Department of Obstetrics and Gynecology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Department of Vascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Clin Genitourin Cancer. 2022 Aug;20(4):e317-e329. doi: 10.1016/j.clgc.2022.02.005. Epub 2022 Feb 24.
Among all types of renal cell cancer (RCC), clear cell renal cell cancer (ccRCC) is the most common and aggressive one. Emerging evidence uncovers that long non-coding RNAs (lncRNAs) are involved in genomic instability, which correlates to the clinical outcomes of patients who suffer from various kinds of cancers.
We gathered expression profiles of transcriptome RNA and clinical information about ccRCC from The Cancer Genome Atlas (TCGA) and The Gene Expression Omnibus (GEO) database. The lncRNA expression profiles and somatic mutation data were combined to identify genome instability-related lncRNAs (GILncRs) by significance analysis of T test. By means of univariate and multivariate cox regression analyses, 3 GILncRs strongly associated with patient prognosis were screened out to build a genomic instability-related risk score (GIRS) model. We use R-version 4.0.4 to draw Kaplan-Meier plots and ROC curves for survival prediction.
The somatic mutation count was higher in genomic unstable group. PBRM1 showed lower expression in genomic unstable group. Three lncRNAs such as LINC00460, AC156455.1, LINC01606 were included in the GIRS model. Patients had poorer prognosis with higher risk score of GIRS model. The somatic mutation count was higher in patients with higher risk score while PBRM1 expression was lower. The GIRS model was independent from other clinical factors. The GIRS model was superior to other 2 published lncRNA signatures in survival prediction.
在所有类型的肾细胞癌（RCC）中，透明细胞肾细胞癌（ccRCC）是最常见和最具侵袭性的一种。新出现的证据揭示了长非编码 RNA（lncRNA）参与基因组不稳定性，这与患有各种癌症的患者的临床结局相关。
我们从癌症基因组图谱（TCGA）和基因表达综合数据库（GEO）中收集了转录组 RNA 的表达谱和 ccRCC 的临床信息。将 lncRNA 表达谱和体细胞突变数据相结合，通过 T 检验的显著性分析来识别与基因组不稳定性相关的 lncRNAs（GILncRs）。通过单变量和多变量 cox 回归分析，筛选出与患者预后强烈相关的 3 个 GILncRs，构建基因组不稳定性相关风险评分（GIRS）模型。我们使用 R 版本 4.0.4 绘制 Kaplan-Meier 图和 ROC 曲线进行生存预测。
基因组不稳定组的体细胞突变计数较高。PBRM1 在基因组不稳定组中的表达较低。LINC00460、AC156455.1 和 LINC01606 这 3 个 lncRNA 被纳入 GIRS 模型。GIRS 模型风险评分较高的患者预后较差。风险评分较高的患者体细胞突变计数较高，而 PBRM1 表达较低。GIRS 模型独立于其他临床因素。GIRS 模型在生存预测方面优于其他 2 个已发表的 lncRNA 特征。
