Fifteen-gene expression based model predicts the survival of clear cell renal cell carcinoma
Autor: | Ping Li, Yan Zhang, Zhaoli Zhou, He Ren |
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Jazyk: | angličtina |
Rok vydání: | 2018 |
Předmět: |
0301 basic medicine
Oncology Male medicine.medical_specialty Observational Study Gene Expression Risk Assessment Cohort Studies 03 medical and health sciences 0302 clinical medicine Renal cell carcinoma Predictive Value of Tests Risk Factors Internal medicine expression medicine Carcinoma Biomarkers Tumor Humans Genetic Testing Carcinoma Renal Cell Survival analysis Proportional Hazards Models Framingham Risk Score Proportional hazards model business.industry ccRCC Reproducibility of Results General Medicine Nomogram medicine.disease Prognosis Survival Analysis Kidney Neoplasms Clear cell renal cell carcinoma Nomograms 030104 developmental biology 030220 oncology & carcinogenesis Multivariate Analysis ComputingMethodologies_DOCUMENTANDTEXTPROCESSING Regression Analysis Female Risk assessment business Research Article |
Zdroj: | Medicine |
ISSN: | 1536-5964 0025-7974 |
Popis: | Supplemental Digital Content is available in the text Clear-cell renal cell carcinoma (ccRCC) is the major renal cell carcinoma subtype, but its postsurgical prognosis varies among individual patients. We used gene expression, machine learning (random forest variable hunting), and Cox regression analysis to develop a risk score model based on 15 genes to predict survival of patients with ccRCC in the The Cancer Genome Atlas dataset (N = 533). We validated this model in another cohort, and analyzed correlations between risk score and other clinical indicators. Patients in the high-risk group had significantly worse overall survival (OS) than did those in the low-risk group (P = 5.6e-16); recurrence-free survival showed a similar pattern. This result was reproducible in another dataset, E-MTAB-1980 (N = 101, P = .00029). We evaluated correlations between risk score and other clinical indicators. Risk was independent of age and sex, but was significantly associated with hemoglobin level, primary tumor size, and grade. Radiation therapy also had no effect on the prognostic value of the risk score. Cox multivariate regression showed risk score to be an important indicator for ccRCC prognosis. We plotted a nomogram for 3-year OS to facilitate use of risk score and other indicators. The risk score model based on expression of the 15 selected genes can predict survival of patients with ccRCC. |
Databáze: | OpenAIRE |
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