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Identification and Verification of Biomarker in Clear Cell Renal Cell Carcinoma via Bioinformatics and Neural Network
1Department of Urinary Surgery, The Fourth Hospital of Hebei Medical University, No. 12 Jiankang Road, 050000, China.
Background:
Clear cell renal cell carcinoma (ccRCC) is the most common subtype of kidney cancer, which represents the 9th most frequently diagnosed cancer. However, the molecular mechanism of occurrence and development of ccRCC is indistinct. Therefore, the research aims to identify the hub biomarkers of ccRCC using numerous bioinformatics tools and functional experiments.
Methods:
The public data was downloaded from the Gene Expression Omnibus (GEO) database, and the differently expressed genes (DEGs) between ccRCC and normal renal tissues were identified with GEO2R. Protein-protein interaction (PPI) network of the DEGs was constructed, and hub genes were screened with cytoHubba. Then, ten ccRCC tumor samples and ten normal kidney tissues were obtained to verify the expression of hub genes with the RT-qPCR. Finally, the neural network model was constructed to verify the relationship among the genes.
Results:
A total of 251 DEGs and ten hub genes were identified. AURKB, CCNA2, TPX2, and NCAPG were highly expressed in ccRCC compared with renal tissue. With the increasing expression of AURKB, CCNA2, TPX2, and NCAPG, the pathological stage of ccRCC increased gradually (P < 0.05). Patients with high expression of AURKB, CCNA2, TPX2, and NCAPG have a poor overall survival. After the verification of RT-qPCR, the expression of hub genes was same as the public data. And there were strong correlations between the AURKB, CCNA2, TPX2, and NCAPG with the verification of the neural network model.
Conclusion:
After the identification and verification, AURKB, CCNA2, TPX2, and NCAPG might be related to the occurrence and malignant progression of ccRCC.
Insights
This study identified four key genes (AURKB, CCNA2, TPX2, and NCAPG) as potential biomarkers for clear cell renal cell carcinoma (ccRCC) progression. Their elevated expression correlates with advanced ccRCC stages and poorer patient survival.
Area of Science:
- Oncology
- Bioinformatics
- Molecular Biology
Background:
- Clear cell renal cell carcinoma (ccRCC) is the most prevalent kidney cancer subtype, yet its underlying molecular mechanisms remain unclear.
- Identifying key molecular drivers is crucial for understanding ccRCC pathogenesis and developing targeted therapies.
Purpose of the Study:
- To identify novel hub biomarkers associated with the occurrence and development of ccRCC.
- To validate the diagnostic and prognostic potential of identified biomarkers using bioinformatics and experimental approaches.
Main Methods:
- Utilized Gene Expression Omnibus (GEO) data to identify differentially expressed genes (DEGs) between ccRCC and normal tissues.
- Constructed protein-protein interaction (PPI) networks to screen for hub genes using cytoHubba.
- Validated hub gene expression in ccRCC and normal samples via RT-qPCR and constructed a neural network model.
Main Results:
- Identified 251 DEGs and ten hub genes, with AURKB, CCNA2, TPX2, and NCAPG showing significant upregulation in ccRCC.
- High expression of these four genes correlated with advanced pathological stages and reduced overall survival in ccRCC patients.
- RT-qPCR and neural network analysis confirmed the expression patterns and inter-gene correlations of the identified hub genes.
Conclusions:
- AURKB, CCNA2, TPX2, and NCAPG are strongly implicated in the occurrence and malignant progression of ccRCC.
- These genes represent potential diagnostic and prognostic biomarkers for ccRCC.
- Further research into these hub genes could elucidate ccRCC development and inform therapeutic strategies.
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