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Updated: Dec 13, 2025
The Use of Reverse Phase Protein Arrays RPPA to Explore Protein Expression Variation within Individual Renal Cell Cancers
Published on: January 22, 2013
Integrated bioinformatics analysis for the identification of potential key genes affecting the pathogenesis of clear
Hao Cui1,2, Lei Xu3, Zhi Li1,2
1Department of Medical Oncology, The First Hospital of China Medical University, Shenyang, Liaoning 110001, P.R. China.
Abstract:
Clear cell renal cell carcinoma (CCRCC) is a typical type of RCC with the worst prognosis among the common epithelial neoplasms of the kidney. However, its molecular pathogenesis remains unknown. Therefore, the aim of the present study was to screen for effective and potential pathogenic biomarkers of CCRCC. The gene expression profile of the GSE16441, GSE36895, GSE40435, GSE46699, GSE66270 and GSE71963 datasets were downloaded from the Gene Expression Omnibus database. First, the limma package in R language was used to identify differentially expressed genes (DEGs) in each dataset. The robust and strong DEGs were explored using the robust rank aggregation method. A total of 980 markedly robust DEGs were identified (429 upregulated and 551 downregulated). According to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis, these DEGs exhibited an obvious enrichment in various cancer-related biological pathways and functions. The Search Tool for the Retrieval of Interacting Genes/Proteins database was used for the construction of a protein-protein interaction (PPI) network, the Cytoscape MCODE plug-in for module analysis and the cytoHubba plug-in to identify hub genes from the aforementioned DEGs. A total of four key modules were identified in the PPI network. A total of six hub genes, including C-X-C motif chemokine ligand 12, bradykinin receptor B2, adenylate cyclase 7, calcium sensing receptor (CASR), kininogen 1 and lysophosphatidic acid receptor 5, were identified. The DEG results of the hub genes were verified using The Cancer Genome Atlas database, and CASR was found to be significantly associated with the prognosis of patients with CCRCC. In conclusion, the present study provided new insight and potential biomarkers for the diagnosis and prognosis of CCRCC.
Insights
Researchers identified key genes linked to clear cell renal cell carcinoma (CCRCC), a kidney cancer with poor prognosis. Calcium sensing receptor (CASR) shows significant association with patient outcomes, offering potential diagnostic and prognostic biomarkers.
Area of Science:
- Oncology
- Genomics
- Bioinformatics
Background:
- Clear cell renal cell carcinoma (CCRCC) presents a poor prognosis among kidney cancers.
- The molecular mechanisms driving CCRCC development are not fully understood.
Purpose of the Study:
- To identify potential pathogenic biomarkers for CCRCC.
- To gain insights into the molecular pathogenesis of CCRCC.
Main Methods:
- Utilized gene expression datasets from the Gene Expression Omnibus database.
- Applied robust rank aggregation to identify robust differentially expressed genes (DEGs).
- Constructed a protein-protein interaction network and identified hub genes using bioinformatics tools.
Main Results:
- Identified 980 robust DEGs, enriched in cancer-related pathways.
- Discovered four key modules and six hub genes within the protein-protein interaction network.
- Validated DEG results using The Cancer Genome Atlas database; Calcium sensing receptor (CASR) was significantly associated with CCRCC patient prognosis.
Conclusions:
- The study provides novel insights into CCRCC molecular pathogenesis.
- Identified potential biomarkers, including CASR, for CCRCC diagnosis and prognosis.
