Identification TRIM46 as a Potential Biomarker and Therapeutic Target for Clear Cell Renal Cell Carcinoma Through Comprehensive Bioinformatics Analyses.
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ABSTRACT:
SUBMITTER: Ren XB
PROVIDER: S-EPMC8645697 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:To comprehensively compare the survival outcomes of clear cell renal cell carcinoma (ccRCC) and papillary renal cell carcinoma (pRCC), the study cohort included ccRCC and pRCC patients in 2004-2017 from the Surveillance, Epidemiology, and End Results (SEER) database, which comprises 18 registries. Primary outcomes including overall mortality (OM) and cancer-specific mortality (CSM) were evaluated. Subgroup analyses were conducted for different ages, race, and disease stages. A total of 112,270 cases were eligible for the current analysis, including 92,209 cases of ccRCC and 20,061 cases of pRCC. Univariate analyses suggested that pRCC has a more favorable outcome than ccRCC in terms of CSM (HR: 0.72, 95% CI: 0.68-0.75, p < 0.001) and OM (HR: 0.90, 95% CI: 0.88-0.93, p < 0.001). Multivariate-adjusted HRs suggested that pRCC has worse survival outcomes than ccRCC (adjusted HR: 1.08 for CSM and 1.05 for OM, both p < 0.05). Subgroup analyses showed that pRCC had a significantly poorer prognosis than ccRCC among patients ≤45 years old (HRCSM : 1.59, 95% CI: 1.31-1.93, p < 0.001; HROM : 1.63, 95% CI: 1.40-1.90, p < 0.001). Among patients with distant metastasis, those with pRCC had a higher risk of CSM and OM than those with ccRCC (HRCSM : 1.28, 95% CI: 1.19-1.39, p < 0.001; HROM : 1.30, 95% CI: 1.21-1.40, p < 0.001). Propensity score analyses for patients ≤45 years old and those with metastasis showed similar results. The lack of information on pRCC subtypes in the SEER database was a limitation. In conclusion, pRCC has poorer survival outcomes than ccRCC among patients younger than 45 years old and patients with distant metastasis.
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Project description: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.
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