ArticleDisease markers2020
Establishment and Validation of a Prognostic Risk Model for Autophagy-Related Genes in Clear Cell Renal Cell Carcinoma.
Article in Disease markers, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Machine learning-based on cytotoxic T lymphocyte evasion gene develops a novel signature to predict prognosis and immunotherapy responses for kidney renal clear cell carcinoma patients.Frontiers in immunology · 2023Article
- Cuproptosis-related lncRNA: Prediction of prognosis and subtype determination in clear cell renal cell carcinoma.Frontiers in genetics · 2022Article
- Establishment and validation of five autophagy-related signatures for predicting survival and immune microenvironment in glioma.Genes & genomics · 2022Article
- Construction and Validation of an Autophagy-Related Prognostic Model for Osteosarcoma Patients.Journal of oncology · 2021Article
- ANGPTL3 Overexpression Suppresses the Development of Oncogenic Properties in Renal Cell Carcinoma via the Wnt/Disease markers · 2021Article
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Authors and funding
8 authors.
Funding
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Abstract
backgroundAutophagy plays an essential role in tumorigenesis. At present, due to the unclear role of autophagy in renal clear cell carcinoma, we studied the potential value of autophagy-related genes (ARGs) in renal clear cell carcinoma (ccRCC).
methodsWe obtained all ccRCC data from The Cancer Genome Atlas (TCGA). We extracted the expression data of ARGs for difference analysis and carried out biological function analysis on the different results. The autophagy risk model was constructed. The 5-year survival rate was assessed using the model, and the predictive power of the model was evaluated from multiple perspectives. Cox regression analysis was use to assess whether the model could be an independent prognostic factor. Finally, the correlation between the model and clinical indicators is analyzed.
resultsThe patients were divided into the high-risk group and low-risk group according to the median of autophagy risk score, and the results showed that the prognosis of the low-risk group was better than that of a high-risk group. The validation results of external data sets show that our model has good predictive value for ccRCC patients. The model can be an independent prognostic factor. Finally, the results show that our model has a stable predictive ability.
conclusionThe autophagy gene model we constructed can be used as an excellent prognostic indicator for ccRCC. Our study provides the possibility of individualized and precise treatment for ccRCC patients.
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