一个 13 基因风险评分系统和列线图生存模型，用于预测透明细胞肾细胞癌的预后。
A 13-gene risk score system and a nomogram survival model for predicting the prognosis of clear cell renal cell carcinoma.
机构信息
Department of Urology, Changhai Hospital, the Second Military Medical University, Shanghai, China.
Department of Urology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China.
出版信息
Urol Oncol. 2020 Mar;38(3):74.e1-74.e11. doi: 10.1016/j.urolonc.2019.12.022. Epub 2020 Jan 14.
BACKGROUND
Renal cell carcinoma (RCC) is the second common malignant tumor in the urinary system, and 85% of RCC cases are clear cell RCC (ccRCC). This study is designed to build a risk score system for ccRCC.
METHODS
The gene methylation and expression data of ccRCC samples were downloaded from The Cancer Genome Atlas database (training set) and ArrayExpress database (validation set). The differentially methylated genes (DMGs) and differentially expressed genes (DEGs) were identified by limma package, and their intersecting genes with negative Pearson correlation coefficients were remained using cor.test function. Prognosis-associated genes were identified by survival package, and the optimal DMGs were obtained using penalized package. After risk score system was built, nomogram survival model was constructed using rms package. Additionally, pathways were enriched for the DEGs between high- and low-risk groups using Gene Set Enrichment Analysis.
RESULTS
There were 3,638 DMGs and 2,702 DEGs between tumor and normal samples. Among the 312 intersecting genes, 43 prognosis-associated genes were identified. A total of 13 optimal DMGs (BTBD19, ADAM8, BGLAP, TNFRSF13C, JPH4, BEST1, GNRH2, UBE2QL1, CHODL, GDF9, UPB1, KCNH3; and ADAMTSL4) were obtained for building the risk score system. After pathological M, pathological T, platelet qualitative, and RS status were revealed to be independent prognostic factors, a nomogram survival model was constructed. For the 920 DEGs between the high- and low-risk samples, 6 significant pathways were enriched.
CONCLUSION
The 13-gene risk score system and the nomogram survival model might be used for prognostic prediction of ccRCC patients.
背景
肾细胞癌（RCC）是泌尿系统中第二常见的恶性肿瘤，其中 85%的 RCC 病例为透明细胞 RCC（ccRCC）。本研究旨在构建 ccRCC 的风险评分系统。
方法
从癌症基因组图谱数据库（训练集）和 ArrayExpress 数据库（验证集）下载 ccRCC 样本的基因甲基化和表达数据。使用 limma 包识别差异甲基化基因（DMGs）和差异表达基因（DEGs），使用 cor.test 函数保留具有负 Pearson 相关系数的交集基因。使用 survival 包识别预后相关基因，并使用 penalized 包获得最优 DMGs。构建风险评分系统后，使用 rms 包构建列线图生存模型。此外，使用 Gene Set Enrichment Analysis 对高风险组和低风险组之间的 DEGs 进行通路富集。
结果
肿瘤与正常样本之间存在 3638 个 DMGs 和 2702 个 DEGs。在 312 个交集基因中，鉴定出 43 个预后相关基因。总共获得 13 个最优 DMGs（BTBD19、ADAM8、BGLAP、TNFRSF13C、JPH4、BEST1、GNRH2、UBE2QL1、CHODL、GDF9、UPB1、KCNH3 和 ADAMTSL4）用于构建风险评分系统。在揭示病理 M、病理 T、血小板定性和 RS 状态为独立预后因素后，构建了列线图生存模型。对于高风险和低风险样本之间的 920 个 DEGs，富集了 6 个显著通路。
结论
13 基因风险评分系统和列线图生存模型可用于预测 ccRCC 患者的预后。
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