Construction and Validation of Protein Expression-related Prognostic Models in Clear Cell Renal Cell Carcinoma

by Selleckchem | Oct 10 2023

Objective: To construct a prognostic evaluation model for clear cell renal cell carcinoma (ccRCC) patients using bioinformatics method and to screen potential drugs for ccRCC.

Methods: ccRCC RNA sequencing data, clinical data, and protein expression data were downloaded from the TCGA database. Univariate Cox and Lasso regression analyses were performed on the combined data to screen out the proteins related to the prognosis, and they were included in a multivariate Cox proportional hazard model. The patients were divided into high and low-risk groups for a survival difference analysis. The predictive power of the model was evaluated on the basis of overall survival, progression-free survival, independent prognostic, clinically relevant receiver operating characteristic (ROC) curve, C-index, principal component, and clinical data statistics analyses. GSEA enrichment and immune function correlation analyses were performed. The samples were divided into different subtypes based on the expression of the risk proteins, and survival analysis of the subtypes was performed. The risk-related protein and RNA sequencing data were analyzed to screen out sensitive drugs with significant differences between the high and low-risk groups.

Results: A total of 469 ccRCC-related proteins were screened, of which 13 proteins with independent prognostic significance were screened by univariate Cox, Lasso, and multivariate Cox regression analyses to construct the prognostic model. The sensitivity and accuracy of the model in predicting the survival of patients with ccRCC were high (1 year: 0.811, 3 years: 0.783, 5 years: 0.777). The 13 proteins were closely related to immunity, and the model proteins were different between kidney and tumor tissues according to the HPA database. The samples were divided into three subtypes, and there were obvious clinical characteristics of the three subtypes in the grade and T, N and M stages. According to the IC50 values, CGP-60474, vinorelbine, doxorubicin, etoposide, FTI-277, JQ12, OSU-03012, pyrimethamine, and other drugs were more sensitive in the high-risk group.

Conclusions: A prognostic model of protein expression in ccRCC was successfully constructed, which had good predictive ability for the prognosis of ccRCC patients. The ccRCC-related proteins in the model can be used as targets for studying the pathogenesis and targeted therapy.

Comments:

The objective of the study was to develop a prognostic evaluation model for clear cell renal cell carcinoma (ccRCC) patients using bioinformatics methods and identify potential drugs for ccRCC. The researchers obtained ccRCC RNA sequencing data, clinical data, and protein expression data from the TCGA database. They employed univariate Cox regression and Lasso regression analyses to identify proteins associated with prognosis, and these proteins were included in a multivariate Cox proportional hazard model. The patients were then divided into high and low-risk groups based on the model, and survival differences between the groups were analyzed.

The predictive power of the model was evaluated using various measures, including overall survival, progression-free survival, clinically relevant receiver operating characteristic (ROC) curves, C-index, principal component analysis, and statistical analysis of clinical data. Additionally, gene set enrichment analysis (GSEA) and immune function correlation analyses were performed. The samples were further divided into different subtypes based on the expression of the risk proteins, and survival analysis was conducted for these subtypes.

In terms of drug screening, the researchers analyzed the risk-related proteins and RNA sequencing data to identify drugs that showed significant differences in sensitivity between the high and low-risk groups. Several drugs, including CGP-60474, vinorelbine, doxorubicin, etoposide, FTI-277, JQ12, OSU-03012, and pyrimethamine, were found to be more sensitive in the high-risk group based on their IC50 values.

Overall, the study successfully constructed a prognostic model using protein expression data in ccRCC, which demonstrated strong predictive ability for the prognosis of ccRCC patients. The identified ccRCC-related proteins in the model can serve as targets for studying the underlying mechanisms and developing targeted therapies for ccRCC.