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Development and verification of a nomogram for prediction of recurrence‐free survival in clear cell renal cell carcinoma
Author(s) -
Chen Yuanlei,
Jiang Shangjun,
Lu Zeyi,
Xue Dingwei,
Xia Liqun,
Lu Jieyang,
Wang Huan,
Xu Liwei,
Li Liyang,
Li Gonghui
Publication year - 2020
Publication title -
journal of cellular and molecular medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.44
H-Index - 130
eISSN - 1582-4934
pISSN - 1582-1838
DOI - 10.1111/jcmm.14748
Subject(s) - nomogram , clear cell renal cell carcinoma , hazard ratio , gene signature , oncology , proportional hazards model , medicine , lasso (programming language) , receiver operating characteristic , gene expression profiling , renal cell carcinoma , confidence interval , bioinformatics , gene expression , gene , biology , computer science , biochemistry , world wide web
Nowadays, gene expression profiling has been widely used in screening out prognostic biomarkers in numerous kinds of carcinoma. Our studies attempt to construct a clinical nomogram which combines risk gene signature and clinical features for individual recurrent risk assessment and offer personalized managements for clear cell renal cell carcinoma. A total of 580 differentially expressed genes (DEGs) were identified via microarray. Functional analysis revealed that DEGs are of fundamental importance in ccRCC progression and metastasis. In our study, 338 ccRCC patients were retrospectively analysed and a risk gene signature which composed of 5 genes was obtained from a LASSO Cox regression model. Further analysis revealed that identified risk gene signature could usefully distinguish the patients with poor prognosis in training cohort (hazard ratio [HR] = 3.554, 95% confidence interval [CI] 2.261‐7.472, P  < .0001, n = 107). Moreover, the prognostic value of this gene‐signature was independent of clinical features ( P  = .002). The efficacy of risk gene signature was verified in both internal and external cohorts. The area under receiver operating characteristic curve of this signature was 0.770, 0.765 and 0.774 in the training, testing and external validation cohorts, respectively. Finally, a nomogram was developed for clinicians and did well in the calibration plots. This nomogram based on risk gene signature and clinical features might provide a practical way for recurrence prediction and facilitating personalized managements of ccRCC patients after surgery.

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