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.
Objective. This study aimed to analyze the cuproptosis-related long non-coding RNA (lncRNA) in patients with bladder urothelial carcinoma (BLCA), construct a prognostic model, and screen its potential drugs. Methods. The transcriptome expression and clinical and mutation burden data related to BLCA were downloaded from The Cancer Genome Atlas database. The prognostic lncRNAs were screened using univariate Cox and Lasso regression analyses, and then included in the multifactor risk ratio model. The risk score of each sample was calculated based on the prognostic model formula, and the patients were divided into high- and low-risk groups for survival difference analysis. Clinically relevant receiver operating characteristic (ROC) curve, C-index principal component analysis, and clinical data statistics were used to evaluate the predictive power of the model. The risk-differential lncRNAs were functionally enriched. We calculated the tumor mutation burden of risk lncRNAs, and survival and the Tumor Immune Dysfunction and Exclusion analyses for high- and low-risk groups. Finally, immunocorrelation analysis and potential drug screening were performed. Results. Eleven lncRNAs with independent prognostic significance were screened out to construct the prognostic model. Survival analysis showed a significant difference in survival between the high- and low-risk groups. The areas under the ROC curve at 1, 3, and 5 years were 0.711, 0.679, and 0.713, respectively. The discrimination between the lncRNA high- and low-risk groups in the constructed model was the most obvious. The risk-differential lncRNAs were closely related to immunity. The treatment drugs with high sensitivity were screened based on the IC50 value. Conclusion. The 11 cuproptosis-related lncRNAs may serve as molecular biomarkers and therapeutic targets for BLCA.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.