FCGR2B, PTPRC, ITGB2, ITGAM. FCGR2B and TYROBP showed negative correlation with overall survival (P < 0.05).
- RT-qPCR confirmed significantly higher expression of CD86, FCGR2A, FCGR2B, TYROBP, LY86, and TLR2 in ccRCC tissues compared to adjacent normal tissues.
Conclusions:
- Bioinformatics approaches are valuable for predicting ccRCC progression.
- Identified DEGs between ccRCC and normal tissues may serve as potential biomarkers for ccRCC.
- Further investigation into the identified hub genes could elucidate ccRCC pathogenesis and inform therapeutic strategies.
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