Department of Urology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Department of Urology, The First People's Hospital of Fuyang, Hangzhou, China.
J Cell Mol Med. 2020 Jan;24(2):1245-1255. doi: 10.1111/jcmm.14748. Epub 2019 Nov 29.
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.
如今，基因表达谱分析已广泛应用于筛选多种癌症的预后生物标志物。我们的研究试图构建一个临床列线图，该列线图将风险基因特征与临床特征相结合，用于个体复发风险评估，并为透明细胞肾细胞癌提供个性化管理。通过微阵列鉴定了 580 个差异表达基因（DEGs）。功能分析表明，DEGs 在 ccRCC 的进展和转移中具有重要意义。在我们的研究中，回顾性分析了 338 名 ccRCC 患者，并从 LASSO Cox 回归模型中获得了由 5 个基因组成的风险基因特征。进一步分析表明，鉴定的风险基因特征可有效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，该基因特征的预后价值独立于临床特征（P=.002）。风险基因特征的疗效在内部和外部队列中均得到验证。该特征在训练、测试和外部验证队列中的接受者操作特征曲线下面积分别为 0.770、0.765 和 0.774。最后，为临床医生开发了一个列线图，在校准图中表现良好。该列线图基于风险基因特征和临床特征，可能为术后复发预测和透明细胞肾细胞癌患者的个性化管理提供一种实用方法。
