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Construction and Validation of Protein Expression-related Prognostic Models in Clear Cell Renal Cell Carcinoma
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. Univa...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Ivyspring International Publisher
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10088890/ https://www.ncbi.nlm.nih.gov/pubmed/37056387 http://dx.doi.org/10.7150/jca.81915 |
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author | Ma, Xuzhan Sun, Libin |
author_facet | Ma, Xuzhan Sun, Libin |
author_sort | Ma, Xuzhan |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10088890 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Ivyspring International Publisher |
record_format | MEDLINE/PubMed |
spelling | pubmed-100888902023-04-12 Construction and Validation of Protein Expression-related Prognostic Models in Clear Cell Renal Cell Carcinoma Ma, Xuzhan Sun, Libin J Cancer Research Paper 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. Ivyspring International Publisher 2023-03-27 /pmc/articles/PMC10088890/ /pubmed/37056387 http://dx.doi.org/10.7150/jca.81915 Text en © The author(s) https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/). See http://ivyspring.com/terms for full terms and conditions. |
spellingShingle | Research Paper Ma, Xuzhan Sun, Libin Construction and Validation of Protein Expression-related Prognostic Models in Clear Cell Renal Cell Carcinoma |
title | Construction and Validation of Protein Expression-related Prognostic Models in Clear Cell Renal Cell Carcinoma |
title_full | Construction and Validation of Protein Expression-related Prognostic Models in Clear Cell Renal Cell Carcinoma |
title_fullStr | Construction and Validation of Protein Expression-related Prognostic Models in Clear Cell Renal Cell Carcinoma |
title_full_unstemmed | Construction and Validation of Protein Expression-related Prognostic Models in Clear Cell Renal Cell Carcinoma |
title_short | Construction and Validation of Protein Expression-related Prognostic Models in Clear Cell Renal Cell Carcinoma |
title_sort | construction and validation of protein expression-related prognostic models in clear cell renal cell carcinoma |
topic | Research Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10088890/ https://www.ncbi.nlm.nih.gov/pubmed/37056387 http://dx.doi.org/10.7150/jca.81915 |
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