Fifteen-gene expression based model predicts the survival of clear cell renal cell carcinoma

Autor: Ping Li, Yan Zhang, Zhaoli Zhou, He Ren
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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