Development and verification of a nomogram for prediction of recurrence-free survival in clear cell renal cell carcinoma
Development and verification of a nomogram for prediction of recurrence-free survival in clear cell renal cell carcinoma
复制标题
用于预测透明细胞肾细胞癌无复发生存的列线图的开发和验证
DOI：
10.1111/jcmm.14748
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发表时间：
2019-11-29
影响因子：
5.3
通讯作者：
Li, Gonghui
中科院分区：
文献类型：
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作者：
Chen, Yuanlei;Jiang, Shangjun;Li, Gonghui
目前，基因表达谱已被广泛应用于筛选多种肿瘤的预后标志物。我们的研究试图构建一个结合风险基因特征和临床特征的临床诺模图，用于个体复发风险评估，并为肾透明细胞癌提供个性化的治疗。通过微阵列共鉴定出580个差异表达基因（DEG）。功能分析显示，DEG在ccRCC进展和转移中具有根本重要性。在我们的研究中，338例ccRCC患者进行了回顾性分析，并从LASSO考克斯回归模型中获得了由5个基因组成的风险基因签名。进一步的分析显示，识别的风险基因标签可以有效地区分训练队列中预后不良的患者（风险比[HR] = 3.554，95%置信区间[CI] 2.261-7.472，P < .0001，n = 107）。此外，该基因标记的预后价值与临床特征无关（P = 0.002）。在内部和外部队列中验证了风险基因签名的有效性。在训练、测试和外部验证队列中，该特征的受试者工作特征曲线下面积分别为0.770、0.765和0.774。最后，为临床医生开发了列线图，并在校准图中表现良好。基于风险基因签名和临床特征的诺模图可能为预测复发和促进ccRCC患者手术后的个性化管理提供实用的方法。
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.